Do Government Policies drive institutional preferences on green investment? Evidence from China

Do Government Policies drive institutional preferences on green investment? 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Evidence from China Wu-E Yang, Pei-Wen Lai, Zhi-Qiu Han, Zhen-Peng Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1790586/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract To improve the guiding role of institutional investors in green investment and provide financial support for green enterprises, Chinese government has issued a series of policies to establish a green finance system. We use a difference-in-difference (DID) analysis to explore whether the implementation of policies could change institutional attitudes to environmental factors when making investment decisions. Considering the effect of investment horizon, we find long-term institutional investors have shown symmetric preferences on green investment, while short-term institutions are more affected by green finance policies. Additionally, we find the investment behavior of short-term institutions are highly associated with abnormal return triggered by the policy effect. Besides, the triple-difference (DDD) analysis further proves that the high level of environmental disclosure would expanse the effect of policies, but high quality of external auditors would reduce the policy effect. The finding would extend the studies of green investment in emerging markets and present new evidence about the policy effect on institutions’ preferences for green investment. green investment institutional preferences environmental information disclosure government policies green finance external audits Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction In the early days of China's reform and opening up, the most important goal is to develop the economy. However, the rapid growth of the economy is at the cost of ecological and environmental resources. At present, China's sustainable economic growth momentum is insufficient, and environmental issues as one of the important limiting factors, are particularly complex and prominent. Under the goal of achieving carbon peaking by 2030 and carbon neutrality by 2060, China's economy is shifting from a stage of high-speed growth to high-quality development. The transformation of the China's overall development strategy requires financial institutions to play a guiding role in resource allocation to promote the improvement of green investment and the development of the low-carbon business. In contrast to developed countries, Chinese governments are a key driver in promoting CSR practices and responsible investment through the guidance of regulations (S. Xu and Liu, 2020 ). Recently, Chinese central government has launched various policies to promote green investment, which can be divided into two modes: improving non-financial reporting disclosure and establishing the green finance system. For green finance, it is designed to encourage more financial capital into green industries by financial loan support, bonds, stocks, insurance and other financial services (J. W. Lee, 2020 ). Since the People’s Bank of China and other Chinese ministries and commissions have promulgated the guidelines for green finance in 2016, the green finance system began taking shape (Feng et al., 2021 ). China became the first country in the world that the central government promote the establishment of a green finance system. In June 2017, the central bank and other four regulators issued a plan of financial standardization system from 2016 to 2020. This action makes the green financial standardization project one of the main projects of financial standardization during the 13th Five-Year Plan. Besides, the State Council approved the construction of pilot zones for green finance reform and innovation in five provinces, namely Zhejiang, Jiangxi, Guangdong, Guizhou and Xinjiang. One of the top priorities in pilot zones is to support financial institutions to set up green finance business units, and encourage venture capital, private equity funds and QFII to participate in green investment. Although China has established a green finance system, green investment in China remains at a stage of beginning. Prior studies pay higher attention to financing constraints (Yu et al., 2021 ) and corporate carbon reduction performance (S. Chen et al., 2021 ), while we focus on institutional investors’ reactions. On the one hand, institutional investors are the main driver of the green finance system by shareholder activism in developed countries. However, the development of the green financial mode is difference in developing countries, which relies more on government regulations to guide financial institutions to support sustainable development (DRC, 2016). Thus, exploring whether policies could change institutions’ investment behaviors in green investment is a prerequisite to further prove whether they could improve corporate environmental performance by engaging corporate governance. On the other hand, institutional investors appear to be more rational (Daniel et al., 1997 ) and possess more information advantages, professional skills, processing capabilities and strong financial strength (Stolin and David, 2008 ). They can timely seize the opportunities when a policy changes, and pay more attention to the fundamentals of enterprises to reduce irrational behavior, especially for Chinese stock market where most trades are made by speculative individual investors (Cano-Berlanga and Giménez-Gómez, 2017 ) and far from being efficient (M. Li et al., 2020 ). Previous studies have proved that institutional investors in emerging stock markets are able to stabilize the market (Qi et al., 2006 ; Vo, 2016 ). Besides, when institutional investors release signals to the market, individual investors who lack sufficient information, tend to observe the trading decisions of institutions and follow selling or buying strategies of institutions. Khurshed et al. ( 2014 ) have proved that small and medium retail investors actively follow the IPO subscription strategy of institutional investors. Therefore, investigating whether green finance policies could change institutional preferences on green investment and strengthen the atmosphere of green investment in the A-share market, may provide more implications and guidance for government policy makers. This paper uses Chinese local ESG rating database to explore policy effect on the relationship between green investment and institutional ownership. Based on the sample period from 2016Q2 to 2019Q1, we find that green finance policies do change institutional preferences on green investment. The rolling window estimation examining the dynamic relationship between institutional ownership and environmental scores also proves our findings. Additionally, comparing with long-term investors, short-term institutions react positively to green finance policies and the change of their holdings are positive associated with abnormal returns aroused by green finance policies. Finally, the mediating effect of internal disclosure and external auditors reveals that the higher level of environmental disclosure would expanse the policy effect, while the higher quality of external audits would reduce the policy effect. Our study complements the prior literature in three aspects. First, research on green finance focuses mainly on green credit (Zhou et al., 2021 ), energy consumption structure (Sun and Chen, 2022 ), and financing constraints (Yu et al., 2021 ), which is in views of corporate management. This paper is the first attempt to examine the green finance policies on institutional ownership to test the effect of policy implement from an investment perspective. Institutional investors, as the major shareholders of enterprises, can participate in the company's decision-making and play a role in promoting the practice of corporate social responsibility (CSR) by shareholder activism (Dyck et al., 2019 ; H.-D. Kim et al., 2019 ). Thus, exploring whether government policies can promote institutional preferences on green investment is of great significance to further promote corporates transform to an environmentally friendly business. Second, we focus on the investment behavior of financial institutional investors in emerging market with a more speculative atmosphere. Previous research has confirmed that short-term institutional investors are characterized by preferences for short-term returns due to pressures on short-term performance (Bushee, 2001 ). But there are little studies show the impact of policy on abnormal returns to explain the intensity of policy effect on short-term institutional investors. This paper provides empirical evidence that although short-term institutions in China are affected by the green finance policies, their reaction to policies are more relative to abnormal returns triggered by the policy effect. Third, our study adds to the existing literature on the mediating effect of heterogeneity in terms of corporate characteristics. Previous studies have disclosed that differences in stock market reactions are related to characteristics of firms like size, product profitability, and ownership structure, etc. (Guo et al., 2020 ). Our paper, however, examines the mediating effect form internal disclosure of environmental performance and external auditors to provide more suggestions for corporate governance. The remainder of this paper is organized as follows. Section 2 describes the related literature and hypothesis. Then we introduce the data and model used in Section 3 and describes the main empirical results in Section 4 . Section 5 empirically examines the mediating effect of internal disclosure and external audits. Finally, we present the conclusion in the final section. 2. Literature And Hypothesis 2.1. Green finance policies and institutional investors Previous studies have proved that when the government introduces economic policies or specific industry revitalization plans, the stock market as a "barometer" of national economic development, will respond to national policies directly, resulting in the rising or falling of the stock prices of listed companies (Y. Jiang and Luo, 2018 ; M. Li et al., 2020 ; You and Zhang, 2008 ). The main explanation of investors’ response to government policies is that when a new environmental policy is launched, rational investors tend to expect those firms would be punished by relevant regulations. They could choose to reduce their investment in heavily polluting firms, and then share values of heavily polluting companies could decline (Y. Chen et al., 2020 ; Guo et al., 2020 ). Besides, a firm who commits to mitigate environmental problems by conducting waste management or hiring environmental auditors (Blomgren, 2011 ), may increase more potential costs and then cause damages to their productivity and profitability (S.-Y. Lee et al., 2015 ; Palmer et al., 1995 ). Additionally, as an emerging market, Chinese stock market is relatively immature due to serious information asymmetry and leakage problems (M. Li et al., 2020 ; Q. Li et al., 2018 ). Thus, on the one hand, Chinese government has given strong supervision and issue more regulations to investors, leading to more policy impact on the stock market comparing with developed markets (Daimin, 2002 ). On the other hand, the extremely strong speculative circumstance in Chinese stock market would make it profitable to gain information advantage (Cano-Berlanga and Giménez-Gómez, 2017 ). Therefore, rational investors like institutional investors who have more information advantages, professional skills, processing capabilities and strong financial strength (Stolin and David, 2008 ), are more likely to seize the opportunities, and react to national policies more quickly. The establishment of the green finance system would reduce the financing costs of green enterprises, and at the same time force brown enterprises to achieve green transformation by adding financing pressure on them (Guojin et al., 2021 ). Institutional investors in China who have more information advantages and the short-term profit-seeking nature than Western institutional investors (Wenjing and Xiaoyan, 2015 ), may increase investment after the introduction of favorable policies to obtain huge returns, and quickly leave the market after the introduction of bearish policies to reduce potential losses. Hence, we formulate the following hypothesis: H1: The implement of green finance policies would affect financial institutional preferences on green investment. 2.2. Green finance policies, investment horizon and short-term return Considering the heterogeneity of financial institutional investors, prior studies have found that long-term institutions focus more on firms’ fundamentals and long-term values (Shirasu and Kawakita, 2021 ), while short-term institutions prefer to engage in more aggressive investment strategies, instead of conducting buy-and-hold strategies on the basis of the corporates’ fundamentals (Lowenstein, 1989 ). Besides, institutional investor shortsightedness indicates that short-term institutional investors prefer short-term returns over long-term value (Bushee, 2001 ). A main interpretation is that they have fiduciary duties for their clients and face more short-term performance pressures (Lang and Mcnichols, 1997 ). For example, the ranking of the annual net worth of mutual funds is a huge constraint and stress on every fund manager. Yan and Zhang ( 2009 ) also find that the positive correlation between institutional ownership and future equity returns is driven by short-term institutions, and propose an argument that short-term institutions are better informed and actively trading to take advantage of their information. Overall, long-term institutional investors who focus more on fundamental analysis, appear to have stable stock holdings, and the policy effect on them will not be particularly significant. However, short-term institutions who prefer to participate in aggressive investments strategies due to short-term performance pressures, may be more responsive to policy in order to obtain higher short-term returns. Research focusing on the abnormal return after environmental policies has revealed that firms with poor environmental performance in heavily-polluting industries have experienced a significant decrease in cumulative abnormal return after the government announcing policies about carbon emission (Ramiah et al., 2013 ). In China, the stock market tends to punish firms with lower environmental scores by reducing their stock prices and encourage those firms with higher environmental performance by providing higher returns (Wang et al., 2019 ; X. D. Xu et al., 2011 ). Accordingly, we propose Hypothesis 2 and 3 for short-term institutions and short-term returns: H2: Green finance policies have more influences on short-term institutional ownership than long-term investors. H3: Changes in investment behaviors of short-term institutional investors are associated with abnormal returns after green finance policies. 2.3. The mediating effect of internal disclosure We compose signaling theory to discuss the mediating effect of internal disclosure on environmental performance. When a company discloses environmental-related reports, it is equivalent to sending a signal to the outside world that the company attaches importance to environmental performance and actively responds to government policy. As signal receivers, investors will interpret the signal as the potential for companies to reduce costs and increase revenues, leading to a positive reaction in the stock market (Y. Chen et al., 2020 ). Stuart et al. ( 2020 ) also indicate that independent announcement on environmental issues can be viewed as a positive ethical signal. However, Lihui and Kedi ( 2017 ) put forward the "disguise effect" hypothesis of CSR information disclosure in China, believing that corporate managers may have moral hazard and use the disclosure of social responsibility information to cover up poor performance or avoid unethical behavior. Therefore, environmental disclosure as a signal does not always bring positive effects. When a company has poor environmental performance but still chooses to disclose environmental indicators, it will bring about a negative reaction in the stock market and exacerbate the reduction of investors' shareholding. Hypothesis 4 for internal disclosure of environment information is thus presented: H4: The higher level of environmental disclosure would expand policy effect on institutional preferences. 2.4. The mediating effect of external audits External auditors may provide assurance of the quality of publicly reported accounting information, and stricter oversight by higher quality auditors will reduce the discretion of inside managers to distort financial reporting, thereby limiting managers' ability to extract wealth from outside shareholders (Fan and Wong, 2005 ; Guedhami et al., 2014 ). This evidence implies that high quality auditors are more likely to protect outside investors from varnishing of company's financial reports by insiders. Additionally, prior research has implied that Big 5 auditors generally have high international reputations and appear to be more independent than local auditors (Fan and Wong, 2005 ). Comparing with non–Big 4 auditors, Big 4 auditors tend to show lower earnings management costs, greater accounting transparency, higher valuations and lower equity financing cost (Guedhami et al., 2014 ). They may also supply better external supervision, thus enhance investor confidence in the company's financial integrity (Goldie et al., 2018 ; J.-B. Kim and Zhang, 2014 ). L. Chen ( 2014 ) also finds that the existence of Big 4 auditors may mitigate the possibility of involving in corporate scandals of a company. Investors would automatically reduce their expectations for the severity of undiscovered misconduct when evaluating stocks of listed companies. Besides, considering institutional investors may originally hold more shares in companies with high external audit quality, the increase of shareholdings in firms with high external audit quality and better environmental performance would be smaller than that hiring non-Big 4 auditors. Hence, the fifth hypothesis is as follows: H5: A stronger external audits would narrow down policy effect on institutional preferences. 3. Data And Method 3.1. Sample selection and data sources We examine the environmental scores and institutional ownership of all listed A-share firms in the China stock market. The data cover the period from the first quarter of 2015 to the second quarter of 2021. We obtain environment ratings from Huazheng ESG database and A-share firms’ information from the Chinese Securities Market and Accounting Research (CSMAR) database. To control for bias, we remove financial companies, Specially Treated (ST) firms on the verge of delisting, and companies that exist less than a year after IPO. Besides, firms with missing data are excluded. All continuous variables are winsorized at the 1st and 99th percentiles to reduce the influence of outliers. The final sample consists of 76,178 firm-quarter observations. 3.1.1. Huazheng environmental ratings The Huazheng ESG database is produced by Sino-Securities Index Information Service (Shanghai) Co.ltd in China. We construct our environmental measures using this database for two reasons. First, it has a larger database of China environmental ratings covering all-listed A-share firms in China across time with the earliest data reporting backtrack to 1990, while the widely used datasets around the world like MSCI ESG Ratings only measure hundreds of firms in the China stock market. Second, it will be better suited to properly measure firms’ performance, since the green investment still be a new topic in China. For instance, indicators like fossil energy utilization, green finance will have a huge difference compared to foreign countries. Thus, it would be better to use China local database to capture the environmental performance of firms in China. These ESG Rating Data are divided into nine levels from AAA to C respectively. Figure 1 shows the tendency of the average scores of firms in sample from 2015Q1 to 2021Q2. And we define AAA to C as 9 to 1 point. Comparing with the ESG scores which is from 7 to 8 point, the average environmental scores are lower, from 4 to 5 point. There is huge room for financial institutions and companies to drive corporate environmental performance. [Insert Fig. 1 here] 3.1.2. Institutional ownership (IO) Institutional investors in China can be classified as financial institutions (like QFII, mutual funds, securities et al.) and General legal person institutional investors (like non-financial publicly listed firms). General legal person institutions are more like internal supervisors in a company due to their holding purpose and trading strategy. Therefore, we only analyze financial institutional investors consisting of QFII, mutual funds, securities, social security funds, insurance companies, trusts, bank and finance companies (accounting for almost 99% among all financial institutional investors). Institutional investors are able to get more information about the company through various channels and professional analysis capabilities. And the changes in institutional investors' shareholdings can convey the information they get. So, we calculate the institutional ownership using the number of institutional holdings divided by the company's outstanding shares. To figure out the heterogeneity of institutional investors, we further compute the portfolio turnover over the past two years as an investment horizon proxy in our research. Following the prior research, this variable is constructed in three steps (Zhang and Yan, 2009). Firstly, we calculate each institutional investor k’s churn rate semiannually by applying the following equation: where P i,t−1 and P i,t are the share prices for stock i at the end of period t-1 and t, and S k,i,t−1 and S k,i,t are the number of shares of stock i held by investor k at the end of period t-1 and t respectively. CR_buy k,t and CR_sell k,t are institution k’s aggregate purchase and sale for period t, respectively. Institution k’s churn rate for period t is then defined as follows: $${CR}_{k,t}=\frac{\text{m}\text{i}\text{n}({CR\_buy}_{k,t},{CR\_sell}_{k,t})}{\sum _{i=1}^{{N}_{k}}\frac{{S}_{k,i,t}{P}_{i,t}+{S}_{k,i,t-1}{P}_{i,t-1}}{2}}$$ we finally calculate the investor turnover of firm i as the weighted average of the total portfolio churn rates of its investors over our sample period, as follows: $${Investor Turnover}_{i}=\sum {W}_{i,t}\left(\frac{1}{4}\sum _{r=1}^{3}{CR}_{k,t-r+1}\right)$$ where W i,t is the proportion of investor i’s ownership in the total ownership held by institutional investors at period t. Given the average churn rate measure, we sort all institutional investors into three tertile portfolios based on the firms’ turnover rate. Those ranked in the top tertile are classified as short-term institutional investors and those ranked in the bottom tertile are classified as long-term institutional investors. 3.1.3. Control variables To mitigate the heterogeneity of different firms and improve the robustness of analysis, eight control variables, namely VOLATILITY, PB, ROE, IPOA, LEV, HHI5, SIZE and AUDIT are included following the previous studies. Following Graves and Waddock (1994), we use the debt-to-asset ratio ( LEV ) as the measurement of firm leverage and ROE as the measurement of firm performance. Besides, VOLATILITY is measured by average monthly volatility of return. Price-to-book ratio ( PB) measure the value of investing in a company. IPOA refers to the number of years that a company exists after IPO, measuring the cognition degree of investors, since stock with longer IPO age could have more open data. Unlike the common practice measured by the natural logarithm of the firm’s market capitalization (Bushee, 2001), we set a dummy variable ( SIZED ) that equals one if the firm is a big company and zero if it is not according to the standard of China’s National Bureau of Statistics 1 to measure this variable. According to Fan and Wong (2005), high-quality auditors could provide better supervision and reliable reports for investors. We add AUDIT as the measure of a dummy variable that equals one if the firm is audited by Big Four global accounting firms. We also set HHI5 , the total squares of the ratio of the top5 large shareholder's ownership to measure the Ownership concentration (F. Jiang et al., 2018). Table 1 shows the explanation of all variables. Table 1 Variable Definitions Variable name Variable symbol Description Dependent variable Institutional ownership IO The number of financial institutional holdings divided by the company's outstanding shares Independent variables Environmental scores E Firms’ environmental ratings, setting AAA to A as environmental strength and CCC to C as environmental weakness Control variables Stock price volatility VOLATILITY Average monthly volatility from the beginning of the quarter to the end of the quarter Price-to-Book Ratio PB Price-to-Book Ratio to measure the value of investing in a company Profitability ROE Rate of Return on Common Stockholders’ Equity to measure the companies’ profitability IPO age IPOA The year a company exists after IPO Firm leverage LEV Liabilities divided by net assets to measure a companies’ debt paying capacity Ownership concentration HHI5 The total squares of the ratio of the top5 large shareholder's ownership to measure the Ownership concentration Firm size dummies DSIZE A dummy variable that equals one if the firm is a big companies according to the standard of China’s National Bureau of Statistics Auditors BIG4 A dummy variable that equals one if the firm is audited by Big Four global accounting firms [Insert Table 1 here] 3.2. Model specification Considering almost 9% of publicly trading firms do not receive financial institutional investors’ investment, it could be hard to get the unbiased result by OLS regression. After conducting the maximum likelihood test, we choose random-effects Tobit regression model instead of pooled panel Tobit regressions to test the institutional preference on ESG ratings. To avoid the Endogeneity, we use one-period lag variables and add industry and time dummy variables in the model. To measure the effect of green finance policies, we treat the plan of financial Standardization system from 2016 to 2020 (the plan) at 2017Q2 as an exogenous regulatory shock and consider a time period from 2016Q2 to 2019Q1 to conduct a DID analysis. We estimate DID models using the following equations: IO i,t = 𝛼 + 𝛽 1 E_weakness i,t−1 + 𝛽 2 E_weakness i,t − 1 *Post +∑𝛽 j Control i,t−1 + 𝜀 i,t (1) IO i,t = 𝛼 + 𝛽 1 E_strength i,t−1 + 𝛽 2 E_strength i,t − 1 *Post +∑𝛽 j Control i,t−1 + 𝜀 i,t (2) where IO i,t is the institutional ownership for firm i in at the end of each quarter t . We divide environmental scores into three groups: weakness, average and strength. Then we set two dummy variables: weakness and strength , and treat average group as control group. The Environmental_weakness (strength) i,t−1 variable is a dummy variable, which equals to 1 when the ESG scores of firm i in quarter t-1 belong to the lowest (highest) group and equals to 0 when they beyond the lowest (highest) group. Standards that divide environmental scores into wea kness and strength are shown in Table 1. Control refers to various firm specific control variables. All of the firm-specific control variables are from quarter t-1 to minimize endogeneity concerns. The dummy variable Post is defined in the following way: the second quarter of 2016 (t-4) to the first quarter of 2017 (t-1) are defined as “pre” period, and the third quarter of 2017 (t + 1) to the first quarter of 2019 (t + 7) are defined as “post” period. By defining pre- and post-periods in this way, there is a clear prediction on the sign of the coefficient β 2 from the above equation. After the policy, financial institutions are expected to improve their investment in firms with environmental strength and reduce their investment in environmental weakness. Hence, β 2 is predicted to be negative in the Eq. (1) and positive in the Eq. (2). 3.3. summary statistics The summary statistics of variables are reported in Table 2. In Panel A that the average IO is 4.93% over our sample period, suggesting financial institutional holdings only account for relatively small proportions of outstanding shares in China, although the maximum holding is 26.104%. Our results are in line with Broadstock et al. (2021) who find that institutional investors remain few in China and retail investors participate in most investment activity. Panel B shows descriptive statistics with two groups: environmental strength and weakness respectively. We find that the average IO in environmental strength group (6.7%) is much higher than that in environmental weakness group (4.42%). This result supports our assumption that institutions prefer firms with higher environmental performance than those with poor environmental performance. The distribution of other variables is within a reasonable range. Table 2 Summary Statistics This table provides summary statistics of financial institutions and firms’ characteristics. The sample consists of 76,178 firm-quarter observations across A-share firms over the 2015-2021 period. All continuous variables are winsorized at the 1st and 99th percentiles. Panel A: Full sample Variables Observation Mean SD Min Median Max IO (%) 76178 4.93 5.659 0.000 2.945 26.104 E 76178 1.73 0.659 1.000 2.000 3.000 VOLATILITY 73533 0.13 0.066 0.037 0.110 0.398 PB 73887 3.78 3.234 0.588 2.816 19.750 LEV 76058 0.37 0.206 0.011 0.354 0.884 IPO A 76066 10.51 7.719 0 9 27 ROE 73766 0.03 0.042 -0.079 0.022 0.195 HHI5 76178 0.16 0.110 0.014 0.128 0.540 DSIZE 76178 0.70 0.460 0.000 1.000 1.000 BIG4 76178 0.06 0.245 0.000 0.000 1.000 Panel B: Sub-samples with environmental strength and weakness Variables Weakness Strength Mean SD Mean SD IO (%) 4.42 5.601 6.70 6.050 E 0.13 0.067 0.11 0.054 VOLATILITY 0.15 0.700 0.15 0.739 PB 3.84 3.123 3.30 3.049 LEV 0.37 0.202 0.39 0.201 IPO A 9.32 7.546 14.26 7.174 ROE 0.03 0.041 0.04 0.043 HHI5 0.15 0.107 0.16 0.110 DSIZE 0.66 0.474 0.84 0.369 BIG4 0.03 0.184 0.13 0.336 [Insert Table 2 here] Table 3 reports the correlation matrix of the variables used in the empirical analyses. The correlation results show that correlations among all variables are pretty low with a 1% significance level. This means multicollinearity will not occur in our regression analyses. Table 3 Correlation Matrix Variables IO E VOLATILITY PB LEV IPOA ROE HHI5 SIZED AUDIT IO (%) 1.000 E 0.128 1.000 VOLATILITY -0.057 -0.086 1.000 PB 0.030 -0.075 0.325 1.000 ROE 0.078 0.037 -0.078 -0.087 1.000 IPOA 0.149 0.195 -0.207 -0.236 0.275 1.000 LEV 0.127 0.041 -0.028 0.103 -0.173 -0.128 1.000 HHI5 -0.115 -0.021 -0.049 -0.065 0.062 -0.046 0.110 1.000 DSIZE 0.121 0.146 -0.090 -0.133 0.136 0.072 0.178 0.074 1.000 AUDIT 0.059 0.094 -0.073 -0.101 0.132 0.104 0.043 0.218 0.108 1.000 [Insert Table 3 here] [1] Statistical Classification of Large, Medium, Small and Micro Enterprises (2017). Retrieved at http://www.stats.gov.cn/tjsj/tjbz/201801/P020180103519867800478.docx 4. Main Empirical Result 4.1. Difference-in-difference (DID) [Insert Table 4 here] Table 4 This table shows the result for the effect of the plan issued in 2017Q2. The scores are calculated using data from the CSMAR database. The sample period is from the second quarter of 2016 to the first quarter of 2019. Variables are winsorized at the 1st and 99th percentiles. The independent variables are lagged by one quarter. The result of t -statistics is reported in parentheses. ***, **, * denote statistical significance at the 1%, 5%, and 10% level, respectively. Dependent variables: Institutional ownership (1) (2) (3) (4) Full example Full example Sub example Sub example Environmental scores : Weakness t-1 -0.123 -0.090 (0.147) (0.171) Strength t-1 0.035 0.338 (0.194) (0.233) Environmental scores*Post -0.190** 0.717*** -0.215** 0.764*** (0.084) (0.120) (0.087) (0.126) Constant 8.087*** 7.775*** 8.065*** 7.755*** (0.251) (0.306) (0.259) (0.312) VOLATILITY t-1 -5.561*** -5.480*** -5.433*** -5.486*** (0.383) (0.481) (0.388) (0.494) PB t-1 0.131*** 0.224*** 0.125*** 0.216*** (0.013) (0.016) (0.014) (0.017) LEV t-1 -0.852*** -0.415** -0.730*** -0.342 (0.180) (0.211) (0.184) (0.218) ROE t-1 3.370*** 2.666*** 3.980*** 3.244*** (0.655) (0.757) (0.678) (0.791) HHI5 t-1 -9.218*** -10.382*** -9.338*** -10.201*** (0.623) (0.775) (0.638) (0.789) IPOA t-1 0.083*** 0.106*** 0.083*** 0.105*** (0.014) (0.017) (0.014) (0.017) SIZED t-1 0.138** 0.060 0.144** 0.061 (0.057) (0.069) (0.058) (0.071) BIG4 2.007*** 2.168*** 1.878*** 2.139*** (0.426) (0.438) (0.429) (0.441) Industry and Year dummies Yes Yes Yes Yes Log likelihood -66102.104 -46434.261 -63311.87 -43922.328 sigma_u 4.794*** 4.930*** 4.798*** 4.920*** sigma_e 2.937*** 2.856*** 2.928*** 2.857*** N 27466 19241 26307 18165 Column (1) and (2) in Table 4 displays the DID result of environmental weakness and strength. From the coefficient of interaction term ( Environmental strength*Post ), there is a 0.19% decrease in the institutional holdings for stocks with poor environmental performance after conducting the plan . While there are also 0.717% increase within high environmental performance. This result provides evidence for our expectation that policies are able to affect institutional preferences. According to the previous studies, we conclude three channels for the changes of institutional environmental preferences before and after the plan . First, the change of corporate environmental performance would raise or reduce the percentage of institutional investors in the environmental strength group. For example, higher environmental scores would attract more responsible investors (Nofsinger et al., 2019 ), while the decrease in environmental scores could make investors who originally invested in average environmental scores naturally become low-scores investors. Second, regulations on ESG information disclosure force corporate to improve their non-financial disclosure (Ioannou and Serafeim, 2019 ), attracting more environmental responsible investors (Meng and Zhang, 2022 ). Third, institutional investor would take action to policies (Y. Jiang and Luo, 2018 ) since government send a signal that policies will tilt toward green produce. To explore the direct impact of policies to financial institutions, we remove firms whose environmental scores or disclosure level have changed during our sample period. Firms’ environmental disclosure data are collected from the Bloomberg database. Result shows in column (3) and (4). The coefficient of interaction term ( Environmental strength*Post ) is -0.215 in environmental weakness and 0.764 in environmental strength, both of which are slightly higher than that in full sample. 4.1.1. Parallel trend test The precondition of DID model is that the changes between treatment and control groups should be consistent during the previous period without the interference of policies. To check it, we execute a parallel trend test that not only can show the tendency of two groups before policy, but also can check the duration of the impact after enacting the policy. [Insert Fig. 2 here] The result for dynamic effect test is reported in Fig. 2 along with a simultaneous 95% confidence band. In panel (a) of Fig. 2 , we can find that there is no significant difference between environmental weakness and control groups before 2017Q2 when the plan was issued, meeting the hypothesis of parallel trend. Then, the coefficients began to decline to negative at 2018Q2, which is a year after the policy is implemented, suggesting the effect of the plan to institutional preferences on environmental weakness lags for a year. Panel (b) shows the result of test between environmental strength and average group. Almost all coefficients are insignificantly before 2017Q2 and become significant positive after the policy enacted, which means the impact of policy on institutional preferences is immediate and persistent especially when a great number of measures about establishing the green financial standardization system conducted after the plan . 4.1.2. The dynamic institutional preferences To prove the change of institutional preferences on environmental criteria, rolling window estimation is conducted. The window for rolling estimation is set to a year (four quarters). By choosing a shorter window, we can effectively control the impact of other events (such as the stock market crash of 2015 and 2018, the COVID-19 global pandemic and etc.) and capture the gradual effect of green finance policies issued. We choose the sample period from the first quarter of 2015 to the second quarter of 2021. Specifically, the first regression is from 2015Q1 to 2015Q4, and the second regression is from 2015Q2 to 2016Q1, and so on. Then we can have insight into the change of institutions’ preferences on green investment in difference period by observing the change of coefficients in different windows. Those coefficients mean that institutions’ interest in strength (weakness) scores would be how much higher (lower) comparing with average scores. The proposed hypothesizes are tested with the model: IO i,t =𝛼 + 𝛽 1 ESG_weakness i,t−1 (ESG_strength i,t−1 )+∑𝛽 j Control i,t − 1 + 𝜀 i,t (3) [Insert Fig. 3 here] Figure 3 demonstrate the dynamic relationship between institutional investors and environmental scores. Besides, we put a cross on the insignificant result with a 10% significance level. As is shown in Fig. 3 , after the announcement and implementation of the plan at 2017, institutional symmetric preferences on environmental scores (namely institutions show higher preferences on environmental strength and a higher aversion to environmental weakness) becomes clearer in recent years. This result supports the research conclusion of the article to a certain extent. 4.2. Do long-term and short-term investors show difference responses on the policy? [Insert Table 5 here] Table 5 This table shows the result for the effect of the plan issued in 2017Q2 considering investment horizon. Following the prior research, we calculate turnover rate and rank the top tertile as short-term institutional investors, the bottom tertile as long-term institutional investors. The scores are calculated using data from the CSMAR database. The sample period is from the second quarter of 2016 to the first quarter of 2019. Variables are winsorized at the 1st and 99th percentiles. The independent variables are lagged by one quarter. The result of t -statistics is reported in parentheses. ***, **, * denote statistical significance at the 1%, 5%, and 10% level, respectively. Dependent variables: Institutional ownership (1) (2) (3) (4) Short-term institutional ownership Short-term institutional ownership Long-term institutional ownership Long term institutional ownership Environmental scores : Weakness t-1 0.274** -0.474*** (0.124) (0.174) Strength t-1 -0.101 0.677*** (0.170) (0.225) Environmental scores*Post -0.378 *** 0.320 *** 0.137 -0.070 (0.068) (0.099) (0.106) (0.137) Constant 3.746*** 3.723*** 1.879*** 1.929*** (0.189) (0.222) (0.276) (0.301) Control variables Yes Yes Yes Yes Industry and Year dummies Yes Yes Yes Yes Log likelihood -40485.921 -29528.477 -57840.552 -40209.529 sigma_u 3.244*** 3.164*** 4.170*** 3.847*** sigma_e 2.267*** 2.227*** 3.107*** 2.832*** N 26307 18165 26307 18165 Table 5 shows the result of policy effect on institutional investors with different investment horizon. Column (1) and (2) displays the DID result of environmental weakness and strength for short-term institutional investors. The coefficient of environmental weakness is significantly positive, and the coefficient of environmental strength is negative although it is insignificant, suggesting that institutions with shorter investment horizon prefer firms with low environmental performance because they typically earn higher returns (Hong and Kacperczyk, 2009 ),and do not show preferences on green investment. However, the coefficient of interaction term ( Environmental weakness*Post ) is negative (-0.378, significantly), and the coefficient of Environmental strength*Post is positive (0.320, significantly), suggesting that the plan did drive short-term institutional investors to reduce the investment in environmental weakness firms and increase holding on firms with environmental strength. Column (3) and (4) indicates the result of long-term institutional investors. Although both the coefficients of interaction term are insignificant, long-term institutions have shown preferences on environmental strength (0.677, significantly) and an aversion to environmental weakness (-0.474, significantly), which is consistent with prior studies who find long-term institutional investment is positively related to CSP (Chang et al., 2020 ; Cox et al., 2004 ; H.-D. Kim et al., 2019 ) and negatively related to poor ESG performance (Nofsinger et al., 2019 ). [Insert Fig. 3 here] Additionally, Fig. 3 shows that in environment strength group, short-term institutions react to green finance policies beginning at the quarter prior to the announcement and only last for a year. Besides, short-term institutions’ reaction in the environmental weakness group tends to be stronger than the whole sample. We assume this difference is driven by short-term returns that be affected by policies. 4.3. dynamic effects on short-term institutions and abnormal returns To illustrate DID results for dynamic policy effects, we treat the periods before the plan as d − 4 ~d − 1 , and the periods after the plan as d 1 ~ d 6 to displace the POST variable. Besides, we use average abnormal return at every quarter calculated by CAPM and short-term institutions as dependent variables to explore the dynamic effect on abnormal return and short-term institutions respectively. Results are shown in Table 6 . We compare changes in short-term institutional ownership and abnormal returns to explore their implicit relations. Table 6 This table shows the result of dynamic difference-in-difference (DID) model for the effect of the plan issued in 2017Q2 on short-term institutions and abnormal return calculated by CAPM. Column (1) and (2) show the results on abnormal returns, and column (3) and (4) show the results on short-term institutions. The scores are calculated using data from the CSMAR database. The sample period is from the second quarter of 2016 to the first quarter of 2019. Variables are winsorized at the 1st and 99th percentiles. The dependent variables are calculated by taking the logarithm of the number of financial institutional holdings and the independent variables are lagged by one quarter. The result of t -statistics is reported in parentheses. ***, **, * denote statistical significance at the 1%, 5%, and 10% level, respectively. Dependent variables: Abnormal return Short-term institutions (1) (2) (3) (4) Environmental weakness Environmental strength Environmental weakness Environmental strength E*d -4 0.170 -0.030 0.255* -0.264 (0.157) (0.234) (0.134) (0.198) E*d -3 0.483*** -0.118 0.286** -0.274 (0.154) (0.222) (0.134) (0.191) E*d -2 0.644*** 0.324 0.236* -0.263 (0.247) (0.293) (0.133) (0.190) E*d -1 0.208 0.055 0.072 0.482** (0.160) (0.241) (0.134) (0.200) E*d 0 0.163 0.408* 0.043 0.465** (0.148) (0.217) (0.127) (0.188) E*d 1 -0.120 0.406* 0.040 0.508*** (0.160) (0.227) (0.125) (0.182) E*d 2 0.090 0.713*** -0.004 0.543*** (0.166) (0.234) (0.125) (0.181) E*d 3 -0.441** 0.726*** -0.324** 0.069 (0.191) (0.266) (0.129) (0.192) E*d 4 -0.476*** 0.922*** -0.387*** 0.066 (0.172) (0.242) (0.119) (0.183) E*d 5 -0.483*** 0.068 -0.392*** 0.085 (0.155) (0.228) (0.118) (0.175) E*d 6 -0.650*** -0.236 -0.404*** 0.040 (0.157) (0.229) (0.118) (0.174) Constant -7.437*** -7.080*** 3.784*** 3.746*** (0.185) (0.210) (0.187) (0.224) Control variables Yes Yes Yes Yes Industry and Year dummies Yes Yes Yes Yes Log likelihood -31938.564 -22114.716 -57822.185 -40209.529 N 25172 17442 26307 18165 [Insert Table 6 here] Column (1) and (2) show the policy effect on abnormal return, and column (3) and (4) show the results of short-term institutions. Comparing results in column (1) and (3), we find the changes of short-term investment behavior on environmental weakness are almost consistent with the decline in abnormal return, both of which begin to significantly decrease at three periods after the plan , and the policy effect last until the end our sample period. In column (2) and (4) for environmental strength, evidence illustrates that the increase on abnormal return is statistically significant at the time the plan issued, and last for five quarters until a year after policy implement. However, the increase of short-term institutional holdings begins at a quarter before the plan , and end at two quarters before the increase in abnormal returns disappear. The empirical results imply that short-tern institutions may use their information advantages to enter the market before the policy released, and then quickly leave the market after obtaining excess returns, which is consistent with our hypothesis that the investment behavior of short-term institutions are associated with abnormal returns. 5. Further Studies 5.1. the role of internal disclosure To test the mediating effect of internal disclosure, we establish triple-difference (DDD) model and estimate DDD models using the following equations: IO i,t =𝛼+𝛽 1 E_weakness i,t−1 +𝛽 2 E_weakness i,t − 1 *Post+𝛽 3 E_weakness i,t−1 *Post*Disclosure +𝛽 4 Post*Disclosure+𝛽 5 E_weakness i,t−1 *Disclosure+𝛽 6 Disclosure+∑𝛽 j Control i,t − 1 +𝜀 i,t (4) IO i,t =𝛼+𝛽 1 E_strength i,t−1 +𝛽 2 E_strength i,t − 1 *Disclosure+𝛽 3 E_strength i,t−1 *Post*Disclosure +𝛽 4 Post*Disclosure+𝛽 5 E_strength i,t−1 *Disclosure+𝛽 6 Disclosure+∑𝛽 j Control i,t − 1 +𝜀 i,t (5) Where Disclosure is a dummy variable that equals to 1 if the listed company discloses its own environmental report separately, and quals to zero if it is not. We focus on the coefficients of Environmental scores*Post* Disclosure ( β 3 ) in Eqs. (4) and (5). [Insert Table 7 here] Table 7 This table shows the result of the mediating effect of environmental information disclosure in the plan issued in 2017Q2. The scores are calculated using data from the CSMAR database. The sample period is from the second quarter of 2016 to the first quarter of 2019. Variables are winsorized at the 1st and 99th percentiles. The independent variables are lagged by one quarter. The result of t -statistics is reported in parentheses. ***, **, * denote statistical significance at the 1%, 5%, and 10% level, respectively. Dependent variables: Institutional ownership (1) (2) Environmental weakness Environmental strength E t-1 : weakness -0.128 (0.183) strength 0.218 (0.250) Environmental scores *Post -0.246*** 0.697*** (0.093) (0.134) Environmental scores*Post*Disclosure -6.475** 0.220 (3.200) (0.993) Environmental scores* Disclosure 8.784*** -2.823*** (3.215) (0.935) Post* Disclosure 0.909** 0.781** (0.405) (0.396) Disclosure 0.266 0.383 (0.272) (0.287) Constant 8.107*** 7.763*** (0.284) (0.340) Control variables Yes Yes Industry and Year dummies Yes Yes Log likelihood -57071.676 -39965.857 sigma_u 4.877*** 4.976*** sigma_e 2.867*** 2.801*** N 22767 15874 In column (2) of Table 7 , given the coefficient of environmental strength*Post is significantly positive, the positive coefficient of Environmental strength*Post* Disclosure shows that comparing with companies that do not self-disclose environmental reports, the effect of policies on institutional preferences for green investment is stronger for companies that have environmental disclosure. This result is consistent with our hypothesis. Besides, the coefficient of Environmental weakness*Post*Disclosure is negative under the negative coefficient of Environmental weakness*Post in column (1). This means that, for firm with self-reported environmental practices, the policy effect on institutional aversion to firms with environmental weakness is stronger. Result in Table 7 consistent with our hypothesis that the higher level of self-environmental disclosure might expand influence of green finance policies, not only on institutional preferences for firms with high environmental performance, but also on their aversion to that with low environmental performance. 5.2. the role of external audits We estimate DDD models using the following equations to test the mediating effect of external auditors: IO i,t =𝛼+𝛽 1 E_weakness i,t−1 +𝛽 2 E_weakness i,t − 1 *Post+𝛽 3 E_weakness i,t−1 *Post*BIG4 +𝛽 4 Post* BIG4+𝛽 5 E_weakness i,t−1 * BIG4 +∑𝛽 j Control i,t − 1 +𝜀 i,t (6) IO i,t =𝛼+𝛽 1 E_strength i,t−1 +𝛽 2 E_strength i,t − 1 *Post+𝛽 3 E_strength i,t−1 *Post*BIG4 +𝛽 4 Post* BIG4+𝛽 5 E_strength i,t−1 * BIG4 +∑𝛽 j Control i,t − 1 +𝜀 i,t (7) Where BIG4 is a dummy variable that equals to 1 if the firm is audited by Big Four global accounting firms, and quals to zero if it is not. We focus on the coefficients of Environmental scores*Post* BIG4 ( β 3 ) in Eqs. (6) and (7). [Insert Table 8 here] Table 8 This table shows the result of the mediating effect of external audits in the plan issued in 2017Q2. The scores are calculated using data from the CSMAR database. The sample period is from the second quarter of 2016 to the first quarter of 2019. Variables are winsorized at the 1st and 99th percentiles. The independent variables are lagged by one quarter. The result of t -statistics is reported in parentheses. ***, **, * denote statistical significance at the 1%, 5%, and 10% level, respectively. Dependent variables: Institutional ownership (1) (2) Environmental weakness Environmental strength E t-1 : weakness -0.054 (0.177) strength 0.190 (0.251) Environmental scores *Post -0.186** 0.842*** (0.091) (0.136) Environmental scores *Post*BIG4 0.681* -1.234*** (0.409) (0.408) Environmental scores* BIG4 -1.363 1.125 (0.852) (0.694) Post* BIG4 1.630*** 1.451*** (0.222) (0.216) Constant 8.383*** 8.092*** (0.266) (0.322) Control variables Yes Yes Industry and Year dummies Yes Yes Log likelihood -63261.298 -43928.388 sigma_u 4.787*** 4.915*** sigma_e 2.923*** 2.853*** N 25147 17394 In Table 8 , given the coefficient of environmental strength*Post is significant positive, the negative coefficient of Environmental strength*Post* BIG4 shows that comparing with firms without Big 4 auditor, the effect of policies on institutional preferences for green investment is weaker for firms that appoint a Big 4 auditor. Additionally, the coefficient of Environmental weakness*Post*BIG4 is positive under the negative coefficient of Environmental weakness*Post in column (1). This means that the stronger external audits supervision of a firm, the weaker policy effect on institutional aversion to its low environmental performance. It worth noting that stronger external audits will narrow down the effect results of green finance policy, which is consistent with our estimation. 7. Conclusion This paper provides a perspective on the evolution of institutional preferences on green investment in the Chinese stock market and the role of government exerting in the evolution. We first examine the degree of response of institutional investors to the green finance policies. The evidence indicates that after the implementation of the plan , financial institutions significantly increased their investment in companies with better environmental performance and reduced their stock holdings for companies with poor environmental performance. We also find that institutional investors react more quickly to environmental strength, but slower to environmental weakness. The rolling window estimation further prove this finding. Considering the heterogeneity of institutional investors, we divide institutional investors into short-term and long-term institutions according to the investment horizon. The study finds that although the preferences of long-term institutional investors are not affected by policies, they have shown a symmetrical preference for environmental performance due to their greater focus on fundamentals, which is consistent with previous research (Cremers et al., 2020 ). In terms of short-term institutional investors, we find that although they are affected by the policy and improve their investment behavior to a certain extent, the change in their behavior is more driven by excess returns. For enterprises with high environmental performance, short-term institutional investors tend to use their own information advantages and professional skills to enter the market in advance and leave before the excess return is reduced, showing a certain degree of speculation (Cano-Berlanga and Giménez-Gómez, 2017 ; Yan and Zhang, 2009 ). For companies with poor environmental performance, the behavior patterns of short-term institutions are also highly correlated with the increase or decrease of excess returns. We further studied the mediating role of environmental disclosure and external audits in policy effects. Consistent with our hypothesis, we find that institutional investors who have better information processing capabilities can distinguish the validity of the information disclosed by the company, and avoid to be affected by the disguise effect of environmental disclosure. Thus, the higher level of environmental information disclosure could expand the policy effect. That is, for companies with a high level of environmental information disclosure, institutional investors' dislike for environmental weakness and preferences for environmental strength are both stronger. Finally, we find that the Big 4 auditors could narrow the policy effect by providing investors with more accurate reporting information and improving investor confidence. Therefore, our findings could have important implications for different market participants. First, the government can introduce more preferential policies, such as appropriate financial subsidies, tax incentives and other means to encourage and guide financial institutions to actively participate in green finance. Since the short-term institutions rely more on excess return instead of changing their investment philosophy, Chinese governments should introduce relevant laws and regulations from the administrative level to deal with those speculative behaviors. Second, for listed enterprises, their brand and corporate reputation may be damaged if there is an environmental problem. This could affect the confidence of investors, resulting in a decrease in the market value of the enterprise and a negative impact on corporate operation and productivity. Companies may thus choose to be silent, because silence is safer than disclosure (Bond and Zeng, 2021 ). Therefore, relying on policy power could be essential to promote corporate information disclosure. Besides, companies need to increase external supervision and environmental performance, rather than attempt to attract external investment through the disguise effect. Finally, the short-sightedness of institutional investors may put short-term pressure on companies, leading to myopic firm behavior (Cremers et al., 2020 ), which would discourage long-term value (Erhemjamts and Huang, 2019 ). Thus, short-term institutional investors should pay more attention to fundamental analysis and transform their trading strategies to reduce myopic behavior. Declarations Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests: The authors have no relevant financial or non-financial interests to disclose. Ethical Approval Consent to Participate ： Not applicable. Consent to Publish ： Not applicable. Authors Contributions ： All authors contributed to the study conception and design. The first draft of the manuscript was written by Pei-Wen Lai. Wu-E Yang provided supervision and review. Zhi-Qiu Han and Zhen-Peng Tang commented on previous versions of the manuscript. All authors read and approved the final manuscript. Availability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Acknowledgments This work was supported by Fujian Provincial Natural Science Foundation of China (No. 2020J01461) and Fujian Provincial Social Science Planning Foundation of China (No. FJ2020B034). References Blomgren A (2011) Does corporate social responsibility influence profit margins? a case study of executive perceptions. Corp Soc Responsib Environ Manag 18(5):263–274 Bond P, Zeng Y (2021) Silence is Safest: Information Disclosure when the Audience’s Preferences are Uncertain.Journal of Financial Economics Broadstock DC, Chan K, Cheng LTW, Wang X (2021) The role of ESG performance during times of financial crisis: Evidence from COVID-19 in China. Financ Res Lett 38:101716 Bushee BJ (2001) Do Institutional Investors Prefer Near-Term Earnings over Long-Run Value? Contemp Acc Res 18(2):207–246 Cano-Berlanga S, Giménez-Gómez J-M (2017) On Chinese stock markets: How have they evolved over time? Ann Oper Res 266(1–2):499–510 Chang K, Kabongo J, Li Y (2020) Geographic proximity, long-term institutional ownership, and corporate social responsibility. Rev Quant Financ Acc 56(1):297–328 Chen L (2014) Local Institutions, Audit Quality, and Corporate Scandals of US-Listed Foreign Firms. J Bus Ethics 133(2):351–373 Chen S, Mao H, Sun J (2021) Low-Carbon City Construction and Corporate Carbon Reduction Performance: Evidence From a Quasi-Natural Experiment in China.Journal of Business Ethics Chen Y, Singhal V, Zhu Q (2020) Environmental policies and financial performance: stock market reaction to firms for their proactive environmental practices recognized by governmental programs. Bus Strategy Environ 30(4):1548–1562 Cox P, Brammer S, Millington A (2004) An Empirical Examination of Institutional Investor Preferences for Corporate Social Performance. J Bus Ethics 52(1):27–43 Cremers M, Pareek A, Sautner Z (2020) Short-Term Investors, Long-Term Investments, and Firm Value: Evidence from Russell 2000 Index Inclusions. Manage Sci 66(10):4535–4551 Daimin S (2002) The effect of impact of policies in stock market fluctuation. Manage World 8:11–15 (in Chinese) Daniel K, Grinblatt M, Titman S, Wermers R (1997) Measuring Mutual Fund Performance with Characteristic-Based Benchmarks.The Journal of Finance, 52 (3) “Green China’s Financial System”Study Group of DRC (2016) The Developmental Logic and Framework of China’s Green Finance. finance forum 21(6):17–28 (in Chinese) Dyck A, Lins KV, Roth L, Wagner HF (2019) Do institutional investors drive corporate social responsibility? International evidence. J Financ Econ 131(3):693–714 Erhemjamts O, Huang K (2019) Institutional ownership horizon, corporate social responsibility and shareholder value. J Bus Res 105:61–79 Fan JPH, Wong TJ (2005) Do External Auditors Perform a Corporate Governance Role in Emerging Markets? Evidence from East Asia. J Accounting Res 43(1):35–72 Feng J, Goodell JW, Shen D (2021) ESG rating and stock price crash risk: Evidence from China. Finance Research Letters Goldie BA, Li L, Masli A (2018) Do Mutual Fund Investors Care About Auditor Quality? Contemp Acc Res 35(3):1505–1532 Graves SB, Waddock SA (1994) Institutional Owners and Corporate Social Performance. Acad Manag J 37(4):1034–1046 Guedhami O, Pittman JA, Saffar W (2014) Auditor Choice in Politically Connected Firms. J Accounting Res 52(1):107–162 Guo M, Kuai Y, Liu X (2020) Stock market response to environmental policies: Evidence from heavily polluting firms in China. Econ Model 86:306–316 Guojin, C., Saijie, D., Xiangqin, Z., & Xiaoyu, J. (2021). China＇s Green Finance Policy，Financing Costs and Firms＇Green Transition: A Central Bank Collateral Framework Perspective. Financial Studies, 12, 75-95 (in Chinese). Hong H, Kacperczyk M (2009) The price of sin: The effects of social norms on markets. J Financ Econ 93(1):15–36 Ioannou I, Serafeim G (2019) The Consequences of Mandatory Corporate Sustainability Reporting. The Oxford Handbook of Corporate Social Responsibility: Psychological and Organizational Perspectives , 452–489 Jiang F, Cai W, Wang X, Zhu B (2018) Multiple large shareholders and corporate investment: Evidence from China. J Corp Finance 50:66–83 Jiang Y, Luo L (2018) Market reactions to environmental policies: Evidence from China. Corp Soc Responsib Environ Manag 25(5):889–903 Khurshed A, Paleari S, Pande A, Vismara SJ (2014) Transparent bookbuilding, certification and initial public offerings. J o F M 19:154–169 Kim H-D, Kim T, Kim Y, Park K (2019) Do long-term institutional investors promote corporate social responsibility activities? J Banking Finance 101:256–269 Kim J-B, Zhang L (2014) Financial Reporting Opacity and Expected Crash Risk: Evidence from Implied Volatility Smirks. Contemp Acc Res 31(3):851–875 Lang M, Mcnichols M (1997) Institutional Trading and Corporate Performance. Working paper, Stanford University. Lee JW (2020) Green Finance and Sustainable Development Goals: The Case of China. J Asian Finance Econ Bus 7(7):577–586 Lee S-Y, Park Y-S, Klassen RD (2015) Market Responses to Firms' Voluntary Climate Change Information Disclosure and Carbon Communication. Corp Soc Responsib Environ Manag 22(1):1–12 Li M, Dong L, Luan J, Wang P (2020) Do environmental regulations affect investors? Evidence from China’s action plan for air pollution prevention.Journal of Cleaner Production, 244 Li Q, Wang J, Bao L (2018) Do institutions trade ahead of false news? Evidence from an emerging market. J Financial Stab 36:98–113 Lihui T, Kedi W (2017) The "disguised effect" of social responsibility information disclosure and the risk of crash of listed companies.Management World(11),146–157 Lowenstein LJCLR (1989) What’s wrong with Wall Street: Short-term gain and the absentee shareholder.Reading, MA: Addison-Wesley., 89 (8) Meng J, Zhang Z (2022) Corporate environmental information disclosure and investor response: Evidence from China's capital market.Energy Economics, 108 Nofsinger JR, Sulaeman J, Varma A (2019) Institutional investors and corporate social responsibility. J Corp Finance 58:700–725 Palmer K, Oates WE, Portney PR (1995) TIGHTENING ENVIRONMENTAL STANDARDS - THE BENEFIT-COST OR THE NO-COST PARADIGM. J Economic Perspect 9(4):119–132 Qi B, Huang M, Chen Z (2006) Institutional Investors and the Volatility of Stock Market.Financial Studies(9),54–64 Ramiah V, Martin B, Moosa I (2013) How does the stock market react to the announcement of green policies? J Banking Finance 37(5):1747–1758 Shirasu Y, Kawakita H (2021) Long-term financial performance of corporate social responsibility.Global Finance Journal, 50 Stolin, David K (2008) Which Money Is Smart? Mutual Fund Buys and Sells of Individual and Institutional Investors.. 63:85–1181 Stuart AC, Bedard JC, Clark CE (2020) Corporate Social Responsibility Disclosures and Investor Judgments in Difficult Times: The Role of Ethical Culture and Assurance. J Bus Ethics 171(3):565–582 Sun H, Chen F (2022) The impact of green finance on China's regional energy consumption structure based on system GMM.Resources Policy, 76 Vo XV (2016) Does institutional ownership increase stock return volatility? Evidence from Vietnam. Int Rev Financial Anal 45:54–61 Wang Y, Delgado MS, Khanna N, Bogan VL (2019) Good news for environmental self-regulation? Finding the right link. J Environ Econ Manag 94:217–235 Wenjing LI, Xiaoyan LU (2015) Do Institutional Investors Care Firm Environmental Performance?Evidence from the Most Polluting Chinese Listed Firms. Financial Studies (in Chinese). Xu S, Liu D (2020) Political connections and corporate social responsibility: Political incentives in China. Bus Ethics: Eur Rev 29(4):664–693 Xu XD, Zeng SX, Tam CM (2011) Stock Market’s Reaction to Disclosure of Environmental Violations: Evidence from China. J Bus Ethics 107(2):227–237 Yan X, Zhang Z (2009) Institutional Investors and Equity Returns: Are Short-term Institutions Better Informed? Rev Financial Stud 22(2):893–924 You H, Zhang X-j (2008) Financial reporting complexity and investor underreaction to 10-K information. Rev Acc Stud 14(4):559–586 Yu C-H, Wu X, Zhang D, Chen S, Zhao J (2021) Demand for green finance: Resolving financing constraints on green innovation in China.Energy Policy, 153 Zhang Z, Yan XS (2009) Institutional Investors and Equity Returns: Are Short-term Institutions Better Informed? SSRN Electronic Journal Zhou G, Liu C, Luo S (2021) Resource Allocation Effect of Green Credit Policy: Based on DID Model.Mathematics, 9 (2) Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 30 Jun, 2022 Reviewers invited by journal 30 Jun, 2022 Editor invited by journal 30 Jun, 2022 Editor assigned by journal 29 Jun, 2022 First submitted to journal 23 Jun, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1790586","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":117685041,"identity":"ac74120c-7e5a-4ba8-9867-5960850cab2d","order_by":0,"name":"Wu-E Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBADOTb25gOkaTHm4zmWQJqWxHkSOQrEKTU4fvbwa942m/Q2hhwGhh8V24jQciYvzZq3LS23jeHsAcaeM7eJ0HIgx8w4t+1wbhtjXwIzYxsxWs6/AWn5n87GzGNApJYbOcaPc9sOJLCxEatF8sYbM+Y/55IN23jYEg4S5Re+8znGH2eU2cnLz3988MGPCiK0KBxgYJOAcQ4QVg8E8g0MzB+IUjkKRsEoGAUjFwAAcJk8dQKo9iUAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-9077-6037","institution":"Fuzhou University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Wu-E","middleName":"","lastName":"Yang","suffix":""},{"id":117685042,"identity":"cbf41ac1-882a-495b-a850-dcc9822e7b07","order_by":1,"name":"Pei-Wen Lai","email":"","orcid":"","institution":"Fuzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Pei-Wen","middleName":"","lastName":"Lai","suffix":""},{"id":117685043,"identity":"13c88fd2-d903-4595-b1ec-55ab5d5e623e","order_by":2,"name":"Zhi-Qiu Han","email":"","orcid":"","institution":"Fuzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhi-Qiu","middleName":"","lastName":"Han","suffix":""},{"id":117685044,"identity":"507fe1cc-4deb-4b9f-a0fa-257e0358261a","order_by":3,"name":"Zhen-Peng Tang","email":"","orcid":"","institution":"Fujian Agriculture and Forestry University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhen-Peng","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2022-06-24 06:38:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1790586/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1790586/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":23727322,"identity":"511b7fd0-7cbd-4d60-8354-69b3c6a181cf","added_by":"auto","created_at":"2022-07-11 21:14:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":502883,"visible":true,"origin":"","legend":"\u003cp\u003eThe Average Environmental Scores over Time\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1790586/v1/2d8b2ac0b7d549f7634c73cc.jpg"},{"id":23727319,"identity":"f0bad649-b484-4ea9-92c1-d9be01b84e49","added_by":"auto","created_at":"2022-07-11 21:14:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":760578,"visible":true,"origin":"","legend":"\u003cp\u003e Results of Parallel Trend Test for Institutional Ownership in Sub Sample\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1790586/v1/bd59cbca797d868334e29477.jpg"},{"id":23727448,"identity":"fbbd3954-0e03-4742-8c4a-3f0760cd1c8c","added_by":"auto","created_at":"2022-07-11 21:19:46","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":842769,"visible":true,"origin":"","legend":"\u003cp\u003eThe Dynamic Results of Institutional Preferences\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1790586/v1/ac15aef6f8fc30bf1eca3f43.jpg"},{"id":23727320,"identity":"e7fa147e-1c60-4148-9e38-09ddf0b0057c","added_by":"auto","created_at":"2022-07-11 21:14:46","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":623114,"visible":true,"origin":"","legend":"\u003cp\u003eResults of Parallel Trend Test for Short-term Institutional Ownership\u0026nbsp;\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1790586/v1/c21756eebb1426d35d18bcda.jpg"},{"id":23727449,"identity":"80b49bc3-709a-41b3-b8cb-d8aa208a7cf2","added_by":"auto","created_at":"2022-07-11 21:19:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":729063,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1790586/v1/761aefd2-fcc7-4e9c-a5c9-ff5ed66a820e.pdf"}],"financialInterests":"","formattedTitle":"Do Government Policies drive institutional preferences on green investment? Evidence from China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn the early days of China's reform and opening up, the most important goal is to develop the economy. However, the rapid growth of the economy is at the cost of ecological and environmental resources. At present, China's sustainable economic growth momentum is insufficient, and environmental issues as one of the important limiting factors, are particularly complex and prominent. Under the goal of achieving carbon peaking by 2030 and carbon neutrality by 2060, China's economy is shifting from a stage of high-speed growth to high-quality development. The transformation of the China's overall development strategy requires financial institutions to play a guiding role in resource allocation to promote the improvement of green investment and the development of the low-carbon business.\u003c/p\u003e \u003cp\u003eIn contrast to developed countries, Chinese governments are a key driver in promoting CSR practices and responsible investment through the guidance of regulations (S. Xu and Liu, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Recently, Chinese central government has launched various policies to promote green investment, which can be divided into two modes: improving non-financial reporting disclosure and establishing the green finance system. For green finance, it is designed to encourage more financial capital into green industries by financial loan support, bonds, stocks, insurance and other financial services (J. W. Lee, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince the People\u0026rsquo;s Bank of China and other Chinese ministries and commissions have promulgated the guidelines for green finance in 2016, the green finance system began taking shape (Feng et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). China became the first country in the world that the central government promote the establishment of a green finance system. In June 2017, the central bank and other four regulators issued a plan of financial standardization system from 2016 to 2020. This action makes the green financial standardization project one of the main projects of financial standardization during the 13th Five-Year Plan. Besides, the State Council approved the construction of pilot zones for green finance reform and innovation in five provinces, namely Zhejiang, Jiangxi, Guangdong, Guizhou and Xinjiang. One of the top priorities in pilot zones is to support financial institutions to set up green finance business units, and encourage venture capital, private equity funds and QFII to participate in green investment.\u003c/p\u003e \u003cp\u003eAlthough China has established a green finance system, green investment in China remains at a stage of beginning. Prior studies pay higher attention to financing constraints (Yu et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and corporate carbon reduction performance (S. Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while we focus on institutional investors\u0026rsquo; reactions. On the one hand, institutional investors are the main driver of the green finance system by shareholder activism in developed countries. However, the development of the green financial mode is difference in developing countries, which relies more on government regulations to guide financial institutions to support sustainable development (DRC, 2016). Thus, exploring whether policies could change institutions\u0026rsquo; investment behaviors in green investment is a prerequisite to further prove whether they could improve corporate environmental performance by engaging corporate governance.\u003c/p\u003e \u003cp\u003eOn the other hand, institutional investors appear to be more rational (Daniel et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) and possess more information advantages, professional skills, processing capabilities and strong financial strength (Stolin and David, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). They can timely seize the opportunities when a policy changes, and pay more attention to the fundamentals of enterprises to reduce irrational behavior, especially for Chinese stock market where most trades are made by speculative individual investors (Cano-Berlanga and Gim\u0026eacute;nez-G\u0026oacute;mez, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and far from being efficient (M. Li et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies have proved that institutional investors in emerging stock markets are able to stabilize the market (Qi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Vo, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Besides, when institutional investors release signals to the market, individual investors who lack sufficient information, tend to observe the trading decisions of institutions and follow selling or buying strategies of institutions. Khurshed et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) have proved that small and medium retail investors actively follow the IPO subscription strategy of institutional investors. Therefore, investigating whether green finance policies could change institutional preferences on green investment and strengthen the atmosphere of green investment in the A-share market, may provide more implications and guidance for government policy makers.\u003c/p\u003e \u003cp\u003eThis paper uses Chinese local ESG rating database to explore policy effect on the relationship between green investment and institutional ownership. Based on the sample period from 2016Q2 to 2019Q1, we find that green finance policies do change institutional preferences on green investment. The rolling window estimation examining the dynamic relationship between institutional ownership and environmental scores also proves our findings. Additionally, comparing with long-term investors, short-term institutions react positively to green finance policies and the change of their holdings are positive associated with abnormal returns aroused by green finance policies. Finally, the mediating effect of internal disclosure and external auditors reveals that the higher level of environmental disclosure would expanse the policy effect, while the higher quality of external audits would reduce the policy effect.\u003c/p\u003e \u003cp\u003eOur study complements the prior literature in three aspects. First, research on green finance focuses mainly on green credit (Zhou et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), energy consumption structure (Sun and Chen, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and financing constraints (Yu et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which is in views of corporate management. This paper is the first attempt to examine the green finance policies on institutional ownership to test the effect of policy implement from an investment perspective. Institutional investors, as the major shareholders of enterprises, can participate in the company's decision-making and play a role in promoting the practice of corporate social responsibility (CSR) by shareholder activism (Dyck et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; H.-D. Kim et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, exploring whether government policies can promote institutional preferences on green investment is of great significance to further promote corporates transform to an environmentally friendly business.\u003c/p\u003e \u003cp\u003eSecond, we focus on the investment behavior of financial institutional investors in emerging market with a more speculative atmosphere. Previous research has confirmed that short-term institutional investors are characterized by preferences for short-term returns due to pressures on short-term performance (Bushee, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). But there are little studies show the impact of policy on abnormal returns to explain the intensity of policy effect on short-term institutional investors. This paper provides empirical evidence that although short-term institutions in China are affected by the green finance policies, their reaction to policies are more relative to abnormal returns triggered by the policy effect.\u003c/p\u003e \u003cp\u003eThird, our study adds to the existing literature on the mediating effect of heterogeneity in terms of corporate characteristics. Previous studies have disclosed that differences in stock market reactions are related to characteristics of firms like size, product profitability, and ownership structure, etc. (Guo et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our paper, however, examines the mediating effect form internal disclosure of environmental performance and external auditors to provide more suggestions for corporate governance.\u003c/p\u003e \u003cp\u003eThe remainder of this paper is organized as follows. Section 2 describes the related literature and hypothesis. Then we introduce the data and model used in Section 3 and describes the main empirical results in Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Section 5 empirically examines the mediating effect of internal disclosure and external audits. Finally, we present the conclusion in the final section.\u003c/p\u003e "},{"header":"2. Literature And Hypothesis","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Green finance policies and institutional investors\u003c/h2\u003e \u003cp\u003ePrevious studies have proved that when the government introduces economic policies or specific industry revitalization plans, the stock market as a \"barometer\" of national economic development, will respond to national policies directly, resulting in the rising or falling of the stock prices of listed companies (Y. Jiang and Luo, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; M. Li et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; You and Zhang, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe main explanation of investors\u0026rsquo; response to government policies is that when a new environmental policy is launched, rational investors tend to expect those firms would be punished by relevant regulations. They could choose to reduce their investment in heavily polluting firms, and then share values of heavily polluting companies could decline (Y. Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Besides, a firm who commits to mitigate environmental problems by conducting waste management or hiring environmental auditors (Blomgren, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), may increase more potential costs and then cause damages to their productivity and profitability (S.-Y. Lee et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Palmer et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, as an emerging market, Chinese stock market is relatively immature due to serious information asymmetry and leakage problems (M. Li et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Q. Li et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Thus, on the one hand, Chinese government has given strong supervision and issue more regulations to investors, leading to more policy impact on the stock market comparing with developed markets (Daimin, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). On the other hand, the extremely strong speculative circumstance in Chinese stock market would make it profitable to gain information advantage (Cano-Berlanga and Gim\u0026eacute;nez-G\u0026oacute;mez, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, rational investors like institutional investors who have more information advantages, professional skills, processing capabilities and strong financial strength (Stolin and David, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), are more likely to seize the opportunities, and react to national policies more quickly.\u003c/p\u003e \u003cp\u003eThe establishment of the green finance system would reduce the financing costs of green enterprises, and at the same time force brown enterprises to achieve green transformation by adding financing pressure on them (Guojin et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Institutional investors in China who have more information advantages and the short-term profit-seeking nature than Western institutional investors (Wenjing and Xiaoyan, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), may increase investment after the introduction of favorable policies to obtain huge returns, and quickly leave the market after the introduction of bearish policies to reduce potential losses. Hence, we formulate the following hypothesis:\u003c/p\u003e \u003cp\u003eH1: The implement of green finance policies would affect financial institutional preferences on green investment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Green finance policies, investment horizon and short-term return\u003c/h2\u003e \u003cp\u003eConsidering the heterogeneity of financial institutional investors, prior studies have found that long-term institutions focus more on firms\u0026rsquo; fundamentals and long-term values (Shirasu and Kawakita, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while short-term institutions prefer to engage in more aggressive investment strategies, instead of conducting buy-and-hold strategies on the basis of the corporates\u0026rsquo; fundamentals (Lowenstein, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1989\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBesides, institutional investor shortsightedness indicates that short-term institutional investors prefer short-term returns over long-term value (Bushee, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). A main interpretation is that they have fiduciary duties for their clients and face more short-term performance pressures (Lang and Mcnichols, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). For example, the ranking of the annual net worth of mutual funds is a huge constraint and stress on every fund manager. Yan and Zhang (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) also find that the positive correlation between institutional ownership and future equity returns is driven by short-term institutions, and propose an argument that short-term institutions are better informed and actively trading to take advantage of their information.\u003c/p\u003e \u003cp\u003eOverall, long-term institutional investors who focus more on fundamental analysis, appear to have stable stock holdings, and the policy effect on them will not be particularly significant. However, short-term institutions who prefer to participate in aggressive investments strategies due to short-term performance pressures, may be more responsive to policy in order to obtain higher short-term returns.\u003c/p\u003e \u003cp\u003eResearch focusing on the abnormal return after environmental policies has revealed that firms with poor environmental performance in heavily-polluting industries have experienced a significant decrease in cumulative abnormal return after the government announcing policies about carbon emission (Ramiah et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In China, the stock market tends to punish firms with lower environmental scores by reducing their stock prices and encourage those firms with higher environmental performance by providing higher returns (Wang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; X. D. Xu et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Accordingly, we propose Hypothesis 2 and 3 for short-term institutions and short-term returns:\u003c/p\u003e \u003cp\u003eH2: Green finance policies have more influences on short-term institutional ownership than long-term investors.\u003c/p\u003e \u003cp\u003eH3: Changes in investment behaviors of short-term institutional investors are associated with abnormal returns after green finance policies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.3. The mediating effect of internal disclosure\u003c/h2\u003e \u003cp\u003eWe compose signaling theory to discuss the mediating effect of internal disclosure on environmental performance. When a company discloses environmental-related reports, it is equivalent to sending a signal to the outside world that the company attaches importance to environmental performance and actively responds to government policy. As signal receivers, investors will interpret the signal as the potential for companies to reduce costs and increase revenues, leading to a positive reaction in the stock market (Y. Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Stuart et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also indicate that independent announcement on environmental issues can be viewed as a positive ethical signal.\u003c/p\u003e \u003cp\u003eHowever, Lihui and Kedi (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) put forward the \"disguise effect\" hypothesis of CSR information disclosure in China, believing that corporate managers may have moral hazard and use the disclosure of social responsibility information to cover up poor performance or avoid unethical behavior.\u003c/p\u003e \u003cp\u003eTherefore, environmental disclosure as a signal does not always bring positive effects. When a company has poor environmental performance but still chooses to disclose environmental indicators, it will bring about a negative reaction in the stock market and exacerbate the reduction of investors' shareholding. Hypothesis 4 for internal disclosure of environment information is thus presented:\u003c/p\u003e \u003cp\u003eH4: The higher level of environmental disclosure would expand policy effect on institutional preferences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.4. The mediating effect of external audits\u003c/h2\u003e \u003cp\u003eExternal auditors may provide assurance of the quality of publicly reported accounting information, and stricter oversight by higher quality auditors will reduce the discretion of inside managers to distort financial reporting, thereby limiting managers' ability to extract wealth from outside shareholders (Fan and Wong, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Guedhami et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This evidence implies that high quality auditors are more likely to protect outside investors from varnishing of company's financial reports by insiders.\u003c/p\u003e \u003cp\u003eAdditionally, prior research has implied that Big 5 auditors generally have high international reputations and appear to be more independent than local auditors (Fan and Wong, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Comparing with non\u0026ndash;Big 4 auditors, Big 4 auditors tend to show lower earnings management costs, greater accounting transparency, higher valuations and lower equity financing cost (Guedhami et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). They may also supply better external supervision, thus enhance investor confidence in the company's financial integrity (Goldie et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; J.-B. Kim and Zhang, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eL. Chen (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) also finds that the existence of Big 4 auditors may mitigate the possibility of involving in corporate scandals of a company. Investors would automatically reduce their expectations for the severity of undiscovered misconduct when evaluating stocks of listed companies. Besides, considering institutional investors may originally hold more shares in companies with high external audit quality, the increase of shareholdings in firms with high external audit quality and better environmental performance would be smaller than that hiring non-Big 4 auditors. Hence, the fifth hypothesis is as follows:\u003c/p\u003e \u003cp\u003eH5: A stronger external audits would narrow down policy effect on institutional preferences.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Data And Method","content":"\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003cdiv\u003e\n \u003ch2\u003e3.1. Sample selection and data sources\u003c/h2\u003e\n \u003cp\u003eWe examine the environmental scores and institutional ownership of all listed A-share firms in the China stock market. The data cover the period from the first quarter of 2015 to the second quarter of 2021. We obtain environment ratings from Huazheng ESG database and A-share firms\u0026rsquo; information from the Chinese Securities Market and Accounting Research (CSMAR) database. To control for bias, we remove financial companies, Specially Treated (ST) firms on the verge of delisting, and companies that exist less than a year after IPO. Besides, firms with missing data are excluded. All continuous variables are winsorized at the 1st and 99th percentiles to reduce the influence of outliers. The final sample consists of 76,178 firm-quarter observations.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ch2\u003e3.1.1. Huazheng environmental ratings\u003c/h2\u003e\n \u003cp\u003eThe Huazheng ESG database is produced by Sino-Securities Index Information Service (Shanghai) Co.ltd in China. We construct our environmental measures using this database for two reasons. First, it has a larger database of China environmental ratings covering all-listed A-share firms in China across time with the earliest data reporting backtrack to 1990, while the widely used datasets around the world like MSCI ESG Ratings only measure hundreds of firms in the China stock market. Second, it will be better suited to properly measure firms\u0026rsquo; performance, since the green investment still be a new topic in China. For instance, indicators like fossil energy utilization, green finance will have a huge difference compared to foreign countries. Thus, it would be better to use China local database to capture the environmental performance of firms in China.\u003c/p\u003e\n \u003cp\u003eThese ESG Rating Data are divided into nine levels from AAA to C respectively. Figure 1 shows the tendency of the average scores of firms in sample from 2015Q1 to 2021Q2. And we define AAA to C as 9 to 1 point. Comparing with the ESG scores which is from 7 to 8 point, the average environmental scores are lower, from 4 to 5 point. There is huge room for financial institutions and companies to drive corporate environmental performance.\u003c/p\u003e\n \u003cp\u003e[Insert Fig.\u0026nbsp;1 here]\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ch2\u003e3.1.2. Institutional ownership (IO)\u003c/h2\u003e\n \u003cp\u003eInstitutional investors in China can be classified as financial institutions (like QFII, mutual funds, securities et al.) and General legal person institutional investors (like non-financial publicly listed firms). General legal person institutions are more like internal supervisors in a company due to their holding purpose and trading strategy. Therefore, we only analyze financial institutional investors consisting of QFII, mutual funds, securities, social security funds, insurance companies, trusts, bank and finance companies (accounting for almost 99% among all financial institutional investors).\u003c/p\u003e\n \u003cp\u003eInstitutional investors are able to get more information about the company through various channels and professional analysis capabilities. And the changes in institutional investors\u0026apos; shareholdings can convey the information they get. So, we calculate the institutional ownership using the number of institutional holdings divided by the company\u0026apos;s outstanding shares.\u003c/p\u003e\n \u003cp\u003eTo figure out the heterogeneity of institutional investors, we further compute the portfolio turnover over the past two years as an investment horizon proxy in our research. Following the prior research, this variable is constructed in three steps (Zhang and Yan, 2009). Firstly, we calculate each institutional investor k\u0026rsquo;s churn rate semiannually by applying the following equation:\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u003cimg 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\"\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003ewhere P\u003csub\u003ei,t\u0026minus;1\u003c/sub\u003e and P\u003csub\u003ei,t\u003c/sub\u003e are the share prices for stock i at the end of period t-1 and t, and S\u003csub\u003ek,i,t\u0026minus;1\u003c/sub\u003e and S\u003csub\u003ek,i,t\u003c/sub\u003e are the number of shares of stock i held by investor k at the end of period t-1 and t respectively. CR_buy\u003csub\u003ek,t\u003c/sub\u003e and CR_sell\u003csub\u003ek,t\u003c/sub\u003e are institution k\u0026rsquo;s aggregate purchase and sale for period t, respectively. Institution k\u0026rsquo;s churn rate for period t is then defined as follows:\u003c/p\u003e\n \u003cdiv\u003e\n \u003cdiv name=\"EquationSource\"\u003e$${CR}_{k,t}=\\frac{\\text{m}\\text{i}\\text{n}({CR\\_buy}_{k,t},{CR\\_sell}_{k,t})}{\\sum _{i=1}^{{N}_{k}}\\frac{{S}_{k,i,t}{P}_{i,t}+{S}_{k,i,t-1}{P}_{i,t-1}}{2}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewe finally calculate the investor turnover of firm i as the weighted average of the total portfolio churn rates of its investors over our sample period, as follows:\u003c/p\u003e\n \u003cdiv\u003e\n \u003cdiv name=\"EquationSource\"\u003e$${Investor Turnover}_{i}=\\sum {W}_{i,t}\\left(\\frac{1}{4}\\sum _{r=1}^{3}{CR}_{k,t-r+1}\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere W\u003csub\u003ei,t\u003c/sub\u003e is the proportion of investor i\u0026rsquo;s ownership in the total ownership held by institutional investors at period t. Given the average churn rate measure, we sort all institutional investors into three tertile portfolios based on the firms\u0026rsquo; turnover rate. Those ranked in the top tertile are classified as short-term institutional investors and those ranked in the bottom tertile are classified as long-term institutional investors.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ch2\u003e3.1.3. Control variables\u003c/h2\u003e\n \u003cp\u003eTo mitigate the heterogeneity of different firms and improve the robustness of analysis, eight control variables, namely \u003cem\u003eVOLATILITY, PB, ROE, IPOA, LEV, HHI5, SIZE and AUDIT\u003c/em\u003e are included following the previous studies.\u003c/p\u003e\n \u003cp\u003eFollowing Graves and Waddock (1994), we use the debt-to-asset ratio (\u003cem\u003eLEV\u003c/em\u003e) as the measurement of firm leverage and \u003cem\u003eROE\u003c/em\u003e as the measurement of firm performance. Besides, \u003cem\u003eVOLATILITY\u003c/em\u003e is measured by average monthly volatility of return. Price-to-book ratio (\u003cem\u003ePB)\u003c/em\u003e measure the value of investing in a company. \u003cem\u003eIPOA\u003c/em\u003e refers to the number of years that a company exists after IPO, measuring the cognition degree of investors, since stock with longer IPO age could have more open data. Unlike the common practice measured by the natural logarithm of the firm\u0026rsquo;s market capitalization (Bushee, 2001), we set a dummy variable (\u003cem\u003eSIZED\u003c/em\u003e) that equals one if the firm is a big company and zero if it is not according to the standard of China\u0026rsquo;s National Bureau of Statistics\u003csup\u003e1\u003c/sup\u003e to measure this variable.\u003c/p\u003e\n \u003cp\u003eAccording to Fan and Wong (2005), high-quality auditors could provide better supervision and reliable reports for investors. We add \u003cem\u003eAUDIT\u003c/em\u003e as the measure of a dummy variable that equals one if the firm is audited by Big Four global accounting firms. We also set \u003cem\u003eHHI5\u003c/em\u003e, the total squares of the ratio of the top5 large shareholder\u0026apos;s ownership to measure the Ownership concentration (F. Jiang et al., 2018). Table 1 shows the explanation of all variables.\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eVariable Definitions\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003cp\u003esymbol\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDependent variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInstitutional ownership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIO\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe number of financial institutional holdings divided by the company\u0026apos;s outstanding shares\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndependent variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnvironmental scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirms\u0026rsquo; environmental ratings, setting AAA to A as environmental strength and CCC to C as environmental weakness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStock price volatility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eVOLATILITY\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage monthly volatility from the beginning of the quarter to the end of the quarter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrice-to-Book Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrice-to-Book Ratio to measure the value of investing in a company\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProfitability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eROE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRate of Return on Common Stockholders\u0026rsquo; Equity to measure the companies\u0026rsquo; profitability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPO age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIPOA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe year a company exists after IPO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirm leverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLEV\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiabilities divided by net assets to measure a companies\u0026rsquo; debt paying capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOwnership concentration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHHI5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe total squares of the ratio of the top5 large shareholder\u0026apos;s ownership to measure the Ownership concentration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirm size dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDSIZE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA dummy variable that equals one if the firm is a big companies according to the standard of China\u0026rsquo;s National Bureau of Statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAuditors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBIG4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA dummy variable that equals one if the firm is audited by Big Four global accounting firms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e[Insert Table 1 here]\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ch2\u003e3.2. Model specification\u003c/h2\u003e\n \u003cp\u003eConsidering almost 9% of publicly trading firms do not receive financial institutional investors\u0026rsquo; investment, it could be hard to get the unbiased result by OLS regression. After conducting the maximum likelihood test, we choose random-effects Tobit regression model instead of pooled panel Tobit regressions to test the institutional preference on ESG ratings. To avoid the Endogeneity, we use one-period lag variables and add industry and time dummy variables in the model.\u003c/p\u003e\n \u003cp\u003eTo measure the effect of green finance policies, we treat \u003cem\u003ethe plan of financial Standardization system from 2016 to 2020 (the plan)\u003c/em\u003e at 2017Q2 as an exogenous regulatory shock and consider a time period from 2016Q2 to 2019Q1 to conduct a DID analysis. We estimate DID models using the following equations:\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eIO\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003ei,t\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e \u003cem\u003e= 𝛼 + 𝛽\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003e1\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e \u003cem\u003eE_weakness\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003ei,t\u0026minus;1\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e \u003cem\u003e+ 𝛽\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eE_weakness\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e*Post +\u0026sum;𝛽\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eControl\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u0026minus;1\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ 𝜀\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e (1)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eIO\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003ei,t\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e \u003cem\u003e= 𝛼 + 𝛽\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003e1\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e \u003cem\u003eE_strength\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003ei,t\u0026minus;1\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e \u003cem\u003e+ 𝛽\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eE_strength\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e*Post +\u0026sum;𝛽\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eControl\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u0026minus;1\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ 𝜀\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e (2)\u003c/p\u003e\n \u003cp\u003ewhere \u003cem\u003eIO\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e is the institutional ownership for firm \u003cem\u003ei\u003c/em\u003e in at the end of each quarter \u003cem\u003et\u003c/em\u003e. We divide environmental scores into three groups: weakness, average and strength. Then we set two dummy variables: \u003cem\u003eweakness\u003c/em\u003e and \u003cem\u003estrength\u003c/em\u003e, and treat average group as control group. The \u003cem\u003eEnvironmental_weakness (strength)\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u0026minus;1\u003c/em\u003e\u003c/sub\u003e variable is a dummy variable, which equals to 1 when the ESG scores of firm \u003cem\u003ei\u003c/em\u003e in quarter \u003cem\u003et-1\u003c/em\u003e belong to the lowest (highest) group and equals to 0 when they beyond the lowest (highest) group. Standards that divide environmental scores into wea\u003cem\u003ekness\u003c/em\u003e and \u003cem\u003estrength\u003c/em\u003e are shown in Table 1. \u003cem\u003eControl\u003c/em\u003e refers to various firm specific control variables. All of the firm-specific control variables are from quarter \u003cem\u003et-1\u003c/em\u003e to minimize endogeneity concerns.\u003c/p\u003e\n \u003cp\u003eThe dummy variable \u003cem\u003ePost\u003c/em\u003e is defined in the following way: the second quarter of 2016 (t-4) to the first quarter of 2017 (t-1) are defined as \u0026ldquo;pre\u0026rdquo; period, and the third quarter of 2017 (t\u0026thinsp;+\u0026thinsp;1) to the first quarter of 2019 (t\u0026thinsp;+\u0026thinsp;7) are defined as \u0026ldquo;post\u0026rdquo; period. By defining pre- and post-periods in this way, there is a clear prediction on the sign of the coefficient \u003cem\u003e\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e from the above equation. After the policy, financial institutions are expected to improve their investment in firms with environmental strength and reduce their investment in environmental weakness. Hence, \u003cem\u003e\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e is predicted to be negative in the Eq. (1) and positive in the Eq. (2).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ch2\u003e3.3. summary statistics\u003c/h2\u003e\n \u003cp\u003eThe summary statistics of variables are reported in Table\u0026nbsp;2. In Panel A that the average IO is 4.93% over our sample period, suggesting financial institutional holdings only account for relatively small proportions of outstanding shares in China, although the maximum holding is 26.104%. Our results are in line with Broadstock et al. (2021) who find that institutional investors remain few in China and retail investors participate in most investment activity. Panel B shows descriptive statistics with two groups: environmental strength and weakness respectively. We find that the average IO in environmental strength group (6.7%) is much higher than that in environmental weakness group (4.42%). This result supports our assumption that institutions prefer firms with higher environmental performance than those with poor environmental performance. The distribution of other variables is within a reasonable range.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Summary Statistics\u003c/p\u003e\n \u003cp\u003eThis table provides summary statistics of financial institutions and firms\u0026rsquo; characteristics. The sample consists of 76,178 firm-quarter observations across A-share firms over the 2015-2021 period. All continuous variables are winsorized at the 1st and 99th percentiles.\u003c/p\u003e\n \u003ctable align=\"left\" border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" width=\"100%\"\u003e\n \u003cp\u003ePanel A: Full sample\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eObservation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.020833333333334%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.715277777777779%\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cem\u003eIO (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e76178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e4.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.020833333333334%\"\u003e\n \u003cp\u003e5.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e2.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.715277777777779%\"\u003e\n \u003cp\u003e26.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e76178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.020833333333334%\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e2.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.715277777777779%\"\u003e\n \u003cp\u003e3.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cem\u003eVOLATILITY\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e73533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.020833333333334%\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.88888888888889%\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.715277777777779%\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cem\u003ePB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e73887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.020833333333334%\"\u003e\n \u003cp\u003e3.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.88888888888889%\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.583333333333334%\"\u003e\n \u003cp\u003e2.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.715277777777779%\"\u003e\n \u003cp\u003e19.750\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cem\u003eLEV\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e76058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.020833333333334%\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.88888888888889%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.715277777777779%\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cem\u003eIPO\u003c/em\u003e\u003cem\u003eA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e76066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e10.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.020833333333334%\"\u003e\n \u003cp\u003e7.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.88888888888889%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.583333333333334%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.715277777777779%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cem\u003eROE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e73766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.020833333333334%\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.88888888888889%\"\u003e\n \u003cp\u003e-0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.715277777777779%\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cem\u003eHHI5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e76178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.020833333333334%\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.88888888888889%\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.715277777777779%\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cem\u003eDSIZE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e76178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.020833333333334%\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.715277777777779%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cem\u003eBIG4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e76178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.020833333333334%\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.715277777777779%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" width=\"100%\"\u003e\n \u003cp\u003ePanel B: Sub-samples with environmental strength and weakness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"17.33102253032929%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"37.08838821490468%\"\u003e\n \u003cp\u003eWeakness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"42.46100519930676%\"\u003e\n \u003cp\u003eStrength\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.68972746331237%\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.17400419287212%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.947589098532493%\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.33102253032929%\"\u003e\n \u003cp\u003e\u003cem\u003eIO (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.584055459272097%\"\u003e\n \u003cp\u003e4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.504332755632582%\"\u003e\n \u003cp\u003e5.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.623916811091853%\"\u003e\n \u003cp\u003e6.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.837088388214905%\"\u003e\n \u003cp\u003e6.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.33102253032929%\"\u003e\n \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.584055459272097%\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.504332755632582%\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.623916811091853%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.837088388214905%\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.33102253032929%\"\u003e\n \u003cp\u003e\u003cem\u003eVOLATILITY\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.584055459272097%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.504332755632582%\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.623916811091853%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.837088388214905%\"\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.33102253032929%\"\u003e\n \u003cp\u003e\u003cem\u003ePB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.584055459272097%\"\u003e\n \u003cp\u003e3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.504332755632582%\"\u003e\n \u003cp\u003e3.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.623916811091853%\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.837088388214905%\"\u003e\n \u003cp\u003e3.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.33102253032929%\"\u003e\n \u003cp\u003e\u003cem\u003eLEV\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.584055459272097%\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.504332755632582%\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.623916811091853%\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.837088388214905%\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.33102253032929%\"\u003e\n \u003cp\u003e\u003cem\u003eIPO\u003c/em\u003e\u003cem\u003eA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.584055459272097%\"\u003e\n \u003cp\u003e9.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.504332755632582%\"\u003e\n \u003cp\u003e7.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.623916811091853%\"\u003e\n \u003cp\u003e14.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.837088388214905%\"\u003e\n \u003cp\u003e7.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.33102253032929%\"\u003e\n \u003cp\u003e\u003cem\u003eROE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.584055459272097%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.504332755632582%\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.623916811091853%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.837088388214905%\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.33102253032929%\"\u003e\n \u003cp\u003e\u003cem\u003eHHI5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.584055459272097%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.504332755632582%\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.623916811091853%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.837088388214905%\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.33102253032929%\"\u003e\n \u003cp\u003e\u003cem\u003eDSIZE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.584055459272097%\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.504332755632582%\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.623916811091853%\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.837088388214905%\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.33102253032929%\"\u003e\n \u003cp\u003e\u003cem\u003eBIG4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.584055459272097%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.504332755632582%\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.623916811091853%\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.837088388214905%\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e[Insert Table\u0026nbsp;2 here]\u003c/p\u003e\n \u003cp\u003eTable 3 reports the correlation matrix of the variables used in the empirical analyses. The correlation results show that correlations among all variables are pretty low with a 1% significance level. This means multicollinearity will not occur in our regression analyses.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCorrelation Matrix\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIO\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eVOLATILITY\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePB\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLEV\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIPOA\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eROE\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHHI5\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSIZED\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUDIT\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIO (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eVOLATILITY\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eROE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIPOA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLEV\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHHI5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDSIZE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAUDIT\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e[Insert Table 3 here]\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e[1] Statistical Classification of Large, Medium, Small and Micro Enterprises (2017). Retrieved at http://www.stats.gov.cn/tjsj/tjbz/201801/P020180103519867800478.docx\u0026nbsp;\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Main Empirical Result","content":"\u003cdiv class=\"Section2\" id=\"Sec14\"\u003e\n \u003ch2\u003e4.1. Difference-in-difference (DID)\u003c/h2\u003e\n \u003cp\u003e[Insert Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e here]\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThis table shows the result for the effect of \u003cem\u003ethe plan\u003c/em\u003e issued in 2017Q2. The scores are calculated using data from the CSMAR database. The sample period is from the second quarter of 2016 to the first quarter of 2019. Variables are winsorized at the 1st and 99th percentiles. The independent variables are lagged by one quarter. The result of \u003cem\u003et\u003c/em\u003e-statistics is reported in parentheses. ***, **, * denote statistical significance at the 1%, 5%, and 10% level, respectively.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDependent variables:\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eInstitutional ownership\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFull example\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFull example\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSub example\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSub example\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEnvironmental scores\u003c/em\u003e:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eWeakness\u003c/em\u003e\u003csub\u003e\u003cem\u003et-1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.147)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.171)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eStrength\u003c/em\u003e\u003csub\u003e\u003cem\u003et-1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.194)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.233)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEnvironmental scores*Post\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.190**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.717***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.215**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.764***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.084)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.087)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.126)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.087***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.775***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.065***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.755***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.251)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.306)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.259)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.312)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eVOLATILITY\u003c/em\u003e \u003csub\u003e\u003cem\u003et-1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.561***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.480***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.433***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.486***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.383)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.481)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.388)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.494)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePB\u003c/em\u003e \u003csub\u003e\u003cem\u003et-1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.131***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.224***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.216***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLEV\u003c/em\u003e \u003csub\u003e\u003cem\u003et-1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.852***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.415**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.730***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.211)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.218)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eROE\u003c/em\u003e \u003csub\u003e\u003cem\u003et-1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.370***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.666***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.980***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.244***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.655)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.757)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.678)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.791)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHHI5\u003c/em\u003e \u003csub\u003e\u003cem\u003et-1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.218***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.382***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.338***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.201***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.623)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.775)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.638)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.789)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIPOA\u003c/em\u003e \u003csub\u003e\u003cem\u003et-1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.083***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.106***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.083***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSIZED\u003c/em\u003e \u003csub\u003e\u003cem\u003et-1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.138**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.144**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.069)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.071)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBIG4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.007***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.168***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.878***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.139***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.426)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.438)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.429)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.441)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIndustry and Year dummies\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLog likelihood\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-66102.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-46434.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-63311.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-43922.328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003esigma_u\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.794***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.930***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.798***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.920***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003esigma_e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.937***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.856***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.928***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.857***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eColumn (1) and (2) in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e displays the DID result of environmental weakness and strength. From the coefficient of interaction term (\u003cem\u003eEnvironmental strength*Post\u003c/em\u003e), there is a 0.19% decrease in the institutional holdings for stocks with poor environmental performance after conducting \u003cem\u003ethe plan\u003c/em\u003e. While there are also 0.717% increase within high environmental performance. This result provides evidence for our expectation that policies are able to affect institutional preferences.\u003c/p\u003e\n \u003cp\u003eAccording to the previous studies, we conclude three channels for the changes of institutional environmental preferences before and after \u003cem\u003ethe plan\u003c/em\u003e. First, the change of corporate environmental performance would raise or reduce the percentage of institutional investors in the environmental strength group. For example, higher environmental scores would attract more responsible investors (Nofsinger et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), while the decrease in environmental scores could make investors who originally invested in average environmental scores naturally become low-scores investors. Second, regulations on ESG information disclosure force corporate to improve their non-financial disclosure (Ioannou and Serafeim, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), attracting more environmental responsible investors (Meng and Zhang, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Third, institutional investor would take action to policies (Y. Jiang and Luo, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) since government send a signal that policies will tilt toward green produce.\u003c/p\u003e\n \u003cp\u003eTo explore the direct impact of policies to financial institutions, we remove firms whose environmental scores or disclosure level have changed during our sample period. Firms\u0026rsquo; environmental disclosure data are collected from the Bloomberg database. Result shows in column (3) and (4). The coefficient of interaction term (\u003cem\u003eEnvironmental strength*Post\u003c/em\u003e) is -0.215 in environmental weakness and 0.764 in environmental strength, both of which are slightly higher than that in full sample.\u003c/p\u003e\n \u003cdiv class=\"Section3\" id=\"Sec15\"\u003e\n \u003ch2\u003e4.1.1. Parallel trend test\u003c/h2\u003e\n \u003cp\u003eThe precondition of DID model is that the changes between treatment and control groups should be consistent during the previous period without the interference of policies. To check it, we execute a parallel trend test that not only can show the tendency of two groups before policy, but also can check the duration of the impact after enacting the policy.\u003c/p\u003e\n \u003cp\u003e[Insert Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e here]\u003c/p\u003e\n \u003cp\u003eThe result for dynamic effect test is reported in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e along with a simultaneous 95% confidence band. In panel (a) of Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, we can find that there is no significant difference between environmental weakness and control groups before 2017Q2 when \u003cem\u003ethe plan\u003c/em\u003e was issued, meeting the hypothesis of parallel trend. Then, the coefficients began to decline to negative at 2018Q2, which is a year after the policy is implemented, suggesting the effect of \u003cem\u003ethe plan\u003c/em\u003e to institutional preferences on environmental weakness lags for a year.\u003c/p\u003e\n \u003cp\u003ePanel (b) shows the result of test between environmental strength and average group. Almost all coefficients are insignificantly before 2017Q2 and become significant positive after the policy enacted, which means the impact of policy on institutional preferences is immediate and persistent especially when a great number of measures about establishing the green financial standardization system conducted after \u003cem\u003ethe plan\u003c/em\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec16\"\u003e\n \u003ch2\u003e4.1.2. The dynamic institutional preferences\u003c/h2\u003e\n \u003cp\u003eTo prove the change of institutional preferences on environmental criteria, rolling window estimation is conducted. The window for rolling estimation is set to a year (four quarters). By choosing a shorter window, we can effectively control the impact of other events (such as the stock market crash of 2015 and 2018, the COVID-19 global pandemic and etc.) and capture the gradual effect of green finance policies issued. We choose the sample period from the first quarter of 2015 to the second quarter of 2021. Specifically, the first regression is from 2015Q1 to 2015Q4, and the second regression is from 2015Q2 to 2016Q1, and so on. Then we can have insight into the change of institutions\u0026rsquo; preferences on green investment in difference period by observing the change of coefficients in different windows. Those coefficients mean that institutions\u0026rsquo; interest in strength (weakness) scores would be how much higher (lower) comparing with average scores. The proposed hypothesizes are tested with the model:\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eIO\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003ei,t\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e \u003cem\u003e=𝛼 + 𝛽\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003e1\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e \u003cem\u003eESG_weakness\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003ei,t\u0026minus;1\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e \u003cem\u003e(ESG_strength\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u0026minus;1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)+\u0026sum;𝛽\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eControl\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ 𝜀\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e (3)\u003c/p\u003e\n \u003cp\u003e[Insert Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e here]\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrate the dynamic relationship between institutional investors and environmental scores. Besides, we put a cross on the insignificant result with a 10% significance level. As is shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, after the announcement and implementation of \u003cem\u003ethe plan\u003c/em\u003e at 2017, institutional symmetric preferences on environmental scores (namely institutions show higher preferences on environmental strength and a higher aversion to environmental weakness) becomes clearer in recent years. This result supports the research conclusion of the article to a certain extent.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec17\"\u003e\n \u003ch2\u003e4.2. Do long-term and short-term investors show difference responses on the policy?\u003c/h2\u003e\n \u003cp\u003e[Insert Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e here]\u003c/p\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThis table shows the result for the effect of \u003cem\u003ethe plan\u003c/em\u003e issued in 2017Q2 considering investment horizon. Following the prior research, we calculate turnover rate and rank the top tertile as short-term institutional investors, the bottom tertile as long-term institutional investors. The scores are calculated using data from the CSMAR database. The sample period is from the second quarter of 2016 to the first quarter of 2019. Variables are winsorized at the 1st and 99th percentiles. The independent variables are lagged by one quarter. The result of \u003cem\u003et\u003c/em\u003e-statistics is reported in parentheses. ***, **, * denote statistical significance at the 1%, 5%, and 10% level, respectively.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDependent variables:\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eInstitutional ownership\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eShort-term institutional ownership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShort-term institutional ownership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLong-term institutional ownership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLong term institutional ownership\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEnvironmental scores\u003c/em\u003e:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eWeakness\u003c/em\u003e\u003csub\u003e\u003cem\u003et-1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.274**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.474***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(0.124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.174)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eStrength\u003c/em\u003e\u003csub\u003e\u003cem\u003et-1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.677***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(0.225)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEnvironmental scores*Post\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.378\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.320\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(0.068)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.099)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(0.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.746***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.723***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.879***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.929***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(0.189)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.222)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.276)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(0.301)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eControl variables\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIndustry and Year dummies\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLog likelihood\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-40485.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-29528.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-57840.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-40209.529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003esigma_u\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.244***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.164***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.170***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.847***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003esigma_e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.267***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.227***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.107***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.832***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e26307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e18165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the result of policy effect on institutional investors with different investment horizon. Column (1) and (2) displays the DID result of environmental weakness and strength for short-term institutional investors. The coefficient of environmental weakness is significantly positive, and the coefficient of environmental strength is negative although it is insignificant, suggesting that institutions with shorter investment horizon prefer firms with low environmental performance because they typically earn higher returns (Hong and Kacperczyk, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e),and do not show preferences on green investment.\u003c/p\u003e\n \u003cp\u003eHowever, the coefficient of interaction term (\u003cem\u003eEnvironmental weakness*Post\u003c/em\u003e) is negative (-0.378, significantly), and the coefficient of \u003cem\u003eEnvironmental strength*Post\u003c/em\u003e is positive (0.320, significantly), suggesting that \u003cem\u003ethe plan\u003c/em\u003e did drive short-term institutional investors to reduce the investment in environmental weakness firms and increase holding on firms with environmental strength.\u003c/p\u003e\n \u003cp\u003eColumn (3) and (4) indicates the result of long-term institutional investors. Although both the coefficients of interaction term are insignificant, long-term institutions have shown preferences on environmental strength (0.677, significantly) and an aversion to environmental weakness (-0.474, significantly), which is consistent with prior studies who find long-term institutional investment is positively related to CSP (Chang et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cox et al., \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e; H.-D. Kim et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) and negatively related to poor ESG performance (Nofsinger et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e[Insert Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e here]\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eAdditionally, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows that in environment strength group, short-term institutions react to green finance policies beginning at the quarter prior to the announcement and only last for a year. Besides, short-term institutions\u0026rsquo; reaction in the environmental weakness group tends to be stronger than the whole sample. We assume this difference is driven by short-term returns that be affected by policies.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec18\"\u003e\n \u003ch2\u003e4.3. dynamic effects on short-term institutions and abnormal returns\u003c/h2\u003e\n \u003cp\u003eTo illustrate DID results for dynamic policy effects, we treat the periods before \u003cem\u003ethe plan\u003c/em\u003e as d\u003csub\u003e\u0026minus;\u0026thinsp;4\u003c/sub\u003e~d\u003csub\u003e\u0026minus;\u0026thinsp;1\u003c/sub\u003e, and the periods after \u003cem\u003ethe plan\u003c/em\u003e as d\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;~\u0026thinsp;d\u003csub\u003e6\u003c/sub\u003e to displace the \u003cem\u003ePOST\u003c/em\u003e variable. Besides, we use average abnormal return at every quarter calculated by CAPM and short-term institutions as dependent variables to explore the dynamic effect on abnormal return and short-term institutions respectively. Results are shown in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. We compare changes in short-term institutional ownership and abnormal returns to explore their implicit relations.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab5\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThis table shows the result of dynamic difference-in-difference (DID) model for the effect of \u003cem\u003ethe plan\u003c/em\u003e issued in 2017Q2 on short-term institutions and abnormal return calculated by CAPM. Column (1) and (2) show the results on abnormal returns, and column (3) and (4) show the results on short-term institutions. The scores are calculated using data from the CSMAR database. The sample period is from the second quarter of 2016 to the first quarter of 2019. Variables are winsorized at the 1st and 99th percentiles. The dependent variables are calculated by taking the logarithm of the number of financial institutional holdings and the independent variables are lagged by one quarter. The result of \u003cem\u003et\u003c/em\u003e-statistics is reported in parentheses. ***, **, * denote statistical significance at the 1%, 5%, and 10% level, respectively.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 15.067%;\"\u003e\n \u003cp\u003eDependent variables:\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" style=\"width: 32.6686%;\"\u003e\n \u003cp\u003eAbnormal return\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 27.391%;\"\u003e\n \u003cp\u003eShort-term institutions\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003eEnvironmental weakness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003eEnvironmental strength\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003eEnvironmental weakness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003eEnvironmental strength\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\n \u003cp\u003e\u003cem\u003eE*d\u003c/em\u003e\u003csub\u003e\u003cem\u003e-4\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e-0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e0.255*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e-0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.157)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.234)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.198)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\n \u003cp\u003e\u003cem\u003eE*d\u003c/em\u003e\u003csub\u003e\u003cem\u003e-3\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e0.483***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e-0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e0.286**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e-0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.154)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.222)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.191)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\n \u003cp\u003e\u003cem\u003eE*d\u003c/em\u003e\u003csub\u003e\u003cem\u003e-2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e0.644***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e0.236*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e-0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.247)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.293)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.133)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.190)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\n \u003cp\u003e\u003cem\u003eE*d\u003c/em\u003e\u003csub\u003e\u003cem\u003e-1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e0.482**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.160)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.241)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.200)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\n \u003cp\u003e\u003cem\u003eE*d\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e0.408*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e0.465**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.217)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.188)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\n \u003cp\u003e\u003cem\u003eE*d\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e-0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e0.406*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e0.508***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.160)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.227)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.125)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.182)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\n \u003cp\u003e\u003cem\u003eE*d\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e0.713***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e0.543***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.166)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.234)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.125)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.181)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\n \u003cp\u003e\u003cem\u003eE*d\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e-0.441**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e0.726***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e-0.324**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.191)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.266)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.192)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\n \u003cp\u003e\u003cem\u003eE*d\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e-0.476***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e0.922***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e-0.387***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.172)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.242)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.183)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\n \u003cp\u003e\u003cem\u003eE*d\u003c/em\u003e\u003csub\u003e\u003cem\u003e5\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e-0.483***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e-0.392***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.155)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.228)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.175)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\n \u003cp\u003e\u003cem\u003eE*d\u003c/em\u003e\u003csub\u003e\u003cem\u003e6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e-0.650***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e-0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e-0.404***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.157)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.229)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.174)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\n \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e-7.437***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e-7.080***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e3.784***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e3.746***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.185)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.210)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e(0.187)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e(0.224)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\n \u003cp\u003e\u003cem\u003eControl variables\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\n \u003cp\u003e\u003cem\u003eIndustry and Year dummies\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\n \u003cp\u003e\u003cem\u003eLog likelihood\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e-31938.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e-22114.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e-57822.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e-40209.529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.067%;\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e25172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e17442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.6159%;\"\u003e\n \u003cp\u003e26307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.1935%;\"\u003e\n \u003cp\u003e18165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e[Insert Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e here]\u003c/p\u003e\n \u003cp\u003eColumn (1) and (2) show the policy effect on abnormal return, and column (3) and (4) show the results of short-term institutions. Comparing results in column (1) and (3), we find the changes of short-term investment behavior on environmental weakness are almost consistent with the decline in abnormal return, both of which begin to significantly decrease at three periods after \u003cem\u003ethe plan\u003c/em\u003e, and the policy effect last until the end our sample period. In column (2) and (4) for environmental strength, evidence illustrates that the increase on abnormal return is statistically significant at the time \u003cem\u003ethe plan\u003c/em\u003e issued, and last for five quarters until a year after policy implement. However, the increase of short-term institutional holdings begins at a quarter before \u003cem\u003ethe plan\u003c/em\u003e, and end at two quarters before the increase in abnormal returns disappear. The empirical results imply that short-tern institutions may use their information advantages to enter the market before the policy released, and then quickly leave the market after obtaining excess returns, which is consistent with our hypothesis that the investment behavior of short-term institutions are associated with abnormal returns.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Further Studies","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.1. the role of internal disclosure\u003c/h2\u003e \u003cp\u003eTo test the mediating effect of internal disclosure, we establish triple-difference (DDD) model and estimate DDD models using the following equations:\u003c/p\u003e \u003cp\u003e \u003cem\u003eIO\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e=\u0026#120572;+\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eE_weakness\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u0026minus;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e+\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e2\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eE_weakness\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e*Post+\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e3\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eE_weakness\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u0026minus;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e*Post*Disclosure +\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e4\u003c/em\u003e \u003c/sub\u003e \u003cem\u003ePost*Disclosure+\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e5\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eE_weakness\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u0026minus;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e*Disclosure+\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e6\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eDisclosure+\u0026sum;\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003ej\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eControl\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e+\u0026#120576;\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u003c/em\u003e \u003c/sub\u003e (4)\u003c/p\u003e \u003cp\u003e \u003cem\u003eIO\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e=\u0026#120572;+\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eE_strength\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u0026minus;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e+\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e2\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eE_strength\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e*Disclosure+\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e3\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eE_strength\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u0026minus;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e*Post*Disclosure +\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e4\u003c/em\u003e \u003c/sub\u003e \u003cem\u003ePost*Disclosure+\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e5\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eE_strength\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u0026minus;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e*Disclosure+\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e6\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eDisclosure+\u0026sum;\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003ej\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eControl\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e+\u0026#120576;\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u003c/em\u003e \u003c/sub\u003e (5)\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eDisclosure\u003c/em\u003e is a dummy variable that equals to 1 if the listed company discloses its own environmental report separately, and quals to zero if it is not. We focus on the coefficients of \u003cem\u003eEnvironmental scores*Post* Disclosure\u003c/em\u003e (\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e) in Eqs.\u0026nbsp;(4) and (5).\u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThis table shows the result of the mediating effect of environmental information disclosure in \u003cem\u003ethe plan\u003c/em\u003e issued in 2017Q2. The scores are calculated using data from the CSMAR database. The sample period is from the second quarter of 2016 to the first quarter of 2019. Variables are winsorized at the 1st and 99th percentiles. The independent variables are lagged by one quarter. The result of \u003cem\u003et\u003c/em\u003e-statistics is reported in parentheses. ***, **, * denote statistical significance at the 1%, 5%, and 10% level, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent variables:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eInstitutional ownership\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental weakness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnvironmental strength\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003et-1\u003c/em\u003e\u003c/sub\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eweakness\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.183)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003estrength\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.250)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEnvironmental scores\u003c/em\u003e \u003cem\u003e*Post\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.246***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.697***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.093)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.134)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEnvironmental scores*Post*Disclosure\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.475**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.993)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEnvironmental scores* Disclosure\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.784***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.823***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.215)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.935)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePost* Disclosure\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.909**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.781**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.405)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.396)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDisclosure\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.272)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.287)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.107***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.763***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.284)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.340)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControl variables\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIndustry and Year dummies\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLog likelihood\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-57071.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-39965.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003esigma_u\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.877***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.976***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003esigma_e\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.867***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.801***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e, given the coefficient of \u003cem\u003eenvironmental strength*Post\u003c/em\u003e is significantly positive, the positive coefficient of \u003cem\u003eEnvironmental strength*Post* Disclosure\u003c/em\u003e shows that comparing with companies that do not self-disclose environmental reports, the effect of policies on institutional preferences for green investment is stronger for companies that have environmental disclosure. This result is consistent with our hypothesis. Besides, the coefficient of \u003cem\u003eEnvironmental weakness*Post*Disclosure\u003c/em\u003e is negative under the negative coefficient of \u003cem\u003eEnvironmental weakness*Post\u003c/em\u003e in column (1). This means that, for firm with self-reported environmental practices, the policy effect on institutional aversion to firms with environmental weakness is stronger.\u003c/p\u003e \u003cp\u003eResult in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e consistent with our hypothesis that the higher level of self-environmental disclosure might expand influence of green finance policies, not only on institutional preferences for firms with high environmental performance, but also on their aversion to that with low environmental performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.2. the role of external audits\u003c/h2\u003e \u003cp\u003eWe estimate DDD models using the following equations to test the mediating effect of external auditors:\u003c/p\u003e \u003cp\u003e \u003cem\u003eIO\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e=\u0026#120572;+\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eE_weakness\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u0026minus;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e+\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e2\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eE_weakness\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e*Post+\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e3\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eE_weakness\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u0026minus;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e*Post*BIG4\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e+\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e4\u003c/em\u003e \u003c/sub\u003e \u003cem\u003ePost* BIG4+\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e5\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eE_weakness\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u0026minus;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e* BIG4 +\u0026sum;\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003ej\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eControl\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e+\u0026#120576;\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u003c/em\u003e \u003c/sub\u003e (6)\u003c/p\u003e \u003cp\u003e \u003cem\u003eIO\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e=\u0026#120572;+\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eE_strength\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u0026minus;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e+\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e2\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eE_strength\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e*Post+\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e3\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eE_strength\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u0026minus;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e*Post*BIG4\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e+\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e4\u003c/em\u003e \u003c/sub\u003e \u003cem\u003ePost* BIG4+\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003e5\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eE_strength\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u0026minus;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e* BIG4 +\u0026sum;\u0026#120573;\u003c/em\u003e \u003csub\u003e \u003cem\u003ej\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eControl\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e+\u0026#120576;\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u003c/em\u003e \u003c/sub\u003e (7)\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eBIG4\u003c/em\u003e is a dummy variable that equals to 1 if the firm is audited by Big Four global accounting firms, and quals to zero if it is not. We focus on the coefficients of \u003cem\u003eEnvironmental scores*Post* BIG4\u003c/em\u003e (\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e) in Eqs.\u0026nbsp;(6) and (7).\u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e8\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThis table shows the result of the mediating effect of external audits in \u003cem\u003ethe plan\u003c/em\u003e issued in 2017Q2. The scores are calculated using data from the CSMAR database. The sample period is from the second quarter of 2016 to the first quarter of 2019. Variables are winsorized at the 1st and 99th percentiles. The independent variables are lagged by one quarter. The result of \u003cem\u003et\u003c/em\u003e-statistics is reported in parentheses. ***, **, * denote statistical significance at the 1%, 5%, and 10% level, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent variables:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eInstitutional ownership\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental weakness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnvironmental strength\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003et-1\u003c/em\u003e\u003c/sub\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eweakness\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.177)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003estrength\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.251)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEnvironmental scores\u003c/em\u003e \u003cem\u003e*Post\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.186**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.842***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.091)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.136)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEnvironmental scores\u003c/em\u003e \u003cem\u003e*Post*BIG4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.681*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.234***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.409)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.408)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEnvironmental scores* BIG4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.852)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.694)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePost* BIG4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.630***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.451***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.222)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.216)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.383***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.092***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.266)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.322)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControl variables\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIndustry and Year dummies\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLog likelihood\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-63261.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-43928.388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003esigma_u\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.787***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.915***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003esigma_e\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.923***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.853***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e8\u003c/span\u003e, given the coefficient of \u003cem\u003eenvironmental strength*Post\u003c/em\u003e is significant positive, the negative coefficient of \u003cem\u003eEnvironmental strength*Post* BIG4\u003c/em\u003e shows that comparing with firms without Big 4 auditor, the effect of policies on institutional preferences for green investment is weaker for firms that appoint a Big 4 auditor. Additionally, the coefficient of \u003cem\u003eEnvironmental weakness*Post*BIG4\u003c/em\u003e is positive under the negative coefficient of \u003cem\u003eEnvironmental weakness*Post\u003c/em\u003e in column (1). This means that the stronger external audits supervision of a firm, the weaker policy effect on institutional aversion to its low environmental performance. It worth noting that stronger external audits will narrow down the effect results of green finance policy, which is consistent with our estimation.\u003c/p\u003e \u003c/div\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis paper provides a perspective on the evolution of institutional preferences on green investment in the Chinese stock market and the role of government exerting in the evolution. We first examine the degree of response of institutional investors to the green finance policies. The evidence indicates that after the implementation of \u003cem\u003ethe plan\u003c/em\u003e, financial institutions significantly increased their investment in companies with better environmental performance and reduced their stock holdings for companies with poor environmental performance. We also find that institutional investors react more quickly to environmental strength, but slower to environmental weakness. The rolling window estimation further prove this finding.\u003c/p\u003e \u003cp\u003eConsidering the heterogeneity of institutional investors, we divide institutional investors into short-term and long-term institutions according to the investment horizon. The study finds that although the preferences of long-term institutional investors are not affected by policies, they have shown a symmetrical preference for environmental performance due to their greater focus on fundamentals, which is consistent with previous research (Cremers et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In terms of short-term institutional investors, we find that although they are affected by the policy and improve their investment behavior to a certain extent, the change in their behavior is more driven by excess returns. For enterprises with high environmental performance, short-term institutional investors tend to use their own information advantages and professional skills to enter the market in advance and leave before the excess return is reduced, showing a certain degree of speculation (Cano-Berlanga and Gim\u0026eacute;nez-G\u0026oacute;mez, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yan and Zhang, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For companies with poor environmental performance, the behavior patterns of short-term institutions are also highly correlated with the increase or decrease of excess returns.\u003c/p\u003e \u003cp\u003eWe further studied the mediating role of environmental disclosure and external audits in policy effects. Consistent with our hypothesis, we find that institutional investors who have better information processing capabilities can distinguish the validity of the information disclosed by the company, and avoid to be affected by the disguise effect of environmental disclosure. Thus, the higher level of environmental information disclosure could expand the policy effect. That is, for companies with a high level of environmental information disclosure, institutional investors' dislike for environmental weakness and preferences for environmental strength are both stronger. Finally, we find that the Big 4 auditors could narrow the policy effect by providing investors with more accurate reporting information and improving investor confidence.\u003c/p\u003e \u003cp\u003eTherefore, our findings could have important implications for different market participants. First, the government can introduce more preferential policies, such as appropriate financial subsidies, tax incentives and other means to encourage and guide financial institutions to actively participate in green finance. Since the short-term institutions rely more on excess return instead of changing their investment philosophy, Chinese governments should introduce relevant laws and regulations from the administrative level to deal with those speculative behaviors.\u003c/p\u003e \u003cp\u003eSecond, for listed enterprises, their brand and corporate reputation may be damaged if there is an environmental problem. This could affect the confidence of investors, resulting in a decrease in the market value of the enterprise and a negative impact on corporate operation and productivity. Companies may thus choose to be silent, because silence is safer than disclosure (Bond and Zeng, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, relying on policy power could be essential to promote corporate information disclosure. Besides, companies need to increase external supervision and environmental performance, rather than attempt to attract external investment through the disguise effect.\u003c/p\u003e \u003cp\u003eFinally, the short-sightedness of institutional investors may put short-term pressure on companies, leading to myopic firm behavior (Cremers et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which would discourage long-term value (Erhemjamts and Huang, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, short-term institutional investors should pay more attention to fundamental analysis and transform their trading strategies to reduce myopic behavior.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003eEthical Approval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eAll authors contributed to the study conception and design. The first draft of the manuscript was written by\u0026nbsp;Pei-Wen Lai.\u0026nbsp;Wu-E Yang\u0026nbsp;provided supervision and review. Zhi-Qiu Han\u0026nbsp;and Zhen-Peng Tang\u0026nbsp;commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\nThis work was supported by Fujian Provincial Natural Science Foundation of China (No. 2020J01461) and Fujian Provincial Social Science Planning Foundation of China (No. FJ2020B034).\n\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eBlomgren A (2011) Does corporate social responsibility influence profit margins? a case study of executive perceptions. Corp Soc Responsib Environ Manag 18(5):263\u0026ndash;274\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBond P, Zeng Y (2021) Silence is Safest: Information Disclosure when the Audience\u0026rsquo;s Preferences are Uncertain.Journal of Financial Economics\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBroadstock DC, Chan K, Cheng LTW, Wang X (2021) The role of ESG performance during times of financial crisis: Evidence from COVID-19 in China. Financ Res Lett 38:101716\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBushee BJ (2001) Do Institutional Investors Prefer Near-Term Earnings over Long-Run Value? Contemp Acc Res 18(2):207\u0026ndash;246\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eCano-Berlanga S, Gim\u0026eacute;nez-G\u0026oacute;mez J-M (2017) On Chinese stock markets: How have they evolved over time? Ann Oper Res 266(1\u0026ndash;2):499\u0026ndash;510\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eChang K, Kabongo J, Li Y (2020) Geographic proximity, long-term institutional ownership, and corporate social responsibility. Rev Quant Financ Acc 56(1):297\u0026ndash;328\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eChen L (2014) Local Institutions, Audit Quality, and Corporate Scandals of US-Listed Foreign Firms. J Bus Ethics 133(2):351\u0026ndash;373\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eChen S, Mao H, Sun J (2021) Low-Carbon City Construction and Corporate Carbon Reduction Performance: Evidence From a Quasi-Natural Experiment in China.Journal of Business Ethics\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eChen Y, Singhal V, Zhu Q (2020) Environmental policies and financial performance: stock market reaction to firms for their proactive environmental practices recognized by governmental programs. Bus Strategy Environ 30(4):1548\u0026ndash;1562\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eCox P, Brammer S, Millington A (2004) An Empirical Examination of Institutional Investor Preferences for Corporate Social Performance. J Bus Ethics 52(1):27\u0026ndash;43\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eCremers M, Pareek A, Sautner Z (2020) Short-Term Investors, Long-Term Investments, and Firm Value: Evidence from Russell 2000 Index Inclusions. Manage Sci 66(10):4535\u0026ndash;4551\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDaimin S (2002) The effect of impact of policies in stock market fluctuation. Manage World 8:11\u0026ndash;15 (in Chinese)\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDaniel K, Grinblatt M, Titman S, Wermers R (1997) Measuring Mutual Fund Performance with Characteristic-Based Benchmarks.The Journal of Finance, \u003cem\u003e52\u003c/em\u003e(3)\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026ldquo;Green China\u0026rsquo;s Financial System\u0026rdquo;Study Group of DRC (2016) The Developmental Logic and Framework of China\u0026rsquo;s Green Finance. finance forum 21(6):17\u0026ndash;28 (in Chinese)\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDyck A, Lins KV, Roth L, Wagner HF (2019) Do institutional investors drive corporate social responsibility? International evidence. J Financ Econ 131(3):693\u0026ndash;714\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eErhemjamts O, Huang K (2019) Institutional ownership horizon, corporate social responsibility and shareholder value. J Bus Res 105:61\u0026ndash;79\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eFan JPH, Wong TJ (2005) Do External Auditors Perform a Corporate Governance Role in Emerging Markets? Evidence from East Asia. J Accounting Res 43(1):35\u0026ndash;72\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eFeng J, Goodell JW, Shen D (2021) ESG rating and stock price crash risk: Evidence from China. Finance Research Letters\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGoldie BA, Li L, Masli A (2018) Do Mutual Fund Investors Care About Auditor Quality? Contemp Acc Res 35(3):1505\u0026ndash;1532\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGraves SB, Waddock SA (1994) Institutional Owners and Corporate Social Performance. Acad Manag J 37(4):1034\u0026ndash;1046\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGuedhami O, Pittman JA, Saffar W (2014) Auditor Choice in Politically Connected Firms. J Accounting Res 52(1):107\u0026ndash;162\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGuo M, Kuai Y, Liu X (2020) Stock market response to environmental policies: Evidence from heavily polluting firms in China. Econ Model 86:306\u0026ndash;316\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eGuojin, C., Saijie, D., Xiangqin, Z., \u0026amp; Xiaoyu, J. (2021). China＇s Green Finance Policy，Financing Costs and Firms＇Green Transition: A Central Bank Collateral Framework Perspective. Financial Studies, 12, 75-95 (in Chinese).\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHong H, Kacperczyk M (2009) The price of sin: The effects of social norms on markets. J Financ Econ 93(1):15\u0026ndash;36\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eIoannou I, Serafeim G (2019) The Consequences of Mandatory Corporate Sustainability Reporting. \u003cem\u003eThe Oxford Handbook of Corporate Social Responsibility: Psychological and Organizational Perspectives\u003c/em\u003e, 452\u0026ndash;489\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eJiang F, Cai W, Wang X, Zhu B (2018) Multiple large shareholders and corporate investment: Evidence from China. J Corp Finance 50:66\u0026ndash;83\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eJiang Y, Luo L (2018) Market reactions to environmental policies: Evidence from China. Corp Soc Responsib Environ Manag 25(5):889\u0026ndash;903\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKhurshed A, Paleari S, Pande A, Vismara SJ (2014) Transparent bookbuilding, certification and initial public offerings. J o F M 19:154\u0026ndash;169\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKim H-D, Kim T, Kim Y, Park K (2019) Do long-term institutional investors promote corporate social responsibility activities? J Banking Finance 101:256\u0026ndash;269\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKim J-B, Zhang L (2014) Financial Reporting Opacity and Expected Crash Risk: Evidence from Implied Volatility Smirks. Contemp Acc Res 31(3):851\u0026ndash;875\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLang M, Mcnichols M (1997) Institutional Trading and Corporate Performance. \u003cem\u003eWorking paper, Stanford University.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLee JW (2020) Green Finance and Sustainable Development Goals: The Case of China. J Asian Finance Econ Bus 7(7):577\u0026ndash;586\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLee S-Y, Park Y-S, Klassen RD (2015) Market Responses to Firms\u0026apos; Voluntary Climate Change Information Disclosure and Carbon Communication. Corp Soc Responsib Environ Manag 22(1):1\u0026ndash;12\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLi M, Dong L, Luan J, Wang P (2020) Do environmental regulations affect investors? Evidence from China\u0026rsquo;s action plan for air pollution prevention.Journal of Cleaner Production, \u003cem\u003e244\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLi Q, Wang J, Bao L (2018) Do institutions trade ahead of false news? Evidence from an emerging market. J Financial Stab 36:98\u0026ndash;113\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLihui T, Kedi W (2017) The \u0026quot;disguised effect\u0026quot; of social responsibility information disclosure and the risk of crash of listed companies.Management World(11),146\u0026ndash;157\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLowenstein LJCLR (1989) What\u0026rsquo;s wrong with Wall Street: Short-term gain and the absentee shareholder.Reading, MA: Addison-Wesley., \u003cem\u003e89\u003c/em\u003e(8)\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMeng J, Zhang Z (2022) Corporate environmental information disclosure and investor response: Evidence from China\u0026apos;s capital market.Energy Economics, \u003cem\u003e108\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eNofsinger JR, Sulaeman J, Varma A (2019) Institutional investors and corporate social responsibility. J Corp Finance 58:700\u0026ndash;725\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003ePalmer K, Oates WE, Portney PR (1995) TIGHTENING ENVIRONMENTAL STANDARDS - THE BENEFIT-COST OR THE NO-COST PARADIGM. J Economic Perspect 9(4):119\u0026ndash;132\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eQi B, Huang M, Chen Z (2006) Institutional Investors and the Volatility of Stock Market.Financial Studies(9),54\u0026ndash;64\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRamiah V, Martin B, Moosa I (2013) How does the stock market react to the announcement of green policies? J Banking Finance 37(5):1747\u0026ndash;1758\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eShirasu Y, Kawakita H (2021) Long-term financial performance of corporate social responsibility.Global Finance Journal, \u003cem\u003e50\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eStolin, David K (2008) Which Money Is Smart? Mutual Fund Buys and Sells of Individual and Institutional Investors.. 63:85\u0026ndash;1181\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eStuart AC, Bedard JC, Clark CE (2020) Corporate Social Responsibility Disclosures and Investor Judgments in Difficult Times: The Role of Ethical Culture and Assurance. J Bus Ethics 171(3):565\u0026ndash;582\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSun H, Chen F (2022) The impact of green finance on China\u0026apos;s regional energy consumption structure based on system GMM.Resources Policy, \u003cem\u003e76\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eVo XV (2016) Does institutional ownership increase stock return volatility? Evidence from Vietnam. Int Rev Financial Anal 45:54\u0026ndash;61\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eWang Y, Delgado MS, Khanna N, Bogan VL (2019) Good news for environmental self-regulation? Finding the right link. J Environ Econ Manag 94:217\u0026ndash;235\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eWenjing LI, Xiaoyan LU (2015) Do Institutional Investors Care Firm Environmental Performance?Evidence from the Most Polluting Chinese Listed Firms. Financial Studies (in Chinese).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eXu S, Liu D (2020) Political connections and corporate social responsibility: Political incentives in China. Bus Ethics: Eur Rev 29(4):664\u0026ndash;693\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eXu XD, Zeng SX, Tam CM (2011) Stock Market\u0026rsquo;s Reaction to Disclosure of Environmental Violations: Evidence from China. J Bus Ethics 107(2):227\u0026ndash;237\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eYan X, Zhang Z (2009) Institutional Investors and Equity Returns: Are Short-term Institutions Better Informed? Rev Financial Stud 22(2):893\u0026ndash;924\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eYou H, Zhang X-j (2008) Financial reporting complexity and investor underreaction to 10-K information. Rev Acc Stud 14(4):559\u0026ndash;586\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eYu C-H, Wu X, Zhang D, Chen S, Zhao J (2021) Demand for green finance: Resolving financing constraints on green innovation in China.Energy Policy, \u003cem\u003e153\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eZhang Z, Yan XS (2009) Institutional Investors and Equity Returns: Are Short-term Institutions Better Informed? \u003cem\u003eSSRN Electronic Journal\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eZhou G, Liu C, Luo S (2021) Resource Allocation Effect of Green Credit Policy: Based on DID Model.Mathematics, \u003cem\u003e9\u003c/em\u003e(2)\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"green investment, institutional preferences, environmental information disclosure, government policies, green finance, external audits","lastPublishedDoi":"10.21203/rs.3.rs-1790586/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1790586/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo improve the guiding role of institutional investors in green investment and provide financial support for green enterprises, Chinese government has issued a series of policies to establish a green finance system. We use a difference-in-difference (DID) analysis to explore whether the implementation of policies could change institutional attitudes to environmental factors when making investment decisions. Considering the effect of investment horizon, we find long-term institutional investors have shown symmetric preferences on green investment, while short-term institutions are more affected by green finance policies. Additionally, we find the investment behavior of short-term institutions are highly associated with abnormal return triggered by the policy effect. Besides, the triple-difference (DDD) analysis further proves that the high level of environmental disclosure would expanse the effect of policies, but high quality of external auditors would reduce the policy effect. The finding would extend the studies of green investment in emerging markets and present new evidence about the policy effect on institutions\u0026rsquo; preferences for green investment.\u003c/p\u003e","manuscriptTitle":"Do Government Policies drive institutional preferences on green investment? Evidence from China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-07-11 21:14:43","doi":"10.21203/rs.3.rs-1790586/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2022-07-01T02:33:28+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-06-30T20:34:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2022-06-30T19:54:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-06-29T05:58:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2022-06-24T02:37:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a3aa615b-53ce-4011-9ff0-a9c6fc33c791","owner":[],"postedDate":"July 11th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-08-19T07:18:19+00:00","versionOfRecord":[],"versionCreatedAt":"2022-07-11 21:14:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1790586","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1790586","identity":"rs-1790586","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}