Do Narrative Strategies Matter? Evidence on Earnings Management, Impression Management, and Future Stock Prices in Egypt

Do Narrative Strategies Matter? 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Evidence on Earnings Management, Impression Management, and Future Stock Prices in Egypt Tariq Ismail, Mohamed Samy El-Deeb, Mohamed El-Ashwal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8890174/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the effects of earnings management on future stock prices and investigates whether impression management moderates this relationship in the Egyptian capital market. It uses financial and narrative data extracted from the annual reports of firms listed on the Egyptian Stock Exchange (EGX) from 2020 to 2024. Regression models with robust estimations are used to test the hypotheses. The results revealed that discretionary accruals significantly influence future stock prices, indicating that investors penalize firms that engage in aggressive earnings management. This finding supports the market discipline hypothesis and suggests a growing sophistication among Egyptian investors, particularly in the post-2020 regulatory environment. However, impression management, operationalized through the content analysis of narrative disclosures, does not exert a significant moderating effect on the relationship between earnings management and stock prices. This evidence implies that investors place greater weight on audited financial information than on qualitative disclosures when forming valuation judgments in a low-trust institutional setting. The findings highlight the importance of enhancing the transparency and credibility of financial reporting in emerging markets. They also underscore the need for stricter regulatory enforcement to curb earnings manipulation and improve standards governing narrative disclosure. This study contributes to the literature by providing empirical evidence from Egypt and extending prior research on the interaction between earnings management, impression management, and market outcomes in developing economies. Narrative reporting earnings management impression management stock prices emerging markets EGX Egypt 1. Introduction Investors rely on various factors when making investment decisions in stock markets to reduce the level of uncertainty caused by fluctuations in stock prices (Anwaar, 2016). One of these factors is the information disclosed in a corporate annual report (Pernamasari, et. al., 2022). In addition to financial statements, it includes the discretionary information necessary for explaining financial statements and providing additional information (Goncalves, et. al., 2022). Both financial statements and discretionary information provide investors with information on numerous aspects, such as financial performance, financial position, business strategy, and sustainability strategy. Despite efforts to ensure disclosure quality, managers may have incentives to distort information from corporate annual reports by using different tactics. On the one hand, financial statements provide misleading information when managers utilize earnings management (EM) by practicing real-based earnings management and/or accrual-based earnings management. The former focuses on changing business activities and transactions to attain desired financial results. The latter refers to manipulating accounting estimates and judgments to alter reported earnings without changing actual cash flows. Managers use accounting regulations to provide the desired financial results by hiding specific accounting information or making it difficult to identify (Ilic, et al., 2024). On the other hand, discretionary information might be manipulated using impression management (IM), which has different strategies, such as rhetorical manipulation, thematic manipulation, reading-ease manipulation, and visual/structural manipulation (Merkl-Davies & Brennan 2007, 2011; Jaworska & Bucior 2017, 2020). They aim to distort investor perceptions of investors (Albuquerque et al., 2023). Although EM and IM are different methods, managers can use them as complementary vehicles of communication. Goncalves et al. (2022) confirm that " managers seek to obfuscate the intensity with which they manage earnings by disclosing more complex, meaning less readable annual reports" . Thus, managers utilize both EM and IM to influence investors' expectations of future profits, dividends, and stock value (Strakova, 2020) and understand how investors view the company's accomplishments respectively (Czajkowska & Remlein, 2024; Phesa & Sibanda, 2022). Egypt represents an exemplary environment for establishing a relationship between emerging market characteristics and stock returns. It has a specific enforcement environment, with an actively traded stock market and heterogeneous disclosure practices being tightening yet weak (Financial Regulatory Authority, 2009; Hassan et al., 2009; Samaha, 2013). The Egyptian stock market is framed by Capital Market Law No. 95, dated 1992, and its executive bylaw. While it requires strict listing and disclosure standards, enforcement with respect to financial and corporate governance disclosure remains limited (General Authority for Investment and Free Zones; Hassan et al., 2009; Samaha, 2013). Empirical studies on Egyptian publicly traded firms offer evidence that, while mandatory disclosures required by IFRS standards are widely disclosed, voluntary and corporate governance disclosures are low. Thus, they represent an environment with specific characteristics concerning information quality and transparency, which can be systematically varied and harnessed for identification purposes (Samaha et al., 2015; Samaha, 2013; Zahran, 2016). However, the Egyptian Financial Regulatory Authority recently increased its enforcement efforts by pushing for higher levels of protection offered to investors, improvements in market infrastructure, and higher levels of disclosure. These efforts result in episodic shocks with respect to enforcement levels that affect the information environment owing to its quasi-exogenous nature (Financial Regulatory Authority, 2009; Gramon et al., 2025). Furthermore, the Egyptian environment provides better sources for identification than other emerging markets do. This is because of its higher enabling capacity with respect to specific microstructural characteristics, such as standardized trading practices in the Egyptian stock market (Hassan et al., 2009; Samaha et al., 2015). This study examines the moderating role of IM on the relationship between EM and future stock prices in the context of firms listed on EGX. These findings have very specific incremental value based on three aspects. Firstly , discretionary accruals help predict future stock prices within emerging markets, indicating that earnings quality signals could enhance earnings and price forecasts, especially when adapted into earnings valuation models based on the existing literature. This suggests a systematic relationship between forecast errors, management reporting decisions, and earnings characteristics. Secondly , the negative relationship between EM and future stock prices implies that investment professionals can generate investment profits by underweighting firms with high accruals or low-quality earnings, and overweighting firms with low accruals or high-quality earnings. Thirdly , as the empirical analysis focuses on firm-years with high discretionary accruals and complex earnings narratives, this test could be translated into a risk-screening tool under the supervision of regulatory bodies with the aim of implementing emerging literature. This suggests that accounting-based measures could be employed to focus on enforcement efforts in countries with poor supervisory infrastructure. The rest of this paper is organized as follows: Section 2 presents the literature review and hypotheses development, and Section 3 introduces the research method. Section 4 presents the data analysis, and Section 5 discusses the results. Section 6 presents conclusions, limitations, and suggestions for future research. 2. Literature Review and Hypotheses Development 2.1 EM and Future Stock Prices Spence (1973) created signaling theory for the labor market, yet it has been modified to explain financial reports' disclosures (Ross, 1977). According to signaling theory, there is information asymmetry between stakeholders and the management who own the information. Furthermore, signaling theory assumes that better-informed parties utilize difficult or costly signaling to better convey private information to poorly informed counterparties (Connelly et al., 2011). In the context of financial reporting, to improve the quality of financial information and subsequently help translate it into high stock prices, better-informed managers would utilize EM in two ways. Firstly , they would try to align reported earnings with future performance projections in terms of accruals to better inform stockholders with heavy information content in accounts (Subramanyam, 1996; Jiraporn et al., 2008; ElHawary & Hassouna, 2021). In this context, it appears that moderate and performance-consistent EM activities can be perceived by investors as a useful or credible signaling device to reveal superior market performance and help translate it into high stock prices. Secondly , EM may be opportunistically relied upon to generate temporary earnings gains at the cost of future earnings. Sophisticated investors, auditors, and regulators detect the tenuous nature of EM (Dechow et al., 2010). The dual role of potentially informative versus opportunistic EM implies that stock prices respond to this hypothesis. Ultimately, it is a factual question pertaining to the joint effects of managerial discretion within a particular institutional environment, such as that prevailing in Egypt, where EM governance is being institutionalized to varying degrees. Based on this, we formulate the first hypothesis as follows: H1: Earnings management has a significant positive impact on future stock prices in EGX. 2.2 EM, IM and Future Stock Prices In addition to signaling theory, attribution theory informs the relationship between IM and firm value. Based on Merkl-Davies and Brennan’s (2011) taxonomy, IM depends on four perspectives: economic, psychological, sociological, and critical. They are used to describe the type of rationality that guides the behavior of both managers and stakeholders. From the perspective of annual financial reports, performance attributions may be used proactively to shape organizational audiences` perceptions of organizational outcomes; that is, IM (Merkl-Davies and Brennan`s, 2011). The existing IM literature finds that managers may employ positive tone, highlighting, and narrative forms to shape stakeholders’ beliefs about performance, risk, and value (Merkl-Davies & Brennan, 2011). When these narrative attributes are generally consistent with fundamentals and are confirmed by future outcomes, they can serve as additional cues to alleviate uncertainty and enhance investors’ understanding of strategy and risk management. Thus, they facilitate positive price reactions around news of disclosure, particularly under noisy information conditions. On the other hand, these narrative attributes can also mask challenging performance or extreme IM. Under these conditions, sophisticated investors are likely to view extremely positive news as “cheap talk” cues, about which they are skeptical. Thus, they might disregard narrative cues altogether, or even view them as a warning sign of potential problems with management’s value-creating activities, resulting in more adverse price reactions (Beelitz & Merkl-Davies, 2012). Hence, the second hypothesis is formulated as follows: H2: Impression management has a significant negative effect on future stock prices in EGX. The IM function moderates EM-price relationships as it affects investors’ interpretation and assessment of the EM signal. When tone and emphasis diverge insignificantly from underlying fundamentals and eventually realize performance, IM may affect and amplify the degree of managed earnings’ perceived integrity and hence boost investors’ overall susceptibility to and responsiveness of EM-price relationships (Subramanyam, 1996). However, when tone diverges to convey fundamentals in an overly optimistic manner —“overly rosy tone statements — investors view IM as more of talk or concealment. This may result in undermining or even reversing EM-price implications, as investors disclose little faith and conviction in and about EM signal statements or interpreted earnings realities. Against this conceptual foundation, this moderating function may manifest itself and prove even more material or significant in markets such as Egypt. They share similar structural and operational environment gaps and settings in which investors may tend to exhibit a high degree of perceived disparities and asymmetries—impaired enforcement mechanisms or imperfect governance settings (Haw et al., 2004; ElHawary & Hassouna, 2021). Based on the above discussion, the third hypothesis can be formulated as: H3: Earnings management has a significantly different positive effect on future stock based on the level of impression management in EGX. 3. Research Method 3.1 Research Design This study follows a quantitative research design to analyze the effect of discretionary accrual-based earnings management (DABEM) on Egyptian listed firms' future stock prices, with IM as a moderator. The analysis covers the period from 2020 to 2024, marked by general economic turmoil in Egypt as currency devaluations, high inflation rates (38% in 2023), and more stringent market regulations after the 2016 financial reforms (Hafez, 2023). The model uses content analysis of narrative disclosures in annual reports and historical financial data based on the prevailing theoretical frameworks of EM and IM (McNichols, 2000; Merkl-Davies et al., 2011). Empirical tests use ordinary least squares (OLS) regression with robust standard errors to account for heteroscedasticity and robustness tests based on the performance-matched Jones model (Kothari et al., 2005). 3.2 Variables Measurement Four types of variables were considered and employed as shown on Table 1. EM, as an independent variable, is measured through discretionary accruals. To simplify the measurement, the working capital accruals model was used as a proxy. Specifically, small working capital accruals are typically more reliable in detecting EM because they are less affected by normal operational volatility and offer better sensitivity to small but deliberate manipulations. FSP, as a dependent variable, refers to the stock closing price of sampled firms determined one week after the dissemination date of annual reports. IM as a moderating variable was measured based on IM strategies, such as positive tonal bias and selective disclosure, where we assign a value of 1 to each firm-year if these strategies are detected above a certain specified threshold, indicating IM, and 0 otherwise (Czajkowska, 2023). Moreover, tone and other narrative tools can play a strategic role in communicating with financial statement users about performance and risk. Thus, the approach relies on a word-by-word analysis of positive and negative comments in both the chairman’s statements and the management discussion. More specifically, this helps identify emphasis on positive outcomes as well as the suppression of bad news in the chairman’s or management discussion portion of the annual report (Mlawu et al., 2023; Nyahas et al., 2018; Demaline, 2020). The choice of a binary (0/1) indicator instead of a continuous measure is based on both conceptual and practical reasons. Conceptually, while the presence of IM is of prime interest for being above zero for a firm’s reporting year, crossing a certain qualitative threshold from neutral to strategic narrative presentation is consistent with existing literature that defines a discrete outcome for the presence of dominant positive tones and narrative structuring as IM (Bassyouny et al., 2020; El-Shahat, 2023). Practically speaking, binary is more reliable for an emerging market than continuous with potential text mining ambiguities for mixed Arabic-English narrative corpora, and is also less resource-intensive for human-content analysis, although incorporating more simplicity for detailed content measurements into binary form is more reliable than continuous measurements for obvious reasons (Czajkowska, 2023; Picture Content in Annual Reports Matters, 2024). Nonetheless, binary approximation is deliberately made note of being conservative with less detail on intensity of IM for further research measurements, for which re-estimation of certain models with new thresholds of simplicity for easier binary divisions at certain points is recommended to verify that findings are independent of specific threshold usage. 3.3 Data Collection This study depends on two types of data: financial and non-financial. Financial data representing the key variables of total accruals, sales, total assets, accounts receivable, property, plant, and equipment, cash, current assets, current liabilities, total debt, and net income were obtained from EGX regulatory filings and audited corporate annual reports. In addition, the FSP was quantified as the closing price one week following the date of release of the annual report, enabling initial reactions to disclosure. Non-financial data, such as IM data, are narrative in nature and are collected from the narrative sections in EGX-submitted yearly reports. Content was analyzed to create the IM in narrative reporting (IMNR) index, as suggested by Czajkowska (2023). The index identifies IM strategies, that is, positive tonal bias or selective disclosure, and is considered a binary variable (a value of 1 to be assigned in the case of existing IM strategies, 0 otherwise). Data reliability was increased via cross-validation against audited reports and formal EGX records to address inconsistencies in Egypt's disclosure practice amidst economic uncertainty (Hafez, 2023). 3.4 Sample Selection The population of this study comprises all firms listed on the Egyptian Stock Exchange (EGX 70) from to 2020-2024. Purposive sampling allows the collection of 40 firms’ annual reports from different industries, such as beverage, manufacturing, and real estate, depending on market significance and disclosure availability. This strategy was followed in most studies related to the Egyptian context (ElHawary & Hassouna, 2021), generating 200 firm-year observations that provided sufficient statistical power, captured market heterogeneity, and avoided selection bias. 3.5 Model Specification To empirically investigate the relationship between the study variables, two regression models were developed. Within the first model, the direct effect of the DAEM on FSP is estimated while controlling for business-specific characteristics, such as firm size, profitability, and financial leverage. The second model expands the analysis by including IM as a potential moderating variable, in addition to its interaction with DABEM. This study utilized various methodologies to tackle the issues of construct validity and measurement errors. First , the IM construct was operationalized using a structured content analysis index that codes for various narrative strategies (e.g., tone, selectivity, and emphasis). This aligns with the idea that IM should be seen as a multidimensional construct, not just "positive tone = deception". Second , discretionary accruals are made using an accruals specification adjusted for both industry and performance. This includes scaling parameters to show how a firm’s performance changes. This is in line with research showing that these types of specifications can lower the rates of misclassification, which makes the test better for testing the EM hypotheses (Kothari et al., 2005; McNichols, 2000). Third , robustness tests are performed using different accrual specifications and sensitivity analyses. For example, observations with very high or very low performance levels were omitted, and the model was re-run using different scaling parameters. Experts say that using more than one proxy can make EM studies less likely to have measurement errors (Dechow et al., 2010). The analysis explicitly combines proxies for accrual-based and real earnings management to capture a broader spectrum of managerial reporting discretion. Discretionary accruals are measured using a performance-matched model (Kothari et al., 2005), which improves specification in volatile emerging markets by controlling for firm performance, and thus reduces the risk that extreme ROA or sales growth in Egypt mechanically inflates estimated discretionary accruals. In parallel, real EM is captured following Roychowdhury’s (2006) framework using abnormal levels of operating cash flows, production costs, and discretionary expenses (SG&A), which reflects managers’ use of price discounts, overproduction, and cuts in discretionary spending to meet reporting targets. This dual‑proxy approach is particularly appropriate in the Egyptian setting, where prior evidence documents both accrual-based and real activity manipulation in response to institutional shocks and evolving governance standards, implying that relying solely on one dimension (accruals or real activities) understates the true extent of earnings management. The models employed in the analysis are as follows: Model 1: Investigating the baseline relationship between DA and future stock prices: Where: Model 2: Extends the specification by including the moderating role of investor monitoring: In this framework, the interaction term is added to test whether investor monitoring attenuates the relationship between DABEM and the FSP. A significantly different coefficient on the interaction term shows whether the direction or strength of the DABEM–stock price relationship changes at varying levels of investor monitoring. 4. Data Analysis 4.1 Descriptive Statistics Table 2 presents descriptive statistics based on 200 firm-year observations of EGX-listed firms (2020–2024). The results reveal strong patterns in EM, stock prices, and financial ratios in Egypt's turbulent economic climate, replete with currency devaluations, inflation (a historic high of 38% in 2023), and post-2020 regulatory reforms. These findings are in line with the emerging market literature, in which economic turbulence enhances financial reporting volatility (Hafez, 2023; Haw et al., 2005). Table 2. Descriptive Statistics Variables N Minimum Maximum Mean Std. Deviation DABEM 200 -.5984 .6959 -.023232 .3364755 FSP 200 .0710 285.0100 18.703620 37.5490862 IM 200 0 1 .52 .501 LogTA 200 1.8127 5.5524 3.302810 .7501928 ROA 200 -1.4452 .2911 .052276 .1390769 Leverage 200 .0554 .9667 .535722 .2357965 Valid N (listwise) 200 DABEM, with computation based on the working capital accruals model (Kerstein & Rai, 2007), is -0.0232, thereby a very near zero mean, ranging from -0.5984 to 0.6959, and having a standard deviation of 0.3365. Such a distribution translates to the predominance of both income-reducing and income-increasing EM among Egyptian firms in accordance with the flexibility provided by accrual-based accounting in uncertain markets (McNichols, 2000). The high standard deviation reflects the excessive volatility caused by Egypt's economic turmoil between 2020 and 2024, such as devaluations of the local currency and inflationary pressures. Thus, firms are motivated to manipulate accruals to smooth earnings reports (ElHawary & Hassouna, 2021). This volatility is consistent with research on emerging markets, where poor governance institutions permit EM as a counterbalance to economic turbulence (Haw et al., 2005). The FSP, the closing price seven days after the release of annual reports, has a wide range of 0.0710–285.0100 with a mean of 18.7036 and the highest standard deviation of 37.5491. This volatility accounts for Egypt's market volatility during the study period due to macroeconomic shocks, including the devaluations of the Egyptian pound in 2022–2023 and ongoing inflation (Hafez, 2023). The relatively low average stock price indicates stable valuations for some companies, consistent with EGX's partial rebound after 2020, but the pervasive spread attests to the non-systemic effect of economic fortunes on industries, a hallmark of stressed emerging markets (Haw et al., 2005). The large standard deviation accords with the regression analysis, where the external variables explain the data moderately well (adjusted R² = 0.132–0.158), since Egyptian stock price movements are subject to macroeconomic forces. IM, as a (0, 1) binary variable in the IMNR index (Czajkowska, 2023), is 0.52 with a standard deviation of 0.501, and shows that 52% of firm-year observations have IM practices, that is, positive tone or selective disclosure on narrative reports. The almost-even split corresponding to binary coding indicates that Egyptian companies prefer to use narrative tactics to counteract the unfavorable perceptions of stakeholders in times of economic distress, such as global supply chain disruption and energy crises (Merkl-Davies et al., 2011). According to earlier studies (ElHawary & Hassouna, 2021), investor pessimism inside Egypt's market lessens the impact of narrative disclosures; hence, equilibrated IM prevalence complements its non-significant influence in regression analysis (Beta = 0.112, p = 0.072). Firm size, the mean of the natural logarithm of total assets (LogTA), reached 3.3028 with a standard deviation of 0.7502. This is a representative distribution of a heterogeneous population of firm sizes to measure scale effects on stock prices in Egypt's heterogeneous market (Hafez, 2023). Moderate heterogeneity is equivalent to the purposive sampling strategy involving companies across different industries, such as financial, industrial, and property, to provide representativeness for the EGX framework 2020–2024. The absence of any significant effect of LogTA on regression analysis (p > 0.05) guarantees Egypt's market, where macroeconomic variables generally dominate firm-specific traits, such as size (ElHawary & Hassouna, 2021). ROA or profitability is 0.0523 on average, with a range of -1.4452 to 0.2911, and a standard deviation of 0.1391. The minimum positive average indicates average profitability, while the extreme range with negative figures indicates humongous losses for certain firms that mirror Egypt's economic recessions included in the span of this study, that is, energy crises and global economic recessions (ElHawary & Hassouna, 2021). Volatility is the differential performance of EGX-listed firms, which is a typical emerging market experience under external shocks (Haw et al., 2005). The failure of the ROA effect in the regressions to achieve significance (p > 0.05) confirms its weak correlation with stock prices (0.085, p > 0.05), where other indicators are even more sensitive among Egyptian investors in the volatile market. Leverage, defined as total debt divided by total assets, has a mean of 0.5357, range of 0.0554 to 0.9667, and standard deviation of 0.2358. The high mean shows that Egyptian companies have high levels of debt, in line with Egypt's high-risk financial setting in 2020–2024, when companies are using debt financing to handle financial uncertainty (Hafez, 2023). The wideness of spread captures heterogeneity in capital compositions, with others attaining their optimal leverage, explaining the strong positive leverage effect in the regression (beta = 0.26, p < 0.01). This finding justifies research in emerging markets, where leverage increases volatility in equity prices owing to financial risk (Haw et al., 2005). In short, descriptive statistics identify Egypt's economic and market position in 2020–2024 as a volatile market with governance problems that impact stock prices, leverage, and DABEM distribution. IM dominance with restricted impact points to investor mistrust, while the discrepancy between LogTA and ROA reflects the variety of EGX-listed firms. These trends confirm the conclusions of the regression and correspond to Egypt's shifting market conditions and emerging market dynamics. 4.2 Correlation Analysis To examine the bivariate relationships among the study variables—DABEM, FSP, IM, firm size, ROA, and leverage—a Pearson correlation matrix was constructed, as presented in Table 3. This analysis, based on 200 firm-year observations from EGX-listed firms from 2020 to 2024, ensures alignment with the regression results and verifies the absence of multicollinearity, with all correlation coefficients below 0.7, consistent with established thresholds (Hair et al., 2010). The correlations are interpreted within the context of Egypt’s volatile economic environment, marked by significant currency devaluations, inflation peaking at 38% in 2023, and enhanced market scrutiny following the post-2020 reforms (Hafez, 2023). Where Egyptian-specific evidence is limited, the emerging market literature provides additional support (Haw et al., 2005). Table 3. Pearson Correlation Analysis Earnings Management Future Stock Price Impression Management TA ROA Leverage Earnings Management Pearson Correlation 1 Future Stock Price Pearson Correlation -.258** 1 Impression Management Pearson Correlation .342** .162* 1 TA Pearson Correlation -.189* .145* .395** 1 ROA Pearson Correlation -.210** .085 .175* .265** 1 Leverage Pearson Correlation -.065 .275** .048 .360** -.082 1 *. Correlation is significant at the 0.01 level (2-tailed), *. Correlation is significant at the 0.05 level (2-tailed). DABEM has a significant negative correlation with future stock prices (0.258, p < 0.01), indicating that higher discretionary accruals are associated with lower stock prices seven days post-release of annual reports. The correlation shows that the Egyptian market penalizes earnings management, indicating greater investor concern after regulatory reforms and foreign investment in 2020–2024 (Hafez, 2023). This finding is congruent with the regression estimates (beta ≈ -0.22, p < 0.01 in Models 1 and 2) and coincides with emerging market research, where markets correct for earnings manipulation due to greater transparency (Haw et al., 2005). In contrast to Mostafa (2017), who found no substantial EM market response in 2012–2015 due to post-revolution distrust, the evidence for the period under consideration shows a more efficient market response because of Egypt's evolving financial landscape. The positive and strong association between DABEM and IM (0.342, p < 0.01) implies that companies with discretionary accruals also practice IM activities, such as tone or selective disclosure, as secondary activities to impress stakeholders. This confirms the practice of utilizing narrative strategies as counterstrategies to mitigate the negative effect of earnings management, a common practice in governance-deficient markets (Merkl-Davies et al., 2011). In the Egyptian context, companies would employ IM to offset investor concerns in times of economic turbulence, such as currency depreciation and oil crises (ElHawary & Hassouna, 2021). The significance of the interaction term DABEM × IM in the regression is buttressed by association, although its non-significance (p = 0.110) suggests a marginal moderating effect. Investors' disinterest in narrative disclosure in Egypt's unpredictable market is indicated by a strong positive weak correlation between IM and FSP (0.162, p < 0.05). This coincides with the near-zero IM effect in the regression (Beta = 0.112, p = 0.072, as shown in Table 6) and with the binary coding of the IMNR index that identifies IM in 52% of observations (Czajkowska, 2023). In Egypt, financial issues such as inflation and supply chain interference between 2020 and 2024 minimize reliance on narrative strategies to the detriment of physical budget interference (ElHawary & Hassouna, 2021). This finding is consistent with the literature on emerging markets, where narrative disclosures lose relevance or become less relevant in low-trust settings (Merkl-Davies et al., 2011). Leverage and FSP correlation (0.275) help explain the lack of regression proof (p > 0.05). This suggests that investors are moderately interested in profitability during Egypt's macroeconomic uncertainty (ElHawary & Hassouna, 2021). The negative correlations between DA and LogTA (-0.189, p < 0.05) and ROA (-0.210, p < 0.01) suggest that less profitable small businesses control earnings more, as required by Egypt's governance failures (ElHawary & Hassouna, 2021). There is no multicollinearity (all correlations < 0.7), which means that regression estimates are reliable, and the results of variance inflation factors (VIFs) confirm this in the robustness checks. These correlations serve as a basis for the regression models, substantiating the substantial negative impact of DABEM, minimal influence of IM, and pivotal role of leverage in Egypt's dynamic market environment from 2020 to 2024. 4.3 Regression Analysis Ordinary least squares (OLS) with robust standard errors was used to estimate two regression models to solve the heteroskedasticity prevalent in Egypt's market (Hafez, 2023). With a variance inflation factor (VIFs) of less than five, no multicollinearity problems were confirmed. 4.3.1 Model 1: Direct Effect of DABEM As shown in Table 4, a higher DABEM is associated with decreases in FSP, as the regression analysis shows a particularly negative coefficient for DA (β = –2.950, standardized β = –.230, p = 0.001). According to Teixeira and Rodrigues (2022), this finding strongly supports the market discipline theory, which posits that investors in the Egyptian equity market have developed a level of sophistication sufficient to identify and punish opportunistic earnings manipulation. Table 4. Regression Results (Model 1) Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -3.750 11.200 -0.335 .738 Earnings management -2.950 0.890 -0.230 -3.315 .001 LogTA 0.650 0.400 0.108 1.625 .106 ROA 11.800 15.200 0.053 0.776 .439 Leverage 39.500 10.100 0.264 3.911 .000 Adjusted R Square= 13.2%, F= 8.125, Sig.= 0.000 a. Dependent Variable: FSP This conclusion conflicts with the previous results of ElHawary and Hassouna (2021), who found a minimal link between DABEM and market value for the years 2012 to 2015. The observed difference might be explained by structural changes in the post-2020 Egyptian regulatory scenario, including more financial reporting monitoring and increased investor awareness. Significantly, the current results more closely match those of other developing nations (e.g., Haw et al., 2005), indicating continuous convergence in the pricing of earnings quality. The impact of size (β = –2.950) reveals that firms with earnings manipulation in the minds of Egyptian investors suffer a severe valuation penalty. The pathology of prior corporate governance failures and the credibility-enhancing impact of recent reforms cause this heightened sensitivity (Hafez, 2023). These findings are in line with the expectations of the efficient market hypothesis and the general literature on the capital market implications of earnings quality (e.g., Subramanyam, 1996). The leverage coefficient is statistically and economically significant (β = 39.500, standardized β = 0.264, p < 0.001). This finding aligns with the theoretical predictions, indicating that leverage amplifies firm-specific risk and heightens stock price volatility, particularly in financially constrained contexts. In Egypt, leverage appears to play a major role as a driver of equity valuation because corporate debt ratios are generally higher there because of the shallow equity base and bank-hierarchical funding structures (Hafez, 2023). This result further supports structural credit risk models, such as Merton's (1974) model, which relates capital structure to equity pricing through risk-related channels. The profitability and size of the firm had no statistically significant effects (LogTA: p = 0.106; ROA: p = 0.439), while the coefficients were positive. This, as well as no significant effect, is consistent with the weak correlations (0.145 and 0.085, respectively) observed, and may reflect the predominance of macro-level determinants—political instability, exchange rate fluctuations, and foreign capital flows—over company-specific factors in the valuation. These results are in line with those of previous research in comparable contexts (ElHawary & Hassouna, 2021; Thanh Liem, 2021). The 13.2% adjusted R 2 has a moderate level of explanatory power, but it is known to be adequate for research in nascent financial markets. Although such levels fall short of those found in developed economies, they are commensurate with the underlying volatility of equity returns in markets that are beset by external shocks and institutional imperfections. The model’s overall significance is also confirmed with an F-statistic of 8.125 (p < 0.001), indicating that together, the explanatory variables significantly account for FSP predictions. 4.3.2 Model 2: Moderating Effect of IM Table (5) depicts Model 2, which extends the baseline regression by including IM and the interaction term between impression management and discretionary accruals (DA × IM) variables to explore how financial and narrative manipulations jointly affect stock prices in the future. At 15.6%, the R² of the model was also somewhat higher than that of Model 1 (13.2%), and the F statistic was still significant (F = 7.326, p < 0.001). The slight improvement in the explanation shows that impression management is enlightening, but it does not significantly increase the predictive capability of the model for the Egyptian example. Table 5. Regression Results (Model 2) Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 2 (Constant) -3.920 11.300 -0.347 .729 DABEM -2.780 0.900 -0.216 -3.089 .002 IM 0.580 0.320 0.114 1.813 .071 DABEM × IM 0.450 0.280 0.098 1.607 .110 LogTA 0.610 0.410 0.101 1.488 .138 ROA 11.200 15.100 0.050 0.742 .459 Leverage 38.700 10.200 0.258 3.794 .000 Adjusted R Square= 15.6%, F= 7.326, Sig.= 0.000 a. Dependent Variable: FSP The DABEM coefficient remained strongly negative (B = –2.780, p = 0.002) and was similar in magnitude to the outcome of Model 1. The similarity in the coefficients provides additional support for the reasoning that Egyptian shareholders punish companies that overly rely on accrual aggressiveness, particularly in the post-2020 period, where increased regulation and more foreign institutional investor monitoring have induced higher market sensitivity against financial misreporting (Hafez, 2023). The negative coefficients also support the contention that Egyptian investors increasingly view earnings management as an early warning sign, conceivably releasing concealed governance vulnerabilities or potential future performance threats. The DABEM and IM interaction terms (B = 0.450, p = 0.110) were not significant; therefore, IM did not significantly dampen the negative association between DABEM and FSP. Hence, narrative disclosure offers no comfort in reducing the punishment for financial mishandling in the market. These results are consistent with investors’ attitudes in low-trust environments, such as Egypt, where narrative techniques may be viewed as rhetorical, trying to validate company value (El-Hawary & Hassouna, 2021; Merkl-Davies et al., 2011). This result substantiates the existing literature that Egyptian investors are predisposed to doubt the absence of narrative disclosures, corroborating robust financial performance (ElHawary & Hassouna, 2021). Czajkowska's (2023) narrative index IMNR also promises to deliver constrained market valuation impacts of narrative reporting in Egypt on behalf of the argument that users of financial statements prefer quantitative performance indicators instead of qualitative disclosure, in agreement with the findings found in emerging economies (Merkl-Davies et al., 2011). The DABEM and IM interaction terms (B = 0.450, p = 0.110) were not significant, that is, IM did not significantly dampen the negative association between DABEM and FSP. Therefore, narrative disclosure does not offer relief in mitigating punishments for financial manipulation in the marketplace. These results are consistent with investor perception in low-trust settings such as Egypt, where narrative frames can be regarded as rhetorical in trying to legitimize firm value (ElHawary & Hassouna, 2021; Merkl-Davies et al., 2011). The control variables largely perform their functions, as in Model 1. Leverage remains strongly positively and significantly affecting future stock prices (B = 38.700, p < 0.001), indicating the previous interpretation that debt in Egypt can also be employed as a measure of expansion or credibility. Conversely, profitability (ROA) and firm size (LogTA) are still statistically insignificant, again suggesting that these measures might be trumped by earnings quality signals to influence investors’ valuation judgments. Notwithstanding that the adjusted R² increases marginally upon including IM variables in the model, the overall explanatory power of the model is still limited to 15.6%. Such a tight fit is likely to react to broader macroeconomic and institutional uncertainties in Egypt, namely exchange rate volatility, inflation uncertainty, and capital flow volatility, between 2020 and 2024 (Hafez, 2023). This suggests that, although financial and story reporting techniques prevail over market valuations, they are, in turn, pushed by an extended range of economic and regulatory drivers that fuel investor reactions. In conclusion, Model 2 favors the leading role of discretionary accruals in explaining FSP in Egypt's capital market, but concludes that IM itself has no significant dampening effect on the market's negative reaction to earnings manipulation. This indicates the changing, but conservative, course of Egyptian investors' attitude towards narrative disclosures and calls for full financial and non-financial transparency programs to restore trust in financial reporting. 4.4 Robustness test To verify the stability and consistency of the EM effect found in the earlier models, a test for robustness was performed by re-estimating discretionary accruals under the performance-matched Jones model suggested by Kothari et al. (2005). The model controls firm performance based on ROA, thus correcting for the endogeneity bias automatically associated with typical accrual models. Considering Egypt's remarkably high diversity and volatile economic situation, this is a welcome corrective measure for properly evaluating the actual impact of earnings management on market value (ElHawary & Hassouna, 2021). The results of Model 3 shown in Table 6 are very much in agreement with the earlier results. Maintaining a statistically significant negative influence on future stock prices (B= –2.900, p = 0.001), the performance-matched discretionary accrual variable (DA_PM) has a standardized coefficient (beta = –0.226), which is nearly identical to that found in Model 2. This finding supports the conclusion that the Egyptian market continues to penalize earnings misrepresentation, regardless of methodological improvements in accrual calculation. Particularly, in an emerging market context increasingly shaped by foreign institutional criteria and demands for IFRS adoption (Hafez, 2023), it also improves the reliability of discretionary accruals as a genuine measure of managerial opportunism. The interaction term DA_PM × IM was also not significant (B = 0.430, p = 0.119) and the IM variable (IM; B = 0.570, p = 0.072) was still not significant. The claim that narrative disclosures, as they are now implemented in Egypt, have little effect on investor responses to financial manipulation is further supported by these data, which almost exactly match those of Model 2. Czajkowska (2023) also came to this conclusion, showing that in capital markets, where investors prioritize audited financial data over qualitative statements, the IMNR index—that is, evaluating the quality of narrative reporting— does not have much explanatory power. Table 6. Robustness Check with Performance-Matched DA Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 3 (Constant) -3.880 11.280 -0.344 .731 Earnings management (DA_PM) -2.900 0.870 -0.226 -3.333 .001 Impression Management 0.570 0.315 0.112 1.810 .072 DA_PM × IM 0.430 0.275 0.094 1.564 .119 LogTA 0.630 0.405 0.105 1.556 .121 ROA 11.250 15.150 0.051 0.743 .458 Leverage 38.800 10.180 0.259 3.811 .000 Adjusted R Square= 15.8%, F= 7.512, Sig.= 0.000 a. Dependent Variable: FStP Leverage shows a strong and statistically significant positive impact (B = 38.800, p 0.05); therefore, it is not very helpful in accounting quality indicators. These results match the structural characteristics of the Egyptian capital market, where governance volatility and poor institutional coverage frequently hide companies’ fundamentals (Hafez, 2023; ElHawary &; Hassouna, 2021). Finally, the modified R² increased slightly to 15.8%, thus enhancing Model 2. This change shows an increased ability to forecast accruals as we consider the state of the firm. The robustness study validates the importance of earnings quality in influencing investor reactions and shows that the main findings hold true, even with different model parameters. It also shows that investors punish firms that use discretionary accruals even if they try to use narrative IM at the same time. This makes the case for better financial transparency and investor education even stronger in the Egyptian market. 5. Discussion of Results This study analyzes the correlation between DABEM and FSP on the Egyptian Stock Exchange from 2020 to 2024, integrating IM as a moderating variable, while controlling for firm size, profitability, and leverage. These findings enhance the literature on emerging markets by offering empirical evidence from Egypt's developing capital market amid substantial economic and regulatory changes. The results are discussed below. 5.1 EM and Market Valuation The empirical analyses consistently demonstrate a statistically significant negative correlation between DABEM and FSP (β ≈ -0.22, p < 0.01 across all model specifications). This finding offers substantial evidence corroborating the market discipline hypothesis, which posits that sophisticated investors detect and sanction opportunistic earnings manipulation. The recorded coefficient magnitude shows that a one-unit increase in discretionary accruals is linked to a drop of approximately 22% in the FSP, which is a big deal for the economy. The descriptive statistics reveal significant variability in EM practices, as discretionary accruals fluctuate between -0.5984 and 0.6959 (mean = -0.0232, SD = 0.3365). This finding suggests that many Egyptian firms use DABEM strategies. This prevalence aligns with theoretical expectations for emerging markets characterized by fragile institutional frameworks and governance structures (Mlawu et al., 2025; Itan et al., 2024). The time frame being examined (2020–2024) witnessed a lot of macroeconomic instability, with currency values dropping by more than 50% and inflation rates reaching 38% in 2023. This may lead managers to engage in earnings-smoothing activities to reduce perceived uncertainty. The robustness of these findings is corroborated by the application of various estimation techniques, including the performance-matched Jones model (Kothari et al., 2005), which yielded consistently negative coefficients (β = -0.226, p = 0.001). This methodological triangulation enhances confidence in the stability and reliability of the primary findings, alleviating potential concerns associated with the model specification and measurement errors inherent in discretionary accrual estimation. The negative coefficient of DABEM indicates that higher discretionary accruals are associated with lower, rather than higher, FSP, and this must be explicitly reconciled with the theoretical framework. In formulating H1, EM was implicitly viewed through a signaling lens as a potentially informative device that could help managers convey private information about favorable future performance in a high–information‑asymmetry environment, suggesting a positive association with stock prices (Subramanyam, 1996; Jiraporn et al., 2008). However, the empirical results for the Egyptian market show that investors appear to interpret EM predominantly as opportunistic earnings manipulation rather than credible signaling; thus, penalize firms with higher discretionary accruals, consistent with evidence that low earnings quality and aggressive EM are priced negatively by capital markets (Healy & Wahlen, 1999; Dechow et al., 2010; ElHawary & Hassouna, 2021). From the perspective of signaling theory, this pattern suggests that in Egypt’s evolving but still imperfect governance and enforcement environment, the perceived cost of manipulation is not high enough to make EM a trustworthy signal. Therefore, rational investors discount EM and, in line with the efficient market hypothesis, incorporate their suspicion of manipulation into prices by attaching a valuation discount to firms exhibiting higher discretionary accruals (Connelly et al., 2011; Haw et al., 2004). Accordingly, the findings refine rather than simply contradict H1, while EM can, in principle, function as a positive signal. The evidence indicates that under current Egyptian institutional conditions, its opportunistic, value-reducing interpretation dominates, and this is how the results are interpreted in light of both signaling theory and market efficiency. 5.2 The Role of IM In contrast to the theoretical expectations informed by signaling theory, IM exhibits a marginally insignificant positive correlation with stock prices (β = 0.112, p = 0.072) and does not moderate the negative relationship between discretionary accruals and market valuations (p = 0.110-0.119). The descriptive analysis indicates that around 52% of firm-year observations demonstrate impression management tactics (mean = 0.52, SD = 0.501), signifying the extensive application of narrative disclosure strategies. The weak correlation between impression management and stock prices (r = 0.162, p < 0.05) indicates that investors do not put much stock in qualitative disclosures when they try to figure out how much a company is worth. In contrast to evidence from developed markets, this finding aligns with emerging market characteristics, where institutional investors exhibit heightened skepticism regarding managerial communications because of governance concerns and information asymmetries (Ajina et al., 2015; Cormier et al., 2010). The negligible moderating effect suggests that audited financial data are more important to Egyptian investors than narrative disclosures when evaluating valuations, and it also shows that impression control tactics do not mitigate unfavorable market reactions to earnings management. 5.3 Control Variables and Market Dynamics The leverage coefficient consistently exhibits a positive and statistically significant correlation with stock prices (β = 0.26, p < 0.01) and is supported by a strong bivariate correlation (r = 0.275, p < 0.01). This surprising outcome illustrates the distinct capital structure dynamics prevalent in emerging markets in contrast to expectations in developed markets. A high mean leverage ratio (0.5357, SD = 0.2358) indicates that the firm relies heavily on debt financing. In Egypt, this could mean that the firm is trustworthy and can obtain money from capital markets instead of being in trouble with money (Elsaman & Alshorbagy, 2011; Evianti & Hasibuan, 2025). Conversely, firm size (LogTA) and profitability (ROA) exhibit no statistically significant associations with stock prices (p > 0.05) despite the presence of weak positive correlations (0.145 and 0.085, respectively). This pattern indicates that macroeconomic factors and systematic risks prevail over idiosyncratic firm characteristics in the Egyptian equity valuation models. The descriptive statistics for firm size (mean = 3.3028, SD = 0.7502) and profitability (mean = 0.0523, SD = 0.1391) illustrate the diverse characteristics of listed entities. However, their restricted explanatory capacity highlights the significance of external economic conditions in emerging market scenarios. 5.4 Implications for Model Fit and Market Efficiency The small adjusted R² values (0.132–0.158) are consistent with findings in studies of emerging markets, where external shocks and institutional factors diminish the utility of firm-specific variables (Mlawu et al., 2025). There was considerable macroeconomic instability during the study period. For example, the currencies lost value, the supply chain faced problems, and the rules changed. All of them make it harder for traditional models of value to make good guesses. Statistically significant F-statistics (p < 0.001) across all model specifications confirm the overall explanatory power of the variable set. These findings suggest an evolution in the efficiency of the Egyptian market, characterized by regulatory reforms instituted post-2020 and increased engagement from foreign institutions, which have enhanced investors' ability to identify and mitigate earnings management practices. This signifies a divergence from previous research that revealed weak or negligible correlations between earnings management and market valuations during Egypt's post-revolution era (2011-2019), implying heightened market sophistication and improved information-processing abilities. 5.5 Theoretical and Practical Implications The observed negative correlation between DABEM and FSP corroborates the market efficiency hypothesis in emerging market contexts, suggesting that Egyptian investors possess adequate analytical skills to detect and penalize opportunistic reporting practices. The negligible influence of IM underscores the constraints of narrative-based disclosure strategies in low-trust institutional settings characterized by investors' pronounced skepticism regarding managerial communications. The positive relationship between leverage and price indicates that financing works differently in emerging markets. In these markets, debt capacity may reflect a firm's quality and growth potential, rather than financial issues. This finding has significant implications for capital structure decisions. This also shows that when traditional theories of developed markets are used in emerging markets, they may need to be changed. Overall, the results show how important it is for financial reporting to be clear and how important it is for regulations to keep improving to make markets work better in emerging economies. The findings indicate that Egyptian capital markets are improving their management of earnings-related information; however, numerous opportunities remain to enhance narrative disclosure and investor education initiatives. 6. Conclusions, Limitations and Directions for Future Research This study provides compelling evidence that DABEM has a significantly negative impact on FSP in the Egyptian market from 2020 to 2024. The working capital accruals model is employed in conjunction with robustness verification using the performance-matched Jones model (Kothari et al., 2005). We find that firms with high discretionary accruals face market-imposed valuation discounts. This finding supports the market discipline hypothesis and shows that the Egyptian market is becoming more sophisticated as investors become less tolerant of earnings manipulation. This aligns with findings from other emerging markets (Haw et al., 2005) and demonstrates the potential impacts of regulatory enhancements post-2020 and increased foreign investor scrutiny (Hafez, 2023), in contrast to earlier Egyptian studies that suggest diminished market sensitivity (e.g., ElHawary & Hassouna, 2021). We investigate the potential moderating effect of IM operationalized through content analysis of narrative disclosures using the IMNR index (Czajkowska, 2023), on this relationship. Our findings indicate that companies can use IM and DABEM simultaneously, as shown by the positive correlation between the two. However, the interaction term (DABEM × IM) is not statistically significant. This means that the way IM is used in Egyptian annual reports right now does not make the negative market reaction to earnings management less aggressive. This finding aligns with views on narrative reporting in low-trust contexts, where qualitative disclosures may be regarded as skepticism (Merkl-Davies et al., 2011). Consistent with the current research on emerging markets, our models show constrained explanatory power (Adjusted R² = 13.2%–15.8%). Factors influencing the entire economy, such as inflation, currency depreciation, and international capital flows, are more significant than those affecting only one company during this turbulent period (Hafez, 2023; Haw et al., 2005). Among the control variables, financial leverage has a major effect on the variation in future stock values. This demonstrates how Egyptians’ market perception is altered by their great debt. As this is the case in most studies, this study has several limitations that should be considered when interpreting the findings and guiding future research. First , the analysis focuses on Egypt, an emerging market, during a short and turbulent period, which limits the generalizability of the results to other contexts with differing institutional features such as governance quality, enforcement intensity, and investor sophistication (ElHawary & Hassouna, 2021). Second , the use of accrual-based models and a binary narrative impression index to measure EM and IM is subject to measurement errors, which makes it difficult to distinguish between opportunistic manipulation and legitimate reporting discretion. Consequently, the estimated effects may be attenuated or biased by proxy noise (Dechow et al., 2010). Third , the observational nature of the study and the reliance on regression models controlling for observable firm characteristics means that unobserved factors, such as governance shocks or investor sentiment, could jointly influence reporting choices and stock price reactions. Thus, the results should be viewed as associative rather than causal (Connelly et al., 2011; Healy & Wahlen, 1999). Future research should explore alternative EM and IM measures, extend the analysis to other markets, and examine regulatory or institutional shocks to allow stronger causal inferences (Roychowdhury, 2006; Elshandidy & Ibrahim, 2024). Bottom of Form However, there are several implications for policy and standard-setters, governments, and market participants in Egypt and other similar emerging markets. This study offers some significant policy implications, where the significant negative market reaction to DABEM confirms that investors possess the ability to detect and penalize such practices. However, the fact that DABEM is present and costs market money shows how important it is for regulatory bodies such as the Egyptian Financial Regulatory Authority (EFRA) to make rules stricter. More assured and severe penalties for earnings manipulation, together with more rigorous audits, may help keep the market in check and reduce the likelihood of companies reporting false information. A favorable response of the market should not be the only factor that ensures accurate reporting. The high level of EM, as evidenced by descriptive statistics and the large market penalty, points to management's potential to abuse their freedom in certain areas of accounting standards, particularly when it comes to working capital accruals. Promoting market efficiency and teaching investors as the market's ability to punish DABEM, especially after 2020, is a good sign that it is becoming more sophisticated. Policymakers should continue working to make the market more efficient, for example, by ensuring that information is shared quickly and widely. In addition, programs that teach investors can help domestic investors better analyze both financial and non-financial information. This would improve overall market trust and discipline. Understanding how leverage works in Egypt, where it has a strong positive effect on future stock prices due to the unique financial dynamics of the market, where debt can mean growth or access to financing. However, high leverage increases this hazard. Regulators must monitor leverage trends and ensure that businesses possess strong risk-management systems. Investors and the financial system as a whole depend on the balance between the need for capital and financial stability. In summary, although Egyptian market forces appear to be heading towards higher standards for earnings quality, proactive legal and policy actions are required to reinforce this trend, boost the general credibility of financial reporting, and foster a more transparent and effective capital market. Hence, future research may be directed towards investigating the effect of different earnings management approaches on future stock prices using other moderators, such as the characteristics of the board of directors, audit quality, and disclosure strategies. Declarations Ethical Approval and Consent to Participate Not applicable Consent for Publication Not applicable Funding The authors received no specific funding Availability of data and material Secondary sources of data are used to complete this study. Competing interests The authors declare that they have no conflict of interest. Funding The authors received no specific funding. Author’s contributions TI developing the original draft, helped in methodology and edited and reviewed the draft and made constructive changes to the draft. MH prepared the original draft as well as reviewing the literature. ME collected the data, analyzed the results, and concludes the draft. All authors have read and approved the manuscript. Authors' information Tariq H. Ismail is a Professor of Accounting at the Faculty of Commerce, Cairo University, Egypt. He is currently the Dean of Business School at the International Academy of Engineering and Media Science, Egypt. He has published numerous articles in a number of high-ranked, peer-reviewed journals, and has many books which had worldwide audience. He had many research grants and excellence awards for the contributions he made in his field. He is the founder and the editor-in-chief of the Academic Journal of Social Sciences and the associate editor of Journal of Humanities and Applied Social Sciences . He is on the editorial board of several reputable International journals. His current research focuses on disclosure quality and financial reporting, accounting in emerging economies, corporate governance, corporate social responsibility, earnings management and narrative reporting. Mohamed Samy El-Deeb is a Professor of accounting and Vice-Dean of Environmental Affairs and Community Service and Head of accounting department, Faculty of management sciences, October University for Modern Sciences and Arts (MSA), 6 of October City, Egypt. His research interest is in governmental accounting, auditing, integrated reporting and environmental, social, and governance reporting, accounting theory, and corporate governance. Mohamed H. El-Ashwal is an Associate Professor of Accounting at Port Said University, Egypt. His research interests are earnings management, sustainability accounting, integrated reporting, corporate sustainable performance, and stock markets. He has presented papers in many national and International conferences References Ajina, A., Lakhal, F., & Sougné, D. (2015). Institutional investors, information asymmetry and stock market liquidity in France. International Journal of Managerial Finance , 11(1), 44-59. 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Evidence on Earnings Management, Impression Management, and Future Stock Prices in Egypt","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eInvestors rely on various factors when making investment decisions in stock markets to reduce the level of uncertainty caused by fluctuations in stock prices (Anwaar, 2016). One of these factors is the information disclosed in a corporate annual report (Pernamasari, et. al., 2022). In addition to financial statements, it includes the discretionary information necessary for explaining financial statements and providing additional information (Goncalves, et. al., 2022). Both financial statements and discretionary information provide investors with information on numerous aspects, such as financial performance, financial position, business strategy, and sustainability strategy. Despite efforts to ensure disclosure quality, managers may have incentives to distort information from corporate annual reports by using different tactics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn the one hand, financial statements provide misleading information when managers utilize earnings management (EM) by practicing real-based earnings management and/or accrual-based earnings management. The former focuses on changing business activities and transactions to attain desired financial results. The latter refers to manipulating accounting estimates and judgments to alter reported earnings without changing actual cash flows. Managers use accounting regulations to provide the desired financial results by hiding specific accounting information or making it difficult to identify (Ilic, et al., 2024). On the other hand, discretionary information might be manipulated using impression management (IM), which has different strategies, such as rhetorical manipulation, thematic manipulation, reading-ease manipulation, and visual/structural manipulation (Merkl-Davies \u0026amp; Brennan 2007, 2011; Jaworska \u0026amp; Bucior 2017, 2020). They aim to distort investor perceptions of investors (Albuquerque et al., 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough EM and IM are different methods, managers can use them as complementary vehicles of communication. Goncalves et al. (2022) confirm that \u0026quot;\u003cem\u003emanagers seek to obfuscate the intensity with which they manage earnings by disclosing more complex, meaning less readable annual reports\u0026quot;\u003c/em\u003e. Thus, managers utilize both EM and IM to influence investors\u0026apos; expectations of future profits, dividends, and stock value (Strakova, 2020) and understand how investors view the company\u0026apos;s accomplishments respectively (Czajkowska \u0026amp; Remlein, 2024; Phesa \u0026amp; Sibanda, 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEgypt represents an exemplary environment for establishing a relationship between emerging market characteristics and stock returns. It has a specific enforcement environment, with an actively traded stock market and heterogeneous disclosure practices being tightening yet weak (Financial Regulatory Authority, 2009; Hassan et al., 2009; Samaha, 2013). The Egyptian stock market is framed by Capital Market Law No. 95, dated 1992, and its executive bylaw. While it requires strict listing and disclosure standards, enforcement with respect to financial and corporate governance disclosure remains limited (General Authority for Investment and Free Zones; Hassan et al., 2009; Samaha, 2013).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEmpirical studies on Egyptian publicly traded firms offer evidence that, while mandatory disclosures required by IFRS standards are widely disclosed, voluntary and corporate governance disclosures are low. Thus, they represent an environment with specific characteristics concerning information quality and transparency, which can be systematically varied and harnessed for identification purposes (Samaha et al., 2015; Samaha, 2013; Zahran, 2016).\u0026nbsp;However,\u0026nbsp;the Egyptian Financial Regulatory Authority\u0026nbsp;recently increased its enforcement efforts by pushing for higher levels of protection offered to investors, improvements in market infrastructure, and higher levels of disclosure. These efforts result in episodic shocks with respect to enforcement levels that affect the information environment owing to its quasi-exogenous nature (Financial Regulatory Authority, 2009; Gramon et al., 2025). Furthermore, the Egyptian environment provides better sources for identification than other emerging markets\u0026nbsp;do. \u0026nbsp;This is because of its higher enabling capacity with respect to specific microstructural characteristics, such as\u0026nbsp;standardized trading practices in the Egyptian stock market (Hassan et al., 2009; Samaha et al., 2015).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study examines the moderating role of IM on the relationship between EM and future stock prices in the context of firms listed on EGX. These findings have very specific incremental value based on three aspects. \u003cem\u003eFirstly\u003c/em\u003e, discretionary accruals help predict future stock prices within emerging markets, indicating that earnings quality signals could enhance earnings and price forecasts, especially when adapted into earnings valuation models based on the existing literature. This suggests a systematic relationship between forecast errors, management reporting decisions, and earnings characteristics. \u003cem\u003eSecondly\u003c/em\u003e, the negative relationship between EM and future stock prices implies that investment professionals can generate investment profits by underweighting firms with high accruals or low-quality earnings, and overweighting firms with low accruals or high-quality earnings. \u003cem\u003eThirdly\u003c/em\u003e, as the empirical analysis focuses on firm-years with high discretionary accruals and complex earnings narratives, this test could be translated into a risk-screening tool under the supervision of regulatory bodies with the aim of implementing emerging literature. This suggests that accounting-based measures could be employed to focus\u0026nbsp;on enforcement efforts\u0026nbsp;in countries with poor supervisory infrastructure.\u003c/p\u003e\n\u003cp\u003eThe rest of this paper is organized as follows: Section 2 presents the literature review and hypotheses development, and Section 3 introduces the research method. Section 4 presents the data analysis, and Section 5 discusses the results. Section 6 presents conclusions, limitations, and suggestions for future research.\u003c/p\u003e"},{"header":"2. Literature Review and Hypotheses Development","content":"\u003cp\u003e\u003cem\u003e2.1 EM and Future Stock Prices\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSpence (1973) created signaling theory for the labor market, yet it has been modified to explain financial reports\u0026apos; disclosures (Ross, 1977). According to signaling theory, there is information asymmetry between stakeholders and the management who own the information. Furthermore, signaling theory assumes that better-informed parties utilize difficult or costly signaling to better convey private information to poorly informed counterparties (Connelly et al., 2011).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the context of financial reporting, to improve the quality of financial information and subsequently help translate it into high stock prices, better-informed managers would utilize EM in two ways. \u003cem\u003eFirstly\u003c/em\u003e, they would try to align reported earnings with future performance projections in terms of accruals to better inform stockholders with heavy information content in accounts (Subramanyam, 1996; Jiraporn et al., 2008; ElHawary \u0026amp; Hassouna, 2021). In this context, it appears that moderate and performance-consistent EM activities can be perceived by investors as a useful or credible signaling device to reveal superior market performance and help translate it into high stock prices. \u003cem\u003eSecondly\u003c/em\u003e, EM may be opportunistically relied upon to generate temporary earnings gains at the cost of future earnings. Sophisticated investors, auditors, and regulators detect the tenuous nature of EM (Dechow et al., 2010).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe dual role of potentially informative versus opportunistic EM implies that stock prices respond to this hypothesis. Ultimately, it is a factual question pertaining to the joint effects of managerial discretion within a particular institutional environment, such as that prevailing in Egypt, where EM governance is being institutionalized to varying degrees. Based on this, we formulate the first hypothesis as follows:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH1:\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;Earnings management has a significant positive impact on future stock prices in EGX.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2 EM, IM and Future Stock Prices\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to signaling theory, attribution theory informs the relationship between IM and firm value. Based on Merkl-Davies and Brennan\u0026rsquo;s (2011) taxonomy, IM depends on four perspectives: economic, psychological, sociological, and critical. They are used to describe the type of rationality that guides the behavior of both managers and stakeholders. From the perspective of annual financial reports, performance attributions may be used proactively to shape organizational audiences` perceptions of organizational outcomes; that is, IM (Merkl-Davies and Brennan`s, 2011). \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe existing IM literature finds that managers may employ positive tone, highlighting, and narrative forms to shape stakeholders\u0026rsquo; beliefs about performance, risk, and value (Merkl-Davies \u0026amp; Brennan, 2011). When these narrative attributes are generally consistent with fundamentals and are confirmed by future outcomes, they can serve as additional cues to alleviate uncertainty and enhance investors\u0026rsquo; understanding of strategy and risk management. Thus, they facilitate positive price reactions around news of disclosure, particularly under noisy information conditions. On the other hand, these narrative attributes can also mask challenging performance or extreme IM. Under these conditions, sophisticated investors are likely to view extremely positive news as \u0026ldquo;cheap talk\u0026rdquo; cues, about which they are skeptical. Thus, they might disregard narrative cues altogether, or even view them as a warning sign of potential problems with management\u0026rsquo;s value-creating activities, resulting in more adverse price reactions (Beelitz \u0026amp; Merkl-Davies, 2012). Hence, the second hypothesis is formulated as follows:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH2:\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;Impression management has a significant negative effect on future stock prices in EGX.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe IM function moderates EM-price relationships as it affects investors\u0026rsquo; interpretation and assessment of the EM signal. When tone and emphasis diverge insignificantly from underlying fundamentals and eventually realize performance, IM may affect and amplify the degree of managed earnings\u0026rsquo; perceived integrity and hence boost investors\u0026rsquo; overall susceptibility to and responsiveness of EM-price relationships (Subramanyam, 1996). However, when tone diverges to convey fundamentals in an overly optimistic manner \u0026mdash;\u0026ldquo;overly rosy tone statements \u0026mdash; investors view IM as more of talk or concealment. This may result in undermining or even reversing EM-price implications, as investors disclose little faith and conviction in and about EM signal statements or interpreted earnings realities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAgainst this conceptual foundation, this moderating function may manifest itself and prove even more material or significant in markets such as Egypt. They share similar structural and operational environment gaps and settings in which investors may tend to exhibit a high degree of perceived disparities and asymmetries\u0026mdash;impaired enforcement mechanisms or imperfect governance settings (Haw et al., 2004; ElHawary \u0026amp; Hassouna, 2021). Based on the above discussion, the third hypothesis can be formulated as:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH3:\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;Earnings management has a significantly different positive effect on future stock based on the level of impression management in EGX.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e"},{"header":"3. Research Method","content":"\u003cp\u003e\u003cem\u003e3.1 Research Design\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study follows a quantitative research design to analyze the effect of discretionary accrual-based earnings management (DABEM) on Egyptian listed firms\u0026apos; future stock prices, with IM as a moderator. The analysis covers the period from 2020 to 2024, marked by general economic turmoil in Egypt as currency devaluations, high inflation rates (38% in 2023), and more stringent market regulations after the 2016 financial reforms (Hafez, 2023). The model uses content analysis of narrative disclosures in annual reports and historical financial data based on the prevailing theoretical frameworks of EM and IM (McNichols, 2000; Merkl-Davies et al., 2011). Empirical tests use ordinary least squares (OLS) regression with robust standard errors to account for heteroscedasticity and robustness tests based on the performance-matched Jones model (Kothari et al., 2005).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2 Variables Measurement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFour types of variables were considered and employed as shown on Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"574\" height=\"619\"\u003e\u003c/p\u003e\n\u003cp\u003eEM, as an independent variable, is measured through discretionary accruals. To simplify the measurement, the working capital accruals model was used as a proxy. Specifically, small working capital accruals are typically more reliable in detecting EM because they are less affected by normal operational volatility and offer better sensitivity to small but deliberate manipulations. FSP, as a dependent variable, refers to the stock closing price of sampled firms determined one week after the dissemination date of annual reports. IM as a\u003cem\u003e\u0026nbsp;\u003c/em\u003emoderating variable was measured based on IM strategies, such as positive tonal bias and selective disclosure, where we assign a value of 1 to each firm-year if these strategies are detected above a certain specified threshold, indicating IM, and 0 otherwise (Czajkowska, 2023). Moreover, tone and other narrative tools can play a strategic role in communicating with financial statement users about performance and risk. Thus, the approach relies on a word-by-word analysis of positive and negative comments in both the chairman\u0026rsquo;s statements and the management discussion. More specifically, this helps identify emphasis on positive outcomes as well as the suppression of bad news in the chairman\u0026rsquo;s or management discussion portion of the annual report (Mlawu et al., 2023; Nyahas et al., 2018; Demaline, 2020).\u003c/p\u003e\n\u003cp\u003eThe choice of a binary (0/1) indicator instead of a continuous measure is based on both conceptual and practical reasons. Conceptually, while the presence of IM is of prime interest for being above zero for a firm\u0026rsquo;s reporting year, crossing a certain qualitative threshold from neutral to strategic narrative presentation is consistent with existing literature that defines a discrete outcome for the presence of dominant positive tones and narrative structuring as IM (Bassyouny et al., 2020; El-Shahat, 2023). Practically speaking, binary is more reliable for an emerging market than continuous with potential text mining ambiguities for mixed Arabic-English narrative corpora, and is also less resource-intensive for human-content analysis, although incorporating more simplicity for detailed content measurements into binary form is more reliable than continuous measurements for obvious reasons (Czajkowska, 2023; Picture Content in Annual Reports Matters, 2024). Nonetheless, binary approximation is deliberately made note of being conservative with less detail on intensity of IM for further research measurements, for which re-estimation of certain models with new thresholds of simplicity for easier binary divisions at certain points is recommended to verify that findings are independent of specific threshold usage.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.3 Data Collection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study depends on two types of data: financial and non-financial. Financial data representing the key variables of total accruals, sales, total assets, accounts receivable, property, plant, and equipment, cash, current assets, current liabilities, total debt, and net income were obtained from EGX regulatory filings and audited corporate annual reports. In addition, the FSP was quantified as the closing price one week following the date of release of the annual report, enabling initial reactions to disclosure. Non-financial data, such as IM data, are narrative in nature and are collected from the narrative sections in EGX-submitted yearly reports. Content was analyzed to create the IM in narrative reporting (IMNR) index, as suggested by Czajkowska (2023). The index identifies IM strategies, that is, positive tonal bias or selective disclosure, and is considered a binary variable (a value of 1 to be assigned in the case of existing IM strategies, 0 otherwise). Data reliability was increased via cross-validation against audited reports and formal EGX records to address inconsistencies in Egypt\u0026apos;s disclosure practice amidst economic uncertainty (Hafez, 2023).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.4 Sample Selection\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe population of this study comprises all firms listed on the Egyptian Stock Exchange (EGX 70) from to 2020-2024. Purposive sampling allows the collection of 40 firms\u0026rsquo; annual reports from different industries, such as beverage, manufacturing, and real estate, depending on market significance and disclosure availability. This strategy was followed in most studies related to the Egyptian context (ElHawary \u0026amp; Hassouna, 2021), generating 200 firm-year observations that provided sufficient statistical power, captured market heterogeneity, and avoided selection bias.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.5 Model Specification\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo empirically investigate the relationship between the study variables, two regression models were developed. Within the first model, the direct effect of the DAEM on FSP is estimated while controlling for business-specific characteristics, such as firm size, profitability, and financial leverage. The second model expands the analysis by including IM as a potential moderating variable, in addition to its interaction with DABEM.\u003c/p\u003e\n\u003cp\u003eThis study utilized various methodologies to tackle the issues of construct validity and measurement errors. \u003cem\u003eFirst\u003c/em\u003e, the IM construct was operationalized using a structured content analysis index that codes for various narrative strategies (e.g., tone, selectivity, and emphasis). This aligns with the idea that IM should be seen as a multidimensional construct, not just \u0026quot;positive tone = deception\u0026quot;. \u003cem\u003eSecond\u003c/em\u003e, discretionary accruals are made using an accruals specification adjusted for both industry and performance. This includes scaling parameters to show how a firm\u0026rsquo;s performance changes. This is in line with research showing that these types of specifications can lower the rates of misclassification, which makes the test better for testing the EM hypotheses (Kothari et al., 2005; McNichols, 2000). \u003cem\u003eThird\u003c/em\u003e, robustness tests are performed using different accrual specifications and sensitivity analyses. For example, observations with very high or very low performance levels were omitted, and the model was re-run using different scaling parameters. Experts say that using more than one proxy can make EM studies less likely to have measurement errors (Dechow et al., 2010).\u003c/p\u003e\n\u003cp\u003eThe analysis explicitly combines proxies for accrual-based and real earnings management to capture a broader spectrum of managerial reporting discretion. Discretionary accruals are measured using a performance-matched model (Kothari et al., 2005), which improves specification in volatile emerging markets by controlling for firm performance, and thus reduces the risk that extreme ROA or sales growth in Egypt mechanically inflates estimated discretionary accruals. In parallel, real EM is captured following Roychowdhury\u0026rsquo;s (2006) framework using abnormal levels of operating cash flows, production costs, and discretionary expenses (SG\u0026amp;A), which reflects managers\u0026rsquo; use of price discounts, overproduction, and cuts in discretionary spending to meet reporting targets. This dual‑proxy approach is particularly appropriate in the Egyptian setting, where prior evidence documents both accrual-based and real activity manipulation in response to institutional shocks and evolving governance standards, implying that relying solely on one dimension (accruals or real activities) understates the true extent of earnings management. The models employed in the analysis are as follows:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 1: Investigating the baseline relationship between DA and future stock prices:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" 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\" width=\"609\" height=\"205\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 2: Extends the specification by including the moderating role of investor monitoring:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"609\" height=\"56\"\u003e\u003c/p\u003e\n\u003cp\u003eIn this framework, the interaction term \u003cimg src=\"data:image/png;base64,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\" width=\"187\" height=\"28\"\u003e\u0026nbsp;is added to test whether investor monitoring attenuates the relationship between DABEM and the FSP. A significantly different coefficient on the interaction term shows whether the direction or strength of the DABEM\u0026ndash;stock price relationship changes at varying levels of investor monitoring.\u003c/p\u003e"},{"header":"4. Data Analysis","content":"\u003cp\u003e\u003cem\u003e4.1 Descriptive Statistics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 presents descriptive statistics based on 200 firm-year observations of EGX-listed firms (2020\u0026ndash;2024). The results reveal strong patterns in EM, stock prices, and financial ratios in Egypt\u0026apos;s turbulent economic climate, replete with currency devaluations, inflation (a historic high of 38% in 2023), and post-2020 regulatory reforms. These findings are in line with the emerging market literature, in which economic turbulence enhances financial reporting volatility (Hafez, 2023; Haw et al., 2005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Descriptive Statistics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"552\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinimum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eDABEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-.5984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.6959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e-.023232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e.3364755\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eFSP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e.0710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e285.0100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e18.703620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e37.5490862\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e.501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eLogTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.8127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e5.5524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e3.302810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e.7501928\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eROA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-1.4452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.2911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e.052276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e.1390769\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eLeverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e.0554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.9667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e.535722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e.2357965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eValid N (listwise)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDABEM, with computation based on the working capital accruals model (Kerstein \u0026amp; Rai, 2007), is -0.0232, thereby a very near zero mean, ranging from -0.5984 to 0.6959, and having a standard deviation of 0.3365. Such a distribution translates to the predominance of both income-reducing and income-increasing EM among Egyptian firms in accordance with the flexibility provided by accrual-based accounting in uncertain markets (McNichols, 2000). The high standard deviation reflects the excessive volatility caused by Egypt\u0026apos;s economic turmoil between 2020 and 2024, such as devaluations of the local currency and inflationary pressures. Thus, firms are motivated to manipulate accruals to smooth earnings reports (ElHawary \u0026amp; Hassouna, 2021). This volatility is consistent with research on emerging markets, where poor governance institutions permit EM as a counterbalance to economic turbulence (Haw et al., 2005).\u003c/p\u003e\n\u003cp\u003eThe FSP, the closing price seven days after the release of annual reports, has a wide range of 0.0710\u0026ndash;285.0100 with a mean of 18.7036 and the highest standard deviation of 37.5491. This volatility accounts for Egypt\u0026apos;s market volatility during the study period due to macroeconomic shocks, including the devaluations of the Egyptian pound in 2022\u0026ndash;2023 and ongoing inflation (Hafez, 2023). The relatively low average stock price indicates stable valuations for some companies, consistent with EGX\u0026apos;s partial rebound after 2020, but the pervasive spread attests to the non-systemic effect of economic fortunes on industries, a hallmark of stressed emerging markets (Haw et al., 2005). The large standard deviation accords with the regression analysis, where the external variables explain the data moderately well (adjusted R\u0026sup2; = 0.132\u0026ndash;0.158), since Egyptian stock price movements are subject to macroeconomic forces.\u003c/p\u003e\n\u003cp\u003eIM, as a (0, 1) binary variable in the IMNR index (Czajkowska, 2023), is 0.52 with a standard deviation of 0.501, and shows that 52% of firm-year observations have IM practices, that is, positive tone or selective disclosure on narrative reports. The almost-even split corresponding to binary coding indicates that Egyptian companies prefer to use narrative tactics to counteract the unfavorable perceptions of stakeholders in times of economic distress, such as global supply chain disruption and energy crises (Merkl-Davies et al., 2011). According to earlier studies (ElHawary \u0026amp; Hassouna, 2021), investor pessimism inside Egypt\u0026apos;s market lessens the impact of narrative disclosures; hence, equilibrated IM prevalence complements its non-significant influence in regression analysis (Beta = 0.112, p = 0.072).\u003c/p\u003e\n\u003cp\u003eFirm size, the mean of the natural logarithm of total assets (LogTA), reached 3.3028 with a standard deviation of 0.7502. This is a representative distribution of a heterogeneous population of firm sizes to measure scale effects on stock prices in Egypt\u0026apos;s heterogeneous market (Hafez, 2023). Moderate heterogeneity is equivalent to the purposive sampling strategy involving companies across different industries, such as financial, industrial, and property, to provide representativeness for the EGX framework 2020\u0026ndash;2024. The absence of any significant effect of LogTA on regression analysis (p \u0026gt; 0.05) guarantees Egypt\u0026apos;s market, where macroeconomic variables generally dominate firm-specific traits, such as size (ElHawary \u0026amp; Hassouna, 2021).\u003c/p\u003e\n\u003cp\u003eROA or profitability is 0.0523 on average, with a range of -1.4452 to 0.2911, and a standard deviation of 0.1391. The minimum positive average indicates average profitability, while the extreme range with negative figures indicates humongous losses for certain firms that mirror Egypt\u0026apos;s economic recessions included in the span of this study, that is, energy crises and global economic recessions (ElHawary \u0026amp; Hassouna, 2021). Volatility is the differential performance of EGX-listed firms, which is a typical emerging market experience under external shocks (Haw et al., 2005). The failure of the ROA effect in the regressions to achieve significance (p \u0026gt; 0.05) confirms its weak correlation with stock prices (0.085, p \u0026gt; 0.05), where other indicators are even more sensitive among Egyptian investors in the volatile market.\u003c/p\u003e\n\u003cp\u003eLeverage, defined as total debt divided by total assets, has a mean of 0.5357, range of 0.0554 to 0.9667, and standard deviation of 0.2358. The high mean shows that Egyptian companies have high levels of debt, in line with Egypt\u0026apos;s high-risk financial setting in 2020\u0026ndash;2024, when companies are using debt financing to handle financial uncertainty (Hafez, 2023). The wideness of spread captures heterogeneity in capital compositions, with others attaining their optimal leverage, explaining the strong positive leverage effect in the regression (beta = 0.26, p \u0026lt; 0.01). This finding justifies research in emerging markets, where leverage increases volatility in equity prices owing to financial risk (Haw et al., 2005).\u003c/p\u003e\n\u003cp\u003eIn short, descriptive statistics identify Egypt\u0026apos;s economic and market position in 2020\u0026ndash;2024 as a volatile market with governance problems that impact stock prices, leverage, and DABEM distribution. IM dominance with restricted impact points to investor mistrust, while the discrepancy between LogTA and ROA reflects the variety of EGX-listed firms. These trends confirm the conclusions of the regression and correspond to Egypt\u0026apos;s shifting market conditions and emerging market dynamics.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.2 Correlation Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo examine the bivariate relationships among the study variables\u0026mdash;DABEM, FSP, IM, firm size, ROA, and leverage\u0026mdash;a Pearson correlation matrix was constructed, as presented in Table 3. This analysis, based on 200 firm-year observations from EGX-listed firms from 2020 to 2024, ensures alignment with the regression results and verifies the absence of multicollinearity, with all correlation coefficients below 0.7, consistent with established thresholds (Hair et al., 2010). The correlations are interpreted within the context of Egypt\u0026rsquo;s volatile economic environment, marked by significant currency devaluations, inflation peaking at 38% in 2023, and enhanced market scrutiny following the post-2020 reforms (Hafez, 2023). Where Egyptian-specific evidence is limited, the emerging market literature provides additional support (Haw et al., 2005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Pearson Correlation Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"102%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEarnings Management\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFuture Stock Price\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImpression \u0026nbsp; Management\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eROA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeverage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEarnings Management\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ePearson Correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFuture Stock Price\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ePearson Correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-.258**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImpression Management\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ePearson Correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e.342**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e.162*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ePearson Correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-.189*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e.145*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e.395**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eROA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ePearson Correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-.210**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e.175*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e.265**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeverage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003ePearson Correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e.275**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e.360**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e-.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*. \u003cem\u003eCorrelation is significant at the 0.01 level (2-tailed), *. Correlation is significant at the 0.05 level (2-tailed).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDABEM has a significant negative correlation with future stock prices (0.258, p \u0026lt; 0.01), indicating that higher discretionary accruals are associated with lower stock prices seven days post-release of annual reports. The correlation shows that the Egyptian market penalizes earnings management, indicating greater investor concern after regulatory reforms and foreign investment in 2020\u0026ndash;2024 (Hafez, 2023). This finding is congruent with the regression estimates (beta \u0026asymp; -0.22, p \u0026lt; 0.01 in Models 1 and 2) and coincides with emerging market research, where markets correct for earnings manipulation due to greater transparency (Haw et al., 2005). In contrast to Mostafa (2017), who found no substantial EM market response in 2012\u0026ndash;2015 due to post-revolution distrust, the evidence for the period under consideration shows a more efficient market response because of Egypt\u0026apos;s evolving financial landscape.\u003c/p\u003e\n\u003cp\u003eThe positive and strong association between DABEM and IM (0.342, p \u0026lt; 0.01) implies that companies with discretionary accruals also practice IM activities, such as tone or selective disclosure, as secondary activities to impress stakeholders. This confirms the practice of utilizing narrative strategies as counterstrategies to mitigate the negative effect of earnings management, a common practice in governance-deficient markets (Merkl-Davies et al., 2011). In the Egyptian context, companies would employ IM to offset investor concerns in times of economic turbulence, such as currency depreciation and oil crises (ElHawary \u0026amp; Hassouna, 2021). The significance of the interaction term DABEM \u0026times; IM in the regression is buttressed by association, although its non-significance (p = 0.110) suggests a marginal moderating effect.\u003c/p\u003e\n\u003cp\u003eInvestors\u0026apos; disinterest in narrative disclosure in Egypt\u0026apos;s unpredictable market is indicated by a strong positive weak correlation between IM and FSP (0.162, p \u0026lt; 0.05). \u0026nbsp;This coincides with the near-zero IM effect in the regression (Beta = 0.112, p = 0.072, as shown in Table 6) and with the binary coding of the IMNR index that identifies IM in 52% of observations (Czajkowska, 2023). In Egypt, financial issues such as inflation and supply chain interference between 2020 and 2024 minimize reliance on narrative strategies to the detriment of physical budget interference (ElHawary \u0026amp; Hassouna, 2021). This finding is consistent with the literature on emerging markets, where narrative disclosures lose relevance or become less relevant in low-trust settings (Merkl-Davies et al., 2011).\u003c/p\u003e\n\u003cp\u003eLeverage and FSP correlation (0.275) help explain the lack of regression proof (p \u0026gt; 0.05). This suggests that investors are moderately interested in profitability during Egypt\u0026apos;s macroeconomic uncertainty (ElHawary \u0026amp; Hassouna, 2021). The negative correlations between DA and LogTA (-0.189, p \u0026lt; 0.05) and ROA (-0.210, p \u0026lt; 0.01) suggest that less profitable small businesses control earnings more, as required by Egypt\u0026apos;s governance failures (ElHawary \u0026amp; Hassouna, 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere is no multicollinearity (all correlations \u0026lt; 0.7), which means that regression estimates are reliable, and the results of variance inflation factors (VIFs) confirm this in the robustness checks. These correlations serve as a basis for the regression models, substantiating the substantial negative impact of DABEM, minimal influence of IM, and pivotal role of leverage in Egypt\u0026apos;s dynamic market environment from 2020 to 2024.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.3 Regression Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOrdinary least squares (OLS) with robust standard errors was used to estimate two regression models to solve the heteroskedasticity prevalent in Egypt\u0026apos;s market (Hafez, 2023). With a variance inflation factor (VIFs) of less than five, no multicollinearity problems were confirmed.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.3.1 Model 1: Direct Effect of DABEM\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 4, a higher DABEM is associated with decreases in FSP, as the regression analysis shows a particularly negative coefficient for DA (\u0026beta; = \u0026ndash;2.950, standardized \u0026beta; = \u0026ndash;.230, p = 0.001). According to Teixeira and Rodrigues (2022), this finding strongly supports the market discipline theory, which posits that investors in the Egyptian equity market have developed a level of sophistication sufficient to identify and punish opportunistic earnings manipulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Regression Results (Model 1)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"bottom\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnstandardized Coefficients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandardized Coefficients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-3.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e11.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e-0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eEarnings management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-2.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e-3.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eLogTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eROA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e11.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e15.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.439\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eLeverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e39.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e10.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e3.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 605px;\"\u003e\n \u003cp\u003e\u003cem\u003eAdjusted R Square= 13.2%, F= 8.125, Sig.= 0.000\u003cbr\u003e\u0026nbsp;a. Dependent Variable: FSP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis conclusion conflicts with the previous results of ElHawary and Hassouna (2021), who found a minimal link between DABEM and market value for the years 2012 to 2015. The observed difference might be explained by structural changes in the post-2020 Egyptian regulatory scenario, including more financial reporting monitoring and increased investor awareness. Significantly, the current results more closely match those of other developing nations (e.g., Haw et al., 2005), indicating continuous convergence in the pricing of earnings quality.\u003c/p\u003e\n\u003cp\u003eThe impact of size (\u0026beta; = \u0026ndash;2.950) reveals that firms with earnings manipulation in the minds of Egyptian investors suffer a severe valuation penalty. The pathology of prior corporate governance failures and the credibility-enhancing impact of recent reforms cause this heightened sensitivity (Hafez, 2023). These findings are in line with the expectations of the efficient market hypothesis and the general literature on the capital market implications of earnings quality (e.g., Subramanyam, 1996).\u003c/p\u003e\n\u003cp\u003eThe leverage coefficient is statistically and economically significant (\u0026beta; = 39.500, standardized \u0026beta; = 0.264, p \u0026lt; 0.001). This finding aligns with the theoretical predictions, indicating that leverage amplifies firm-specific risk and heightens stock price volatility, particularly in financially constrained contexts. In Egypt, leverage appears to play a major role as a driver of equity valuation because corporate debt ratios are generally higher there because of the shallow equity base and bank-hierarchical funding structures (Hafez, 2023). This result further supports structural credit risk models, such as Merton\u0026apos;s (1974) model, which relates capital structure to equity pricing through risk-related channels.\u003c/p\u003e\n\u003cp\u003eThe profitability and size of the firm had no statistically significant effects (LogTA: p = 0.106; ROA: p = 0.439), while the coefficients were positive. This, as well as no significant effect, is consistent with the weak correlations (0.145 and 0.085, respectively) observed, and may reflect the predominance of macro-level determinants\u0026mdash;political instability, exchange rate fluctuations, and foreign capital flows\u0026mdash;over company-specific factors in the valuation. These results are in line with those of previous research in comparable contexts (ElHawary \u0026amp; Hassouna, 2021; Thanh Liem, 2021).\u003c/p\u003e\n\u003cp\u003eThe 13.2% adjusted R\u003csup\u003e2\u003c/sup\u003e has a moderate level of explanatory power, but it is known to be adequate for research in nascent financial markets. Although such levels fall short of those found in developed economies, they are commensurate with the underlying volatility of equity returns in markets that are beset by external shocks and institutional imperfections. The model\u0026rsquo;s overall significance is also confirmed with an F-statistic of 8.125 (p \u0026lt; 0.001), indicating that together, the explanatory variables significantly account for FSP predictions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.3.2 Model 2: Moderating Effect of IM\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable (5) depicts Model 2, which extends the baseline regression by including IM and the interaction term between impression management and discretionary accruals (DA \u0026times; IM) variables to explore how financial and narrative manipulations jointly affect stock prices in the future. At 15.6%, the R\u0026sup2; of the model was also somewhat higher than that of Model 1 (13.2%), and the F statistic was still significant (F = 7.326, p \u0026lt; 0.001). The slight improvement in the explanation shows that impression management is enlightening, but it does not significantly increase the predictive capability of the model for the Egyptian example.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Regression Results (Model 2)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"bottom\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnstandardized Coefficients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandardized Coefficients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-3.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e11.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e.729\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eDABEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-2.780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-3.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eDABEM \u0026times; IM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eLogTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e.138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eROA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e11.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e15.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e.459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eLeverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e38.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e10.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAdjusted R Square= 15.6%, F= 7.326, Sig.= 0.000\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ea. Dependent Variable: FSP\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe DABEM coefficient remained strongly negative (B = \u0026ndash;2.780, p = 0.002) and was similar in magnitude to the outcome of Model 1. The similarity in the coefficients provides additional support for the reasoning that Egyptian shareholders punish companies that overly rely on accrual aggressiveness, particularly in the post-2020 period, where increased regulation and more foreign institutional investor monitoring have induced higher market sensitivity against financial misreporting (Hafez, 2023). The negative coefficients also support the contention that Egyptian investors increasingly view earnings management as an early warning sign, conceivably releasing concealed governance vulnerabilities or potential future performance threats.\u003c/p\u003e\n\u003cp\u003eThe DABEM and IM interaction terms (B = 0.450, p = 0.110) were not significant; therefore, IM did not significantly dampen the negative association between DABEM and FSP. Hence, narrative disclosure offers no comfort in reducing the punishment for financial mishandling in the market. These results are consistent with investors\u0026rsquo; attitudes in low-trust environments, such as Egypt, where narrative techniques may be viewed as rhetorical, trying to validate company value (El-Hawary \u0026amp; Hassouna, 2021; Merkl-Davies et al., 2011). This result substantiates the existing literature that Egyptian investors are predisposed to doubt the absence of narrative disclosures, corroborating robust financial performance (ElHawary \u0026amp; Hassouna, 2021). Czajkowska\u0026apos;s (2023) narrative index IMNR also promises to deliver constrained market valuation impacts of narrative reporting in Egypt on behalf of the argument that users of financial statements prefer quantitative performance indicators instead of qualitative disclosure, in agreement with the findings found in emerging economies (Merkl-Davies et al., 2011).\u003c/p\u003e\n\u003cp\u003eThe DABEM and IM interaction terms (B = 0.450, p = 0.110) were not significant, that is, IM did not significantly dampen the negative association between DABEM and FSP. Therefore, narrative disclosure does not offer relief in mitigating punishments for financial manipulation in the marketplace. These results are consistent with investor perception in low-trust settings such as Egypt, where narrative frames can be regarded as rhetorical in trying to legitimize firm value (ElHawary \u0026amp; Hassouna, 2021; Merkl-Davies et al., 2011).\u003c/p\u003e\n\u003cp\u003eThe control variables largely perform their functions, as in Model 1. Leverage remains strongly positively and significantly affecting future stock prices (B = 38.700, p \u0026lt; 0.001), indicating the previous interpretation that debt in Egypt can also be employed as a measure of expansion or credibility. Conversely, profitability (ROA) and firm size (LogTA) are still statistically insignificant, again suggesting that these measures might be trumped by earnings quality signals to influence investors\u0026rsquo; valuation judgments. Notwithstanding that the adjusted R\u0026sup2; increases marginally upon including IM variables in the model, the overall explanatory power of the model is still limited to 15.6%. Such a tight fit is likely to react to broader macroeconomic and institutional uncertainties in Egypt, namely exchange rate volatility, inflation uncertainty, and capital flow volatility, between 2020 and 2024 (Hafez, 2023). This suggests that, although financial and story reporting techniques prevail over market valuations, they are, in turn, pushed by an extended range of economic and regulatory drivers that fuel investor reactions.\u003c/p\u003e\n\u003cp\u003eIn conclusion, Model 2 favors the leading role of discretionary accruals in explaining FSP in Egypt\u0026apos;s capital market, but concludes that IM itself has no significant dampening effect on the market\u0026apos;s negative reaction to earnings manipulation. This indicates the changing, but conservative, course of Egyptian investors\u0026apos; attitude towards narrative disclosures and calls for full financial and non-financial transparency programs to restore trust in financial reporting.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.4 Robustness test\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo verify the stability and consistency of the EM effect found in the earlier models, a test for robustness was performed by re-estimating discretionary accruals under the performance-matched Jones model suggested by Kothari et al. (2005). The model controls firm performance based on ROA, thus correcting for the endogeneity bias automatically associated with typical accrual models. Considering Egypt\u0026apos;s remarkably high diversity and volatile economic situation, this is a welcome corrective measure for properly evaluating the actual impact of earnings management on market value (ElHawary \u0026amp; Hassouna, 2021).\u003c/p\u003e\n\u003cp\u003eThe results of Model 3 shown in Table 6 are very much in agreement with the earlier results. Maintaining a statistically significant negative influence on future stock prices (B= \u0026ndash;2.900, p = 0.001), the performance-matched discretionary accrual variable (DA_PM) has a standardized coefficient (beta = \u0026ndash;0.226), which is nearly identical to that found in Model 2. This finding supports the conclusion that the Egyptian market continues to penalize earnings misrepresentation, regardless of methodological improvements in accrual calculation. Particularly, in an emerging market context increasingly shaped by foreign institutional criteria and demands for IFRS adoption (Hafez, 2023), it also improves the reliability of discretionary accruals as a genuine measure of managerial opportunism.\u003c/p\u003e\n\u003cp\u003eThe interaction term DA_PM \u0026times; IM was also not significant (B = 0.430, p = 0.119) and the IM variable (IM; B = 0.570, p = 0.072) was still not significant. The claim that narrative disclosures, as they are now implemented in Egypt, have little effect on investor responses to financial manipulation is further supported by these data, which almost exactly match those of Model 2. \u0026nbsp;Czajkowska (2023) also came to this conclusion, showing that in capital markets, where investors prioritize audited financial data over qualitative statements, the IMNR index\u0026mdash;that is, evaluating the quality of narrative reporting\u0026mdash; does not have much explanatory power.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Robustness Check with Performance-Matched DA\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnstandardized Coefficients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandardized Coefficients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-3.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e11.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e.731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eEarnings management (DA_PM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-2.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-3.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eImpression Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eDA_PM \u0026times; IM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eLogTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eROA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e11.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e15.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e.458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eLeverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e38.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e10.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAdjusted R Square= 15.8%, F= 7.512, Sig.= 0.000\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ea. Dependent Variable: FStP\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLeverage shows a strong and statistically significant positive impact (B = 38.800, p 0.05); therefore, it is not very helpful in accounting quality indicators. These results match the structural characteristics of the Egyptian capital market, where governance volatility and poor institutional coverage frequently hide companies\u0026rsquo; fundamentals (Hafez, 2023; ElHawary \u0026amp;; Hassouna, 2021).\u003c/p\u003e\n\u003cp\u003eFinally, the modified R\u0026sup2; increased slightly to 15.8%, thus enhancing Model 2. This change shows an increased ability to forecast accruals as we consider the state of the firm. The robustness study validates the importance of earnings quality in influencing investor reactions and shows that the main findings hold true, even with different model parameters. It also shows that investors punish firms that use discretionary accruals even if they try to use narrative IM at the same time. This makes the case for better financial transparency and investor education even stronger in the Egyptian market.\u003c/p\u003e"},{"header":"5. Discussion of Results","content":"\u003cp\u003eThis study analyzes the correlation between DABEM and FSP on the Egyptian Stock Exchange from 2020 to 2024, integrating IM as a moderating variable, while controlling for firm size, profitability, and leverage. These findings enhance the literature on emerging markets by offering empirical evidence from Egypt\u0026apos;s developing capital market amid substantial economic and regulatory changes. The results are discussed below.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5.1 EM and Market Valuation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe empirical analyses consistently demonstrate a statistically significant negative correlation between DABEM and FSP (\u0026beta; \u0026asymp; -0.22, p \u0026lt; 0.01 across all model specifications). This finding offers substantial evidence corroborating the market discipline hypothesis, which posits that sophisticated investors detect and sanction opportunistic earnings manipulation. The recorded coefficient magnitude shows that a one-unit increase in discretionary accruals is linked to a drop of approximately 22% in the FSP, which is a big deal for the economy.\u003c/p\u003e\n\u003cp\u003eThe descriptive statistics reveal significant variability in EM practices, as discretionary accruals fluctuate between -0.5984 and 0.6959 (mean = -0.0232, SD = 0.3365). This finding suggests that many Egyptian firms use DABEM strategies. This prevalence aligns with theoretical expectations for emerging markets characterized by fragile institutional frameworks and governance structures (Mlawu et al., 2025; Itan et al., 2024). The time frame being examined (2020\u0026ndash;2024) witnessed a lot of macroeconomic instability, with currency values dropping by more than 50% and inflation rates reaching 38% in 2023. This may lead managers to engage in earnings-smoothing activities to reduce perceived uncertainty.\u003c/p\u003e\n\u003cp\u003eThe robustness of these findings is corroborated by the application of various estimation techniques, including the performance-matched Jones model (Kothari et al., 2005), which yielded consistently negative coefficients (\u0026beta; = -0.226, p = 0.001). This methodological triangulation enhances confidence in the stability and reliability of the primary findings, alleviating potential concerns associated with the model specification and measurement errors inherent in discretionary accrual estimation.\u003c/p\u003e\n\u003cp\u003eThe negative coefficient of DABEM indicates that higher discretionary accruals are associated with lower, rather than higher, FSP, and this must be explicitly reconciled with the theoretical framework. In formulating H1, EM was implicitly viewed through a signaling lens as a potentially informative device that could help managers convey private information about favorable future performance in a high\u0026ndash;information‑asymmetry environment, suggesting a positive association with stock prices (Subramanyam, 1996; Jiraporn et al., 2008). However, the empirical results for the Egyptian market show that investors appear to interpret EM predominantly as opportunistic earnings manipulation rather than credible signaling; thus, penalize firms with higher discretionary accruals, consistent with evidence that low earnings quality and aggressive EM are priced negatively by capital markets (Healy \u0026amp; Wahlen, 1999; Dechow et al., 2010; ElHawary \u0026amp; Hassouna, 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFrom the perspective of signaling theory, this pattern suggests that in Egypt\u0026rsquo;s evolving but still imperfect governance and enforcement environment, the perceived cost of manipulation is not high enough to make EM a trustworthy signal. Therefore, rational investors discount EM and, in line with the efficient market hypothesis, incorporate their suspicion of manipulation into prices by attaching a valuation discount to firms exhibiting higher discretionary accruals (Connelly et al., 2011; Haw et al., 2004). Accordingly, the findings refine rather than simply contradict H1, while EM can, in principle, function as a positive signal. The evidence indicates that under current Egyptian institutional conditions, its opportunistic, value-reducing interpretation dominates, and this is how the results are interpreted in light of both signaling theory and market efficiency.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5.2 The Role of IM\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn contrast to the theoretical expectations informed by signaling theory, IM exhibits a marginally insignificant positive correlation with stock prices (\u0026beta; = 0.112, p = 0.072) and does not moderate the negative relationship between discretionary accruals and market valuations (p = 0.110-0.119). The descriptive analysis indicates that around 52% of firm-year observations demonstrate impression management tactics (mean = 0.52, SD = 0.501), signifying the extensive application of narrative disclosure strategies. The weak correlation between impression management and stock prices (r = 0.162, p \u0026lt; 0.05) indicates that investors do not put much stock in qualitative disclosures when they try to figure out how much a company is worth.\u003c/p\u003e\n\u003cp\u003eIn contrast to evidence from developed markets, this finding aligns with emerging market characteristics, where institutional investors exhibit heightened skepticism regarding managerial communications because of governance concerns and information asymmetries (Ajina et al., 2015; Cormier et al., 2010). The negligible moderating effect suggests that audited financial data are more important to Egyptian investors than narrative disclosures when evaluating valuations, and it also shows that impression control tactics do not mitigate unfavorable market reactions to earnings management.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5.3 Control Variables and Market Dynamics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe leverage coefficient consistently exhibits a positive and statistically significant correlation with stock prices (\u0026beta; = 0.26, p \u0026lt; 0.01) and is supported by a strong bivariate correlation (r = 0.275, p \u0026lt; 0.01). This surprising outcome illustrates the distinct capital structure dynamics prevalent in emerging markets in contrast to expectations in developed markets. A high mean leverage ratio (0.5357, SD = 0.2358) indicates that the firm relies heavily on debt financing. In Egypt, this could mean that the firm is trustworthy and can obtain money from capital markets instead of being in trouble with money (Elsaman \u0026amp; Alshorbagy, 2011; Evianti \u0026amp; Hasibuan, 2025).\u003c/p\u003e\n\u003cp\u003eConversely, firm size (LogTA) and profitability (ROA) exhibit no statistically significant associations with stock prices (p \u0026gt; 0.05) despite the presence of weak positive correlations (0.145 and 0.085, respectively). This pattern indicates that macroeconomic factors and systematic risks prevail over idiosyncratic firm characteristics in the Egyptian equity valuation models. The descriptive statistics for firm size (mean = 3.3028, SD = 0.7502) and profitability (mean = 0.0523, SD = 0.1391) illustrate the diverse characteristics of listed entities. However, their restricted explanatory capacity highlights the significance of external economic conditions in emerging market scenarios.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5.4 Implications for Model Fit and Market Efficiency\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe small adjusted R\u0026sup2; values (0.132\u0026ndash;0.158) are consistent with findings in studies of emerging markets, where external shocks and institutional factors diminish the utility of firm-specific variables (Mlawu et al., 2025). There was considerable macroeconomic instability during the study period. For example, the currencies lost value, the supply chain faced problems, and the rules changed. All of them make it harder for traditional models of value to make good guesses. Statistically significant F-statistics (p \u0026lt; 0.001) across all model specifications confirm the overall explanatory power of the variable set.\u003c/p\u003e\n\u003cp\u003eThese findings suggest an evolution in the efficiency of the Egyptian market, characterized by regulatory reforms instituted post-2020 and increased engagement from foreign institutions, which have enhanced investors\u0026apos; ability to identify and mitigate earnings management practices. This signifies a divergence from previous research that revealed weak or negligible correlations between earnings management and market valuations during Egypt\u0026apos;s post-revolution era (2011-2019), implying heightened market sophistication and improved information-processing abilities.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5.5 Theoretical and Practical Implications\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe observed negative correlation between DABEM and FSP corroborates the market efficiency hypothesis in emerging market contexts, suggesting that Egyptian investors possess adequate analytical skills to detect and penalize opportunistic reporting practices. The negligible influence of IM underscores the constraints of narrative-based disclosure strategies in low-trust institutional settings characterized by investors\u0026apos; pronounced skepticism regarding managerial communications.\u003c/p\u003e\n\u003cp\u003eThe positive relationship between leverage and price indicates that financing works differently in emerging markets. In these markets, debt capacity may reflect a firm\u0026apos;s quality and growth potential, rather than financial issues. This finding has significant implications for capital structure decisions. This also shows that when traditional theories of developed markets are used in emerging markets, they may need to be changed.\u003c/p\u003e\n\u003cp\u003eOverall, the results show how important it is for financial reporting to be clear and how important it is for regulations to keep improving to make markets work better in emerging economies. The findings indicate that Egyptian capital markets are improving their management of earnings-related information; however, numerous opportunities remain to enhance narrative disclosure and investor education initiatives.\u003c/p\u003e"},{"header":"6. Conclusions, Limitations and Directions for Future Research","content":"\u003cp\u003eThis study provides compelling evidence that DABEM has a significantly negative impact on FSP in the Egyptian market from 2020 to 2024. The working capital accruals model is employed in conjunction with robustness verification using the performance-matched Jones model (Kothari et al., 2005). We find that firms with high discretionary accruals face market-imposed valuation discounts. This finding supports the market discipline hypothesis and shows that the Egyptian market is becoming more sophisticated as investors become less tolerant of earnings manipulation. This aligns with findings from other emerging markets (Haw et al., 2005) and demonstrates the potential impacts of regulatory enhancements post-2020 and increased foreign investor scrutiny (Hafez, 2023), in contrast to earlier Egyptian studies that suggest diminished market sensitivity (e.g., ElHawary \u0026amp; Hassouna, 2021).\u003c/p\u003e\n\u003cp\u003eWe investigate the potential moderating effect of IM operationalized through content analysis of narrative disclosures using the IMNR index (Czajkowska, 2023), on this relationship. Our findings indicate that companies can use IM and DABEM simultaneously, as shown by the positive correlation between the two. However, the interaction term (DABEM \u0026times; IM) is not statistically significant. This means that the way IM is used in Egyptian annual reports right now does not make the negative market reaction to earnings management less aggressive. This finding aligns with views on narrative reporting in low-trust contexts, where qualitative disclosures may be regarded as skepticism (Merkl-Davies et al., 2011).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsistent with the current research on emerging markets, our models show constrained explanatory power (Adjusted R\u0026sup2; = 13.2%\u0026ndash;15.8%). Factors influencing the entire economy, such as inflation, currency depreciation, and international capital flows, are more significant than those affecting only one company during this turbulent period (Hafez, 2023; Haw et al., 2005). Among the control variables, financial leverage has a major effect on the variation in future stock values. This demonstrates how Egyptians\u0026rsquo; market perception is altered by their great debt.\u003c/p\u003e\n\u003cp\u003eAs this is the case in most studies, this study has several limitations that should be considered when interpreting the findings and guiding future research. \u003cem\u003eFirst\u003c/em\u003e, the analysis focuses on Egypt, an emerging market, during a short and turbulent period, which limits the generalizability of the results to other contexts with differing institutional features such as governance quality, enforcement intensity, and investor sophistication (ElHawary \u0026amp; Hassouna, 2021). \u003cem\u003eSecond\u003c/em\u003e, the use of accrual-based models and a binary narrative impression index to measure EM and IM is subject to measurement errors, which makes it difficult to distinguish between opportunistic manipulation and legitimate reporting discretion. Consequently, the estimated effects may be attenuated or biased by proxy noise (Dechow et al., 2010). \u003cem\u003eThird\u003c/em\u003e, the observational nature of the study and the reliance on regression models controlling for observable firm characteristics means that unobserved factors, such as governance shocks or investor sentiment, could jointly influence reporting choices and stock price reactions. Thus, the results should be viewed as associative rather than causal (Connelly et al., 2011; Healy \u0026amp; Wahlen, 1999). Future research should explore alternative EM and IM measures, extend the analysis to other markets, and examine regulatory or institutional shocks to allow stronger causal inferences (Roychowdhury, 2006; Elshandidy \u0026amp; Ibrahim, 2024).\u0026nbsp;Bottom of Form\u003c/p\u003e\n\u003cp\u003eHowever, there are several implications for policy and standard-setters, governments, and market participants in Egypt and other similar emerging markets. This study offers some significant policy implications, where the significant negative market reaction to DABEM confirms that investors possess the ability to detect and penalize such practices. However, the fact that DABEM is present and costs market money shows how important it is for regulatory bodies such as the Egyptian Financial Regulatory Authority (EFRA) to make rules stricter. More assured and severe penalties for earnings manipulation, together with more rigorous audits, may help keep the market in check and reduce the likelihood of companies reporting false information. A favorable response of the market should not be the only factor that ensures accurate reporting. The high level of EM, as evidenced by descriptive statistics and the large market penalty, points to management\u0026apos;s potential to abuse their freedom in certain areas of accounting standards, particularly when it comes to working capital accruals.\u003c/p\u003e\n\u003cp\u003ePromoting market efficiency and teaching investors as the market\u0026apos;s ability to punish DABEM, especially after 2020, is a good sign that it is becoming more sophisticated. Policymakers should continue working to make the market more efficient, for example, by ensuring that information is shared quickly and widely. In addition, programs that teach investors can help domestic investors better analyze both financial and non-financial information. This would improve overall market trust and discipline.\u003c/p\u003e\n\u003cp\u003eUnderstanding how leverage works in Egypt, where it has a strong positive effect on future stock prices due to the unique financial dynamics of the market, where debt can mean growth or access to financing. However, high leverage increases this hazard. Regulators must monitor leverage trends and ensure that businesses possess strong risk-management systems. Investors and the financial system as a whole depend on the balance between the need for capital and financial stability.\u003c/p\u003e\n\u003cp\u003eIn summary, although Egyptian market forces appear to be heading towards higher standards for earnings quality, proactive legal and policy actions are required to reinforce this trend, boost the general credibility of financial reporting, and foster a more transparent and effective capital market. Hence, future research may be directed towards investigating the effect of different earnings management approaches on future stock prices using other moderators, such as the characteristics of the board of directors, audit quality, and disclosure strategies.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no specific funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSecondary sources of data are used to complete this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no specific funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTI developing the original draft, helped in methodology and edited and reviewed the draft and made constructive changes to the draft. MH \u0026nbsp;prepared the original draft as well as reviewing the literature. ME collected the data, analyzed the results, and concludes the draft. All authors have read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTariq H. Ismail\u003c/strong\u003e is a Professor of Accounting at the Faculty of Commerce, Cairo University, Egypt. He is currently the Dean of Business School at the International Academy of Engineering and Media Science, Egypt. He has published numerous articles in a number of high-ranked, peer-reviewed journals, and has many books which had worldwide audience. He had many research grants and excellence awards for the contributions he made in his field. He is the founder and the editor-in-chief of \u003cem\u003ethe Academic Journal of Social Sciences\u003c/em\u003e and the associate editor of \u003cem\u003eJournal of Humanities and Applied Social Sciences\u003c/em\u003e. He is on the editorial board of several reputable International journals. His current research focuses on disclosure quality and financial reporting, accounting in emerging economies, corporate governance, corporate social responsibility, earnings management and narrative reporting.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMohamed Samy El-Deeb\u003c/strong\u003e is a Professor of accounting and Vice-Dean of Environmental Affairs and Community Service and Head of accounting department, Faculty of management sciences, October University for Modern Sciences and Arts (MSA), 6 of October City, Egypt. His research interest is in governmental accounting, auditing, integrated reporting and environmental, social, and governance reporting, accounting theory, and corporate governance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMohamed H. El-Ashwal\u003c/strong\u003e is an Associate Professor of Accounting at Port Said University, Egypt. His research interests are earnings management, sustainability accounting, integrated reporting, corporate sustainable performance, and stock markets. He has presented papers in many national and International conferences\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAjina, A., Lakhal, F., \u0026amp; Sougn\u0026eacute;, D. (2015). Institutional investors, information asymmetry and stock market liquidity in France. \u003cem\u003eInternational Journal of Managerial Finance\u003c/em\u003e, 11(1), 44-59.\u003c/li\u003e\n\u003cli\u003eAlbuquerque, F., Stoltzemburg, V. and Cariano, A. 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In \u003cem\u003eEconomic Research Forum 22nd Annual Conference\u003c/em\u003e (Working Paper). \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Narrative reporting, earnings management, impression management, stock prices, emerging markets, EGX, Egypt","lastPublishedDoi":"10.21203/rs.3.rs-8890174/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8890174/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study examines the effects of earnings management on future stock prices and investigates whether impression management moderates this relationship in the Egyptian capital market. It uses financial and narrative data extracted from the annual reports of firms listed on the Egyptian Stock Exchange (EGX) from 2020 to 2024. Regression models with robust estimations are used to test the hypotheses. The results revealed that discretionary accruals significantly influence future stock prices, indicating that investors penalize firms that engage in aggressive earnings management. This finding supports the market discipline hypothesis and suggests a growing sophistication among Egyptian investors, particularly in the post-2020 regulatory environment. However, impression management, operationalized through the content analysis of narrative disclosures, does not exert a significant moderating effect on the relationship between earnings management and stock prices. This evidence implies that investors place greater weight on audited financial information than on qualitative disclosures when forming valuation judgments in a low-trust institutional setting. The findings highlight the importance of enhancing the transparency and credibility of financial reporting in emerging markets. They also underscore the need for stricter regulatory enforcement to curb earnings manipulation and improve standards governing narrative disclosure. This study contributes to the literature by providing empirical evidence from Egypt and extending prior research on the interaction between earnings management, impression management, and market outcomes in developing economies.","manuscriptTitle":"Do Narrative Strategies Matter? Evidence on Earnings Management, Impression Management, and Future Stock Prices in Egypt","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-03 05:13:59","doi":"10.21203/rs.3.rs-8890174/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7f187f78-2a7c-4957-92ba-922bf1f9685a","owner":[],"postedDate":"March 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-03T05:13:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-03 05:13:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8890174","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8890174","identity":"rs-8890174","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}