Dividend Announcements and capital market efficiency : Evidence from the algerian stock market

Dividend Announcements and capital market efficiency : Evidence from the algerian stock market | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dividend Announcements and capital market efficiency : Evidence from the algerian stock market OMAR BOUFAMA, ELENA ROGOVA This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5321164/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 The aim of the study is to examine the efficiency of the Algerian financial market by analyzing the reaction to dividend announcements between 2013 and 2022. Algeria represents a good example of small capital markets, with thin trading and rare events. To examine the market reaction, we modified the common approach to event study analysis by estimating abnormal returns based on monthly data. Our findings suggest that market information is distributed slowly and prices do not fully incorporate new information. These findings indicate that the financial market in Algeria does not satisfy the semi-strong form of market efficiency. The paper presents one of the first studies to investigate the efficiency of the Algerian financial market based on the response to dividend announcements. Capital market development Event study Financial markets in Africa Dividend announcements Market efficiency Abnormal returns Algerian financial market Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction In recent decades, the development of capital markets in Africa has become increasingly important for economic growth, long-term financing for real sector and infrastructure projects, and serving the needs of foreign investors. The number of African stock exchanges rose from five in the 1980s to 29 in 2023, while capitalization grew from $ 260 billion in 2002 to $ 1.6 trillion in 2021 (Oxford Business Group, 2022 ). However, stock markets in Africa, excluding South Africa, are relatively underdeveloped compared to those in developed and emerging economies globally (Ntim et al., 2011 ). They are small in both market capitalization and the number of listed companies. According to Farid ( 2013 ), their market capitalization accounts for less than 2% of the world's total. Additionally, even the largest stock exchange, such as the Johannesburg stock exchange, has only around 350 listed companies. African financial markets are also less liquid than those in other parts of the world and thinly traded (Mlambo & Biekpe, 2005 ). These considerations raise a question about their efficiency. Following the Efficient Market Hypothesis (EMH), the market has informational efficiency if stock prices fully and immediately reflect all available information. This means that stock price changes are random and unpredictable, making it impossible for investors to consistently outperform the market, as all market anomalies are arbitraged away immediately. Fama ( 1970 ) justified three forms of market efficiency. The weak form suggests that all past market prices are reflected in current prices. The semi-strong form of EMH extends beyond historical prices and suggests that all publicly available information is instantly priced into the market. This includes financial statements, news releases, economic indicators, and other public disclosures. Therefore, neither technical analysis nor fundamental analysis can yield superior returns consistently. The most extreme version of EMH, the strong form, asserts that all information, both public and private, is fully reflected in stock prices, and is inherent for large, liquid markets. Researchers generally agree that African capital markets operate in the weak form of efficiency (Ntim et al., 2011 ). However, several points should be considered. Firstly, African markets are much less studied than those globally. Secondly, some studies suggest that these markets do not satisfy the efficient market hypothesis due to their small size and thin trading (Ferreira et al., 2018 ; Okorie & Nadarajah, 2020 ). Thirdly, researchers prefer focusing on larger markets and neglecting smaller ones. However, such markets are crucial for economic growth in emerging countries and need improvement in efficiency. This is especially relevant to North African markets because of the geographical location of these countries close to Europe that creates unique opportunities to attract international investors (Saâdaoui, 2024 ). This study aims to address gaps in market efficiency research by examining the Algerian financial market. Despite the Algiers Stock Exchange's existence since 1999, it remains small and has limited internationalization and growth incentives. The financial market development is far behind the rates of economic growth. According to World Bank, (The World Bank, 2023 ), Algeria’s GDP growth reached 3.2% in 2022, and the current account surplus reached 9.5% of GDP. Despite positive recent macroeconomic developments, Algeria's economic prospects remain sensitive to volatile oil prices. To achieve sustainable growth and diversification, financial market development is crucial. To the best of our knowledge, this research is among the very few that investigate the efficiency issues in the Algerian financial market, which is underexplored. Due to the lack of information, the difficulty of obtaining digital data for listed shares, the reduced number of shares registered on the stock exchange, and a thin trading, we use event study approach as the main method of research focusing on dividend announcements made by the listed companies. The remaining sections of this research paper are structured as follows: The second section provides a literature review on the subject. It is followed by an overview of the Algiers Stock Exchange and its specific factors that influenced the choice of research approach. The next section outlines the methodology of the study. After it, the section presenting and discussing the study results follows. The last section concludes the study and discusses its limitations and potential directions for further research. Literature Review Researchers test African stock market efficiency using various methods. Ntim et al. ( 2011 ) concluded based on previous studies that different countries showed conflicting results due to the limitations of conventional techniques like autocorrelation and unit root tests. Using non-parametric Wright’s ranks and signs test (Wright, 2000 ), they concluded that the majority of the African continent-wide stock price indices returns are weak-form efficient, but by contrast, none of the individual national indices tested (Botswana, Egypt, Ghana, Kenya, Mauritius, Morocco, Nigeria, and Tunisia) turned to be efficient. This was supported by other studies for Egypt and Morocco, which found these markets are not efficient in the weak form but show weak efficiency for seasonal effects when tested by non-parametric tests (Omran & Farrar, 2006 ; Nwachukwu & Shitta, 2015 ). Considering such specific features of African markets as slow reaction of investors to the new information and nonsynchronous trading, some researchers are concluding that market efficiency in these markets is time-various and make attempts to examine long-run dependencies (Cajueiroa & Tabak, 2004 ) or explore the multifractal nature of these indices (Saâdaoui, 2024 ). As researchers fail to determine the efficiency of African stock markets based on random walk pattern, and Algeria, with a limited number of listed companies and rare trading looks inappropriate for conventional methods used in these studies, an alternative approach is necessary. Fama et al. ( 1969 ) introduced event study methodology for testing semi-strong market efficiency. This approach examines how stock prices react to specific events like dividend announcements, acquisitions, financial report publications etc. Dividend announcements are often used by scholars to test market efficiency. For American market, many studies confirmed the informational content of dividend announcements (Woolridge, 1982 ; Bajaj & Vijh, 1990 ; Dyl & Weigand, 1998 ; Lie, 2005 ). However, Benartzi et al. ( 1997 ) found low evidence that justified the informational content of dividends. In a study on the Greek stock market, Dasilas and Leventis ( 2011 ) supported the informational content of dividends and revealed that companies’ executives can use dividend distribution to signal their forecasts and expectations about the financial situation of their firms to the market. Numerous studies also examined how stock prices react to dividend announcements. In the US, scholars revealed a statistically significant and positive relationship between dividend announcements and security prices (Aharony & Swary, 1980 ; Asquith & Mullins, 1983 ; Michaely et al., 1995 ; Yoon & Starks, 1995 ). For the French, UK and Portuguese markets, mixed results were found: while in the UK and Portugal the market did not react significantly to dividend announcements, the French market showed a positive reaction to the announcements dividends decline and partially confirmed the informational content of dividends (Vieira & Raposo, 2007 ). Other scholars also explored volumes of stock trade around the dates of dividend announcements. For example, (Gurgul et al., 2003 ) report on the confirmation of the EMH and the information content of dividends in the Austrian market. As for emerging markets, a study of the Warsaw Stock Exchange found that dividend announcements elicit an immediate market reaction consistent with the signaling hypothesis and informational content of dividends (Mrzyglod & Nowak, 2017 ). The Amman Stock Exchange also exhibited a market reaction aligned with signaling theory, with stock prices more sensitive to dividend amounts than changes (Al-Shattarat et al., 2013 ). In contrast, the Karachi Stock Exchange showed no significant abnormal returns around dividend announcements in 2005–2009, supporting market efficiency (Khan et al., 2016 ). However, a later study found a negative reaction to dividend decreases but no positive reaction to increases after the introduction of a new capital gains tax, suggesting informational content dividends and market inefficiency (Tauseef, 2023 ). Several studies of financial markets in Africa applied similar approaches and proved the feasibility of using event study for testing market efficiency based on information content of dividend announcements. Research in Ghana (Osamoah, 2010 ; Sare et al., 2014 ), Nigeria (Campbell & Ohuocha, 2011 ; Ozo & Arun, 2019 ), and Kenya (Owira, 2016 ) found positive abnormal returns around dividend events. As for the Algerian market, very few attempts were made to study its efficiency. Khadri et al. ( 2023 ) provides an overview of Algiers Stock Exchange and uses VAR model to test the market efficiency. The findings indicate no significant effect of financial statements information on stock returns. Bedrouni et al . (2022) applied event study to analyze market reaction to dividend announcements made in 2018–2019 for one company (Biopharm) and found significant negative reaction in the day following the announcement, but the scope and period were too limited for making conclusions. Algiers Stock Exchange: An Overview Algiers Stock Exchange (SGVB) was launched in 1997. It is one of the smallest stock exchanges globally, with market capitalization of 3.953 USD billion in April 2024 (CEIC Data, 2024). Five companies are listed, with four forming DZAIRINDEX (Table 1 ). The fifth company, AOM INVEST SPA, is listed on the SMEs Market, launched in 2012. Table 1 List of companies traded at Algiers Stock Exchange Company Stock Sector of Economy Date of the first listing day Number of shares Marketplace ALLIANCE ASSURANCES Insurance 07/03/2011 9.287.217 Main Market BIOPHARM Pharmaceuticals 20/04/2016 24.245.781 Main Market EGH EL AURASSI Hospitality and Tourism 03/01/2008 6.000.000 Main Market GROUPE SAIDAL Pharmaceuticals 03/01/2008 10.000.000 Main Market AOM INVEST SPA Hospitality and Tourism 12/12/2018 4.596.030 SME’s Market Source: website of Algeria clearing center, https://www.algerieclearing.dz/index.php/fr/ In 2022, 203 transactions with shares were completed in the main market (203.2 thousand of shares traded) and 8 at the SME’s market. In 2021, the number of exchange transactions amounted to 219 transactions compared to 179 in 2020, 434 in 2019 and 440 in 2018.Trade on the equity compartment reached 131.956 million dinars (DZD) in 2022, up from 127.907 million in 2021 and 78.458 million DZD in 2020, but still below the 248.990 million recorded in 2019 and 205.797 million in 2018 (Beddaride, 2022 ). The market has seen a recovery after COVID-19, but the number of investors remains small, with only 21,287 active accounts in 2022, mostly individual investors (20,272 accounts) (Commission d'Organisation et de Surveillance des Opérations de Bourse, 2023 ). It is worth noting that the Algiers Stock Exchange has only three trading days per week (Sundays, Tuesdays, and Thursdays). The Algiers stock exchange is not only small but also plays a minor role in the country’s economy. The stock market capitalization to GDP ratio is less than 0.3%, compared to neighboring Tunisia's 20% and Morocco's 60% (Yatim, 2023 ). Despite recent growth (Fig. 1), this ratio remains one of the smallest globally. Source: (The World Bank, 2024 ). Figure 1. Stock market capitalization to GDP ratio in Algeria (%) Low level of participation of stock market in GDP shows the low ability of allocation and mobilizing national savings to serve the needs of the growing economy (Yatim, 2023 ). According to (Haouas et al., 2024 ), one of the reasons why Algeria is underperforming in terms of economic growth (considering its rich national resources) is the weak capital allocation. The country needs better infrastructure for investors. Data and Study Methodology Data description The primary sample of the study includes all companies listed on the main market of Algiers Stock Exchange on August 1, 2023, and those that distributed dividends to their shareholders. The fifth company, AOM INVEST SPA, was excluded due to its stock not being part of the Algiers Stock Exchange index and being traded in the SME marketplace. We collected dividend announcements from Algerian companies between 2013 and 2022 and monthly closing prices for the securities and the stock exchange index from the «Il Boursa» database related to the Algerian Stock Exchange. Abnormal returns were calculated using both equity returns and the equity index. The Algiers Stock Exchange website provides time series data on stock prices and the stock market index, updated three times a week (Sunday, Tuesday, Thursday). Due to the small number of stocks and missing daily closing prices, we used monthly data instead. This approach is consistent with (Fernandes and Ferreira, 2009 ) that used monthly data instead of daily or weekly data to avoid non-synchronous trading issues. As (Ellis & Keys, 2014 ) mentions, African markets often face data insufficiencies, including missing data, thin trading, small stock numbers etc. These issues can be addressed by giving more attention to the sample details. Given the complexity of the Algiers Stock Exchange, which has only five listed securities, we encountered missing data. To address this, we replaced missing data with the average of previous and subsequent monthly prices, as shown below: $$Average\;share\;price=\frac{{{P_{t - 1}}+{P_{t+1}}}}{2}$$ 1 , where P t−1 denotes share price at month t-1 ; P t+1 – share price at month t + 1. To address insufficient data, we conducted individual event studies to analyze the impact of dividends on stock prices. For each listed company, we required specific criteria to determine abnormal returns around the event date: (a) dividend announcement dates must be published on the Algiers Stock Exchange website, verified through media publications; (b) stock prices must be available 10 months before and after the event date; and (c) we selected dividend operations from 2014 to 2020 that allowed us to determine the event period and estimation period. The example of information needed to conduct an event study for each company is summarized in Table 2 . Table 2 Dividend announcement dates (example) Alliance Assurances Biopharm El-Aurassi Saidal Dividend Announcement Date 05/06/2016 26/06/2018 30/05/2019 27/06/2019 Dividend Payout Date 16/06/2016 31/07/2018 01/07/2019 05/08/2019 Dividend per share (DZD) 35.00 100.00 30.00 40.50 Source: collected by authors from ‘Il Borsa’ database Methodology To answer the questions posed in this paper, we apply event study. This involves several steps: data collection, preparation, and calculation of event window size; estimating normal returns; calculating abnormal returns and cumulative abnormal returns; and testing results. Since the Algiers Stock Exchange does not provide daily data, we use monthly data. We define the event date as the month of dividend announcement (t = 0), which may differ from the dividend payment date (Table 2 ). This process involves carefully examining the information provided by each company to identify the specific date on which the dividend distribution to shareholders was announced. Then we determine the event window and the estimation window. The event window spans 21 months (-10; +10), starting 10 months before the announcement month and extending until 10 months after the announcement month including the announcement month (t = 0) itself. The estimation window is used to determine the parameters of the yield-generating model, which is then employed to calculate the abnormal profitability during the event period. It precedes the event window. In our study, the length of the estimation window varies across companies due to data availability constraints. Figure 2 illustrates the design of the event period and the estimation period for Alliance Assurances company: After organizing the sample and determining the event period and estimation period dates, we calculate the sample stock return, R it , and market return, R mt , as follows. $${R_{it}}=\frac{{S{P_{it}} - S{P_{it - 1}}}}{{S{P_{it - 1}}}},$$ 2 where R it denotes an actual return of stock i at month t; SP it and SP it−1 are closing prices of stock i at months t and t-1; $${R_{mt}}=\frac{{dzairinde{x_t} - dzairinde{x_{t - 1}}}}{{dzairinde{x_{t - 1}}}}$$ 3 , where R mt denotes an actual return of the Algiers Stock Exchange index (dzairindex) at month t; dzairindex t - closing Algiers Stock Exchange index at month t; dzairindex t−1 - closing Algiers Stock Exchange index at month t-1. The estimation of abnormal returns for the companies in the sample is based on the market model (Fama et al., 1969 ), which is widely used in event studies because of its performance and simplicity. According to this model, the expected return \(\widehat {{{R_{it}}}}\) is the result of the following simple regression equation: $$\widehat {{{R_{it}}}}={\alpha _i}+{\beta _i}{R_{mt}}+{\varepsilon _{it}},$$ 4 where α i , ß i are coefficients of the model for stock i; \({\varepsilon _{it}}\) - residual error of stock i at month t. Abnormal return \(\:{\text{A}\text{R}}_{\text{i}\text{t}}\) is defined as the actual return \(\:{\text{R}}_{\text{i}\text{t}}\) minus expected return calculated using the market model: $$A{R_{it}}={R_{it}} - \widehat {{{R_{it}}}}.$$ 5 To analyze the price changes of securities around the event date for four companies listed on the Algiers stock exchange, we calculate the average cross-sectional monthly abnormal return as follows: $$AA{R_t}=\frac{1}{N}\sum\limits_{{i=1}}^{N} {A{R_{it}},}$$ 6 where AAR t denotes an average abnormal return at month t; N - the number of observations in the study sample. To gauge the comprehensive effect of the examined event over a specific time frame (event window), we can aggregate individual abnormal returns into a cumulative abnormal return. It represents the cumulative impact of all abnormal returns. The cumulative abnormal return (CAR) is calculated as follows: $$CA{R_{it}}=\sum\limits_{{t={t_1}}}^{{{t_2}}} {A{R_{it}}.}$$ 7 Finally, cumulative average abnormal returns ( CAAR ) are calculated as follows: $$CAA{R_(}_{{{t_1},{t_2})}}=\frac{1}{N}\sum\limits_{{i=1}}^{N} {CA{R_{it}}} .$$ 8 Cumulative average abnormal return provides a comprehensive measure of the overall impact of an event on stock returns. In particular, if the impact of the event being studied does not occur exactly in the month of the event, then this estimator can be very important. For statistical tests that can be performed, the null hypothesis confirms that the abnormal return on month t within the event window is equal to zero: \(H0:E(A{R_t})=0.\) This implies that the event in question has no influence on the price of the securities. Assuming that individual abnormal returns during the event period are normally distributed, the t-student parametric test, as applied by Brown and Warner (1980, 1985), is used to measure the impact that may occur on the day (month) of the event as follows: $$t=\frac{{AA{R_t}}}{{\sigma (AA{R_t})}},$$ 9 where AAR t denotes an average abnormal return at month t, σ(AAR t ) - standard deviation of average abnormal returns. The test procedure consists in dividing the sum of the abnormal return of the event period for all securities on the root of the variance of abnormal return, during the estimation period. The preceding equation is thus reproduced according to the formula below: $$t=\frac{{\frac{1}{N}\sum\limits_{{i=1}}^{N} {A{R_{i0}}} }}{{\frac{1}{N}\sqrt {\sum\limits_{{i=1}}^{N} {\frac{1}{{T - 1}}{{\left( {A{R_{it}} - \sum\limits_{{t=1}}^{T} {\frac{{A{R_{it}}}}{T}} } \right)}^2}} } }},$$ 10 where AR i0 presents an abnormal return for stock i during the month of the event (t = 0); T – the length of the estimation period. The second statistical test assesses the overall effect of dividend announcements on the profitability of the securities. This t-test can be applied to one or more sub-periods within the event window, or to the full event period (-10, + 10). To determine the significance of the cumulative average abnormal return (CAAR), we use the following test: $$t=\frac{{CAA{R_{{T_1},{T_2}}}}}{{{{\left( {{T_2} - {T_1}+1} \right)}^{{1 \mathord{\left/ {\vphantom {1 2}} \right. \kern-0pt} 2}}}\sigma (AA{R_t})}},$$ 11 where T 1 , T 2 denote lower and upper limits of the cumulative period. In addition, we utilize the Beaver test (Beaver, 1968 ), which complements t-statistic tests that depend on models generating returns. This test assesses the magnitude of variations (abnormal returns) experienced by a security due to the event under examination. The Beaver test applies to all securities in the sample and consists in transforming abnormal returns without considering their signs. This transformation involves calculating the square of each security’s abnormal return. The test is expressed as a ratio: $${U_{it}}=\frac{{{{\left( {A{R_{it}}} \right)}^2}}}{{{{\left( {\sigma _{{it}}^{2}\left( {1+(\frac{1}{k})+\frac{{{{({R_{mt}} - \overline {{{R_m}}} )}^2}}}{{\sum\nolimits_{1}^{k} {{{({R_{mk}} - \overline {{{R_m}}} )}^2}} }}} \right)} \right)}^{{\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 2}}\right.\kern-0pt}\!\lower0.7ex\hbox{$2$}}}}}},$$ 12 where \(\overline {{{R_m}}}\) is an average market return calculated for the estimated period; k - number of observations in the estimation period; \(\:{{{\sigma\:}}_{\text{i}\text{t}}}^{2}\) - variance of abnormal return for stock i. If U it =1, it indicates that the variance observed during the event period is the same as that calculated during the estimation period and that the share price reaction within the event window does not differ significantly from a normal one. If \(\:{\text{U}}_{\text{i}\text{t}}\:>1\) this means that the abnormal return is higher than normal and shows a significant reaction. If \(\:{\text{U}}_{\text{i}\text{t}}\:<1\) , then the abnormal return is lower than normal, and the market reaction to the announcement of the event is insignificant. Results and Discussion Table 3 shows abnormal returns (AR) for four companies within event windows (-10; +10) around the month of their dividend announcements. For Alliance Assurances, we observe a positive but insignificant reaction at the month of announcement ( \(\:t=0)\) . Almost half of abnormal returns in the event window are significant. EGH El Aurassi and Biopharm cause negative reactions at the month of announcement (-1.70% and − 7.50%, respectively), with Biopharm's being significant at the 1% level. Saidal’s returns show a significant reaction of -8.69% only once, eight months after the announcement. These results indicate that the Algerian market reacted to dividend announcements. Table 3 Summary results for abnormal returns around dividend announcement month Month Alliance Assurances EGH El Aurassi Biopharm Saidal AR t-statistics AR t-statistics AR t-statistics AR t-statistics -10 0.0518 3.9201 *** -0.0194 -0.7596 0.0018 0.0981 -0.0312 -1.0995 -9 0.0393 2.9722 -0.0038 -0.1490 0.0105 0.5611 -0.0205 -0.7221 -8 0.0050 0.3748 0.0068 0.2669 0.0008 0.0402 -0.0126 -0.4419 -7 -0.0882 -6.6667 *** -0.0038 -0.1481 -0.0050 -0.2675 0.0399 1.4049 -6 -0.0361 -2.7274 *** 0.0005 0.0199 0.0005 0.0246 0.0072 0.2550 -5 -0.0389 -2.9388 *** 0.0078 0.3050 -0.0115 -0.6186 -0.0078 -0.2732 -4 0.0074 0.5572 -0.0072 -0.2810 0.0008 0.0443 -0.0392 -1.3783 -3 -0.0242 -1.8288 0.0103 0.4025 0.0226 1.2127 -0.0049 -0.1727 -2 0.0341 2.5761 ** -0.0027 -0.1055 0.0011 0.0604 -0.0037 -0.1292 -1 -0.0480 -3.6274 *** -0.0047 -0.1853 0.0136 0.7306 -0.0175 -0.6172 0 0.0044 0.3322 -0.0170 -0.6627 0.0087 0.4678 0.0014 0.0480 1 -0.0546 -4.1277 *** 0.0233 0.9091 0.0060 0.3191 -0.0056 -0.1970 2 0.0027 0.2022 -0.0123 -0.4806 -0.0747 -4.0068 *** -0.0224 -0.7900 3 0.0095 0.7191 -0.0039 -0.1529 -0.0144 -0.7701 -0.0234 -0.8254 4 0.0163 1.2293 -0.0065 -0.2535 0.0049 0.2607 -0.0400 -1.4080 5 -0.0060 -0.4543 -0.0079 -0.3097 0.0044 0.2352 -0.0034 -0.1212 6 0.0040 0.3009 -0.0029 -0.1124 -0.0046 -0.2450 -0.0047 -0.1658 7 0.0051 0.3842 -0.0005 -0.0188 0.0146 0.7850 -0.0051 -0.1798 8 0.0041 0.3100 -0.0009 -0.0349 0.0014 0.0726 -0.0869 -3.0591 *** 9 -0.0263 -1.9896 * -0.0001 -0.0052 0.0101 0.5409 -0.0140 -0.4929 10 -0.0076 -0.5719 0.0051 0.1987 0.0090 0.4819 0.0445 1.5664 *, **, *** denotes significance at 10%, 5%, and 1% level consequently. Source: authors’ calculations Figure 3 provides visual evidence of market reaction to companies’ dividend announcements, highlighting peaks in the abnormal return curves. EGH El Aurassi and Biopharm show peaks. For EGH El Aurassi, the peak occurred during the first month after the announcement date. For Biopharm, the peak was negative and occurred in the second month after the announcement date. Source: authors’ calculations Figure 3. Abnormal returns around the month of dividend announcement for all companies Table 4 presents the minimum, maximum, and average of abnormal returns (AAR) for the event period. The average abnormal returns are statistically insignificant for the entire event period. Upon examining Table 4 , we observe that approximately half of the values are positive but near zero and have no statistical significance. The largest rate of average abnormal return (1.37%) occurred during the second month before the dividend announcement date. Table 4 Summary results for abnormal returns around dividend announcement month near here Month Min AR t Max AR t AAR t σ(AR t ) t-statistics Significance -10 -0.0316 0.0514 0.0072 0.0392 0.3682 -9 -0.0209 0.0388 0.0128 0.0298 0.8595 -8 -0.0129 0.0273 0.0065 0.0165 0.7837 -7 -0.0886 0.0396 -0.0078 0.0567 -0.2749 -6 -0,0365 0.0270 -0.0005 0.0265 -0.0383 -5 -0.0393 0.0150 -0.0061 0.0242 -0.5079 -4 -0.0395 0.0274 -0.0031 0.0281 -0.2194 -3 -0.0247 0.0492 0.0074 0.0313 0.4736 -2 -0.0040 0.0336 0.0137 0.0198 1.3831 * -1 -0.0485 0.0402 -0.0077 0.0368 -0.4186 0 -0.0169 0.0353 0.0058 0.0217 0.5394 1 -0.0550 0.0325 -0.0013 0.0394 -0.0641 2 -0.0481 0.0022 -0.0202 0.0212 -1.9065 * 3 -0.0238 0.0122 -0.0016 0.0163 -0.1947 4 -0.0403 0.0314 0.0001 0.0311 0.0075 5 -0.0078 0.0310 0.0032 0.0186 0.3457 6 -0.0051 0.0220 0.0044 0.0123 0.7191 7 -0.0055 0.0412 0.0100 0.0212 0.9421 8 -0.0873 0.0279 -0.0141 0.0504 -0.5613 9 -0.0268 0.0367 -0.0011 0.0275 -0.0826 10 -0.0080 0.0442 0.0192 0.0247 1.5560 * *, **, *** denotes significance at the 10%, 5%, and 1% level consequently. Source: authors’ calculations In Table 5 we present the results of the CAR and the CAAR calculation, which show the overall stocks behavior around the date of the dividend announcement for all companies in the sample. For the CAR, we observe that three of the four rates are positive at the announcement date (t = 0), with Biopharm recording the highest rate of 3.53% and El Aurassi recording the lowest rate of -1.69%. Only Biopharm demonstrated a positive CAR during all event windows, with the highest rate of 33.61% for the event window (-10,0) \(\:.\) Fig. 4 clearly shows the peak for all four companies during the dividend announcement month (t = 0). Table 5 Cumulative abnormal return and cumulative average abnormal return around dividend announcement Window (Event Period) CAR Alliance Assurances EGH El Aurassi Biopharm Saidal CAAR (-10,0) -0.0987 -0.0322 0.3361 -0.0926 0.0282 (-5,0) -0.0681 -0.0130 0.1948 -0.0737 0.0100 (-4,0) -0.0288 -0.0208 0.1798 -0.0656 0.0161 (-3,0) -0.0357 -0.0137 0.1524 -0.0261 0.0192 (-2,0) -0.0110 -0.0241 0.1032 -0.0208 0.0118 (-1,0) -0.0446 -0.0215 0.0755 -0.0168 -0.0019 0 0.0039 -0.0169 0.0353 0.0010 0.0058 (0,1) -0.0511 0.0065 0.0679 -0.0049 0.0046 (0,2) -0.0489 -0.0057 0.0197 -0.0277 -0.0157 (0,3) -0.0399 -0.0095 0.0319 -0.0515 -0.0172 (0,4) -0.0241 -0.0159 0.0634 -0.0918 -0.0171 (0,5) -0.0306 -0.0238 0.0943 -0.0956 -0.0139 (0,10) -0.0537 -0.0226 0.2576 -0.1636 0.0044 Source: authors’ calculations Source: authors’ calculations Figure 4. Cumulative abnormal return around dividend announcement month near here Regarding the CAAR, which assesses the total sample’s response to the dividend announcements, we see that the effect was positive for five event windows preceding the month of the announcement, namely (-10,0), (-5,0), (-4,0), (-3,0), and (-2,0). The effect was also positive during the announcement date and the subsequent window. However, it turned negative during the periods following the announcement, as Fig. 5 illustrates. Source: authors’ calculations. Figure 5. Cumulative average abnormal return around dividend announcement month near here Table 6 presents the results of Beaver test. It is observed that all values during the event period are less than one and sometimes equal to zero. This means that the abnormal return is lower than the normal return calculated during the estimation period and the market reaction was weak and insignificant. Table 6 Results of Beaver test near here Month Alliance Assurances EGH El Aurassi Biopharm Saidal -10 0.194 0.0143 0.0422 0.0312 -9 0.092 0.0005 0.0717 0.0151 -8 0.0015 0.0018 0.0386 0.0058 -7 0.564 0.0005 0.024 0.0542 -6 0.0954 0 0.0379 0.0015 -5 0.1116 0.0024 0,0117 0.0023 -4 0.0034 0.0019 0.0384 0.0529 -3 0.0447 0.0041 0.1211 0.001 -2 0.083 0.0003 0.0401 0.0006 -1 0.0999 0.0008 0.0846 0.0102 0 0.0011 0.0108 0.0634 0 1 0.1833 0.0198 0.0516 0.0012 2 0.0004 0.0056 0.117 0.0181 3 0.0057 0.0006 0.0078 0.0197 4 0.0183 0.0016 0.0517 0.0568 5 0.0031 0.0023 0.05 0.0005 6 0.0009 0.0003 0.0245 0.0009 7 0.0016 0 0.0889 0.001 8 0.001 0 0.0405 0.2598 9 0.0525 0 0.0703 0.0072 10 0.0048 0.001 0.0662 0.0679 Regarding Alliance Assurances, we note that it experienced a weak but statistically significant abnormal return before the dividend announcement date, which could be attributed to the leak of information or rumors. On the other hand, we observe that abnormal returns persisted for a longer period, and the market did not rapidly correct the share price. We find that EGH El Aurassi shareholders did not receive statistically significant abnormal returns. This means that dividend announcement had no effect on the company's shares. This analysis allows us to conclude that the efficiency of the Algiers Stock Exchange was very low, meaning it has poor information efficiency and barely approaches weak form efficiency rather than a semi-strong form efficiency. Finally, we observed that Biopharm and Saidal had a somewhat delayed reaction, which could be due to a slower market response in incorporating dividend announcements information into stock prices. Though we do not have enough empirical evidence on Algiers stock markets, our results are consistent with (Khadri et al., 2023 ) regarding Algerian market and support results obtained for other markets in Africa (Campbell & Ohuocha, 2011 ; Ozo & Arun, 2019 ; Sare et al., 2014 ) in terms of informational content of dividends and market inefficiency. Conclusion This article presents an empirical study on the Algerian financial market's response to dividend announcements. The study aims to answer two key questions: how stocks react to dividend announcements and whether the Algerian financial market has informational efficiency in its semi-strong form. We consider this study as one of the first empirical studies testing the Algerian market. The results are mixed. Generally, average abnormal returns were weak, but we observed a statistical significance for abnormal returns values of Alliance Assurances, and mostly positive returns for Biopharm shares. Cumulative abnormal returns peaked for each stock at the date of the event. However, the results were inconsistent, leading us to conclude that the Algerian financial market is not efficient in the semi-strong form. The study has limitations, the most serious is the small number of stock and thin trading that enabled us to use monthly prices and long event windows. The low number of events that drive share prices also restricted the choice of methods and impacted the results. Nevertheless, the sample expansion by testing market reaction to information about other events, such as the publication of annual reports, information on CEO changes etc., could be an interesting direction for further research. Summing up, our study highlights the inconsistency between Algeria’s economic growth and its small and inefficient financial market and emphasizes the need for government actions to address this problem. This inconsistency is important not only for Algeria, however. Many African capital markets are underperforming and need in improvement to serve the needs of growing economies. Declarations Author Contribution O.B : Empirical study References Aharony, J., & Swary, I. (1980). Quarterly Dividend and Earnings Announcements and Stockholders' Returns: An Empirical Analysis. The Journal of Finance, 35 (1), 1-12. doi:10.2307/2327176 Al-Shattarat, W., Atmeh, M., & Al-Shattarat, B. (2013). Dividend Signalling Hypothesis In Emerging Markets: More Empirical Evidence. Journal of Applied Business Research, 29 (2), 461-468. doi:10.19030/jabr.v29i2.7650 Asquith, P., & Mullins, D. (1983). The impact of initiating dividend payments on shareholders wealth. The Journal of Business, 56 (1), 77-96. Bajaj, M., & Vijh, A. (1990). Dividend clienteles and the information content of dividend changes. Journal of Financial Economics, 26 (2), 193-219. doi:10.1016/0304-405X(90)90003-I Beaver, W. (1968). The information content of annual earnings announcements. Journal of Accounting Research , 67-92. Beddaride, D. (2022, March 07). Algeria: The Algiers Stock Exchange confirms its recovery and strong growth in 2021 with +63% . Retrieved January 21, 2024, from Ecomnews Med: https://www.ecomnewsmed.com/en/2022/05/07/algeria-the-algiers-stock-exchange-confirms-its-recovery-and-strong-growth-in-2021-with-63/ Beer, F. (1993). Dividend signalling equilibria: quantitative evidence from the Brussels stock exchange. Financial Review , 139-157. Benartzi, S., Michaely, R., & Thaler, R. (1997). Do changes in dividends signal the future or the past ? The Journal of Finance, 52 (3), 1007-1034. doi:10.2307/2329514 Cajueiroa, D., & Tabak, B. (2004). The Hurst exponent over time: testing the assertion that emerging markets are becoming more efficient. Physica A, 336 , 521-537. doi:10.1016/j.physa.2003.12.031 Campbell, K., & Ohuocha, C. (2011). The stock market reaction to stock dividends in Nigeria and their information content. Managerial Finance, 37 (3), 295-311. doi:10.1108/03074351111113333 Commission d'Organisation et de Surveillance des Opérations de Bourse. (2023). Rapport Annuel 2022 ACTIVITÉ DU MARCHÉ DES VALEURS MOBILIÈRES. Algiers. Retrieved January 21, 2024, from https://www.cosob.org/wp-content/uploads/2023/12/COSOB-Rapport-annuel-2022.pdf Dasilas, A., & Leventis, S. (2011). Stock market reaction to dividend announcements: Evidence from the Greek stock market. International Review of Economics and Finance, 20 (2), 302-3011. doi:10.1016/j.iref.2010.06.003 Dyl, E., & Weigand, R. (1998). The information content of dividend initiations: Additional evidence. Financial Management, 27 (3), 27-35. doi:10.2307/3666272 Ellis, K., & Keys, P. (2014). Event study methodology: An overview and special considerations for African markets. In Advancing Research Methodology in the African Context: Techniques, Methods, and Designs. Research Methodology in Strategy and Management, Volume 10 (pp. 69-87). Emerald. doi:10.1108/S1479-838720140000010005 Fama, E. (1965). The Behavior of Stock-Market Prices. The Journal of Business, 38 (1), 34-105. Retrieved from http://www.jstor.org/stable/2350752 Fama, E. (1970). Efficient capital markets: a review of theory and empirical work. The Journal of Finance, 25 (2), 383-417. doi:10.2307/2325486 Fama, E., Fisher, L., Jensen, M., & Roll, R. (1969). The Adjustment of Stock Prices to New Information. International Economic Review, 10 (1), 1-21. doi:10.2307/2525569 Farid, S. (2013). Financial Integration in African Emerging Markets. African Economic Conference 2013 "Regional Integration in Africa". Johannesburg. Retrieved from https://archive.uneca.org/sites/default/files/uploaded-documents/AEC/2013/financial_integration_in_african_emerging_markets.pdf Fernandes, M., & Ferreira, N. (2009). Insider trading laws and stock price informativeness. Review of Financial Studies, 22 (5), 1845-1887. Ferreira, P., Dionisio, A., & Correia, J. (2018). Non-linear dependencies in African stock markets: Was subprime crisis an important factor? Physica A: Statistical Mechanics and Its Applications, 505 , 680-687. doi:10.1016/j.physa.2018.03.060 Gurgul, H., Mestel, R., & Schleicher, C. (2003). Stock market reaction to dividend announcements: Empirical evidence from the Austrian stock market. Financial Markets and Portfolio Management , 332-350. Haouas, A., Ochi, A., & Labidi, M. (2024). Sources of Algeria's economic growth, 1979–2019: Augmented growth accounting framework and growth regression method. Regional Science Policy & Practice, 16 (3), 12448. doi:10.1111/rsp3.12448 Khadri, N., Waked, S., Bouali, S., & Omri, M. (2023). EFFECT OF ACCOUNTING DISCLOSURE ON RETURNS OF SHARES IN THE ALGERIAN STOCK EXCHANGE: APPLICATION OF VAR MODEL. Les Cahiers du Cread, 39 (2), 285-313. doi:10.4314/cread.v39i2.10 Khan, N., Burton, B., & Power, D. (2016). Share price behaviour around dividend announcements in Pakistan. Afro-Asian Journal of Finance and Accounting, 6 (4), 351-373. doi:10.1504/AAJFA.2016.080522 Lie, E. (2005). Operating performance following dividend decreases and omissions. Journal of Corporate Finance, 12 (1), 27-53. doi:10.1016/j.jcorpfin.2004.04.004 Michaely, R., Thaler, R., & Womack, K. (1995). Price Reactions to Dividend Initiations and Omissions: Overreaction or Drift? The Journal of Finance, 50 (2), 573-608. doi:10.2307/2329420 Mlambo, C., & Biekpe, N. (2005). Thin trading on African stock markets: Implications for market efficiency testing. Investment Analysts Journal, 34 (61), 29-40. doi:10.1080/10293523.2005.11082466 Mrzyglod, U., & Nowak, S. (2017). Market reactions to dividends announcements and payouts: Empirical evidence from the Warsaw Stock Exchange. Journal: Contemporary Economics, 11 (2), 187-204. Retrieved from https://ssrn.com/abstract=3176998 Ntim, C., Opong, K., Danbolt, G., & Dewotor, F. (2011). Testing the weak-form efficiency in African markets. Managerial Finance, 37 (3), 196-218. doi:10.1108/03074351111113289 Nwachukwu, J., & Shitta, O. (2015). Testing the weak-form efficiency of stock markets: A comparative stidy of emerging and industrialised economies. International Journal of Emerging Markets, 10 (3), 409-426. doi:10.1108/IJoEM-07-2013-0115 Okorie, I., & Nadarajah, S. (2020). On nonlinear dependencies in African stock markets. Economic Notes: Review of Banking, Finance and Monetary Economics (49), e12137. doi:10.1111/ecno.12137 Omran, M., & Farrar, S. (2006). Tests of weak form efficiency in the Middle East emerging markets. Studies in Economics and Finance, 23 (1), 13-26. doi:10.1108/10867370610661927 Osamoah, G. (2010). The Impact of dividend announcement on share price behaviour in Ghana. Journal of Business & Economics Research, 8 (4), 47-58. doi:10.19030/jber.v8i4.702 Owira, D. (2016). The effect of dividend announcements on stock returns of companies listed at at the Nairobi Security Exchange. Master's thesis, University of Nairobi, Nairobi. Retrieved January 21, 2024, from http://erepository.uonbi.ac.ke/bitstream/handle/11295/99595/Owira_The%20Effect%20Of%20Dividend %20Announcements%20On%20Stock%20Returns%20Of%20Companies%20Listed%20At%20The% 20Nairobi%20Securities%20Exchange.pdf?sequence=1 Oxford Business Group. (2022). African Stock Exchange Focus Report. Oxford Business Group. Retrieved from https://www.bvm-ac.org/wp-content/uploads/2022/08/ASEA-African-Exchanges-Focus-Report.pdf Ozo, F., & Arun, T. (2019). Stock market reaction to cash dividends: evidence from the Nigerian stock market. Managerial Finance, 45 (3), 366-380. doi:10.1108/MF-09-2017-0351 Saâdaoui, F. (2024). Segmented multifractal detrended fluctuation analysis for assessing. Chaos, Solitons and Fractals, 181 , 14652. doi:10.1016/j.chaos.2024.114652 Sare, Y., Pearl-Kumah, S., & Salakpi, A. (2014). Market Reaction To Dividend Initiation Announcements on the Ghana Stock Exchange: The Case of Industrial Analysis. Asian Economic and Financial Review, 14 (4), 440-450. Retrieved January 21, 2024, from https://archive.aessweb.com/index.php/5002/article/view/1170 Tauseef, S. (2023). Market reaction to the annual cash dividend changes in Pakistan. Afro-Asian Journal of Finance and Accounting, 13 (3), 305-319. doi:10.1504/AAJFA.2023.132202 The World Bank. (2023). Algeria Economic Update: Winds Remain Favorable. Spring 2023. The World Bank. Retrieved from https://documents1.worldbank.org/curated/en/099607506202340553/pdf/IDU029af6e650ac6404e170bc79062eb3b0de925.pdf The World Bank . (2024). Retrieved January 30, 2024, from Market capitalization of listed domestic companies (% of GDP) - Algeria: https://data.worldbank.org/indicator/CM.MKT.LCAP.GD.ZS?locations=DZ Vieira, E., & Raposo, C. (2007, January 9). Signaling With Dividends? The Signaling Effects of Dividend Change Announcements: New Evidence From Europe. Working Paper . Retrieved January 20, 2024, from https://ssrn.com/abstract=955768 Woolridge, J. (1982). The information content of dividend changes. The Journal of Financial Research, 5 (3), 237-247. doi:10.1111/j.1475-6803.1982.tb00298.x Wright, J. (2000). Alternative variance-ratio tests using ranks and signs. Journal of Business and Economic Statistics, 18 (1), 1-9. doi:10.2307/1392131 Yatim, M. (2023). The Algerian Stock Market, Perspectives and Constraints. Finance and Markets Journal, 10 (2), 55-70. Yoon, P., & Starks, L. (1995). Signaling, Investment Opportunities, and Dividend Announcements. The Review of Financial Studies, 8 (4), 995-1018. Additional Declarations No competing interests reported. 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BOUFAMA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYFACHiC2AWL2BgaGBCAtwczGIMHAwExASxqIPkCyFokECF+CgYAWfgbeg495Euyi+We+MXvwsI3BXrKdLfHGDwZre1xaJBv4ko15EpJzZ9zOMTdIbGNInM3MdtiyhyEdpzUGB3jMpHl/MOc23M4xk0g48z9Bjpm9TYKH4TAbXi08CfW582+eAWlhsAdpkfzDcJiHgJbDuRtu8AC1VDAwAh12TBpoiwROvzTzJRvOSTieu/FMWhlIS+LMZrZkaxmDdANcWvjZew8+eJNQnTvv+OFtkj8MGOwlzh8zvPmmAneI4Qp/nHaMglEwCkbBKCAGAAAnYEkGV1Jv8wAAAABJRU5ErkJggg==","orcid":"","institution":"University of 20 august 1955 Skikda","correspondingAuthor":true,"prefix":"","firstName":"OMAR","middleName":"","lastName":"BOUFAMA","suffix":""},{"id":370475861,"identity":"1a036deb-1c14-42c2-ae74-8f0963395977","order_by":1,"name":"ELENA ROGOVA","email":"","orcid":"","institution":"Saint Petersburg Academic University","correspondingAuthor":false,"prefix":"","firstName":"ELENA","middleName":"","lastName":"ROGOVA","suffix":""}],"badges":[],"createdAt":"2024-10-23 19:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5321164/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5321164/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67773318,"identity":"6e862bac-b0f8-4edc-a6c6-adf666bd9fff","added_by":"auto","created_at":"2024-10-29 14:35:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53509,"visible":true,"origin":"","legend":"\u003cp\u003eStock market capitalization to GDP ratio in Algeria (%)\u003c/p\u003e\n\u003cp\u003eSource: (The World Bank, 2024).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5321164/v1/f2499845d1a0319ed1e5a0de.png"},{"id":67773315,"identity":"b44b9978-cf33-4145-818c-479540ff4ba1","added_by":"auto","created_at":"2024-10-29 14:35:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78089,"visible":true,"origin":"","legend":"\u003cp\u003eTimeline for event study (example: Alliance Assurances)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5321164/v1/30a95811109718e33fdb8e85.png"},{"id":67773316,"identity":"91b7b93b-1c80-46cd-b7f7-aa0298cd4c37","added_by":"auto","created_at":"2024-10-29 14:35:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":165576,"visible":true,"origin":"","legend":"\u003cp\u003eAbnormal returns around the month of dividend announcement for all companies\u003c/p\u003e\n\u003cp\u003eSource: authors’ calculations\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5321164/v1/85dea0d0014010205eb2fee8.png"},{"id":67775090,"identity":"5a62ebba-6883-4911-9f1c-c1eeecbb8f06","added_by":"auto","created_at":"2024-10-29 14:51:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":168261,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative abnormal return around dividend announcement month near here\u003c/p\u003e\n\u003cp\u003eSource: authors’ calculations\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5321164/v1/9a830de0df655b9d3e505142.png"},{"id":67774591,"identity":"90245280-23f7-442e-8ba0-25ddbbb95648","added_by":"auto","created_at":"2024-10-29 14:43:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":55039,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative average abnormal return around dividend announcement month near here\u003c/p\u003e\n\u003cp\u003eSource: authors’ calculations.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5321164/v1/cd6c5b0c32513ac3147b293e.png"},{"id":68025505,"identity":"ddd39bb9-1c73-4c61-91f2-33c4447d72a4","added_by":"auto","created_at":"2024-11-01 13:08:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1298313,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5321164/v1/b039cc83-6a37-4646-ad87-739bc49760dd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dividend Announcements and capital market efficiency : Evidence from the algerian stock market","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent decades, the development of capital markets in Africa has become increasingly important for economic growth, long-term financing for real sector and infrastructure projects, and serving the needs of foreign investors. The number of African stock exchanges rose from five in the 1980s to 29 in 2023, while capitalization grew from \u003cspan\u003e$\u003c/span\u003e260\u0026nbsp;billion in 2002 to \u003cspan\u003e$\u003c/span\u003e1.6 trillion in 2021 (Oxford Business Group, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, stock markets in Africa, excluding South Africa, are relatively underdeveloped compared to those in developed and emerging economies globally (Ntim et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). They are small in both market capitalization and the number of listed companies. According to Farid (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), their market capitalization accounts for less than 2% of the world's total. Additionally, even the largest stock exchange, such as the Johannesburg stock exchange, has only around 350 listed companies. African financial markets are also less liquid than those in other parts of the world and thinly traded (Mlambo \u0026amp; Biekpe, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). These considerations raise a question about their efficiency.\u003c/p\u003e \u003cp\u003eFollowing the Efficient Market Hypothesis (EMH), the market has informational efficiency if stock prices fully and immediately reflect all available information. This means that stock price changes are random and unpredictable, making it impossible for investors to consistently outperform the market, as all market anomalies are arbitraged away immediately. Fama (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1970\u003c/span\u003e) justified three forms of market efficiency. The weak form suggests that all past market prices are reflected in current prices. The semi-strong form of EMH extends beyond historical prices and suggests that all publicly available information is instantly priced into the market. This includes financial statements, news releases, economic indicators, and other public disclosures. Therefore, neither technical analysis nor fundamental analysis can yield superior returns consistently. The most extreme version of EMH, the strong form, asserts that all information, both public and private, is fully reflected in stock prices, and is inherent for large, liquid markets.\u003c/p\u003e \u003cp\u003eResearchers generally agree that African capital markets operate in the weak form of efficiency (Ntim et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, several points should be considered. Firstly, African markets are much less studied than those globally. Secondly, some studies suggest that these markets do not satisfy the efficient market hypothesis due to their small size and thin trading (Ferreira et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Okorie \u0026amp; Nadarajah, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thirdly, researchers prefer focusing on larger markets and neglecting smaller ones. However, such markets are crucial for economic growth in emerging countries and need improvement in efficiency. This is especially relevant to North African markets because of the geographical location of these countries close to Europe that creates unique opportunities to attract international investors (Sa\u0026acirc;daoui, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aims to address gaps in market efficiency research by examining the Algerian financial market. Despite the Algiers Stock Exchange's existence since 1999, it remains small and has limited internationalization and growth incentives. The financial market development is far behind the rates of economic growth.\u003c/p\u003e \u003cp\u003eAccording to World Bank, (The World Bank, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Algeria\u0026rsquo;s GDP growth reached 3.2% in 2022, and the current account surplus reached 9.5% of GDP. Despite positive recent macroeconomic developments, Algeria's economic prospects remain sensitive to volatile oil prices. To achieve sustainable growth and diversification, financial market development is crucial.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this research is among the very few that investigate the efficiency issues in the Algerian financial market, which is underexplored. Due to the lack of information, the difficulty of obtaining digital data for listed shares, the reduced number of shares registered on the stock exchange, and a thin trading, we use event study approach as the main method of research focusing on dividend announcements made by the listed companies.\u003c/p\u003e \u003cp\u003eThe remaining sections of this research paper are structured as follows: The second section provides a literature review on the subject. It is followed by an overview of the Algiers Stock Exchange and its specific factors that influenced the choice of research approach. The next section outlines the methodology of the study. After it, the section presenting and discussing the study results follows. The last section concludes the study and discusses its limitations and potential directions for further research.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eResearchers test African stock market efficiency using various methods. Ntim et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) concluded based on previous studies that different countries showed conflicting results due to the limitations of conventional techniques like autocorrelation and unit root tests. Using non-parametric Wright\u0026rsquo;s ranks and signs test (Wright, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), they concluded that the majority of the African continent-wide stock price indices returns are weak-form efficient, but by contrast, none of the individual national indices tested (Botswana, Egypt, Ghana, Kenya, Mauritius, Morocco, Nigeria, and Tunisia) turned to be efficient. This was supported by other studies for Egypt and Morocco, which found these markets are not efficient in the weak form but show weak efficiency for seasonal effects when tested by non-parametric tests (Omran \u0026amp; Farrar, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Nwachukwu \u0026amp; Shitta, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Considering such specific features of African markets as slow reaction of investors to the new information and nonsynchronous trading, some researchers are concluding that market efficiency in these markets is time-various and make attempts to examine long-run dependencies (Cajueiroa \u0026amp; Tabak, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) or explore the multifractal nature of these indices (Sa\u0026acirc;daoui, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs researchers fail to determine the efficiency of African stock markets based on random walk pattern, and Algeria, with a limited number of listed companies and rare trading looks inappropriate for conventional methods used in these studies, an alternative approach is necessary. Fama et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1969\u003c/span\u003e) introduced event study methodology for testing semi-strong market efficiency. This approach examines how stock prices react to specific events like dividend announcements, acquisitions, financial report publications etc.\u003c/p\u003e \u003cp\u003eDividend announcements are often used by scholars to test market efficiency. For American market, many studies confirmed the informational content of dividend announcements (Woolridge, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Bajaj \u0026amp; Vijh, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Dyl \u0026amp; Weigand, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Lie, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). However, Benartzi et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) found low evidence that justified the informational content of dividends. In a study on the Greek stock market, Dasilas and Leventis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) supported the informational content of dividends and revealed that companies\u0026rsquo; executives can use dividend distribution to signal their forecasts and expectations about the financial situation of their firms to the market. Numerous studies also examined how stock prices react to dividend announcements. In the US, scholars revealed a statistically significant and positive relationship between dividend announcements and security prices (Aharony \u0026amp; Swary, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Asquith \u0026amp; Mullins, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Michaely et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Yoon \u0026amp; Starks, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). For the French, UK and Portuguese markets, mixed results were found: while in the UK and Portugal the market did not react significantly to dividend announcements, the French market showed a positive reaction to the announcements dividends decline and partially confirmed the informational content of dividends (Vieira \u0026amp; Raposo, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Other scholars also explored volumes of stock trade around the dates of dividend announcements. For example, (Gurgul et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) report on the confirmation of the EMH and the information content of dividends in the Austrian market.\u003c/p\u003e \u003cp\u003eAs for emerging markets, a study of the Warsaw Stock Exchange found that dividend announcements elicit an immediate market reaction consistent with the signaling hypothesis and informational content of dividends (Mrzyglod \u0026amp; Nowak, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The Amman Stock Exchange also exhibited a market reaction aligned with signaling theory, with stock prices more sensitive to dividend amounts than changes (Al-Shattarat et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In contrast, the Karachi Stock Exchange showed no significant abnormal returns around dividend announcements in 2005\u0026ndash;2009, supporting market efficiency (Khan et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, a later study found a negative reaction to dividend decreases but no positive reaction to increases after the introduction of a new capital gains tax, suggesting informational content dividends and market inefficiency (Tauseef, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies of financial markets in Africa applied similar approaches and proved the feasibility of using event study for testing market efficiency based on information content of dividend announcements. Research in Ghana (Osamoah, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Sare et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Nigeria (Campbell \u0026amp; Ohuocha, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ozo \u0026amp; Arun, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and Kenya (Owira, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found positive abnormal returns around dividend events.\u003c/p\u003e \u003cp\u003eAs for the Algerian market, very few attempts were made to study its efficiency. Khadri et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) provides an overview of Algiers Stock Exchange and uses VAR model to test the market efficiency. The findings indicate no significant effect of financial statements information on stock returns. Bedrouni \u003cem\u003eet al\u003c/em\u003e. (2022) applied event study to analyze market reaction to dividend announcements made in 2018\u0026ndash;2019 for one company (Biopharm) and found significant negative reaction in the day following the announcement, but the scope and period were too limited for making conclusions.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAlgiers Stock Exchange: An Overview\u003c/h2\u003e \u003cp\u003eAlgiers Stock Exchange (SGVB) was launched in 1997. It is one of the smallest stock exchanges globally, with market capitalization of 3.953 USD billion in April 2024 (CEIC Data, 2024). Five companies are listed, with four forming DZAIRINDEX (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The fifth company, AOM INVEST SPA, is listed on the SMEs Market, launched in 2012.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of companies traded at Algiers Stock Exchange\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompany Stock\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSector of Economy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDate of the first listing day\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of shares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarketplace\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALLIANCE ASSURANCES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e07/03/2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.287.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMain Market\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIOPHARM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePharmaceuticals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20/04/2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.245.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMain Market\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGH EL AURASSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHospitality and Tourism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e03/01/2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.000.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMain Market\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGROUPE SAIDAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePharmaceuticals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e03/01/2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.000.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMain Market\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAOM INVEST SPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHospitality and Tourism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12/12/2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.596.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSME\u0026rsquo;s Market\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: website of Algeria clearing center, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.algerieclearing.dz/index.php/fr/\u003c/span\u003e\u003cspan address=\"https://www.algerieclearing.dz/index.php/fr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn 2022, 203 transactions with shares were completed in the main market (203.2 thousand of shares traded) and 8 at the SME\u0026rsquo;s market. In 2021, the number of exchange transactions amounted to 219 transactions compared to 179 in 2020, 434 in 2019 and 440 in 2018.Trade on the equity compartment reached 131.956\u0026nbsp;million dinars (DZD) in 2022, up from 127.907\u0026nbsp;million in 2021 and 78.458\u0026nbsp;million DZD in 2020, but still below the 248.990\u0026nbsp;million recorded in 2019 and 205.797\u0026nbsp;million in 2018 (Beddaride, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The market has seen a recovery after COVID-19, but the number of investors remains small, with only 21,287 active accounts in 2022, mostly individual investors (20,272 accounts) (Commission d'Organisation et de Surveillance des Op\u0026eacute;rations de Bourse, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It is worth noting that the Algiers Stock Exchange has only three trading days per week (Sundays, Tuesdays, and Thursdays).\u003c/p\u003e \u003cp\u003eThe Algiers stock exchange is not only small but also plays a minor role in the country\u0026rsquo;s economy. The stock market capitalization to GDP ratio is less than 0.3%, compared to neighboring Tunisia's 20% and Morocco's 60% (Yatim, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite recent growth (Fig.\u0026nbsp;1), this ratio remains one of the smallest globally.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSource: (The World Bank, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFigure 1. Stock market capitalization to GDP ratio in Algeria (%)\u003c/p\u003e \u003cp\u003eLow level of participation of stock market in GDP shows the low ability of allocation and mobilizing national savings to serve the needs of the growing economy (Yatim, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). According to (Haouas et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), one of the reasons why Algeria is underperforming in terms of economic growth (considering its rich national resources) is the weak capital allocation. The country needs better infrastructure for investors.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData and Study Methodology\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData description\u003c/h2\u003e \u003cp\u003eThe primary sample of the study includes all companies listed on the main market of Algiers Stock Exchange on August 1, 2023, and those that distributed dividends to their shareholders. The fifth company, AOM INVEST SPA, was excluded due to its stock not being part of the Algiers Stock Exchange index and being traded in the SME marketplace. We collected dividend announcements from Algerian companies between 2013 and 2022 and monthly closing prices for the securities and the stock exchange index from the \u0026laquo;Il Boursa\u0026raquo; database related to the Algerian Stock Exchange. Abnormal returns were calculated using both equity returns and the equity index.\u003c/p\u003e \u003cp\u003eThe Algiers Stock Exchange website provides time series data on stock prices and the stock market index, updated three times a week (Sunday, Tuesday, Thursday). Due to the small number of stocks and missing daily closing prices, we used monthly data instead. This approach is consistent with (Fernandes and Ferreira, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) that used monthly data instead of daily or weekly data to avoid non-synchronous trading issues. As (Ellis \u0026amp; Keys, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) mentions, African markets often face data insufficiencies, including missing data, thin trading, small stock numbers etc. These issues can be addressed by giving more attention to the sample details. Given the complexity of the Algiers Stock Exchange, which has only five listed securities, we encountered missing data. To address this, we replaced missing data with the average of previous and subsequent monthly prices, as shown below:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$Average\\;share\\;price=\\frac{{{P_{t - 1}}+{P_{t+1}}}}{2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e \u003cp\u003ewhere P\u003csub\u003et\u0026minus;1\u003c/sub\u003e denotes share price at month \u003cem\u003et-1\u003c/em\u003e; P\u003csub\u003et+1\u003c/sub\u003e \u0026ndash; share price at month t\u0026thinsp;+\u0026thinsp;1.\u003c/p\u003e \u003cp\u003eTo address insufficient data, we conducted individual event studies to analyze the impact of dividends on stock prices. For each listed company, we required specific criteria to determine abnormal returns around the event date: (a) dividend announcement dates must be published on the Algiers Stock Exchange website, verified through media publications; (b) stock prices must be available 10 months before and after the event date; and (c) we selected dividend operations from 2014 to 2020 that allowed us to determine the event period and estimation period.\u003c/p\u003e \u003cp\u003eThe example of information needed to conduct an event study for each company is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDividend announcement dates (example)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlliance Assurances\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBiopharm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEl-Aurassi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSaidal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDividend Announcement Date\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e05/06/2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26/06/2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30/05/2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27/06/2019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDividend Payout Date\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16/06/2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31/07/2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e01/07/2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e05/08/2019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDividend per share (DZD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: collected by authors from \u0026lsquo;Il Borsa\u0026rsquo; database\u003c/p\u003e \u003c/div\u003e"},{"header":"Methodology","content":"\u003cp\u003eTo answer the questions posed in this paper, we apply event study. This involves several steps: data collection, preparation, and calculation of event window size; estimating normal returns; calculating abnormal returns and cumulative abnormal returns; and testing results. Since the Algiers Stock Exchange does not provide daily data, we use monthly data. We define the event date as the month of dividend announcement (t\u0026thinsp;=\u0026thinsp;0), which may differ from the dividend payment date (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This process involves carefully examining the information provided by each company to identify the specific date on which the dividend distribution to shareholders was announced. Then we determine the event window and the estimation window. The event window spans 21 months (-10; +10), starting 10 months before the announcement month and extending until 10 months after the announcement month including the announcement month (t\u0026thinsp;=\u0026thinsp;0) itself.\u003c/p\u003e \u003cp\u003eThe estimation window is used to determine the parameters of the yield-generating model, which is then employed to calculate the abnormal profitability during the event period. It precedes the event window. In our study, the length of the estimation window varies across companies due to data availability constraints. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the design of the event period and the estimation period for Alliance Assurances company:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter organizing the sample and determining the event period and estimation period dates, we calculate the sample stock return, R\u003csub\u003eit\u003c/sub\u003e, and market return, R\u003csub\u003emt\u003c/sub\u003e, as follows.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${R_{it}}=\\frac{{S{P_{it}} - S{P_{it - 1}}}}{{S{P_{it - 1}}}},$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere R\u003csub\u003eit\u003c/sub\u003e denotes an actual return of stock i at month t; SP\u003csub\u003eit\u003c/sub\u003e and SP\u003csub\u003eit\u0026minus;1\u003c/sub\u003e are closing prices of stock i at months t and t-1;\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${R_{mt}}=\\frac{{dzairinde{x_t} - dzairinde{x_{t - 1}}}}{{dzairinde{x_{t - 1}}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e \u003cp\u003ewhere R\u003csub\u003emt\u003c/sub\u003e denotes an actual return of the Algiers Stock Exchange index (dzairindex) at month t; dzairindex\u003csub\u003et\u003c/sub\u003e - closing Algiers Stock Exchange index at month t; dzairindex\u003csub\u003et\u0026minus;1\u003c/sub\u003e - closing Algiers Stock Exchange index at month t-1.\u003c/p\u003e \u003cp\u003eThe estimation of abnormal returns for the companies in the sample is based on the market model (Fama et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1969\u003c/span\u003e), which is widely used in event studies because of its performance and simplicity. According to this model, the expected return \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\widehat {{{R_{it}}}}\\)\u003c/span\u003e\u003c/span\u003e is the result of the following simple regression equation:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\widehat {{{R_{it}}}}={\\alpha _i}+{\\beta _i}{R_{mt}}+{\\varepsilon _{it}},$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere α\u003csub\u003ei\u003c/sub\u003e, \u0026szlig;\u003csub\u003ei\u003c/sub\u003e are coefficients of the model for stock i;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varepsilon _{it}}\\)\u003c/span\u003e\u003c/span\u003e - residual error of stock i at month t.\u003c/p\u003e \u003cp\u003eAbnormal return \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{A}\\text{R}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is defined as the actual return \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{R}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e minus expected return calculated using the market model:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$A{R_{it}}={R_{it}} - \\widehat {{{R_{it}}}}.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTo analyze the price changes of securities around the event date for four companies listed on the Algiers stock exchange, we calculate the average cross-sectional monthly abnormal return as follows:\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$AA{R_t}=\\frac{1}{N}\\sum\\limits_{{i=1}}^{N} {A{R_{it}},}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere AAR\u003csub\u003et\u003c/sub\u003e denotes an average abnormal return at month t; N - the number of observations in the study sample.\u003c/p\u003e \u003cp\u003eTo gauge the comprehensive effect of the examined event over a specific time frame (event window), we can aggregate individual abnormal returns into a cumulative abnormal return. It represents the cumulative impact of all abnormal returns. The cumulative abnormal return (CAR) is calculated as follows:\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$CA{R_{it}}=\\sum\\limits_{{t={t_1}}}^{{{t_2}}} {A{R_{it}}.}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFinally, cumulative average abnormal returns (\u003cem\u003eCAAR\u003c/em\u003e) are calculated as follows:\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$$CAA{R_(}_{{{t_1},{t_2})}}=\\frac{1}{N}\\sum\\limits_{{i=1}}^{N} {CA{R_{it}}} .$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eCumulative average abnormal return provides a comprehensive measure of the overall impact of an event on stock returns. In particular, if the impact of the event being studied does not occur exactly in the month of the event, then this estimator can be very important.\u003c/p\u003e \u003cp\u003eFor statistical tests that can be performed, the null hypothesis confirms that the abnormal return on month t within the event window is equal to zero: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(H0:E(A{R_t})=0.\\)\u003c/span\u003e\u003c/span\u003e This implies that the event in question has no influence on the price of the securities. Assuming that individual abnormal returns during the event period are normally distributed, the t-student parametric test, as applied by Brown and Warner (1980, 1985), is used to measure the impact that may occur on the day (month) of the event as follows:\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$$t=\\frac{{AA{R_t}}}{{\\sigma (AA{R_t})}},$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere AAR\u003csub\u003et\u003c/sub\u003e denotes an average abnormal return at month t, σ(AAR\u003csub\u003et\u003c/sub\u003e) - standard deviation of average abnormal returns.\u003c/p\u003e \u003cp\u003eThe test procedure consists in dividing the sum of the abnormal return of the event period for all securities on the root of the variance of abnormal return, during the estimation period. The preceding equation is thus reproduced according to the formula below:\u003cdiv id=\"Equ10\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ10\" name=\"EquationSource\"\u003e\n$$t=\\frac{{\\frac{1}{N}\\sum\\limits_{{i=1}}^{N} {A{R_{i0}}} }}{{\\frac{1}{N}\\sqrt {\\sum\\limits_{{i=1}}^{N} {\\frac{1}{{T - 1}}{{\\left( {A{R_{it}} - \\sum\\limits_{{t=1}}^{T} {\\frac{{A{R_{it}}}}{T}} } \\right)}^2}} } }},$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e10\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere AR\u003csub\u003ei0\u003c/sub\u003e presents an abnormal return for stock i during the month of the event (t\u0026thinsp;=\u0026thinsp;0); T \u0026ndash; the length of the estimation period.\u003c/p\u003e \u003cp\u003eThe second statistical test assesses the overall effect of dividend announcements on the profitability of the securities. This t-test can be applied to one or more sub-periods within the event window, or to the full event period (-10, + 10). To determine the significance of the cumulative average abnormal return (CAAR), we use the following test:\u003cdiv id=\"Equ11\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ11\" name=\"EquationSource\"\u003e\n$$t=\\frac{{CAA{R_{{T_1},{T_2}}}}}{{{{\\left( {{T_2} - {T_1}+1} \\right)}^{{1 \\mathord{\\left/ {\\vphantom {1 2}} \\right. \\kern-0pt} 2}}}\\sigma (AA{R_t})}},$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e11\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e denote lower and upper limits of the cumulative period.\u003c/p\u003e \u003cp\u003eIn addition, we utilize the Beaver test (Beaver, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1968\u003c/span\u003e), which complements t-statistic tests that depend on models generating returns. This test assesses the magnitude of variations (abnormal returns) experienced by a security due to the event under examination. The Beaver test applies to all securities in the sample and consists in transforming abnormal returns without considering their signs. This transformation involves calculating the square of each security\u0026rsquo;s abnormal return. The test is expressed as a ratio:\u003cdiv id=\"Equ12\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ12\" name=\"EquationSource\"\u003e\n$${U_{it}}=\\frac{{{{\\left( {A{R_{it}}} \\right)}^2}}}{{{{\\left( {\\sigma _{{it}}^{2}\\left( {1+(\\frac{1}{k})+\\frac{{{{({R_{mt}} - \\overline {{{R_m}}} )}^2}}}{{\\sum\\nolimits_{1}^{k} {{{({R_{mk}} - \\overline {{{R_m}}} )}^2}} }}} \\right)} \\right)}^{{\\raise0.7ex\\hbox{$1$} \\!\\mathord{\\left/ {\\vphantom {1 2}}\\right.\\kern-0pt}\\!\\lower0.7ex\\hbox{$2$}}}}}},$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e12\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\overline {{{R_m}}}\\)\u003c/span\u003e\u003c/span\u003e is an average market return calculated for the estimated period; k - number of observations in the estimation period; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{{\\sigma\\:}}_{\\text{i}\\text{t}}}^{2}\\)\u003c/span\u003e\u003c/span\u003e - variance of abnormal return for stock i.\u003c/p\u003e \u003cp\u003eIf U\u003csub\u003eit\u003c/sub\u003e=1, it indicates that the variance observed during the event period is the same as that calculated during the estimation period and that the share price reaction within the event window does not differ significantly from a normal one. If \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{U}}_{\\text{i}\\text{t}}\\:\u0026gt;1\\)\u003c/span\u003e\u003c/span\u003e this means that the abnormal return is higher than normal and shows a significant reaction. If \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{U}}_{\\text{i}\\text{t}}\\:\u0026lt;1\\)\u003c/span\u003e\u003c/span\u003e, then the abnormal return is lower than normal, and the market reaction to the announcement of the event is insignificant.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows abnormal returns (AR) for four companies within event windows (-10; +10) around the month of their dividend announcements. For Alliance Assurances, we observe a positive but insignificant reaction at the month of announcement (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t=0)\\)\u003c/span\u003e\u003c/span\u003e. Almost half of abnormal returns in the event window are significant. EGH El Aurassi and Biopharm cause negative reactions at the month of announcement (-1.70% and \u0026minus;\u0026thinsp;7.50%, respectively), with Biopharm's being significant at the 1% level. Saidal\u0026rsquo;s returns show a significant reaction of -8.69% only once, eight months after the announcement. These results indicate that the Algerian market reacted to dividend announcements.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary results for abnormal returns around dividend announcement month\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAlliance Assurances\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eEGH El Aurassi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eBiopharm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eSaidal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003et-statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et-statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003et-statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003et-statistics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.9201\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.7596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.0312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-1.0995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.9722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.1490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.0205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.7221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.0126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.4419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.6667\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.1481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.2675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.4049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.7274\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.2550\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.9388\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.6186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.0078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.2732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.2810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.0392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-1.3783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.8288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.2127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.0049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.1727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.5761\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.1055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.0037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.1292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.6274\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.1853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.0175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.6172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.6627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0480\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.1277\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.0056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.1970\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.4806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.0068\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.0224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.7900\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.1529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.7701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.0234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.8254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.2535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.0400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-1.4080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.4543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.3097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.1212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.1124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.2450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.0047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.1658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.0051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.1798\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.0869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-3.0591\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.9896\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.0140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.4929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.5719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.5664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*, **, *** denotes significance at 10%, 5%, and 1% level consequently.\u003c/p\u003e \u003cp\u003eSource: authors\u0026rsquo; calculations\u003c/p\u003e \u003cp\u003eFigure 3 provides visual evidence of market reaction to companies\u0026rsquo; dividend announcements, highlighting peaks in the abnormal return curves. EGH El Aurassi and Biopharm show peaks. For EGH El Aurassi, the peak occurred during the first month after the announcement date. For Biopharm, the peak was negative and occurred in the second month after the announcement date.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSource: authors\u0026rsquo; calculations\u003c/p\u003e \u003cp\u003eFigure 3. Abnormal returns around the month of dividend announcement for all companies\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the minimum, maximum, and average of abnormal returns (AAR) for the event period. The average abnormal returns are statistically insignificant for the entire event period. Upon examining Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, we observe that approximately half of the values are positive but near zero and have no statistical significance. The largest rate of average abnormal return (1.37%) occurred during the second month before the dividend announcement date.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary results for abnormal returns around dividend announcement month \u003cem\u003enear here\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin AR\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax AR\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAAR\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eσ(AR\u003csub\u003et\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et-statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.2749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,0365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.5079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.2194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.3831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.4186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.9065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.1947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.5613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.5560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*, **, *** denotes significance at the 10%, 5%, and 1% level consequently.\u003c/p\u003e \u003cp\u003eSource: authors\u0026rsquo; calculations\u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e we present the results of the CAR and the CAAR calculation, which show the overall stocks behavior around the date of the dividend announcement for all companies in the sample. For the CAR, we observe that three of the four rates are positive at the announcement date (t\u0026thinsp;=\u0026thinsp;0), with Biopharm recording the highest rate of 3.53% and El Aurassi recording the lowest rate of -1.69%. Only Biopharm demonstrated a positive CAR during all event windows, with the highest rate of 33.61% for the event window (-10,0)\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:.\\)\u003c/span\u003e\u003c/span\u003e Fig.\u0026nbsp;4 clearly shows the peak for all four companies during the dividend announcement month (t\u0026thinsp;=\u0026thinsp;0).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCumulative abnormal return and cumulative average abnormal return around dividend announcement\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWindow\u003c/p\u003e \u003cp\u003e(Event Period)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eCAR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlliance Assurances\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEGH\u003c/p\u003e \u003cp\u003eEl Aurassi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBiopharm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSaidal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCAAR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(-10,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(-5,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(-4,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(-3,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(-2,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(-1,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(0,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(0,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(0,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(0,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(0,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(0,10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.1636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSource: authors\u0026rsquo; calculations\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSource: authors\u0026rsquo; calculations\u003c/p\u003e \u003cp\u003eFigure 4. Cumulative abnormal return around dividend announcement month \u003cem\u003enear here\u003c/em\u003e\u003c/p\u003e \u003cp\u003eRegarding the CAAR, which assesses the total sample\u0026rsquo;s response to the dividend announcements, we see that the effect was positive for five event windows preceding the month of the announcement, namely (-10,0), (-5,0), (-4,0), (-3,0), and (-2,0). The effect was also positive during the announcement date and the subsequent window. However, it turned negative during the periods following the announcement, as Fig.\u0026nbsp;5 illustrates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSource: authors\u0026rsquo; calculations.\u003c/p\u003e \u003cp\u003eFigure 5. Cumulative average abnormal return around dividend announcement month \u003cem\u003enear here\u003c/em\u003e\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the results of Beaver test. It is observed that all values during the event period are less than one and sometimes equal to zero. This means that the abnormal return is lower than the normal return calculated during the estimation period and the market reaction was weak and insignificant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Beaver test \u003cem\u003enear here\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlliance Assurances\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEGH\u003c/p\u003e \u003cp\u003eEl Aurassi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBiopharm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSaidal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,0117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e 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colname=\"c2\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2598\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding Alliance Assurances, we note that it experienced a weak but statistically significant abnormal return before the dividend announcement date, which could be attributed to the leak of information or rumors. On the other hand, we observe that abnormal returns persisted for a longer period, and the market did not rapidly correct the share price. We find that EGH El Aurassi shareholders did not receive statistically significant abnormal returns. This means that dividend announcement had no effect on the company's shares. This analysis allows us to conclude that the efficiency of the Algiers Stock Exchange was very low, meaning it has poor information efficiency and barely approaches weak form efficiency rather than a semi-strong form efficiency. Finally, we observed that Biopharm and Saidal had a somewhat delayed reaction, which could be due to a slower market response in incorporating dividend announcements information into stock prices.\u003c/p\u003e \u003cp\u003eThough we do not have enough empirical evidence on Algiers stock markets, our results are consistent with (Khadri et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) regarding Algerian market and support results obtained for other markets in Africa (Campbell \u0026amp; Ohuocha, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ozo \u0026amp; Arun, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sare et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) in terms of informational content of dividends and market inefficiency.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis article presents an empirical study on the Algerian financial market's response to dividend announcements. The study aims to answer two key questions: how stocks react to dividend announcements and whether the Algerian financial market has informational efficiency in its semi-strong form. We consider this study as one of the first empirical studies testing the Algerian market.\u003c/p\u003e \u003cp\u003eThe results are mixed. Generally, average abnormal returns were weak, but we observed a statistical significance for abnormal returns values of Alliance Assurances, and mostly positive returns for Biopharm shares. Cumulative abnormal returns peaked for each stock at the date of the event. However, the results were inconsistent, leading us to conclude that the Algerian financial market is not efficient in the semi-strong form.\u003c/p\u003e \u003cp\u003eThe study has limitations, the most serious is the small number of stock and thin trading that enabled us to use monthly prices and long event windows. The low number of events that drive share prices also restricted the choice of methods and impacted the results. Nevertheless, the sample expansion by testing market reaction to information about other events, such as the publication of annual reports, information on CEO changes etc., could be an interesting direction for further research.\u003c/p\u003e \u003cp\u003eSumming up, our study highlights the inconsistency between Algeria\u0026rsquo;s economic growth and its small and inefficient financial market and emphasizes the need for government actions to address this problem. This inconsistency is important not only for Algeria, however. Many African capital markets are underperforming and need in improvement to serve the needs of growing economies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eO.B : Empirical study\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAharony, J., \u0026amp; Swary, I. (1980). Quarterly Dividend and Earnings Announcements and Stockholders\u0026apos; Returns: An Empirical Analysis. \u003cem\u003eThe Journal of Finance, 35\u003c/em\u003e(1), 1-12. doi:10.2307/2327176\u003c/li\u003e\n\u003cli\u003eAl-Shattarat, W., Atmeh, M., \u0026amp; Al-Shattarat, B. (2013). Dividend Signalling Hypothesis In Emerging Markets: More Empirical Evidence. \u003cem\u003eJournal of Applied Business Research, 29\u003c/em\u003e(2), 461-468. doi:10.19030/jabr.v29i2.7650\u003c/li\u003e\n\u003cli\u003eAsquith, P., \u0026amp; Mullins, D. (1983). 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The Signaling Effects of Dividend Change Announcements: New Evidence From Europe. \u003cem\u003eWorking Paper\u003c/em\u003e. Retrieved January 20, 2024, from https://ssrn.com/abstract=955768\u003c/li\u003e\n\u003cli\u003eWoolridge, J. (1982). The information content of dividend changes. \u003cem\u003eThe Journal of Financial Research, 5\u003c/em\u003e(3), 237-247. doi:10.1111/j.1475-6803.1982.tb00298.x\u003c/li\u003e\n\u003cli\u003eWright, J. (2000). Alternative variance-ratio tests using ranks and signs. \u003cem\u003eJournal of Business and Economic Statistics, 18\u003c/em\u003e(1), 1-9. doi:10.2307/1392131\u003c/li\u003e\n\u003cli\u003eYatim, M. (2023). The Algerian Stock Market, Perspectives and Constraints. \u003cem\u003eFinance and Markets Journal, 10\u003c/em\u003e(2), 55-70.\u003c/li\u003e\n\u003cli\u003eYoon, P., \u0026amp; Starks, L. (1995). Signaling, Investment Opportunities, and Dividend Announcements. \u003cem\u003eThe Review of Financial Studies, 8\u003c/em\u003e(4), 995-1018.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Capital market development, Event study, Financial markets in Africa, Dividend announcements, Market efficiency, Abnormal returns, Algerian financial market","lastPublishedDoi":"10.21203/rs.3.rs-5321164/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5321164/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe aim of the study is to examine the efficiency of the Algerian financial market by analyzing the reaction to dividend announcements between 2013 and 2022. Algeria represents a good example of small capital markets, with thin trading and rare events. To examine the market reaction, we modified the common approach to event study analysis by estimating abnormal returns based on monthly data. Our findings suggest that market information is distributed slowly and prices do not fully incorporate new information. These findings indicate that the financial market in Algeria does not satisfy the semi-strong form of market efficiency. The paper presents one of the first studies to investigate the efficiency of the Algerian financial market based on the response to dividend announcements.\u003c/p\u003e","manuscriptTitle":"Dividend Announcements and capital market efficiency : Evidence from the algerian stock market","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-29 14:35:47","doi":"10.21203/rs.3.rs-5321164/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":"668a60ae-45d8-4dc6-bdab-b0d60a544c7e","owner":[],"postedDate":"October 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-01T13:08:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-29 14:35:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5321164","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5321164","identity":"rs-5321164","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}