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This study suggests a new measure for a firm's operating cost flexibility. Flexible firms are less risky and, therefore, require lower stock returns. This analysis of 126,202 firm-year observations from the U.S. cross-section of stock returns finds that the new measure explains a negative significant rate of return. The new measure's impact extends beyond that of operating leverage. In addition, the new measure's impact is both statistically and economically significant, and it is sustainable for a variety of in-sample and out-of-sample robustness tests. The new findings are beneficial to researchers and practitioners alike. [ABSTRACT FROM AUTHOR]

Abstract In the field of empirical asset pricing, the challenges of high dimensionality, non-linear relationships, and interaction effects have led to the increasing popularity of machine learning (ML) methods. This study investigates the performance of ML methods when predicting different measures of stock returns from various factor models and investigates the feature importance and interaction effects among firm-specific variables and macroeconomic factors in this context. Our findings reveal that neural network models exhibit consistent performance across different stock return measures when they rely solely on firm-specific characteristic variables. However, the inclusion of macroeconomic factors from the financial market, real economic activities, and investor sentiment leads to substantial improvements in the model performance. Notably, the degree of improvement varies with the specific measures of stock returns under consideration. Furthermore, our analysis indicates that, after the inclusion of macroeconomic factors, there is a dissimilarity in model performance, variable importance, and interaction effects among macroeconomic and firm-specific variables, particularly concerning abnormal returns derived from the Fama–French three- and five-factor models compared with excess returns. This divergence is primarily attributed to the extent to which these factor models remove the variance associated with the macroeconomic variables. These findings collectively offer valuable insights into the efficacy of neural network models for stock return predictions and contribute to a deeper understanding of the intricate relationship between factor models, stock returns, and macroeconomic conditions in the domain of empirical asset pricing.

This comparative analysis of the investment performance of eight portfolio strategies in the Taiwan stock market utilizes monthly data from Taiwan-listed companies from 1990 to 2021; the portfolio for each year comprises the top 100 companies based on market capitalization. Setting aside the equally and value-weighted portfolio strategies, the six strategies remaining are grounded in the mean-variance framework, fundamental index model, predictive blends model, and single-index model, respectively. Employing the rolling-window method, we compute the following monthly out-of-sample performance metrics for the portfolios: average excess return, standard deviation, Sharpe ratio, certainty equivalent return (CER), single-year cumulative returns, and value-at-risk. Portfolio weights are determined using a five-year estimation period and remain fixed (buy and hold) during the sixth year. In addition to scrutinizing the portfolios’ performance on a yearly basis, we also assess their cumulative long-term performance over multiple years. We investigate the impact of stock market fluctuations on various investment strategies, considering periods of boom and bust as a test of robustness. We posit here that CER, rather than the Sharpe ratio, is a suitable performance measurement index. We further explore the influence of portfolio diversification on investment performance. Generally, our findings suggest an inverse relationship between investment performance and the HHI, indicating that diversification tends to enhance investment performance while mitigating investment risk. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the field of empirical asset pricing, the challenges of high dimensionality, non-linear relationships, and interaction effects have led to the increasing popularity of machine learning (ML) methods. This study investigates the performance of ML methods when predicting different measures of stock returns from various factor models and investigates the feature importance and interaction effects among firm-specific variables and macroeconomic factors in this context. Our findings reveal that neural network models exhibit consistent performance across different stock return measures when they rely solely on firm-specific characteristic variables. However, the inclusion of macroeconomic factors from the financial market, real economic activities, and investor sentiment leads to substantial improvements in the model performance. Notably, the degree of improvement varies with the specific measures of stock returns under consideration. Furthermore, our analysis indicates that, after the inclusion of macroeconomic factors, there is a dissimilarity in model performance, variable importance, and interaction effects among macroeconomic and firm-specific variables, particularly concerning abnormal returns derived from the Fama–French three- and five-factor models compared with excess returns. This divergence is primarily attributed to the extent to which these factor models remove the variance associated with the macroeconomic variables. These findings collectively offer valuable insights into the efficacy of neural network models for stock return predictions and contribute to a deeper understanding of the intricate relationship between factor models, stock returns, and macroeconomic conditions in the domain of empirical asset pricing. [ABSTRACT FROM AUTHOR]

AbstractInvestor sentiment is the result of irrational speculations about the future asset values driven by the market participants. Though scholarly works on investor sentiment are evolving in both developed and emerging markets, the literature in the Indian context is relatively modest. To fill this void, the study aims to examine sentiment-return relations based on a sectoral analysis framework. The study considered Nifty 500 and sectoral indices return such as Automobile, Information Technology, Metal, Fast-Moving Consumer Goods, and Public Sector Undertakings. To measure investor sentiment a unique sentiment index (INDex) using seven indirect proxy sentiment indicators suitable for the Indian stock market is proposed. To test the framed hypothesis, the study employs Principal Component Analysis and OLS regression. The results uncover that there exists a strong significant positive sentiment effect on Nifty 500 and selected sectoral indices return. The findings assist academicians, practitioners, investors, and policymakers in enhancing their understanding of the sentiment-return nexus in the Indian stock market and thereby guide them to ensure caution while making investment decisions.

This study examines the relationship between intangible capital (IC) and stock performance during the two recent crisis periods, the GFC and COVID-19. By categorizing IC into Knowledge Capital (KC) and Organizational Capital (OC), we analyze the impact of each capital on the crisis return in the manufacturing sector. The results show that a greater KC and OC are significantly associated with higher crisis returns during both periods. In addition, we find evidence that generalist CEOs strengthen this relationship while specialist CEOs do not. Within firms led by a generalist CEO, the CEO's tenure positively moderates the association between each factor of intangible capital and crisis period returns. This study emphasizes the pivotal role of KC and OC as a protective buffer against external shocks, particularly when the market pays more attention to corporate sustainability. [ABSTRACT FROM AUTHOR]

The objective of this study is to determine the impact of variable volatility of stock price, trading frequency, and share returns on bid-ask spreads either partially or simultaneously for LQ45 index member companies. The quantitative method is the method used in the study, the population in this study was 45 companies listed in the LK45 index, whereas, for the sample this study used 8 companies with the observation period 2019-2022, the findings in this research had a negative and significant effect on the price-ask variable bid spread, while for Frequency Trading and Share Return Variables did not affect the bid-ash spread it was marked by the reevaluation value of trading currency and stock return variability, while to test jointly on the entire variable value of the price of stocks, frequencies, and trade per share significantly affected the return of stocks in terms of the value of bid-assets being divided. This study concludes that by understanding the impact of stock price volatility and trading frequency, investors can develop better strategies to manage risk and maximize profits. Keywords: Stock Price Volatility; Trading Frequency; Stock Return; Bid-ask spread

The aim of this research is to analyze and test whether there is an influence of company size and profitability on stock returns with moderation proxied by inflation. Quantitative methods were used in this research. Manufacturing companies in the goods and consumption sectors were used as the population in the research, the data of which was obtained from the 2019-202 BEI. In collecting samples, this research used criteria in determining the sample so that 54 samples were obtained based on the specified criteria. This research data was analyzed using MRA analysis version 26 with SPSS tools. The results of the research prove that profitability has no effect on stock returns, stock returns are able to influence inflation and company size, inflation is able to strengthen the influence of company size and profitability on stock returns.

In this paper, I examine the relationship of taxation with performance and risk with the usage of a sample of 76 non-financial companies traded on the Athens Stock Exchange. The period covered by my study spans from 2018 to 2022, while correlation and panel data analysis is conducted. Both financial performance and stock return are considered, while risk concerns the volatility of the companies' share prices. The explanatory variables used concern figures reported both in the balance sheet and the profit and loss statement and include net deferred tax, deferred tax asset, deferred tax liability, total tax expense/revenue, income tax, and deferred tax expense/revenue. The empirical results reveal a positive relationship of financial performance with net deferred tax, total tax expense/revenue, income tax and deferred tax expense/revenue. Moreover, deferred tax asset is found to affect financial performance in a negative fashion, while deferred tax liability bears a positive influence on financial performance. The opposite relationships with deferred tax asset and deferred tax liability are detected in the case of stock return and risk. Finally, evidence of a negative relationship of total tax and income tax with stock risk is obtained.

This study suggests a new measure for a firm’s operating cost flexibility. Flexible firms are less risky and, therefore, require lower stock returns. This analysis of 126,202 firm-year observations from the U.S. cross-section of stock returns finds that the new measure explains a negative significant rate of return. The new measure’s impact extends beyond that of operating leverage. In addition, the new measure’s impact is both statistically and economically significant, and it is sustainable for a variety of in-sample and out-of-sample robustness tests. The new findings are beneficial to researchers and practitioners alike.

The aim of the article is to analyze graphs of weakly correlated stocks. Characteristics of these graphs such as number of edges, histogram of vertices degrees, degrees distribution, hubs and cliques are investigated. Pearson correlation and Kendall correlation are used to construct these graphs. Graphs constructed by the traditional procedure and by Holm procedure are compared. Obtained results are exemplified on the data of French stock market. In particular it is shown that reliable maximum cliques contain very few nodes despite the large number of edges in the graph of weakly correlated stocks. [ABSTRACT FROM AUTHOR]

In this paper, I examine the relationship of taxation with performance and risk with the usage of a sample of 76 non-financial companies traded on the Athens Stock Exchange. The period covered by my study spans from 2018 to 2022, while correlation and panel data analysis is conducted. Both financial performance and stock return are considered, while risk concerns the volatility of the companies' share prices. The explanatory variables used concern figures reported both in the balance sheet and the profit and loss statement and include net deferred tax, deferred tax asset, deferred tax liability, total tax expense/revenue, income tax, and deferred tax expense/revenue. The empirical results reveal a positive relationship of financial performance with net deferred tax, total tax expense/revenue, income tax and deferred tax expense/revenue. Moreover, deferred tax asset is found to affect financial performance in a negative fashion, while deferred tax liability bears a positive influence on financial performance. The opposite relationships with deferred tax asset and deferred tax liability are detected in the case of stock return and risk. Finally, evidence of a negative relationship of total tax and income tax with stock risk is obtained. [ABSTRACT FROM AUTHOR]

Purpose -- This study aims to investigate the influence of macroeconomic factors, including economic growth, inflation, the rupiah exchange rate, and interest rates, as well as corporate financial performance indicators such as leverage and profitability, on the stock returns of Indonesian Property and Real Estate Sub-Sector companies. Method -- The research data consists of annual data obtained from the Indonesia Stock Exchange, the Indonesia Central Bureau of Statistics, and Bank Indonesia for the period from 2017 to 2021. The research population comprises 36 Property and Real Estate companies listed on the Indonesia Stock Exchange. Out of these, 28 companies meet the sample requirements. The analysis employs panel data regression to examine the data. Result -- The results indicate that inflation and exchange rates have a negative and statistically significant impact on stock returns. On the other hand, economic growth, interest rates, return on equity, and the debt-to-equity ratio exhibit a positive influence on stock returns, although these effects are not statistically significant. Contribution -- This study's distinctive contribution is its focused analysis of the Property and Real Estate Sub-Sector in Indonesia. It stands out by exploring the intricate relationship between macroeconomic factors and financial performance variables concerning the stock returns of companies within this sub-sector. This nuanced approach provides valuable insights into a specific industry within the Indonesian market, shedding light on previously unexplored dynamics and contributing to a deeper understanding of how these factors affect stock returns in this context. [ABSTRACT FROM AUTHOR]

This paper employs a machine learning approach to capture firm‐pair production similarity, which depicts how firms' production processes resemble each other using textual data in corporate MD&As. We show that production‐linked firms' average return has strong predictive power on focal firm's future stock return. A hedging portfolio yields an annualised return of 11.69%, which cannot be subsumed by existing factor models. For mechanism tests, we find that the main findings are stronger in firms with higher information asymmetry and higher costs of arbitrage. The production‐linkage measure also predicts future unexpected earnings, suggesting it possibly includes valuable information on firm fundamentals. [ABSTRACT FROM AUTHOR]

This study examines the impact of the flight-to-liquidity (FTL) phenomenon on the excess stock return by applying the previously developed generalised method of moments (GMM) framework. For this purpose, we use the data covering the period from 2004 to 2018 for 122 public companies listed on the Pakistan Stock Exchange (PSX). This study uses six proxies to measure the expected and unexpected illiquidity. The empirical investigation reveals that expected and unexpected illiquidities greatly influence smaller firms more notably than larger ones, which induces FTL phenomena into the market. Moreover, a FTL phenomenon triggered the Pakistani equity market during the financial crisis, when a significant decline appeared and the less liquid stocks were strongly affected. The results reveal that FTL risk is priced in the Pakistan equity market, making large stocks relatively more attractive in times of dire liquidity. These findings further suggest that the market participants in the Pakistan equity market, including policymakers, regulators and investors, should not ignore FTL phenomena while designing their portfolios. [ABSTRACT FROM AUTHOR]

This study investigates the unsymmetrical effect from air quality (AQ) to stock return (SR) in China's different industries. Depending on quantile-on-quantile (QQ) test, it draws the important results in following aspects. For tourism, iron and steel, and automobile industries, their coefficient values between AQ and SR turn into negative from positive with deteriorating AQ. Conversely, the coefficients in the wind power, hydro power, thermal power, environmental protection, and medical equipment industries turn positive from negative. Some contributions are thus drawn when compared to existing literatures. Government industrial policy is regarded as an important supplement in explaining mechanism from AQ to SR, except investor sentiment. Industrial heterogeneity is seriously treated in this paper due to different industries have different responses to AQ. Besides, the QQ test is able to capture nexus between AQ and SR in specific quantiles through embedding non-parametric estimation into conventional quantile approach. Therefore, investors should avoid biased trading decisions under different air qualities. Meanwhile, government intervention is paid special attention when appearing serious air pollution. [ABSTRACT FROM AUTHOR]

This study aims to predict Taiwan stock returns through gold and crude oil futures prices using monthly data from TAIEX and 19 stock sector indexes from January 1996 to December 2020. By using a 60-month rolling window horizon, we compare the forecast performances of various regression models, where the forecast performances are measured by MAE (mean absolute error) and R2 OS (out-of-sample R-square). In addition to using spot returns and the first principal component of futures returns on gold and crude oil, five traditional financial variables (dividend to price ratio, earnings to price ratio, market price to book value ratio, long-term yield, and short-term yield) are added to the regression model to explain and predict stock returns. Given that the regression models have included these traditional financial variables, the empirical results reveal that adding gold or crude oil price information to the model substantially improves its explanatory ability. Additionally, except during periods of high stock returns, the forecast ability of crude oil price information on stock returns is significantly better than traditional forecast variables. Furthermore, although gold prices are not as accurate as crude oil prices in predicting stock returns, their predictive capabilities are often better than the traditional financial variables. [ABSTRACT FROM AUTHOR]

Investors’ attitudes were severely altered by the Covid-19 crisis, which had an intolerable effect on volatile returns in Nigerian stock market and other stock markets worldwide. Psychological trauma was experienced by stock investors due to the inability to foresee returns following the worldwide shock of Covid-19 and the subsequent drop in stock investments brought on by the unpredictability of macroeconomic behavior. This study analyzed the link effect of investor psyche on stock returns in Nigeria between March 2019 and December 2022. For this research, Sentiment Index (SMI) model was employed for the connection between investors psyche and returns on the Nigerian stock market. It was found that, investors psyche during Covid-19 pandemic negatively affected stock returns, resulting from negative concerns about their survival and financial security than the stock market, thus triggered sharp decline in stock investment. Thus, study concluded that Covid-19 outbreak impacted stock returns negatively. The research recommends that the Nigerian stock exchange commission should increase disclosure of market information, which is related to the magnitude and potentials of stock returns and degree of volatility at all times. The concerned volatility and potentials during the Covid-19 era were also influential to enhance investors’ confidence.

In this study, the explanatory power of the macro variables in relation to the variation of stock returns has been discussed in terms of the economy of the USA. To make an analysis of the cross-section of the stock returns, 131 Macroeconomic variables between 1964 and 2007 have been put into use. Summing up the information in 131 monthly series, dynamic factor analysis is used to take out 8 potential factors. So that the pragmatic presentation of the factor model can be measured, Fama-Macbeth’s test procedure of two phases is applied. In addition to the variables included in the literature such as market risk factor, size factor, value factor, and momentum factors, it is found that the macro factors are highly influential on the explanation of the common variation in U.S stock returns. The tests stated above have been performed by means of Fama French 49 industry portfolios, apart from Fama French 100 portfolios that have been formed on size and book. Furthermore, the factor model is established and intended for certain periods of boom and recession. The relations established between latent factors and stock returns appear to be unimportant during the downturn periods.

The decisions of the central bank, as the most important monetary institution of any country, have an impact on the companies whose shares are bought and sold in the stock exchange, and this market is dependent on the policies of the central bank. Considering the importance of this issue, in the present study, the effect of central bank independence on the stock returns of active industries in the Tehran Stock Exchange during the period of 1370 to 1399 will be investigated by using the ARDL Bounding Test. Results of the model indicate that increasing the degree of central bank independence increases the stock returns of active industries in the Tehran Stock Exchange. Also, the findings show that the impact of GDP growth, interest rate, oil price and turnover ratio on stock returns is positive and on the other hand, inflation rate and real exchange rate have a negative effect on stock returns.

Despite the increased interest in socially and environmentally responsible investments, there is a lack of consensus about the effects they exert on companies’ financial performance. The paper examines the reaction of Russian companies’ stock prices to the news about ESG investments made by these companies. The theoretical basis is stakeholder theory suggesting that firms can achieve long-term success solely by protecting the interests of all their stakeholders – not only shareholders, but also employees, local communities, and future generations. To implement the research, we use the event study on a sample of 202 news items about ESG investments of 11 Russian companies topping the ESG-rating by RA-Expert agency. Data were collected from news search engine Factiva and Yahoo!Finance. The data sample include news and stock prices over the period of 2019–2022. According to the research findings, news about Russian companies’ investments in environmental projects, solution of social problems and corporate governance issues do not lead to a rise in stock returns for these companies. Thus, Russian investors do not consider ESG projects and practices as an important decision-making criterion that increases the investment attractiveness of companies.

The book-to-market ratio is known as an anomaly variable in the financial literature. This variable has a high explanatory power in predicting the returns of companies in different capital markets across world; But understanding why it has the power to explain is still a matter of debate. In this study, we seek a clear understanding of the explanatory power of the ratio of book-to-market ratio in explaining the annual return of cross-sectional data of stocks on the Tehran Stock Exchange. Book value can be divided into two parts: retained earnings and contributed capital, which have different economic meanings for readers of financial statements. Our hypothesis is that the predictive power of the book-to-market ratio arises from a component of book value that could be a good proxy for underlying earnings yield. Using the method of Fama and Macbeth (1973), we regress the annual return of cross-sectional data of companies listed on the Tehran Stock Exchange for the years 2001-2019 on the ratio of book-to-market ratio and its two components. Neither component of book-to-market ratio could eliminate the predictive power of this ratio; however, the ratio of retained Earnings-to-market ratio could show predictive power along with the book-to-market ratio. We contribute to the literature by providing additional evidence from Tehran's Stock Exchange.1- IntroductionThe book-to-market ratio is known as an anomaly variable in the financial literature. It has appeared as a key explanatory variable with high explanatory power in predicting the returns of firms in capital markets across the world, however, understanding the mechanism through which this financial factor functions and its origin of the explanatory power is still a matter of research debates. Empirical researches on the returns and “book to market value” can be divided into two strands. The first group aims to examine the existence of abnormal returns on the ratio of "book to market value" in the stock markets. This stream of works aim to answer the question of whether the "book to market value" is able to predict companies' returns in capital markets or the returns is caused by other sources including random noise. Rosenberg et al. (1985) show, for instance, that in the US capital market, the strategy of the "book to market value" can yield abnormal returns for investors. In terms of this strategy, at the beginning of each month, the shares with a high "book to market value" are bought and the shares that have a low "book to market value" ratio are sold. A relationship between the ratio and average stock returns for the period 1981-1981 in the capital markets of Switzerland, France, Germany and the United Kingdom has also been observed by Coppole, Rollie and Sharp (1992). The second stream of studies on the "book to market value" seeks to understand the cause of its explanatory power. This issue is an active research area and is still subject of discussions and has been studied from various aspects. One of the most highly cited of them is Fama and French (1993), which attributes high returns in stocks with a higher magnitude of "book to market value", to higher systematic risk. In contrast, Daniel and Titman (1997) introduces the hypothesis of equity characteristics and by providing empirical evidence argues that the returns premia on high book-to-market stocks does not arises because of the co-movements of these stocks with pervasive factors. It is the characteristics of the share rather than the covariance structure of returns that appear to explain the cross-sectional variation in stock returns. So, these are not associated with greater risk tolerance. Ball, Gerakos, Linnaeus, and Nikolaev (2020) examines the "book to market value" through its components (retained earnings and contributed capital) in the US capital market. He argues that the ability of "book to market value" to predict the cross-sectional returns is not because of its intrinsic information contents, but it appears as an appropriate proxy for the actual profitability of the firms, because, the retained earnings component of the book value of equity includes the accumulation and, hence, the averaging of past earnings, instead the contributed capital-to-market has no predictive power. HypothesesWe contribute to the literature by providing additional evidence from Tehran's Stock Exchange. Our study aims to provide further evidence to clarify explanatory power of the ratio in predicting the variations of annual returns in cross-sectional data for stocks in the Tehran Stock Exchange. Our hypothesis is that the predictive power of the book-to-market ratio arises from a component of book value that could be an appropriate proxy for underlying earnings yield. Data and Identification methodologyWe use the annual returns and financial statements of all shares traded from the beginning of 2001 to the end of 2020 in Tehran Stock Exchange. Annual returns are calculated from price data recorded and reported in the “tseclient” software and accounting data are downloaded from “codal.ir” website. In this research, financial companies listed in the TSE have not been included in our working sample due to their special nature. Because, by nature of their activities, they have high financial leverage, which is normal for companies active in the financial field. The characteristics might be interpreted as a financially critical situation, whereas, the it is not so for firm that are active in financial fields. The information extracted from the financial statements is matched with the annual return of 1 month after the end of the financial year. The reason for this identification strategy is to make sure that the published financial information affects the share price. For example, if the company's financial year is at the end of March, we will assume that this information was available to the public at the end of April. Findings Following the statistical method of Fama and Macbeth (1973), we regress the annual return for cross-sectional data of companies listed on the Tehran Stock Exchange over the years 2001-2019 on the ratio of book-to-market ratio and its two components as well. Neither component of book-to-market ratio could eliminate the predictive power of book-to-market; however, the ratio of retained Earnings-to-market ratio could show predictive power along with the book-to-market ratio. Table (1) reports the Fama and Macbeth (1973) regressions in which, outcome of interest is returns and determinants of the regression are the log of "Book to Market Value", log of " Retained Earnings to the Market Value " and log of "Contributed Capital to Market value". We include a few controlling variables that are identified theoretically as determinants of returns.Table(1): Contributed Capital and Retained Earnings in the Fama and Macbeth Regression(1)(2)(3)(4)(5)(6)Variables-0.129**-0.128**-0.116**-0.0901**-0.126**-0.103**Log(Market Value)(-2.680)(-2.762)(-2.492)(-2.474)(-2.257)(-2.228)0.498** 0.210** 0.508** Log( Book-to-Maket)(2.744) (2.471) (2.342) 10.53***8.557** 9.914**Log(Retained Earnings to market Value) (2.992)(2.426) (2.890) 0.371***0.004060.255***Log(Contributed Capital) (3.446)(0.0438)(3.343) 0.619***0.560*** 0.415**Binary if profit>0 (3.382)(3.058) (2.256)2.973**-19.64***-15.47**2.429**2.959**-18.34**Constant(2.731)(-2.907)(-2.272)(2.825)(2.534)(-2.806) 3,7943,7943,7943,7943,7943,794#OBS0.1210.1440.1880.0990.1350.189R-Square212121212121# Groups*** p

Stock return is the result of profit on a stock investment made by investors. There are 2 factors that affect stock returns, namely: external and internal. This study aims to obtain empirical evidence regarding Return on Equity (ROE) and Gross Domestic Product (GDP) on stock returns and test the Coprorate Social Responsibility (CSR) factor in moderating the effect of ROE and GDP on stock returns. The population in this research is food and beverage companies listed on the Indonesia Stock Exchange for the period 2017-2021. The sample in this study was taken using purposive sampling technique and census technique. Based on the criteria and sampling techniques that have been determined, there are 12 companies samples and 60 units of analysis. The data analysis technique used is multiple linear regression analysis and Moderating Regression Analysis (MRA). The results of the analysis show that ROE has a positive and significant effect on stock returns and GDP has not significant effect. CSR significantly moderates the effect of ROE and GDP on stock returns. The theoretical implications of this study confirm the signal theory and stakeholder theory based on the test results that have been conducted. The practical implications of this research can provide knowledge that can be used as a basis for consideration for company management and other parties.

Stock return is an indicator of banking performance in Indonesia. This study aims to empirically test non-performing loans and loan deposit ratios on stock returns mediated by return on assets at commercial banks listed on the Indonesian stock exchange in 2016-2018. The sample used in this study was 20 bank companies that met predetermined criteria. The data that has been collected is then analyzed using Path analysis to test the proposed hypothesis. The findings of this study indicate that non-performing loans and loan deposit ratios each have a significant effect on stock returns and are mediated by return on assets. Based on these findings, it is recommended that banking companies, in managing financial ratios, must run more optimally to maximize stock returns obtained by banks. Non-performing loan, loan deposit ratio is a bank's financial ratio to assess its performance. These financial ratios have a purpose to determine the bank's ability to optimize the level of lending to the public, generate profits from the activities carried out and reject the risks from its operational activities. For banking companies, the findings of this study can be used in policy-making related to the delivery of information on bank performance reports to investors.

This research aimed to identify how to examine and analyze the effects of foreign direct investment and profitability on the stock returns in manufacturing companies registered on IDX during 2016 – 2018. The total population was 91 companies registered and filtered into 32 registered companies according to the sample criteria and analyzed through Eviews 10 software. The research result referred that the foreign direct investment received by multinational companies could not yet affect stock return directly. Also, foreign direct investment was not able to influence the return on asset (ROA), but it was able to affect the return on equity (ROE). Further, ROA was not able to affect the increase of stock returns, while ROE was able to affect the rise of stock returns. Another research finding showed that ROE was the only one that could be a mediation variable in the relationship between foreign direct investment affecting stock returns. At the same time, ROA could not be a mediation variable. For the next studies, the researchers suggested exploring the other financial performance variables suited to foreign direct investment to affect the stock returns. In practice implication from this research, the investors must see how strong the capital owned by a company that accepts foreign direct investment and the relation, how the capital they receive can improve or maintain the company's financial performance within a certain period of time.

The aim of the present study is to examine the effect of the events related to the “Panama leaks” on the stock market in Pakistan. For this purpose, we have analyzed a total of 24 events related to the “Panama leaks” in Pakistan. These events are further divided into three broad categories i.e. 8 events are related to the news/statements by the opposition parties, 8 events are related to news/statements by Sharif’s family/sitting governments, and 8 events are related to the news/statements by the Apex court in Pakistan. The analyzed period begins on the 1st of March 2015 and has lasted until the 30th of July 2018. This research applies the event study methodology, and overall findings show that “Panama leaks-related news has a significant impact on stock returns in Pakistan. What is more, all the news related to the Apex court in the Panama leaks show a significant positive impact on stock returns. This study provides a guideline to investors, regulators, policymakers, and other stakeholders by proving that all the news related to the “Panama leaks” have a significant impact on stock returns in Pakistan. Future researchers can extend this study by analyzing the impact of the “Panama leaks” on stock returns in all the countries to which the “Panama leaks” are connected.

AbstractThe study uses wavelet power spectrum and wavelet coherence transformation methodologies to examine how geopolitical risk affected the returns on stocks, oil, and gold during the GFC, COVID-19, and Russia-Ukraine war-three disruptive events that affected the world’s financial markets. For better diversification benefits during the turbulent times, we further investigate the degree of co-movement in frequency and time domains. We observe that GPR has high variations during Russia-Ukraine war period compared to COVID-19 period and is shown to have least variation during the GFC period. WTI crude oil and DJGI indexes are observed to have high variations during GFC, and COVID-19 periods followed by Russia-Ukraine war. We further observe that GOLD offers better diversification opportunity as well as leading movement against WTI and DJGI during disruptive events in financial markets. The results provide new understanding of how geopolitical risk affects financial assets for international investors, fund managers, and regulators, which would further aid to find risky and safer haven possibilities during the turmoil periods.

This study aims to investigate investor sentiment and its effect on the industrial sector in Indonesia. In the study, investor sentiment was extracted from data obtained from search engines. Then the data were used to see how the sentiment affected the stock return in each listed industrial company. Fifteen industrial sector companies listed on the stock exchange were selected; each was analyzed for their search volume and the effect on stock returns using panel data regression from March 2020 to April 2022. The result shows that investor sentiment affects the level of stock returns in the industrial sector in Indonesia. This result indicates that with the increase in search volume on search engines, which reflects positive sentiment, there will be an increase in stock trading transactions and vice versa. This study's findings will help investors make investment decisions, especially in the industrial sector. [ABSTRACT FROM AUTHOR]

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Objective Financial markets are considered to be a kind of complex network due to the interaction and interrelationship of its various actors. Therefore, the development of stock communication networks is an important issue for discovering potential connections between different companies. Therefore, the purpose of this research is to investigate and analyze the communication structure of companies in the capital market based on price, return, and trading volume of stock with a network approach. Methods This study employs a quantitative research methodology, specifically focusing on network analysis. The data were collected in the period between 2017 and 2021 and analyzed using the network analysis method and with the help of Excel 2021, SPSS 26, UCINET 6, and its complementary package NetDraw. In addition to the general analysis and review of the price, returns, and trading volume of the stock correlation network, the performance of each node in the network was also investigated using micro-indices. Centrality is one of the most important micro-concepts of network analysis, which examines the influence and importance of people in the network. Also, it was studied with three indices of degree, betweenness, and closeness of centrality of network nodes. Results The results indicated that as the correlation basis expands, there is a decrease in communication between different companies along with an increase in irregularity. The higher the correlation basis, the less likely it is to be affected by a common variable or interaction, which will reduce the correlation between stocks. In addition, there are companies in the communicative network that are in a better position than the rest. They may have higher connections, faster access to information, and greater influence over other units' price changes. As a result, these companies can play a decisive and leading role, or in other words, the role of key and influential actors in the governing structure. Also, the results showed that in the second period of 2021 compared to the first period, communication networks were much more erratic and scattered. Conclusion Considering the importance of capital market communication networks and the decisive role of key companies, relationship pattern analysis helps to increase transparency, reduce risk, and, consequently, improve decision-making, policy-making, portfolio optimization, etc by revealing the network structure of the capital market. The results of this research can lead to a better understanding of the forces that create the communication structure of companies and enable policymakers to make better decisions. Therefore, all users are suggested to pay attention to the communication structure governing the companies in the capital market and the role and impact of their position on the advancement of the company's goals in their decisions and to consider this communication structure and the way power is distributed. [ABSTRACT FROM AUTHOR]

The purpose of this research is to study and analyze the composition of the risk management committee and audit committee, and to develop a performance evaluation form of the composition of the risk management committee and audit committee regarding prediction of the return of companies registered in the Stock Exchange of Thailand. Samples used in this study are 317 companies in 5 years interval between 2017 – 2021. Path analysis is used in this study. In addition, seven experts in the related fields are invited to an in-depth interview and the results are summarized in accordance with the questionnaire in order to support the results that are in line with the hypothesis. The results of statistical analysis will be used to develop the Performance Evaluation Form of the Composition of the Risk Management Committee and Audit Committee. [ABSTRACT FROM AUTHOR]

Fluctuations in returns from investment in stocks make these risky. This factor should be kept in mind in stock investment decisions, which determines the relevance of this research. Through the study, the volatility in the stock returns of BRICS nations is analysed for inferring on the riskiness associated with investing in the respective nations, which is the aim of the research. For this study, the daily returns of five indexes representing each of the nation namely Ibovespa (Brazil), Moex (Russia), Nifty 50 (India), Hang Seng Index (HSI, China), and FTSE/JSE All Share Index (JALSH, South Africa) for a period of 14 years are collected and analysed. Both unconditional and conditional volatility in returns is analysed for each of the nations for imparting clearer and more comprehensive picture of the volatility in returns. Such an in-depth and long period analysis of volatility of the returns of the emerging BRICS economies is a novelty of the research that determined that no volatility model can be said as perfect for all economies for all time. The GARCH (1, 1) model was used to study for the returns of all the five indexes. The results of the study point out that the daily returns of all these indexes are heteroscedastic, implying presence of varying variance. Accordingly, the study м that the BRICS nations’ index returns are more volatile and riskier, and authors are recommended to invest in those indexes with lesser conditional volatility.

The relationship between investor sentiment and market dynamics is a highly intriguing research topic for both academics and the financial industry. By using Spearman rank correlation analysis, this paper aims to explore investors’ decision-making behavior (rational and irrational) on stock returns during and after the COVID-19 outbreak. While irrational factors of the study were measured by the Google Search Volume Index and trading volume, rational factors were measured by profitability and size. The study used three different characteristics of subsectors manufacturing industry namely Food and Beverage, Pharmacy, and Cigarettes that are listed in the Indonesia Stock Market. To the best of our knowledge, our study is the first to examine and compare the level of rationality of investors in a wide range of industries and sectors during and after the COVID-19 pandemic. Our finding supports the notion that both sentiments have an effect on stock returns indicating that cognitions, emotions, and the noise of traders still have an impact on the market. While overall rational sentiment has a more significant correlation with stock returns during the economic recovery phase, there was a highly significant correlation between irrational factors and stock returns during pandemic uncertainty conditions. Moreover, investors tend to be irrational and overreact when making investment decisions in Cigarette sectors during the COVID-19 pandemic. In contrast, after the pandemic, the correlation of rational sentiment of investors toward the Pharmacy industry is still higher than others.

This study investigates the market response to the timeliness of audit reports, specifically before and during the coronavirus disease 2020 (COVID-19) pandemic. We use the earnings response coefficient (ERC) as our proxy to assess the market reaction to the timeliness of audit reports. We applied the purposive sampling method to all companies listed on the Indonesia Stock Exchange (IDX), except for the financial industry, and obtained 977 firm-year observations as our final sample. Using linear multiple regression models in our analysis, we discovered no indication of a market response to the timeliness of audit reports for our full sample during 2018–2020. However, we find evidence that during the COVID-19 pandemic in 2020, the market reacted more positively to the audit report timeliness compared to the pre-COVID-19-pandemic period. Our results indicate that investors were more tolerant of delayed audit reports during the COVID-19 pandemic due to the increased audit efforts and longer time needed to gather sufficient evidence to issue audit reports.

The nexus between stock return and macroeconomic indicators have been a debatable phenomenon for a long time. It has also be one of the major issues for domestic and foreign investors in building efficient and optimum stock portfolio in the capital market. However, the unstable macroeconomic indicators adversely affect the level of stock return in the Nigerian capital market. Against this background, this study investigates the effect of macroeconomic factors on stock return in the Nigerian capital market. The study employed secondary data obtained from Nigeria Stock Exchange (NSE) fact book and Central Bank of Nigeria (CBN) statistical bulletin within the period of 1998 to 2019. The data obtained was subjected to Autoregressive Distribution Lag (ARDL) method of analysis. Findings revealed that money supply and aggregate industrial production positively and significantly affect stock return with co-efficient and P-value at (β=0.466098, P

This study aims to investigate investor sentiment and its effect on the industrial sector in Indonesia. In the study, investor sentiment was extracted from data obtained from search engines. Then the data were used to see how the sentiment affected the stock return in each listed industrial company. Fifteen industrial sector companies listed on the stock exchange were selected; each was analyzed for their search volume and the effect on stock returns using panel data regression from March 2020 to April 2022. The result shows that investor sentiment affects the level of stock returns in the industrial sector in Indonesia. This result indicates that with the increase in search volume on search engines, which reflects positive sentiment, there will be an increase in stock trading transactions and vice versa. This study's findings will help investors make investment decisions, especially in the industrial sector.

Purpose — This study aims to investigate the influence of macroeconomic factors, including economic growth, inflation, the rupiah exchange rate, and interest rates, as well as corporate financial performance indicators such as leverage and profitability, on the stock returns of Indonesian Property and Real Estate Sub-Sector companies. Method — The research data consists of annual data obtained from the Indonesia Stock Exchange, the Indonesia Central Bureau of Statistics, and Bank Indonesia for the period from 2017 to 2021. The research population comprises 36 Property and Real Estate companies listed on the Indonesia Stock Exchange. Out of these, 28 companies meet the sample requirements. The analysis employs panel data regression to examine the data. Result — The results indicate that inflation and exchange rates have a negative and statistically significant impact on stock returns. On the other hand, economic growth, interest rates, return on equity, and the debt-to-equity ratio exhibit a positive influence on stock returns, although these effects are not statistically significant. Contribution — This study's distinctive contribution is its focused analysis of the Property and Real Estate Sub-Sector in Indonesia. It stands out by exploring the intricate relationship between macroeconomic factors and financial performance variables concerning the stock returns of companies within this sub-sector. This nuanced approach provides valuable insights into a specific industry within the Indonesian market, shedding light on previously unexplored dynamics and contributing to a deeper understanding of how these factors affect stock returns in this context.

In recent years, a great deal of attention has been devoted to the use of neural networks in portfolio management, particularly in the prediction of stock prices. Building a more profitable portfolio with less risk has always been a challenging task. In this study, we propose a model to build a portfolio according to an equity-market-neutral (EMN) investment strategy. In this portfolio, the selection of stocks comprises two steps: a prediction of the individual returns of stocks using LSTM neural network, followed by a ranking of these stocks according to their predicted returns. The stocks with the best predicted returns and those with the worst predicted returns constitute, respectively, the long side and the short side of the portfolio to be built. The proposed model has two key benefits. First, data from historical quotes and technical and fundamental indicators are used in the LSTM network to provide good predictions. Second, the EMN strategy allows for the funding of long-position stocks by short-sell-position stocks, thus hedging the market risk. The results show that the built portfolios performed better compared to the benchmarks. Nonetheless, performance slowed down during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The returns obtained from investment decisions made by businesses, individuals, and organizations. Utilizing panel data regression, the research collected data from the annual reports and financial statements of mining firms listed in the Indonesian sharia stock index between 2016 and 2020. The findings suggest that ROA, ROE, and PER positively impact stock returns. Effective management of these variables by companies, leading to higher profits, incentivises investor engagement. However, EPS does not significantly influence stock returns, as low earnings per share fail to accurately reflect the anticipated returns for investors, diminishing investor interest. Therefore, it can be concluded that ROA, ROE, EPS, and PER collectively influence stock returns. [ABSTRACT FROM AUTHOR]

Objective: Contratum strategies are hybrid strategies in which, like the reverse strategy, the portfolio ranking is based on a long-term time horizon. However, the time horizon of their maintenance is like medium-term momentum strategies. Momentarian or momentary strategies are among the combined strategies whose portfolio ranking and stock selection are based on the medium-term time horizon, just like the momentum strategy; however, unlike reverse strategy, they are kept in long term. In this research, an attempt has been made to provide an optimal model for stock selection based on momentum, reverse, and hybrid trading strategies using the GWO algorithm and dynamic panel. Methods: Considering the fact that the purpose of the current research is to answer the questions and test the existing theories in a specific field, it can be categorized as applied research (research and development), and due to its possibility of obtaining a descriptive explanation of a phenomenon, it can be considered as the pseudotype, experiential, descriptive and post-event. In addition, due to description, inference, and problem-solving using quantitative values, it is in the scope of quantitative research. To estimate the model, the information of 175 companies in the period from 2012 to 2021 was used, as well as the software E-Views 12 and MATLAB 2021. Based on the results of 8 time periods of 3, 6, 9, 12, 24, 36, 48, and 60 months, it was analyzed according to different momentum and reverse and combined strategies in loser, winner, and loser-winner, winner-loser positions. It should be noted that two methods of the dynamic panel and gray wolves were used to estimate the strategies. Results: The existence of a momentum strategy was confirmed in the Tehran Stock Exchange, which ensures the hypothesis of underreaction in Iran's capital market. Investors in this market show less reaction to the change of the fundamentals affecting the stock price. Accordingly, the correction of the stock price of these companies is done slowly until it reaches its intrinsic value. Of course, factors such as the volatility limit, the base volume, and the trading node law also fuel this slowness and cause the price correction process to take place with a delay by the formation of buying and selling queues. This helps to use the momentum strategy and obtain abnormal returns in this market. As a result, it can be said that the momentum policy and, naturally, based on the research results, combined approaches cannot create abnormal profits for their investors in the capital market of Iran. Conclusion: Financial market activists and investors are advised to use mixed strategies to improve their buying and selling decisions. Due to the improvement of the results of the gray wolf method compared to the simple panel method, they should use artificial intelligence methods instead of regression methods in the calculations and formation of the optimal portfolio.

CEO compensation remains at the forefront of corporate financial discourse – primarily because of its interconnection with firm performance, growth and survivability. Diverse literature exists on CEO compensation and shareholder return, albeit with different findings and/or disagreements. This paper aims to apply a single independent variable enquiry – the CEO total payment to understand how it might influence shareholder return. Research employs a quantitative approach by using a simple OLS technique with data drawn from a random sample of fifty-six companies listed in the S&P 500 Index. The S&P 500 is a stock market index tracking the stock performance of 500 large companies listed on stock exchanges in the United States. It is one of the most commonly followed equity indices. The OLS result yields a P-value of 0.0001, with a negative regression coefficient, which thus indicates a significant negative relationship between the CEO total payment and shareholder return. The result corroborates some extant research regarding the inability of agency theory to provide an overriding explanation for the CEO pay and return relationship. Further research is therefore encouraged to apply more variables, particularly those that may capture CEO's opportunistic behaviour in future CEO and stock return analysis.

This research aims to get empirical evidence the effect of earnings management in increasing stock return received by investor. Earnings management is an activity to manipulated financial statement. Earnings management can be used to improve company performance when the company has a bad performance or it can be used to maintain company performance. There are 2 earnings management, accrual and real activity manipulation. This research used both to see which one can increase stock return. This research used multiple regression analysis to test the hypothesis and the samples are manufacturing companies listed on Indonesia Stock Exchange from 2017-2019. 86 companies are selected as samples using purposive sampling. The results showed that earnings management is a negative signal for investors. When a negative signal given to investors, it will cause a decrease in stock return received by investor. This research provides an overview of earnings management practice can make investor suffer losses, so that capital market supervisors can increase the monitoring of this manipulation practices.

Given the importance of estimating stock returns, the purpose of this study was to identify the factors affecting stock returns by using the meta-analysis method. Meta-analysis is the main tool for combining results in social and behavioral research. It allows researchers to combine quantitative results of studies, explain the compatibility of results, and achieve a single result. Based on the research background, the impacts of different factors on stock returns were considered. A model of factors affecting stock return is designed in the analysis of empirical studies. This research used the meta-analysis method to comprehensively examine the factors affecting stock returns and assess the different factors that had been examined in various studies over the past years. In a similar research, the researcher had selected the factors affecting stock returns and only tested the selected factors via general categories. However, in this research, a more comprehensive study was conducted to separately examine all the factors whose effects on stock returns had been tested in different studies. Therefore, some of the factors investigated in this research had not been studied in the previous research. Accordingly, a model of factors affecting stock returns was developed. In this research, 422 studies were collected and 102 of them were analyzed. Totally, 153 factors were extracted from these studies and finally, 16 factors were tested. The type of effect size calculated in this study was r. Among the examined factors, earnings per share, operating cash flow, net operating assets, operating earnings, market return, earnings quality, and return on equity had an impact on stock returns. Since the majority of the factors affecting stock returns were related to the financial reports of companies, i.e., their financial status and performance, those factors could be considered to investigate investment opportunities.Keywords: Stock Return, Financial Investment, Fundamental Variables, Financial Ratios, Meta-analysis. IntroductionThe stock return is considered as one of the most important criteria in financial decisions. So far, many studies have been conducted to predict stock returns and provide a comprehensive and reliable model for investors and financial activists. The Capital Asset Pricing Model (CAPM) is one of the most important models in the financial field, especially for estimating stock returns (Murthy et al., 2017; Graham & Harvey, 2001). Although the experimental tests initially confirmed the predictive power of this model regarding the positive linear relationship between systematic risk and stock returns, the results of recent studies indicated that the beta coefficient alone had the power to explain the differences in average stock returns, while other variables were also effective in explaining the differences in stock returns (Fama & French, 1992). Despite examining the effects of various factors, combining the results and presenting a comprehensive model of the factors affecting the stock return, have always been of interest. Meta-analysis methodology, which is the main tool for combining results in social and behavioral research, allows researchers to combine quantitative results of studies (Asgarnezhad Nouri et al., 2016). Therefore, the purpose of this meta-analysis was to expand the pattern of factors affecting the stock returns. Method and DataIn this research, the meta-analysis method was used to comprehensively examine the factors affecting stock returns, as well as the different factors that had been examined in various studies over the past years. In this meta-analysis, 422 studies were collected and 102 of them were analyzed. Totally 153 factors were extracted from these studies and finally, 16 factors were tested. The r effect size was used as the effect size measure, which was calculated following Cohen (1977), Card (2015), and Rosenthal and DiMatteo (2001). FindingsAt the significance level of 1%, the heterogeneity of the effect size of the studies was confirmed. As a result, due to the heterogeneity of the effect size, the model of random effects was used. In addition, the value of the I2 statistic for all the factors was greater than 70, which showed that the heterogeneity of the effect size of the studies was at a high level. In general, among the investigated factors, earnings per share, operating cash flow, net operating assets, operating earnings, market return, earnings quality, and return on equity had an impact on the stock returns. The results of the methods of evaluating publication bias also showed that there was no significant bias in this meta-analysis ContributionIn similar meta-analyses, the researcher had selected the factors affecting stock returns and only tested the selected factors in general categories, while in this research, all the factors whose effects on stock returns had been tested in different studies were separately examined in a more comprehensive study to develop a pattern of factors affecting stock returns. In this meta-analysis, the effects of earnings-to-sales ratio, earnings per share, operating earnings, return on assets, earnings quality, and economic added value on stock returns were investigated, while these factors had not been investigated in similar meta-analyzes. The results showed the positive impact of earnings per share, operating earnings, and earnings quality on stock returns among these factors. Conclusion According to the research results, investors and capital market participants are suggested to pay attention to the variable of earnings per share as one of the influencing factors on the stock market for predicting stock returns. The earnings per share represents the company’s performance in terms of profitability. It is part of the variables of fundamental analysis based on the analytical approach. Among other factors affecting stock returns, we could mention operating cash flow and net operating assets. Therefore, to choose stocks for investment, investors are suggested to examine the company's operations and specifically the items related to the company's cash flow and net operating assets. Market returns were among other factors that affect stock returns. Therefore, it is suggested that investors pay attention to changes in the market yield to estimate the changes in the share value. Based on the research findings, the earnings quality was another factor affecting stock returns. Continuity and reproducibility of earnings, transparency and growing earnings, reflection of the economic reality of the company, etc. were the issues raised about the earnings quality. Hence, the investors are suggested to pay attention to it when examining different stocks. Also, among the financial ratios examined in this research, the return on equity had an impact on the stock return. Therefore, investors are suggested to pay attention to the ratio that is focused on the company's net earnings and shareholders' equity to choose stocks and check their value changes. In general, since the majority of the factors affecting stock returns based on this research were related to the company's financial reports, i.e, the company's financial status and performance, these factors can be considered to investigate investment opportunities.

Investor sentiment is the result of trading behavior and irrational beliefs of investors leading to high volatility and market mispricing. This review aims to study the entire spectrum of articles in the domain of investor sentiment using a bibliometric analysis approach. To this end, the study analyzes a total of 1,919 articles published in the Scopus database between 1979 and 2022. The review uncovers major themes, leading authors, influencing articles, trend topics, top contributing countries, and affiliations. The review shows that the research in the domain of investor sentiment is growing exponentially with an annual growth rate of 15.88%, and the year 2020 witnessed the highest number of scientific productions accounting for 252 (13.68%) total publications. The results display that the USA and China are leading countries in terms of the total contribution and volume of studies from respective authors. The review also reveals that existing research in the field has mainly focused on themes such as market efficiency, asset pricing, stock returns, sentiment analysis, IPO underpricing, overreaction, and volatility, whereas Covid-19 and Bitcoin depicted as emerging themes from recent scholarly works.

This study aims to test and analyze the effect of capital structure, profitability, investment opportunity set, firm value, earnings per share, and dividend policy, on stock returns. Our research uses regression analysis to determine and analyze the influence of independent variables on dependent variables. The objects in this study are companies incorporated in LQ45 for the period 2013 - 2021. The reason for choosing LQ45 is because it is a type of index used to measure the price performance of stocks that have high liquidity and large market capitalization and are supported by good firm fundamentals. The results of the study found that the capital structure with indicators debt to equity ratio has a significant negative effect on stock return. Profitability with indicators return on equity and investment opportunity set with indicators price earnings ratio have a positive and significant effect on stock return. While other findings from our study are firm value with price to book value indicators, profitability from the investor's point of view represented by earning per share indicators, and dividend policy with dividend payout ratio indicators have no effect on stock return. [ABSTRACT FROM AUTHOR]