University of Isfahan Journal of Asset Management and Financing 2383-1189 10 3 2022 09 23 Application of Deep Learning Architectures in Stock Price Forecasting: A Convolutional Neural Network ‎Approach Application of Deep Learning Architectures in Stock Price Forecasting: A Convolutional Neural Network ‎Approach 1 20 26474 10.22108/amf.2022.129205.1673 FA Amir Sharif Far Ph. D. Student, Department of Financial Management, Faculty of Management and Economics, Science and Research Branch, Islamic ‎Azad University, Tehran. Iran. ‎ Maryam Khaliliaraghi Assistant Professor, Department of Business Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran. Iran. Iman Raeesi Vanani Associate Professor, Department of Industrial Management. Faculty of Management and Accounting, Allameh Tabataba'i University, ‎Tehran. Iran.‎ Mirfeyz Fallahshams Associate Professor, Department of Financial Management, Faculty of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran. Journal Article 2021 06 28 Algorithms based on a Convolutional Neural Network (CNN), which is a branch of Deep Learning (DL), have seen significant progress in picture and video analyses in recent years. Success of these new models has led to widespread use of them in various fields, including text mining and time series data. DL is part of a broader family of machine learning methods that attempts to model high-level concepts using learning at multiple levels and layers and extract features of higher levels from the raw input. This survey investigated the abilities of different CNN architectures to predict stock prices. Upon running the model with various architectures and parameters for the stock price of Esfahan Steel Company, the results showed that a CNN with max-pooling layers (a combination of Batch size=64, filters=256, and ReLU Activation Function) and Mean Absolute Percentage Error (MAPE) of 1.79% and Normalized Root Mean Square Error (NRMSE) of 2.71% had a higher prediction accuracy than other CNN architectures and Recurrent Neural Network (RNN). Introduction Among the various deep learning techniques that have many applications in different sciences, specific algorithms like Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) have been used by researchers due to their characteristics of financial time series (Sezer, Gudelek, & Ozbayoglu, 2020). CNN is a feed-forward Artificial Neural Network (ANN) that takes its inputs as 2-D matrices. Unlike a fully connected neural network like Multi-Layer Perception (MLP) neural network, the locality of data within its input vector (or matrix) is important (Sezer & Ozbayoglu, 2018). CNN has different architectures. Usually one specific architecture is focused on in each study conducted in this field. In this study, however, the architectures used in various studies were surveyed in the first level and each selected architecture was optimized by using different parameters in the second level. Finally, the best performances of the architectures with various parameters were compared to choose the optimized model. The effective studies in model development are shown in Table 1. Table (1) Effective studies in model development Art. Method Dataset Feature Set Horizon Livieris, E. Pintelas, & P. Pintelas (2020) Using two convolutional layers with different filters Gold Price data 1 day Gao, Zhang, &Yang (2020) Simple CNN S & P500 CSI300 Nikkei225 Price data, volume, technical indicators 1 day CNN with a dropout layer Gudelek, Boluk, & Ozbayoglu (2017) CNN with dropout and max-pooling layers ETF Price data, technical indicators 1 day Ji, Zou, He, & Zhu (2019) CNN with a max-pooling layer Future carbon price Price data 7 days Li & Dai (2020) CNN with a max-pooling layer Bitcoin Price data 1 day Method and Data Based on the previous studies on CNN application, three different architectures of CNN were investigated as shown in Figure 1. Figure (1) The process of choosing an optimal CNN algorithm For selecting the best CNN architecture, all the three models were surveyed with various parameters. It is worth noting that the parameters that affected CNN included items like number of filters in the CNN layer, Batch size, and Activation Function. In this study, the data obtained from Esfahan Steel Company during the period of 2018-2021were used. The input data consisted of two categories, including price data (Open, High, Low, Close, and Volume) and technical indicators based on the surveys of Kara et al. (2011) and Patel et al., (2015). Python 3.8 with Keras Library was used to execute the model. In this study, the dataset was divided into a training set and a testing set, which covered about the first 80% and last 20% of the raw dataset, respectively. Findings Comparison of the three defined architectures with various parameters led to the optimized model. It should be noted that the selected model was the result of running it 54 times with different layers and parameters. In this study, the two performance measures of Mean Absolute Percentage Error (MAPE) and Normalized Root Mean Square Error (NRMSE) were selected to evaluate the predictive power of our proposed models. In Table 2, the errors of the best performances of each of the three architectures with different parameters and the RNN model (another DL model) were compared to choose the optimized model. Based on the results, the accuracy of the best performance of the second CNN architecture was higher than those of the others. Table (2) Comparison of the errors of the selected models Error Method MAPE NRMSE RNN 2.46% 2.79% Best performance of the first CNN architecture 2.13% 3.09% Best performance of the second CNN architecture 1.79% 2.71% Best performance of the third CNN architecture 2.18% 3.26% Conclusion and discussion In this paper, the predictive powers of the various architectures of CNN models were investigated. The results demonstrated that the best performance of the second CNN architecture with the Max-Pooling layer and combination of Batch size of 64, filter of 256, and ReLU Activation Function and MAPE and NRMSE errors of 1.79 and 2.71%, respectively, provided higher prediction accuracy than other CNN and RNN architectures. The outcome of this survey was supported by research of Ji et al. (2019) on Carbon future price forecasting and that of Li & Dai (2020) on Bitcoin price forecasting by using a CNN model with convolutional and Max-Pooling layers. However, Gao et al. (2020) proposed a convolutional layer with a dropout layer and Gudelek et al. (2017) used a convolutional layer with dropout and Max-Pooling layers for predicting ETF prices. Their results were not confirmed by this paper since using a convolutional layer with a Max-Pooling layer had a better performance than other architectures. Algorithms based on a Convolutional Neural Network (CNN), which is a branch of Deep Learning (DL), have seen significant progress in picture and video analyses in recent years. Success of these new models has led to widespread use of them in various fields, including text mining and time series data. DL is part of a broader family of machine learning methods that attempts to model high-level concepts using learning at multiple levels and layers and extract features of higher levels from the raw input. This survey investigated the abilities of different CNN architectures to predict stock prices. Upon running the model with various architectures and parameters for the stock price of Esfahan Steel Company, the results showed that a CNN with max-pooling layers (a combination of Batch size=64, filters=256, and ReLU Activation Function) and Mean Absolute Percentage Error (MAPE) of 1.79% and Normalized Root Mean Square Error (NRMSE) of 2.71% had a higher prediction accuracy than other CNN architectures and Recurrent Neural Network (RNN). Introduction Among the various deep learning techniques that have many applications in different sciences, specific algorithms like Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) have been used by researchers due to their characteristics of financial time series (Sezer, Gudelek, & Ozbayoglu, 2020). CNN is a feed-forward Artificial Neural Network (ANN) that takes its inputs as 2-D matrices. Unlike a fully connected neural network like Multi-Layer Perception (MLP) neural network, the locality of data within its input vector (or matrix) is important (Sezer & Ozbayoglu, 2018). CNN has different architectures. Usually one specific architecture is focused on in each study conducted in this field. In this study, however, the architectures used in various studies were surveyed in the first level and each selected architecture was optimized by using different parameters in the second level. Finally, the best performances of the architectures with various parameters were compared to choose the optimized model. The effective studies in model development are shown in Table 1. Table (1) Effective studies in model development Art. Method Dataset Feature Set Horizon Livieris, E. Pintelas, & P. Pintelas (2020) Using two convolutional layers with different filters Gold Price data 1 day Gao, Zhang, &Yang (2020) Simple CNN S & P500 CSI300 Nikkei225 Price data, volume, technical indicators 1 day CNN with a dropout layer Gudelek, Boluk, & Ozbayoglu (2017) CNN with dropout and max-pooling layers ETF Price data, technical indicators 1 day Ji, Zou, He, & Zhu (2019) CNN with a max-pooling layer Future carbon price Price data 7 days Li & Dai (2020) CNN with a max-pooling layer Bitcoin Price data 1 day Method and Data Based on the previous studies on CNN application, three different architectures of CNN were investigated as shown in Figure 1. Figure (1) The process of choosing an optimal CNN algorithm For selecting the best CNN architecture, all the three models were surveyed with various parameters. It is worth noting that the parameters that affected CNN included items like number of filters in the CNN layer, Batch size, and Activation Function. In this study, the data obtained from Esfahan Steel Company during the period of 2018-2021were used. The input data consisted of two categories, including price data (Open, High, Low, Close, and Volume) and technical indicators based on the surveys of Kara et al. (2011) and Patel et al., (2015). Python 3.8 with Keras Library was used to execute the model. In this study, the dataset was divided into a training set and a testing set, which covered about the first 80% and last 20% of the raw dataset, respectively. Findings Comparison of the three defined architectures with various parameters led to the optimized model. It should be noted that the selected model was the result of running it 54 times with different layers and parameters. In this study, the two performance measures of Mean Absolute Percentage Error (MAPE) and Normalized Root Mean Square Error (NRMSE) were selected to evaluate the predictive power of our proposed models. In Table 2, the errors of the best performances of each of the three architectures with different parameters and the RNN model (another DL model) were compared to choose the optimized model. Based on the results, the accuracy of the best performance of the second CNN architecture was higher than those of the others. Table (2) Comparison of the errors of the selected models Error Method MAPE NRMSE RNN 2.46% 2.79% Best performance of the first CNN architecture 2.13% 3.09% Best performance of the second CNN architecture 1.79% 2.71% Best performance of the third CNN architecture 2.18% 3.26% Conclusion and discussion In this paper, the predictive powers of the various architectures of CNN models were investigated. The results demonstrated that the best performance of the second CNN architecture with the Max-Pooling layer and combination of Batch size of 64, filter of 256, and ReLU Activation Function and MAPE and NRMSE errors of 1.79 and 2.71%, respectively, provided higher prediction accuracy than other CNN and RNN architectures. The outcome of this survey was supported by research of Ji et al. (2019) on Carbon future price forecasting and that of Li & Dai (2020) on Bitcoin price forecasting by using a CNN model with convolutional and Max-Pooling layers. However, Gao et al. (2020) proposed a convolutional layer with a dropout layer and Gudelek et al. (2017) used a convolutional layer with dropout and Max-Pooling layers for predicting ETF prices. Their results were not confirmed by this paper since using a convolutional layer with a Max-Pooling layer had a better performance than other architectures.

https://amf.ui.ac.ir/article_26474_124b469bb6a000b8772b245b3b2d97ef.pdf

University of Isfahan Journal of Asset Management and Financing 2383-1189 10 3 2022 09 23 An Analysis of the Effects of Board Relations Network Structure in Determining Labor Investment Efficiency: The Monitoring Role of Institutional Owners An Analysis of the Effects of Board Relations Network Structure in Determining Labor Investment Efficiency: The Monitoring Role of Institutional Owners 21 46 26894 10.22108/amf.2022.130793.1701 FA Reza Taghizadeh Assistant Professor, Department of Accounting and Finance, Faculty of Economics, Management and Accounting, Yazd University, Yazd, Iran Gholamreza Rezaei Assistant Professor, Department of Accounting, Faculty of Management and Economics, University of Sistan and Baluchestan, Zahedan, Iran Mohammad Sadeghzadeh Maharlui Assistant Professor, Department of Accounting, Faculty of Economics, Management and Social Sciences, Shiraz University, Shiraz, Iran Ramin Zeraatgari Assistant Professor, Department of Accounting, Faculty of Management and Economics, University of Sistan and Baluchestan, Zahedan, Iran. Journal Article 2021 10 02 The purpose of this paper was to investigate the effects of board relations network structure on labor investment efficiency. Besides, the role of institutional owners in these relationships was studied. This paper had a quantitative approach based on graphic techniques. The sample included 117 companies in Tehran Stock Exchange (TSE) from 2009 to 2020. The social network analysis and regression analysis approaches were used to conduct the research tests. Evidence showed that the indicators of the degree (negative effect) and closeness (positive effect) of board relations network structure affect labor investment efficiency. In addition, monitoring of institutional owners can have a positive effect on labor investment efficiency. The intensity of the relationship between the degree index and labor investment efficiency depends on the level of this variable. Moreover, the negative relationship between over-investment in labor and the betweenness index depends on the level of institutional owner monitoring. Introduction One of the main issues that has received a lot of attention in recent years is investment decisions in labor. Labor investment inefficiency can occur through over-(under-) investment. Both over-investment and under-investment in labor can cause distortions in labor investment. In this regard, one of the phenomena that have attracted the attention of many researchers in recent years is the network of board member relationships and the position of companies in this network due to the boards’ role in key decisions, such as hiring and firing employees. However, in the previous studies, factors, such as conditional conservatism, financial reporting quality, and accounting information comparability, have been regarded as the influential and determining factors of labor investment efficiency. However, among them, the important issue of board network is ignored. Accordingly, the purpose of this study was to examine the effects of board’s structure relations network in determining labor investment efficiency. In addition, the monitoring role of institutional owners was examined since it reduced the agency problems in labor decisions. Method and Data This study was arranged in two phases: In the first phase, the relationship between the companies was examined based on the shareholders, who appointed the board members. This phase was performed via network analysis method by using PreMap software version 1, UCINET version 6, and NetDraw. In the second phase, it was examined whether the position and situation in the relationship network affected investment efficiency in the labor. This phase was performed through regression analysis by using Eviews software version 9. The sample included all the companies listed in Tehran Stock Exchange (TSE) from 2009 to 2017 and had the following criteria: first, their fiscal year ending in Esfand (final month based on the Iranian calendar) was taken into account and second, they had to be uncategorized as banks and financial institutions. Third, they had not to have a long trading interval (more than 3 months). Based on the above-mentioned criteria, 117 companies (819 year-company) were selected for research analysis. The independent variables of this research were the company’s position in the board’s relations network (Board_net) and Institutional Ownership Change (IOC). Labor investment efficiency (| Ab_Net_Hiring |) was the dependent variable of this research. It should be noted that the research tests were performed with the help of social network analysis approach and regression analysis. Findings Evidence showed that the effects of the boards’ relations network on the labor investment efficiency were different according to the various indicators. To be more precise, the degree index had negative effects; the betweenness index had no significant effects; and the closeness index had positive effects on the labor investment efficiency. Evidence from further studies at the level of over-investment and under-investment groups in labor showed the same results. Furthermore, institutional owners' monitoring had no effects on the labor investment (both over-investment and under-investment) efficiency in the companies. However, if the firm's fixed effects were controlled, this variable (institutional owner’s monitoring) could have a positive effect on the labor investment efficiency. Besides, the research results revealed that the relationship between the indicators of the board’s relations network structure and labor investment efficiency was not related to the level of institutional owner’s monitoring. To be more precise, in both groups of the strong and weak institutional owners’ monitoring, there were a negative and positive relationship between the indicators of degree and closeness and the labor investment efficiency, respectively. However, the Paternoster test showed that the intensity of the degree index impact on the labor investment efficiency could be greater in the group of companies with strong institutional ownership monitoring. Conclusion and discussion The findings showed the network of board members' relationships could be introduced as an indicator of labor investment efficiency. In general, the results of this article should be considered by shareholders in selecting board members in order to increase the company’s value by improving labor investment efficiency. This evidence can be very important for employment regulators; for example, the organization in charge of legislation in the field of labor can use the results of the present study to pass laws and impose mandatory restrictions on the selection of board members of various companies and change labor investment efficiency. The purpose of this paper was to investigate the effects of board relations network structure on labor investment efficiency. Besides, the role of institutional owners in these relationships was studied. This paper had a quantitative approach based on graphic techniques. The sample included 117 companies in Tehran Stock Exchange (TSE) from 2009 to 2020. The social network analysis and regression analysis approaches were used to conduct the research tests. Evidence showed that the indicators of the degree (negative effect) and closeness (positive effect) of board relations network structure affect labor investment efficiency. In addition, monitoring of institutional owners can have a positive effect on labor investment efficiency. The intensity of the relationship between the degree index and labor investment efficiency depends on the level of this variable. Moreover, the negative relationship between over-investment in labor and the betweenness index depends on the level of institutional owner monitoring. Introduction One of the main issues that has received a lot of attention in recent years is investment decisions in labor. Labor investment inefficiency can occur through over-(under-) investment. Both over-investment and under-investment in labor can cause distortions in labor investment. In this regard, one of the phenomena that have attracted the attention of many researchers in recent years is the network of board member relationships and the position of companies in this network due to the boards’ role in key decisions, such as hiring and firing employees. However, in the previous studies, factors, such as conditional conservatism, financial reporting quality, and accounting information comparability, have been regarded as the influential and determining factors of labor investment efficiency. However, among them, the important issue of board network is ignored. Accordingly, the purpose of this study was to examine the effects of board’s structure relations network in determining labor investment efficiency. In addition, the monitoring role of institutional owners was examined since it reduced the agency problems in labor decisions. Method and Data This study was arranged in two phases: In the first phase, the relationship between the companies was examined based on the shareholders, who appointed the board members. This phase was performed via network analysis method by using PreMap software version 1, UCINET version 6, and NetDraw. In the second phase, it was examined whether the position and situation in the relationship network affected investment efficiency in the labor. This phase was performed through regression analysis by using Eviews software version 9. The sample included all the companies listed in Tehran Stock Exchange (TSE) from 2009 to 2017 and had the following criteria: first, their fiscal year ending in Esfand (final month based on the Iranian calendar) was taken into account and second, they had to be uncategorized as banks and financial institutions. Third, they had not to have a long trading interval (more than 3 months). Based on the above-mentioned criteria, 117 companies (819 year-company) were selected for research analysis. The independent variables of this research were the company’s position in the board’s relations network (Board_net) and Institutional Ownership Change (IOC). Labor investment efficiency (| Ab_Net_Hiring |) was the dependent variable of this research. It should be noted that the research tests were performed with the help of social network analysis approach and regression analysis. Findings Evidence showed that the effects of the boards’ relations network on the labor investment efficiency were different according to the various indicators. To be more precise, the degree index had negative effects; the betweenness index had no significant effects; and the closeness index had positive effects on the labor investment efficiency. Evidence from further studies at the level of over-investment and under-investment groups in labor showed the same results. Furthermore, institutional owners' monitoring had no effects on the labor investment (both over-investment and under-investment) efficiency in the companies. However, if the firm's fixed effects were controlled, this variable (institutional owner’s monitoring) could have a positive effect on the labor investment efficiency. Besides, the research results revealed that the relationship between the indicators of the board’s relations network structure and labor investment efficiency was not related to the level of institutional owner’s monitoring. To be more precise, in both groups of the strong and weak institutional owners’ monitoring, there were a negative and positive relationship between the indicators of degree and closeness and the labor investment efficiency, respectively. However, the Paternoster test showed that the intensity of the degree index impact on the labor investment efficiency could be greater in the group of companies with strong institutional ownership monitoring. Conclusion and discussion The findings showed the network of board members' relationships could be introduced as an indicator of labor investment efficiency. In general, the results of this article should be considered by shareholders in selecting board members in order to increase the company’s value by improving labor investment efficiency. This evidence can be very important for employment regulators; for example, the organization in charge of legislation in the field of labor can use the results of the present study to pass laws and impose mandatory restrictions on the selection of board members of various companies and change labor investment efficiency.

https://amf.ui.ac.ir/article_26894_37439860a752e25e687e311200c5df2c.pdf

University of Isfahan Journal of Asset Management and Financing 2383-1189 10 3 2022 09 23 Analysis of Market Reaction to Risk Disclosure Factors Analysis of Market Reaction to Risk Disclosure Factors 47 66 26794 10.22108/amf.2022.125358.1596 FA Zahra Tabatabaei Ph.D. Student, Department of Accounting, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran Seyed Abbas Hashemi Associate Professor, Department of Accounting, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran Hadi Amiri Assistant Professor, Department of Economics, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran Journal Article 2020 12 28 Disclosure of information, especially information about company risks, provides investors with useful information about the inherent risks of the company. Whether this information signal has information content or not, it will lead to an increase in the investors' understanding and ultimately their reactions and changes in stock prices. Therefore, the aim of this study was to analyze the market reaction to risk disclosure factors, which included financial risk, operational risk, and strategic risk. To measure risk disclosure in this study, a method based on the content analysis of the board of directors' activity report to the general meeting of shareholders was used. Also, to investigate the market reaction, abnormal returns accumulated around the date of publication of the board of directors’ activity report were utilized. In the period of 2011-2019, among the companies listed on Tehran Stock Exchange (TSE), 655 years-companies were selected as a sample. Multiple regression was applied to test the research hypotheses. The results indicated that the total risk disclosure and types of risk disclosure, including financial risk, operational risk, and strategic risk, had a positive and significant effect on the abnormal returns on stock accumulation. In other words, the disclosure of risk factors had information content and the market reacted to their disclosure in the report of the board of directors. Introduction Disclosure of information, especially information about company risks, provides useful information to investors about the company's inherent risks. If this information signal has information content, it will increase investors' understanding and they eventually react and the stock price changes. Therefore, in the purpose of the present study was to analyze market reaction to risk disclosure factors, which included financial risk, operational risk, and strategic risk. Method and Data To measure risk disclosure, a method based on content analysis of the board of directors' activity report to general meeting of shareholders was used. To examine the market reaction, absolute value of cumulative abnormal return around the publication date of the board of directors' activity report was also utilized. In the period of 2011-2019, among the companies listed on Tehran Stock Exchange (TSE), 655 years-companies were selected as the sample. Multiple regression was also applied to test the research hypotheses. Findings In this study, the investors’ reaction to total risk disclosure and different categories of risk disclosure were investigated. Based on this, 4 hypotheses were formulated. The first hypothesis, which was about the effect of total risk disclosure by the company on the absolute value of cumulative abnormal return of the company in the period around the reporting date, was not rejected. The results of this hypothesis indicated that the investors responded to risk disclosure in financial reporting and as the rate of risk disclosure in the financial reporting increased, investors’ reactions increased. The second hypothesis on the effect of financial risk disclosure by the company on the absolute value of cumulative abnormal return in the period around the reporting date was not rejected. The results of this hypothesis test indicated that the higher the level of financial risk disclosure was in financial reporting, the higher the investors’ responses were. The third hypothesis on the effect of disclosure of non-financial operational risk by the company on the absolute value of cumulative abnormal return of the company in the period around the reporting date was not rejected. The results of this hypothesis test indicated that the higher the disclosure of non-financial operational risk was in financial reporting, the higher the investors’ responses were. The fourth hypothesis based on the effect of strategic risk disclosure by the company on the absolute value of cumulative abnormal return of the company in the period around the reporting date was not rejected. The results of this hypothesis test indicated that the higher the rate of strategic non-financial risk disclosure was in financial reporting, the higher the rate of the investors’ responses was. Conclusion and discussion Risk disclosure factors decrease investors’ information risk because when the company discloses the information related to its existing risks, the risk of adverse selection and information asymmetry are reduced. In fact, there is a kind of signal-giving condition about the company's performance and situation, which improves investors' understanding of the company's situation. Therefore, the higher the information content of risk disclosure information is, the more investors use this information in their economic decisions and take this information into account in the stock price. In general, the results of this study indicated a significant and direct effect between risk disclosure and its types with the absolute value of cumulative abnormal return. In other words, according to the theory of signaling in uncertainty conditions, receiving any information about the risks that the company faced could lead to reconsideration of investors' previous beliefs about the company's risks and the investors reactions, which affected risk disclosure. This indicated that in addition to the numerical information of financial statements, the disclosure of textual information about the risks faced by the company in the activity report of the board of directors had information content and the market in return became close to the publication of the board of directors’ report since they reacted to the disclosure of information related to the company risks. Disclosure of information, especially information about company risks, provides investors with useful information about the inherent risks of the company. Whether this information signal has information content or not, it will lead to an increase in the investors' understanding and ultimately their reactions and changes in stock prices. Therefore, the aim of this study was to analyze the market reaction to risk disclosure factors, which included financial risk, operational risk, and strategic risk. To measure risk disclosure in this study, a method based on the content analysis of the board of directors' activity report to the general meeting of shareholders was used. Also, to investigate the market reaction, abnormal returns accumulated around the date of publication of the board of directors’ activity report were utilized. In the period of 2011-2019, among the companies listed on Tehran Stock Exchange (TSE), 655 years-companies were selected as a sample. Multiple regression was applied to test the research hypotheses. The results indicated that the total risk disclosure and types of risk disclosure, including financial risk, operational risk, and strategic risk, had a positive and significant effect on the abnormal returns on stock accumulation. In other words, the disclosure of risk factors had information content and the market reacted to their disclosure in the report of the board of directors. Introduction Disclosure of information, especially information about company risks, provides useful information to investors about the company's inherent risks. If this information signal has information content, it will increase investors' understanding and they eventually react and the stock price changes. Therefore, in the purpose of the present study was to analyze market reaction to risk disclosure factors, which included financial risk, operational risk, and strategic risk. Method and Data To measure risk disclosure, a method based on content analysis of the board of directors' activity report to general meeting of shareholders was used. To examine the market reaction, absolute value of cumulative abnormal return around the publication date of the board of directors' activity report was also utilized. In the period of 2011-2019, among the companies listed on Tehran Stock Exchange (TSE), 655 years-companies were selected as the sample. Multiple regression was also applied to test the research hypotheses. Findings In this study, the investors’ reaction to total risk disclosure and different categories of risk disclosure were investigated. Based on this, 4 hypotheses were formulated. The first hypothesis, which was about the effect of total risk disclosure by the company on the absolute value of cumulative abnormal return of the company in the period around the reporting date, was not rejected. The results of this hypothesis indicated that the investors responded to risk disclosure in financial reporting and as the rate of risk disclosure in the financial reporting increased, investors’ reactions increased. The second hypothesis on the effect of financial risk disclosure by the company on the absolute value of cumulative abnormal return in the period around the reporting date was not rejected. The results of this hypothesis test indicated that the higher the level of financial risk disclosure was in financial reporting, the higher the investors’ responses were. The third hypothesis on the effect of disclosure of non-financial operational risk by the company on the absolute value of cumulative abnormal return of the company in the period around the reporting date was not rejected. The results of this hypothesis test indicated that the higher the disclosure of non-financial operational risk was in financial reporting, the higher the investors’ responses were. The fourth hypothesis based on the effect of strategic risk disclosure by the company on the absolute value of cumulative abnormal return of the company in the period around the reporting date was not rejected. The results of this hypothesis test indicated that the higher the rate of strategic non-financial risk disclosure was in financial reporting, the higher the rate of the investors’ responses was. Conclusion and discussion Risk disclosure factors decrease investors’ information risk because when the company discloses the information related to its existing risks, the risk of adverse selection and information asymmetry are reduced. In fact, there is a kind of signal-giving condition about the company's performance and situation, which improves investors' understanding of the company's situation. Therefore, the higher the information content of risk disclosure information is, the more investors use this information in their economic decisions and take this information into account in the stock price. In general, the results of this study indicated a significant and direct effect between risk disclosure and its types with the absolute value of cumulative abnormal return. In other words, according to the theory of signaling in uncertainty conditions, receiving any information about the risks that the company faced could lead to reconsideration of investors' previous beliefs about the company's risks and the investors reactions, which affected risk disclosure. This indicated that in addition to the numerical information of financial statements, the disclosure of textual information about the risks faced by the company in the activity report of the board of directors had information content and the market in return became close to the publication of the board of directors’ report since they reacted to the disclosure of information related to the company risks.

https://amf.ui.ac.ir/article_26794_e5aa6493ce3c7785fb7d40d6209916e9.pdf

University of Isfahan Journal of Asset Management and Financing 2383-1189 10 3 2022 09 23 Dimensions of Risk Management Development in the Business Model and Culture of Financial Industry Organizations in Iran Dimensions of Risk Management Development in the Business Model and Culture of Financial Industry Organizations in Iran 67 94 26995 10.22108/amf.2022.134365.1749 FA Mojtaba Rostami Noroozabad Department of Financial Management, Faculty of Management, Islamic Azad University, North Tehran Branch, Tehran, Iran Mohammad Hossein Badami M.S, Department of Financial Management and Insurance, Faculty of Management, University of Tehran, Tehran, Iran Maryam Esna Ashari Assistant Professor, Property and Casualty Insurance Research Group, Insurance Research Institute, Tehran, Iran Journal Article 2022 07 13 The purpose of this research was to survey the dimensions of implementation and development of risk management in financial industry organizations in Iran so as to reveal the level of risk management implementation and its degree of maturity, including the level of risk management culture, risk management governance, and integrity of risk management systems and processes, in the financial industry organizations.This research, which was a survey in terms of its nature and an applied one in terms of purpose, was performed among 173 organizations from all the companies and institutions in the country's financial industry. A questionnaire was used to collect the required data and information. The results showed that the risk management culture in the country's financial industry organizations was at a slightly lower than average level. Risk governance was at a slightly higher than weak level and the integrity of risk management systems and processes was lower than average. This research investigated enterprise risk management and its development among Iran’s financial industry organizations for the first time. Introduction Risk management has become a major concern in today's dynamic global environment. In the structure of corporate governance, risk management is a vital issue. Therefore, interest in the establishment of Enterprise Risk Management (ERM) has grown a lot in recent years. Since 2007 when the global financial crisis began, risk management has undergone a huge change. In Iran, various organizations and supervisory institutions have gradually paid special attention to the issue of risk management. However, many companies and institutions have not devoted enough respect to risk management and have not implemented the relevant standards to an acceptable extent. This research aimed to fully explore the dimensions of risk management implementation and development in various financial organizations by answering the following questions in order to reveal the implementation level of risk management and its degree of maturity among those organizations. To what extent the risk management culture is developed among financial industry organizations in Iran? What is the state of risk governance among the companies and institutions of the Iran`s financial industry? How is the integration of risk management systems and processes in the companies and institutions of the Iran`s financial industry? Method and Data. The research data were collected with a qualitative approach by using a questionnaire. The sampling frame and the number of samples for this study were determined based on the classification of the supervisory body of each member in the research population. In this regard, 250 out of 465 organizations, including insurance companies, state-owned banks, private banks, leasing companies, investment banks, portfolio management companies, investment companies, brokerages, holding companies, investment consulting companies, and rating agencies, were randomly selected and the research questionnaire was distributed among them. Out of 250 questionnaires, 215 questionnaires were completed and returned, while 42 questionnaires were unusable. Finally, 173 questionnaires were used for data analysis. Findings Regarding the answer to the first main question and in terms of the development of risk management culture in the organizations of the country's financial industry, the state of risk culture was at a slightly lower than average level. In response to the second main question, the state of risk governance in the financial industry companies and institutions was at a slightly higher than weak level. In response to the third main question, the integration of risk management systems and processes in the companies and institutions of the country's financial industry was lower than the average. Conclusion and discussion The results of the analyses showed that the risk culture in the organizations of the country's financial industry was at a slightly lower than average level. Also, risk governance was at a higher than weak level and the integrity of the risk management systems and processes in the organizations of the financial industry was lower than the average. In this study, for the country’s organizations of the financial industry, the average abilities to manage financial and non-financial risks were estimated at 16 and 18%, respectively. In general, one of the important reasons for the low level of risk management implementation and lack of institutionalization of risk management culture in the financial institutions was the lack of serious attention to generalization of the importance and position of risk management to preventing future possible crises in financial institutions by the supervisory institutions. The senior managers and board directors` understanding of risk management and the benefits of its implementation in the relevant institutions, low level of risk management knowledge, and lack of experts for risk management implementation, as well as the showcase of risk management to gain satisfaction and minimum approval of the supervisory institutions and their relevant regulations were among the other reasons. The purpose of this research was to survey the dimensions of implementation and development of risk management in financial industry organizations in Iran so as to reveal the level of risk management implementation and its degree of maturity, including the level of risk management culture, risk management governance, and integrity of risk management systems and processes, in the financial industry organizations.This research, which was a survey in terms of its nature and an applied one in terms of purpose, was performed among 173 organizations from all the companies and institutions in the country's financial industry. A questionnaire was used to collect the required data and information. The results showed that the risk management culture in the country's financial industry organizations was at a slightly lower than average level. Risk governance was at a slightly higher than weak level and the integrity of risk management systems and processes was lower than average. This research investigated enterprise risk management and its development among Iran’s financial industry organizations for the first time. Introduction Risk management has become a major concern in today's dynamic global environment. In the structure of corporate governance, risk management is a vital issue. Therefore, interest in the establishment of Enterprise Risk Management (ERM) has grown a lot in recent years. Since 2007 when the global financial crisis began, risk management has undergone a huge change. In Iran, various organizations and supervisory institutions have gradually paid special attention to the issue of risk management. However, many companies and institutions have not devoted enough respect to risk management and have not implemented the relevant standards to an acceptable extent. This research aimed to fully explore the dimensions of risk management implementation and development in various financial organizations by answering the following questions in order to reveal the implementation level of risk management and its degree of maturity among those organizations. To what extent the risk management culture is developed among financial industry organizations in Iran? What is the state of risk governance among the companies and institutions of the Iran`s financial industry? How is the integration of risk management systems and processes in the companies and institutions of the Iran`s financial industry? Method and Data. The research data were collected with a qualitative approach by using a questionnaire. The sampling frame and the number of samples for this study were determined based on the classification of the supervisory body of each member in the research population. In this regard, 250 out of 465 organizations, including insurance companies, state-owned banks, private banks, leasing companies, investment banks, portfolio management companies, investment companies, brokerages, holding companies, investment consulting companies, and rating agencies, were randomly selected and the research questionnaire was distributed among them. Out of 250 questionnaires, 215 questionnaires were completed and returned, while 42 questionnaires were unusable. Finally, 173 questionnaires were used for data analysis. Findings Regarding the answer to the first main question and in terms of the development of risk management culture in the organizations of the country's financial industry, the state of risk culture was at a slightly lower than average level. In response to the second main question, the state of risk governance in the financial industry companies and institutions was at a slightly higher than weak level. In response to the third main question, the integration of risk management systems and processes in the companies and institutions of the country's financial industry was lower than the average. Conclusion and discussion The results of the analyses showed that the risk culture in the organizations of the country's financial industry was at a slightly lower than average level. Also, risk governance was at a higher than weak level and the integrity of the risk management systems and processes in the organizations of the financial industry was lower than the average. In this study, for the country’s organizations of the financial industry, the average abilities to manage financial and non-financial risks were estimated at 16 and 18%, respectively. In general, one of the important reasons for the low level of risk management implementation and lack of institutionalization of risk management culture in the financial institutions was the lack of serious attention to generalization of the importance and position of risk management to preventing future possible crises in financial institutions by the supervisory institutions. The senior managers and board directors` understanding of risk management and the benefits of its implementation in the relevant institutions, low level of risk management knowledge, and lack of experts for risk management implementation, as well as the showcase of risk management to gain satisfaction and minimum approval of the supervisory institutions and their relevant regulations were among the other reasons.

https://amf.ui.ac.ir/article_26995_b162aa89c8eb1db60fecb58aeb5c48e7.pdf

University of Isfahan Journal of Asset Management and Financing 2383-1189 10 3 2022 09 23 Factors Affecting the Interest of Individuals to Participate and Invest in Social Crowdfunding Projects Factors Affecting the Interest of Individuals to Participate and Invest in Social Crowdfunding Projects 95 118 27357 10.22108/amf.2022.132608.1722 FA Seyed Mohammadbagher Jafari Associate Professor, Department of Industrial and Technology Management, Faculty of Management and Accounting, College of Farabi, University of Tehran, Tehran, Iran 0000-0003-2042-2756 Pegah Poor Zanjani M.A., Department of Industrial and Technology Management, Faculty of Management and Accounting, College of Farabi, University of Tehran, Tehran, Iran Journal Article 2022 05 07 Crowdfunding has become a modern and favorite financing channel worldwide. Crowdfunding is a new financing method that helps entrepreneurs acquire the financial resources needed for their projects. This study aimed to investigate the factors affecting individuals' interest in participating and investing in social crowdfunding projects. Using the Structural Equation Modeling (SEM) technique, the developed model was tested with the help of Amos software via the data obtained from 318 individuals. This paper examined the direct and indirect contextual variables: awareness of need, altruism, reputation, psychological benefits, efficacy, funder’s trust, and institution-based trust in the investment intention. Finally, it was observed that awareness of need, altruism, values, reputation, psychological benefits, efficacy, funder’s trust, and institution-based trust were the factors influencing people's intention to invest in social crowdfunding projects. Keywords: funding, crowdfunding, social crowdfunding. Introduction Crowdfunding is considered a new source of funding and is becoming an increasingly employed tool by entrepreneurs seeking financing for their ventures and investors and searching for non-traditional alternatives of investment. It can be a crucial alternative financing method, especially for micro-entrepreneurs, new ventures, and nonprofit associations (Ferreira, Papaoikonomou & Terceno, 2022). Traditional forms of philanthropy and methods of giving are in the throes of change; with the emergence of crowdfunding, a new rapidly expanding source of innovative financing has emerged that has enabled entrepreneurs to forego traditional financiers, such as venture capitalists, and instead target a geographically dispersed 'crowd' of consumers, lenders, and small investors (Mollick 2014). The term 'social crowdfunding' was applied here to denote online raising of money for social causes by using the 'crowd'. In social crowdfunding, the projects have a social aspect and financial support is aimed at helping to reduce the society's problems and altruistic goals, while investors do not expect a direct return from their support. Social crowdfunding projects reduce social problems of the society, such as employment of unmarried women, environmental protection, printing of useful books, entertainment projects, and similar cases. It is typically classified into 4 models of financing, i.e., donation-based, reward-based, lending-based, and equity-based crowdfunding (Belleflamme et al., 2014). Method and Data This study investigated the mechanisms identified by Bekkers & Wiepking (2011), Srkoc, Zarim, & Hockerts (2013), and Liang, Wu, & Huang (2019) and applied them in the context of social crowdfunding. The sample of this study consisted of the people, who participated in social crowdfunding projects, and the sampling method was a randomized mixed method. The conceptual model of this research and the relationship between the variables and their effects on each other were tested via a survey method by using an online questionnaire. For this purpose, the data of 318 individuals were collected. The Structural Equation Modeling (SEM) technique was utilized for the analysis with the help of Amos software. Findings Social crowdfunding, which individuals and investors manifest without financial incentives, plays an important role in the development of the economy and reduction of poverty, thus increasing the country's welfare. This study found that awareness of need, altruism, reputation, psychological benefits, efficacy, funder’s trust, and institution-based trust influenced the studied people's intention to invest in social crowdfunding projects. Moreover, values, reputation, psychological benefits, efficacy of the factors influencing the funders' trust, reputation, psychological benefits, and efficacy of the factors affecting trust in the institution-based trust-building process were identified. The funding level also moderated the relationship between the funders' trust and investment intention. Conclusion and discussion Crowdfunding is a new phenomenon that has grown tremendously in recent years. In this paper, the factors affecting the individuals' interest in participating and investing in social crowdfunding projects were investigated. Based on the findings, 11 mechanisms were identified as the most important factors influencing the individuals’ interest to participate and invest in social crowdfunding projects: (1) awareness of need, (2) solicitation, (3) altruism, (4) costs and benefits, (5) values, (6) reputation, (7) psychological benefits, (8) efficacy, (9) funder’s trust, (10) institution-based trust, and (11) investment intention. These mechanisms can provide a basic theoretical framework for future research to explain social crowdfunding. Thus, the important roles of awareness of need, altruism, psychological benefits, efficacy, reputation, values, funder’s trust, and institution-based trust were discussed as the key variables affecting investment intention. Crowdfunding has become a modern and favorite financing channel worldwide. Crowdfunding is a new financing method that helps entrepreneurs acquire the financial resources needed for their projects. This study aimed to investigate the factors affecting individuals' interest in participating and investing in social crowdfunding projects. Using the Structural Equation Modeling (SEM) technique, the developed model was tested with the help of Amos software via the data obtained from 318 individuals. This paper examined the direct and indirect contextual variables: awareness of need, altruism, reputation, psychological benefits, efficacy, funder’s trust, and institution-based trust in the investment intention. Finally, it was observed that awareness of need, altruism, values, reputation, psychological benefits, efficacy, funder’s trust, and institution-based trust were the factors influencing people's intention to invest in social crowdfunding projects. Keywords: funding, crowdfunding, social crowdfunding. Introduction Crowdfunding is considered a new source of funding and is becoming an increasingly employed tool by entrepreneurs seeking financing for their ventures and investors and searching for non-traditional alternatives of investment. It can be a crucial alternative financing method, especially for micro-entrepreneurs, new ventures, and nonprofit associations (Ferreira, Papaoikonomou & Terceno, 2022). Traditional forms of philanthropy and methods of giving are in the throes of change; with the emergence of crowdfunding, a new rapidly expanding source of innovative financing has emerged that has enabled entrepreneurs to forego traditional financiers, such as venture capitalists, and instead target a geographically dispersed 'crowd' of consumers, lenders, and small investors (Mollick 2014). The term 'social crowdfunding' was applied here to denote online raising of money for social causes by using the 'crowd'. In social crowdfunding, the projects have a social aspect and financial support is aimed at helping to reduce the society's problems and altruistic goals, while investors do not expect a direct return from their support. Social crowdfunding projects reduce social problems of the society, such as employment of unmarried women, environmental protection, printing of useful books, entertainment projects, and similar cases. It is typically classified into 4 models of financing, i.e., donation-based, reward-based, lending-based, and equity-based crowdfunding (Belleflamme et al., 2014). Method and Data This study investigated the mechanisms identified by Bekkers & Wiepking (2011), Srkoc, Zarim, & Hockerts (2013), and Liang, Wu, & Huang (2019) and applied them in the context of social crowdfunding. The sample of this study consisted of the people, who participated in social crowdfunding projects, and the sampling method was a randomized mixed method. The conceptual model of this research and the relationship between the variables and their effects on each other were tested via a survey method by using an online questionnaire. For this purpose, the data of 318 individuals were collected. The Structural Equation Modeling (SEM) technique was utilized for the analysis with the help of Amos software. Findings Social crowdfunding, which individuals and investors manifest without financial incentives, plays an important role in the development of the economy and reduction of poverty, thus increasing the country's welfare. This study found that awareness of need, altruism, reputation, psychological benefits, efficacy, funder’s trust, and institution-based trust influenced the studied people's intention to invest in social crowdfunding projects. Moreover, values, reputation, psychological benefits, efficacy of the factors influencing the funders' trust, reputation, psychological benefits, and efficacy of the factors affecting trust in the institution-based trust-building process were identified. The funding level also moderated the relationship between the funders' trust and investment intention. Conclusion and discussion Crowdfunding is a new phenomenon that has grown tremendously in recent years. In this paper, the factors affecting the individuals' interest in participating and investing in social crowdfunding projects were investigated. Based on the findings, 11 mechanisms were identified as the most important factors influencing the individuals’ interest to participate and invest in social crowdfunding projects: (1) awareness of need, (2) solicitation, (3) altruism, (4) costs and benefits, (5) values, (6) reputation, (7) psychological benefits, (8) efficacy, (9) funder’s trust, (10) institution-based trust, and (11) investment intention. These mechanisms can provide a basic theoretical framework for future research to explain social crowdfunding. Thus, the important roles of awareness of need, altruism, psychological benefits, efficacy, reputation, values, funder’s trust, and institution-based trust were discussed as the key variables affecting investment intention.

https://amf.ui.ac.ir/article_27357_35bfeaa3e27a077a64e0d844ea2015a4.pdf

University of Isfahan Journal of Asset Management and Financing 2383-1189 10 3 2022 09 23 Development of the Model of Factors Affecting Stock Returns Development of the Model of Factors Affecting Stock Returns 119 142 27271 10.22108/amf.2023.136130.1774 FA Reza Raei Professor, Department of Financial Management and Insurance, Faculty of Management, University of Tehran, Tehran, Iran Shapoor Mohammadi Associate Professor, Department of Financial Management and Insurance, Faculty of Management, University of Tehran, Tehran, Iran Alireza Ajam Ph. D. Student of Financial Management and Insurance, Faculty of Management, University of Tehran, Tehran, Iran Journal Article 2022 12 28 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. Introduction The 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 Data In 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). Findings At 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 Contribution In 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. 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. Introduction The 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 Data In 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). Findings At 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 Contribution In 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.

https://amf.ui.ac.ir/article_27271_80a48bc50d874c426bc8cb9fa65228aa.pdf