جستجوی مقالات مرتبط با کلیدواژه « بازده سهام » در نشریات گروه « مالی »

Idiosyncratic volatility of stock returns is caused by idiosyncratic risk. While proper stock portfolio diversification can mitigate idiosyncratic risk, the precise relationship between idiosyncratic volatility and stock returns remains ambiguous. The present study aims to shed light on this relationship by examining the effect of investor attention. It analyzed a sample of 176 companies listed on the Tehran Stock Exchange from 2016 to 2022. Employing the market model, the authors measured idiosyncratic volatility and abnormal trading volume to proxy for investor attention. The findings revealed that idiosyncratic volatility exerts a negative and significant impact on future stock returns. Moreover, it was found that investor attention amplified this negative effect, possibly due to behavioral biases arising from limited attention. Notably, this research diverges from prior studies by emphasizing the role of investor attention, an understudied variable, in shaping the idiosyncratic volatility-stock returns nexus. 

The investigation of risk-return relationships has a rich history in financial literature. According to the traditional asset pricing model, only systematic risk significantly impacts asset returns, while idiosyncratic or unsystematic risk that can be eliminated by diversification by the investor does not affect asset returns. However, empirical evidence concerning the link between idiosyncratic volatility and stock returns is ambiguous. Some studies such as Malkiel and Xu (1997), and Goyal and Santa-Clara (2003) find a positive relationship while some other studies such as Ang et al. (2006) and Jiang et al. (2009) document a negative association. Based on previous studies, investor attention can play an important role in shaping stock prices and affect the relationship between idiosyncratic volatility and future stock returns. Therefore, this study aims to investigate the effect of investor attention on the relationship between idiosyncratic volatility and future stock returns.

To test the research hypotheses, we utilize multivariate regression models and panel data methods. The sample consists of 176 companies listed on the Tehran Stock Exchange from 2016 to 2022. The independent variable is the idiosyncratic volatility of stocks, which is calculated through the market model. In addition, the moderating variable, the investor's attention, has been measured by the abnormal volume.

The empirical findings indicate that idiosyncratic volatility has a negative and significant effect on future stock returns. In other words, the higher the idiosyncratic volatility of the stock return, the lower the future monthly return. Moreover, the investor's attention exacerbates the negative effect of idiosyncratic volatility on future stock returns. That is, the greater investor attention causes the idiosyncratic volatility to have a stronger negative effect on future monthly stock returns.

The results of the first hypothesis show that idiosyncratic volatility has a negative and significant effect on future returns. In other words, as idiosyncratic volatility increases, stock returns will decrease in the next month. This result is consistent with Ang et al. (2006), Jiang et al. (2009), and Hur and Singh (2022). In the second hypothesis, the effect of investor attention on the negative relationship between idiosyncratic volatility and stock returns was investigated, and it was expected that investor attention would mitigate the negative effect of idiosyncratic volatility on future stock returns. However, the results indicate investor's attention amplifies the negative effect of idiosyncratic volatility on future stock returns. This result diverges from the conclusions drawn by Hur and Singh (2022). This unexpected outcome may be attributed to the behavioral bias associated with limited attention among investors. When attention is constrained, investors may react inaccurately to available information or evaluate it incorrectly, leading to exacerbated negative effects of idiosyncratic volatility on future stock returns.

Access to sustainable water resources has recently been raised as a major challenge facing water-dependent companies. The mining industry is one of the huge water users, which can be severely affected by water crises. The purpose of the current research is to test the pricing of water risk in mining companies of the Tehran Stock Exchange (TSE) and to estimate the drop in the value of their shares in the event of a water crisis.

Considering that the availability of water resources hinges on rainfall levels in the catchment area, we assessed water risk using the Standard Precipitation Index (SPI). Employing seasonal data from 2007 to 2011, it used a dynamic panel regression model to assess the effect of water risk on stock returns, controlling for factors such as profitability, leverage, firm size, and capital intensity among 11 companies. Then, the probable loss in a water crisis scenario was calculated using the stress test in the framework of value at risk. Also, the effect of drought hazard index, firm size, and covariance of market return with SPI on the value at risk of firms were investigated.

The significance of the water risk effect on stock returns of mining companies indicates the pricing and the confirmation power of this risk factor to explain the changes in stock returns over time, in a way that one unit increase in water risk will cause a decrease of about 3% in the excess return of the firm’s stock three quarters later. Also, the results showed that the amount of rainfall and drought in the region affects the firm's stock returns only up to three seasons, and its long-term effect is zero with a slight tolerance. Additional research findings indicate that during water crises, mining companies may experience a decline in value ranging from 15 to 45 percent. However, there was no direct relationship between the drought risk index and the value at risk of the firm’s value. Further investigations confirmed the existence of a strong relationship between the value at risk and the correlation coefficient of SPI and the market return. This means that the more positive the correlation between SPI and market returns, the greater the value at risk of the firm in water crisis conditions.

Water risk can explain part of the changes in stock returns of mining companies. Although companies may be situated in similar drought-prone regions, the level of value at risk for their stocks varies i.e., in case of any water resource crisis, other factors affect water risk other than drought. The shareholders of the larger company will suffer relatively fewer losses due to water risk.

Predicting the valuation of blockTrade transaction allows the market to evaluate control over companies in an efficient manner.In this research, by measuring the indicators affecting block transactions in three active industries in Tehran Stock Exchange During the period of 1390 to the end of 1400, on a daily basis with utilization a deep learning neural network, specifically the GRU model. The study focused on industries with a significant number of market participants, namely basic metals (steel), automotive and parts manufacturing (Khodro), and pharmaceuticals (Darou). The results of the hypothesis testing indicate that, at three industry level, Nine variables significantly affect blockTrade transaction valuation: stock returns, block size, trading volume, company size, price fluctuations, industry returns, market returns, institutional ownership, and market-to-book ratio It affects the valuation of block transactions. At the separate level of industries, the results of the effect of financial indicators on the valuation of blockTrade transaction in each industry are different from other industries, which indicates the independence of industries from each other. The findings of this research will help the managers of industries in the stock market and the users of the valuation indices of blockTrade transactions in better evaluation of pricing.

Investing in the stock market is very important as a profitable opportunity in the economy. The purpose of this article is to investigate the asymmetry in the amount and direction of the influence of exchange rate impulses and global crude oil prices on stock returns in the Tehran Stock Exchange. For this purpose, daily data (2011:11:26--2021:3:19) and the Nonlinear Autoregressive Distributed Lag Model approach of NARDL have been used. The results of Pesaran et al.'s co-integration test (2001) showed the existence of long-term relationship between stock returns with exchange rate and global crude oil price. In the following, based on the results of the Wald test and the research model, the asymmetric effects of exchange rate shocks on stock returns in the short and long term were confirmed. In the short term, positive exchange rate impulses on stock returns were not significant. Also, in the long term, the effect of negative exchange rate shocks is more than its positive shocks on stock returns. The impact of oil prices on stock returns was confirmed in the short term. The findings of the research showed that the exchange rate has a greater impact on stock returns in the Tehran Stock Exchange than the price of oil.

Currently, due to economic fluctuations in Iran, the stock market and consequently the economy has undergone a huge transformation. This has created instability in macroeconomic change, known as systematic risk. The purpose of this study is to investigate and determine the effect of independent variables to estimate systematic risk and then determine the effect of systematic risk on stock return performance. For this purpose, two hypotheses were determined and the annual data of the member companies of Tehran Stock Exchange in the period 1399-1387 were used to test the hypotheses. To test the hypotheses, the Rough-set method was used to select independent variables affecting systematic risk, then the regression method with panel data was used to estimate systematic risk and the arbitrage pricing theory method was used to relate systematic risk to stock return performance. Findings from the first hypothesis in the systemic risk evaluation part showed that the variables of sales growth fluctuations, profit margin, debt to equity ratio have a significant effect on systematic risk but interest rates do not have a significant effect on systematic risk. Also, the findings of the second hypothesis in the effect of systematic risk on stock returns show a significant and positive effect of systematic risk on stock returns.

This study investigates the analysis of Granger causality in mean patterns to measure the number of lags of the cross-correlation between the standard-residuals of returns and the volume of transactions during the crisis period. For this purpose, based on daily data from April 2020 to April 2021, structural breakpoints were first determined and then the relationship between the volume of daily transactions and price changes of the Tehran Stock Exchange was investigated using the GARCH-ARMA model. Finally, the causality was investigated in the average between returns and volume of transactions for each sub-period. The results showed when the prices fall sharply during the crisis period, market participants tend to use the volume of past transactions to predict current returns. Also, the results showed when there is an upward price movement in the post-crisis period, it is observed the correlations are significant from lag 2 to 20. These observations suggest causality-in-the-mean between both series occurs asymmetrically after the crisis period. Such asymmetric behavior supports the trader heterogeneity hypothesis from two perspectives, firstly, the degree of significant cross-correlation between the standardized residuals of both series is stronger in the highest interval before the crisis than after the crisis. Second, the time frame for past trading volume to correlate with current returns becomes longer after the crisis. The results of the research can be used to predict stock prices at a time when we are facing the fall of the stock market and the emergence of behavioral phenomena, especially negative market sentiments.

In the present study, stock price forecasts were evaluated based on fundamental, technical and economic factors. For this purpose, three groups of fundamental, technical and economic factors were studied. In data analysis, fitting of least squares error regressions was used for the share price data of 30 companies with more than 50% of the stock market value in 2020 and the stock prices of companies were analyzed unbalanced from 2002 to 2020. The results showed that each of the fundamental, technical and economic factors alone can predict stock returns, while the technical and economic factors did not have additional information content than the fundamental factors.

Studying and analyzing the behavior of fluctuations in the return of securities requires the discovery of a behavioral pattern of stock returns that Based on this model, shareholders are able to choose the best stocks by evaluating their stocks and other stocks in the market, and as a result, they can decide to keep, sell or replace the stocks. The main purpose of this study is to study and analyze the behavioral patterns of stock market fluctuations to show different characteristics of different layers, appropriate strategies with different horizons and evaluate the level of economic activity of investors. In this research, using discrete wavelet transform with maximum overlap in MATLAB software, stock market fluctuations in different time periods are examined and analyzed; For this purpose, Return variances of effective stock market indices during the years 2011-2020 are compared and analyzed.The results of the research showed that the wavelet variance of the total index is lower in different scales compared to the wavelet variance of the cash return index,also the wavelet variance of the stock return is more than the moving average of the stock return. According to the movement scales of each of the stock returns and the moving average of the stock returns during the long-term scales,the wavelet variance was less and the co-movement was less, but during the short-term time scales, the co-movement increased and the variance of the wavelet return was higher among them. In the end, it was suggested that investors formulate their investment strategies in a dynamic and intelligent manner according to the analysis of the behavior of the fluctuations of total indices, price and cash yield and their moving average in different time layers.

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.
 
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.
 
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.
 
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.

Pyramidal and network ownership structures, which give ultimate owners greater control rights over companies than cash flow rights, are prevalent in the Iranian stock market. Given the complex shareholding networks and significant changes in the ownership structures of large Iranian firms over the past two decades, this study aims to examine the relationship between ownership structure and company performance. Four measures are used to analyze ownership structure: cash flow right of the largest shareholder, wedge between control right and cash flow right of the largest shareholder, difference in cash flow rights of the two largest shareholders, and type of ownership. The study investigates their relationship with the performance and stock returns of 261 listed companies during the period of 2015 to 2020. The results indicate a positive and significant association between the cash flow right of the largest shareholder and firm performance, but only when the largest shareholder owns at least 15% of the company's shares. For firms with only one owner and less than 50% in control rights, there is no significant correlation between the cash flow right of the largest shareholder and performance. The study also reveals that ownership by insurance companies has a positive correlation with firm performance, while quasi-governmental ownerships have a negative correlation with performance.

The return of assets and various investment opportunities are two of the most basic principles of optimal allocation of resources in the economy. Liquidity is one of the most important factors affecting the risk and expected return of assets, and liquidity risks arise from it. The main purpose of this study is to determine the effect of different liquidity risks on the stock returns using the LCAPM model with the most optimal illiquidity measure. To test the hypotheses of research, a sample of 167 companies listed on the Tehran Stock Exchange during the years 1389 to 1398 has been selected. In order to select the optimal illiquidity measure from among six different measures, Principal Component Analysis approach is used and PSpread measure has been chosen. For estimating the model, the Generalized Method of Moments method has been used. The results of this study show that investors price liquidity risks in bearish markets in addition to the illiquidity effect, however, they do not pay much attention to these risks in the bullish markets.

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.

The Stock Market is one of the main pillars of the financial market. Increasing the analytical power of capital market investors is one of the factors that play an effective role in the development of such a market. Many investors use technical analysis to make their trading decisions. In this study, the efficiency of buying stocks of insurance companies in Tehran Stock Exchange was examined by Technical Analysis which includes Stochastic Oscillator, Exponential Moving Average, and Hull Moving Average. Then, the study compares Technical Analysis with Buy and Hold Method. To do this, the trading positions that created based on daily periods for 14 insurance companies in Tehran Stock Exchange between 2017:3 and 2019:9 have been reviewed and compared with the Buy and Hold Method. Results indicate that the quarterly returns getting by Technical Method are positive in overall, and the number of trades with negative returns is less than the number of trades with positive returns. Similarly, the average return on a trade with a loss has always been lower than the average return with a profit. Moreover, the number of days with negative returns was less than the number of days with positive returns in all insurance companies. Findings show that using Technical Method was more profitable than the Buy and Hold Method in all companies which have been investigated.

Some studies have shown that investors' emotions can influence their financial and investment decisions, as well as stock returns; On the other hand, stock risk can also affect this relationship; Therefore, the present study has examined the moderating role of corporate governance system on the relationship between investors' emotional tendencies and changes in stock returns of companies listed on the Tehran Stock Exchange. In this study, the independent variable is investors 'feelings and the dependent variable is stock return fluctuations. To test the research hypotheses, the research model estimation method in the form of panel data and analysis based on the model of fixed effects and random effects by Eviews software based on composite data has been used. The sample includes 139 companies listed on the Tehran Stock Exchange in the period 1394-1399, which have been selected by a systematic elimination method. Findings showed that there is a significant relationship between investors' emotional tendencies and fluctuations in stock returns. Also showed that the variable of institutional shareholders can moderate the relationship between investors' emotional tendencies and fluctuations in returns.

The change in stock price is not only affected by the intrinsic value provided by accounting information, but also by the behavior of investors, which is referred to as investor sentiment. If the investor's feelings originate from false mental beliefs and irrelevant information, it may cause wrong predictions and market price fluctuations; Therefore, investors' feelings can affect the stock market and its events, because investors usually invest with an optimistic or pessimistic attitude towards the future. This study aims to develop and evaluate a model that looks for the impact of investors' emotions on financial decisions and stock returns and economic fluctuations in companies listed on the Tehran Stock Exchange. This research is based on the purpose of basic research. To test the hypotheses, a multivariate regression model was used and then they were analyzed using EViews and Stata software. It is a statistic that the sample size is equal to 105 companies according to the screening method and after removing outlier observations. In this research, panel data with fixed effects have been used. The results of this research show that personality traits have an effect on people's transactional behavior and investors' performance and their investment performance, and there is a significant positive relationship between investor's feelings in financial decisions and stock returns. Also, other results of the research show that more transactions are done in the stock exchange in periods when investor sentiment is optimistic and the market is in recession

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.

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.

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.

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.

The purpose of this study is to investigate the relationship between financial and banking components and stock returns of banks listed on the stock exchange. This is an applied research and has been conducted by the descriptive-analytical method. Regression models and panel data have also been used to analyze the data. The dependent variable of the research on the stock return of the listed banks and 9 independent variables corresponding to the six elements of the CAMELS method include capital adequacy ratio, Debt ratio, asset quality, per capita net profit, net income to total financial facilities, net profit margin, deposit to asset ratio, total loan to total deposit ratio, and income changes to interest rate changes. The banks surveyed include 15 banks during the period 2011 to 2020. The results show the positive effect and significant coefficients of debt ratio variables, per capita net profit, net income to total financial facilities and income changes to interest rate changes, on stock returns of banks is at a 95% confidence level that corresponds to the elements of assets, management and interest rate sensitivity in the CAMELS rating system.

Monte Carlo Markov chain methods are a set of algorithms for sampling possible distributions based on building a Markov chain with desirable properties. One of the most common Markov Monte Carlo chain algorithms is the Metropolis-Hastings algorithm. Therefore, the purpose of this study is to evaluate the relationship between financial distress and stock returns using the Metropolis-Hastings algorithm in the Tehran Stock Exchange. For this purpose, 151 companies were selected from the Tehran Stock Exchange in the period 2011 to 2020 using systematic elimination sampling method. R software was used to test the research hypotheses. Altman Z and Olson's score were also used to calculate financial distress. Also, in evaluating the relationship using the Metropolis-Hastings algorithm, two different previous distributions for the research variables were used. The results of the study showed that for Altman's financial distress variable, the accuracy of estimating financial distress was higher with the previous non-informed distribution. For O-Olson's financial helplessness variable, the precision of financial distress was higher with Zelner's previous distribution. Meanwhile, in the previous non-informed distribution, the effect of financial distress was not significant and was significant in Zelner's previous distribution.

This research is based on an eight-factor model consisting of the Fama and French five-factor model plus the variables of market stress, market fragility, and market liquidity risk in order to investigate the explanatory power of this model in Tehran securities market during the years 2009 to 2018 for 117 companies on a monthly basis. The results of the research indicated that the explanatory capability of the eight factor model is better than the Fama and Farnach five-factor model in the Iranian capital market.The results show that fragility has a significant negative relationship with stock returns. The results also show that stress has a significant negative relationship with stock returns and but liquidity risk has a significant positive relationship with stock returns. This result can be of interest to policymakers in the field of finance and investment and other stakeholders.Keywords: Liquidity Risk, Fragility, Market Stress, Fama and French Five-Factor Model, Stock Return

The major part of the research of the last two decades in the field of assets pricing models has been to more accurately identify the variables affecting stock returns and ultimately reduce errors and better explain stock returns in these models. On the other hand, in recent years, extensive research has been conducted on behavioral finance issues to explain stock returns by the psychological and behavioral characteristics of investors. In this regard, this study seeks to analyze the behavior of stock returns based on assets pricing models in the framework of prospect theory in companies listed on the Tehran Stock Exchange. This study was conducted using a sample of 120 companies listed on the Tehran Stock Exchange in the period from 2009 to 2020 and using the Fama-Macbeth regression approach. The results show that prospect theory has a significant effect on explaining stock returns in capital asset pricing models and the three-factor model and the five-factor model of Fama and French, so that with the introduction of prospect theory to this model their unexplained returns have been significantly reduced.