stock returns

This study aimed to investigate the effect of fraction resulted of bad news on stock returns emphasizing the regulatory power of information disclosure policies that for this goal, the study population is consisted of the companies listed on the Tehran Stock Exchange during a five years' period (2010-2014). Data of selected statistical sample using systematic elimination method has been collected from 122 companies. This study objectively is a practical research. In terms of type of research design because of relying on historical data, is ex post facto and its inference method is inductive and in correlation type. This study includes six main hypotheses. In this study to assess the hypotheses, the linear regression has been used. To analyze the data and test hypotheses, the EVIEWS software is used. the results of this study suggest that the fraction resulted of bad news has an effect on stock returns, abnormal cumulative returns and the stock crash risk, as well as the fraction resulted of bad news has an effect on the interaction of regulatory power of information disclosure policies, stock crash risk, the abnormal cumulative returns and stock returns.

Recent studies show that individual investors tend to speculate on stock markets and hold shares with a lottery-like return. For this speculation of people have a significant impact on stock returns, individual investors must trade the same shares with the same time. The purpose of this study was to investigate the effect of the speculative bubble on the stock returns of companies in Iran. Following the design of the speculative bubble specification indexes, the transaction information was collected from the stock market in the five-year period from 2011 to 2015 and a sample of 106 companies was selected by systematic elimination method, which totaled 530 year-company. In this research, linear regression and correlation analysis were used to analyze the hypotheses of the research. To analyze the data and test the hypotheses, Eviews software was used. What can be said in the summing-up and conclusion of the general test of research hypotheses is that there is a speculative bubble in the Tehran Stock Exchange index. In addition, the speculative bubble has an impact on stock returns, and this effect has been confirmed in conditions of market boom and downswing. The results obtained in this study are consistent with the documents referred to in the theoretical framework of the research and financial literature.

Identifying the causal relations between trading volume and stock returns and between trading volume and return volatility plays a vital role in identifying profitable investment opportunities. In this study, the Granger causality test was conducted to analyze the causal relationships between the mentioned variables in Tehran Stock Exchange. Consequently, the Vector Auto Regression (VAR) model was employed to determine the conditional mean equations of returns and volume. Moreover, the bivariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model was used to model the conditional variance equation, stating the relationship between volume and return volatility. According to the results, no bilateral causal relationship can be ascertained between returns, volume, and return volatility. In other words, return and return volatility could barely predict volume; therefore, volume cannot be the Granger causality of the other two variables. However, stock returns were found to have an important role in determining the volume. Likewise, return volatility can be used to predict volume accurately. In fact, stock returns and the return volatility were both the Granger causalities of the volume.

Behavioral finance is a new issue raised by some financial intellectuals over the past two decades and has been quickly addressed by professors, experts, and students throughout the world. Investigating the factors affecting investment decisions is carried out in the field of behavioral finance; in other words, the focus of behavioral finance is on the specific charac-teristics of human behavior and applying them in asset pricing. Empirically, pricing models rarely include psychological factors, but the noticeable point is that nowadays, researchers have found behavioral factors influencing empirical asset pricing models that can manipulate returns on asset mispricing. Behavioral asset pricing is the result of applying behavioral finance theories within traditional asset pricing theories. Thus, despite the existence of many asset pricing models, due to their weaknesses and lack of comprehensiveness, as well as the necessity of reviewing behavioral factors, this study aims to model asset pricing through behavioral models. Using the data from 141 listed firms in Tehran Stock Exchange over the years 2008 to 2017 and multivariate regression, this study is an attempts to model asset pric-ing through employing behavioral models and Fama-French approach. Using Fama-French approach, the results showed that accounting information risk, investors’ trading behavior, and investors' sentiment have a direct and significant impact on asset pricing.

The purpose of this study is to compare the impacts of momentum on stock returns of companies listed in Tehran Stock Exchange in different market conditions. For this purpose, the sample size is 120 months from 2008 to 2017. The research hypotheses are estimated using multivariate linear regression using time series method. Based on the results of the hypotheses test, the momentum in each of the market conditions, including normal, ascending and descending conditions, has a positive and direct effect on the stock returns of the companies listed in Tehran Stock Exchange, which indicates the principle of investors' insatiability in the stock exchange Tehran seeking to maximize its return on investment with a certain risk that in a downside mode of market, their insatiability exits less and faster than their momentum conditions, which is a reason for investors' loss evasion in this situation.

Stock return predictability has been extensively considered as a stylized reality. Theories indicate that returns should change along the time, and various studies have presented evidence on this point. On the other hand, there is an optimal portfolio in each regime, and one cannot claim that a specific portfolio can minimize risk and returns in each regime. On the other hand, the financial conditions index (FCI) is an important index to specify monetary policy conditions. Regarding the importance of the issue, this research aims to present a comprehensive index, including all monetary transmission mechanisms. In this regard, it is attempted to improve the efficiency of stock return predictability in Iran's economy by incorporating an FCI and identifying relationships between FCI and stock returns using the TVP-DMA model, which can resolve shortcomings of traditional models. The study is applied research in terms of purpose. Seasonal data over the period of April 1991 to July 2019 is used. The results based on TPV, DMS, and DMA models indicate that liquidity growth rate, economic growth rate, unemployment rate, exchange rate, financial condition index, oil revenues, misery index, and budget deficit, has significantly affected factors of stock returns in 30, 50, 11, 49, 66, 54, 7, and 84 periods of 104 periods, respectively. Accordingly, budget deficit, financial condition index, oil revenues, and economic growth are the most effective factors of stock returns predictability in Iran. Further, the incorporation of flexibility in coefficients of the financial development index leads to higher forecast accuracy.

Analysis of investors’ behavior is a new scientific field in financial behavior, which is a good tool for acquiring this knowledge. Uncertain circumstances cause investors to make systematic mistakes and face cognitive biases in their expectations and decision making, including in assessing stock returns. The purpose of this study is to investigate the extent to which investors follow the pattern of belief updating as a cognitive bias in the use of earnings accounting information to evaluate the stock returns of 205 active companies in Tehran Stock Exchange. Multivariate regression analysis was performed using Eviews 9 software. The results show that by controlling year and industry effects, investors use profitability for most of the past years in following the pattern of belief updating based on two indicators of reducing realized earnings per share and equity returns. The research model is also confirmed by the sensitivity analysis and controlling company effects based on the equity return index. However, it is not confirmed by the two indicators of realized earnings per share and return on assets.

Spiritual and emotional intelligence in the field of organizational behavior is very important for the performance of companies, concurrent with stock prices, and the expected return of investors. Managers with these two skills have a remarkable ability to create commitment in employees, strengthen the spirit of self-control in employees and eliminate weaknesses in the control of the company. Is their spirituality and affects the effectiveness of the company. Therefore, based on this argument, the present study examines the effect of managers' spiritual and emotional intelligence on the concurrence of stock prices and stock returns. For this purpose, King (2008) questionnaire and emotional intelligence based on Bar-On (2001) questionnaire were used to measure the spiritual intelligence of managers. The hypotheses were tested using the statistical method of regression analysis with composite data using the information of 60 companies listed on the Tehran Stock Exchange in 2019. The findings of the research hypothesis indicate that the emotional and spiritual intelligence of managers affects the concurrence of companies' stock prices. The results also include the effect of managers 'emotional and spiritual intelligence on companies' stock returns.

The main purpose of this study is to determine the real and dynamic relationship between returns and stock market fluctuations in different time horizons so that its key results can measure the predictive power of returns and stock market fluctuations in determining the level of economic activity of investors. Accurate measurement of these relationships helps investors predict stock market movements in the future and Develop, plan and implement their own investment strategies in each time horizon. In this research Maximal overlap discrete wavelet transform has been used to investigate the relationships between industries return fluctuations and stock main indices in different time horizons from 2011 to 2020 have been estimated. The results showed that the wavelet variance of the rate of return varies in different industries. The return of investment companies on various time scales is equal to the investment return of the banking industry. The variance of the total index is less on different time scales than the value of the cash index.

This study examined the extent to which stock returns can be explained by fundamental financial information. To this end, the accuracy of predicting future stock returns under conditions of mispricing stocks, including underpriced and overpriced stocks, was evaluated. The empirical results are based on a sample of 140 companies listed on the Tehran Stock Exchange during 2006-2022. The applied methodology is based on multivariate regression analysis of panel data. In particular, the six-factor models of Fama-French (2018), Nichols-Wallen-Wyland (2017), and Rhodes croft-Robinson-Viswanathan (2005) have been calculated to investigate the extent of explaining future stock valuation and returns. The results show that most of the changes in the stock valuation of the investigated companies are explained by using fundamental financial factors. Specifically, the results indicate that undervalued companies have earned higher stock returns in the coming year compared to overvalued companies. In other words, in undervalued companies, the stock return has increased in the following year, and in overvalued companies, the future stock return has decreased. According to the provided results, investors and stock exchange regulatory bodies are advised to more sensitively examine companies that have a lower rating of incorrect stock valuation based on the models introduced in this research.

Momentum strategy, despite its outstanding performance, offers different results at different time intervals. In this study, we aimed to provide an opti-mal model for stock selection based on momentum, reverse and hybrid trad-ing strategies using the data panel model. The present research method was applied on the information of 180 companies in the period 2011 to 2021 was used to estimate the model. (Eviews12) software has been used to estimate the models. Based on the results of 8 time periods of 3, 6, 9, 12, 24, 36, 48 and 60 months based on different momentum and inverse strategies and a combination of loser, winner and loser-winner, winner-loser were analyzed. According to the data panel method, the studied strategies in small companies give more additional returns to investors than large companies. Also, based on the results of hybrid strategies, investors will receive more additional returns in the long run than simple momentum strategies.