نشریه چشم انداز مدیریت مالی
پیاپی 36 (زمستان 1400)

In recent years, the concept of trading strategies value and Momentum strategies has become important.The present study investigates the role of financial reporting quality on the willingness of short-term institutional investors and the timing of achieving the return of each of the two strategies. Investigating data related to the companies listed on the Tehran Stock Exchange from 2013 to 2020 indicate that The results indicate that the tendency of transient institutional investors to invest in companies with poor financial reporting decreases during the both value and momentum strategies. Institutional investors also achieve abnormal returns in the momentum strategy faster than value strategy.

Accurate assessment of systematic risks in financial markets can lead to favorable capital allocation. Systematic tail risk is adverse events that, if they occur, can affect stock returns. Therefore, the purpose of this study is to investigate the effect of tail risk on excess stock returns. In the present study, two criteria of cumulative tail risk and combined tail covariance risk were used to measure tail risk. For this purpose, using the systematic removal method, a sample of 136 companies listed on the Tehran Stock Exchange (TSE) in the period 2009 to 2019 was selected. Research hypotheses were tested using the five-factor model of Fama and French regression. The results showed that the combination of size portfolio and tail risk and the combination of value portfolio and tail risk have a negative effect on excess stock returns. In addition, the results indicate that the combination of profitability portfolio and tail risk and the combination of investment portfolio and tail risk does not lead to excess stock returns. In general, the results showed that tail risk can be added to asset pricing models in addition to the variables of the five-factor model of Fama and French.

Due to the increasing growth of social networks in recent years, investors in various markets, in addition to reviewing and analyzing classic market information, also pay attention to news and information published on social networks. By examining and evaluating the relationship between news and information published on social networks and changes in stock prices, it is possible to understand the impact of the information on stock prices and predict the future trend.In this article, using the methods of sentiment analysis and text mining, the impact of public thoughts and feelings caused by news on the Internet and cyberspace on stock prices is examined. The information used in this research includes content published on the social network Twitter about stocks and real stock price data of the top 5 companies on the US stock exchange. Using the presented method, general feelings about a text are estimated and a general score is considered for it. Then, using back testing methods and adopting different trading strategies, the impact of these emotions on the share price trend will be examined and the obtained results will be compared with and without the effect of emotion analysis. According to the results of this study, the effectiveness of strategies based on sentiments analysis is significantly higher than technical analysis-based methods.

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.

Due to the dynamic trend of stock prices and the volatile nature of the market, asset price forecasting plays a key role in creating an efficient strategy. In addition, the results of price forecasting are a prerequisite for creating a portfolio with an optimal structure. Accordingly, the purpose of this research is to provide a hybrid model to help investors in selecting optimal portfolios. For this reason, ten top preferable industries have been selected among the active industries of Tehran Stock Exchange using the Improved Analytical Hierarchy Process method, Then, the price of selected active industries' stocks has been predicted daily, monthly, bi-annually, and annually, using a Long Short Term Memory Recurrent Neural Network. In the next step, three portfolios with different time horizons have been selected by using the Combined Compromise Solution method, and finally, optimal weights have been determined and an efficient frontier has been drawn using Mixed-Integer Quadratic Program and Branch and Cut Algorithm based on Limited Asset Markowitz Model. According to the results of this research, the proposed model gives higher returns to investors due to the risk in constituting portfolios with specified time horizons in contrast to traditional approaches.

The theory of signaling indicates the role of the signal in the management of the company's stock market. Information asymmetry that cannot be removed for confidentiality reduces shareholders` wealth. But by sending a signal (dividend ratio, debt ratio, ownership concentration), the relationship between information asymmetry and discount rate is adjusted, leading to increasing the wealth of shareholders. Due to the cost of signaling for companies, error signaling strategy that do not benefit the shareholders, reduces the shareholders` wealth. Using three corporate financial signals, three required return proxy and one information asymmetry proxy, 18 information asymmetry management strategies were designed and tested. With the information of 300 companies listed on the Tehran Stock Exchange and the OTC market of Iran from 2008 to 2020, the difference between the relationship of information asymmetry (PIN) and the cost of capital between signaled and not signaled companies was studied. The results indicate that debt ratio and ownership concentration signals have been able to moderate the relationship between the discount rate resulting from the Gordon model and information asymmetry. In addition, the dividend ratio signal has been able to moderate the relationship between the discount rate of CAPM and information asymmetry.

In the financial literature, a bubble is a situation in which the market price of a commodity is traded at a significant difference from its intrinsic value. Determining the true value is difficult, usually after a sudden drop in price and the so-called bubble burst; They notice the bubble. The bubble is characterized by its temporary nature. In this case, investors rush into the market without considering the real market value of a commodity, which causes the market price of a commodity to be separated from its real value. Given the importance of this issue; the present study is based on the study of factors affecting the prediction of stock price bubble irregularity. In this study and in order to achieve the objectives of the research; Data of 99 companies were extracted for a period of ten years from the beginning of 2009 to the end of 2020, the research variables were calculated and the necessary statistical tests were performed. The method of this research is descriptive-correlational and its design is Fundamental using post-event approach. The findings indicated that among the proposed variables; Financial performance and Market Performance are effective in predicting stock irregularities.