مجله راهبرد مدیریت مالی
سال یازدهم شماره 1 (پیاپی 40، بهار 1402)

providing a model for better identifying the pattern of exchange rate effects on the capital market and forecast the next one and two years of the Iranian capital market is The purpose of this study. In this regard, using 4 specification criteria (Maximum likelihood, Akaik, Schwartz and Hannan Quinn) we compare the fitness of two-regime Markov switching Model(2R-MSM) with three-regime Markov switching model(3R-MSM) relating to exchange rate fluctuations and the relationship between exchange rate fluctuations and capital market. Afterward we forecast the regime for relationship of exchange rate and capital market. results show in recognizing the pattern of exchange rate effects on the capital market, 3R-MSM outperforms 2R-MSM. According to the research findings, if the foreign exchange market fluctuates severely, an increase in the exchange rate will certainly increase capital market return, but when foreign exchange market is almost stable, the effect of this variable on the capital market can be positive or negative. According to the estimated probabilities, in the coming years we are expected to see a positive effect of the exchange rate on the capital market in a low volatility environment.

Partly on the issue that value stocks earn higher returns than growth stocks, there is agreement, but the interpretation of the cause is controversial and there is no clear explanation for this stock feature. According to investment-based asset pricing theory, the financial inflexibility is the reason for expected return of value firms covary more with recessions than the expected returns of growth firms. The present study seeks to determine the effectiveness of the value anomaly of financial inflexibility with attention to the business cycle. In order to achieve the research goals using systematic removal sampling method, the monthly data of 450 year - firm has been used during the period from 2008 to 2017. To test the research hypotheses, regression method using time series and panel data was used. The results of the research show that the financial inflexibility leads to a positive risk premium in the stock level and investment portfolios and value firms gain higher future returns than growth firms due to compensate for the risk of financial inflexibility, and Finally, the effect of financial inflexibility on stock risk premium is not constant during the business cycle, and in times of recession, value companies are more exposed to risk of financial inflexibility than growth companies.Partly on the issue that value stocks earn higher returns than growth stocks, there is agreement, but the interpretation of the cause is controversial and there is no clear explanation for this stock feature. According to investment-based asset pricing theory, the financial inflexibility is the reason for expected return of value firms covary more with recessions than the expected returns of growth firms. The present study seeks to determine the effectiveness of the value anomaly of financial inflexibility with attention to the business cycle. In order to achieve the research goals using systematic removal sampling method, the monthly data of 450 year - firm has been used during the period from 2008 to 2017. To test the research hypotheses, regression method using time series and panel data was used. The results of the research show that the financial inflexibility leads to a positive risk premium in the stock level and investment portfolios and value firms gain higher future returns than growth firms due to compensate for the risk of financial inflexibility, and Finally, the effect of financial inflexibility on stock risk premium is not constant during the business cycle, and in times of recession, value companies are more exposed to risk of financial inflexibility than growth companies.

The purpose of this study was to compare the efficiency of machine learning models (32 models) and statistical models (14 models) in predicting the financial risk of listed 145 companies in Tehran Stock Exchange during the period 2010 to 2020 and selecting the best model using advanced optimization techniques. Findings of the research using the test of comparing the accuracy of prediction coefficients, indicates that with 99 percent confidence, the prediction accuracy of machine learning models is higher than statistical models. Also, the best machine learning model after optimization was the evolutionary support vector machine model with 99.86 percent prediction accuracy and the value of the area under the curve was 0.998. In addition, accrual financial ratios with 99.45 percent predictive accuracy and operating financial ratios with 98.62 percent predictive accuracy were able to perform better than other financial ratios in using the evolutionary support vector to predict financial risk. on the other side, the projected financial risk varied according to different industries. Therefore, it was found that machine learning models can be used as an important tool in predicting corporate financial risk due to the lack of limitations that statistical models face.

In the present study, the role of recession, boom and the quality of institutions in the impact of natural resource rents on Iran's financial development index in 1984-2018 has been investigated. For this purpose, first, the principal component analysis model has been used to extract the indicators of institutional quality and financial development. Then to study the effect of natural resource rents on financial development in the country during periods of recession and boom and in two modes with and without considering the institutional quality index, the threshold structural autoregressive model has been used. According to the research results, the impact of oil resources rent in the country on financial development does not depend on recession and boom, and the determining factor in the curse or blessing of oil resources rent in the country is the quality of institutions. If the rent of resources and the quality of institutions increase at the same time, it is possible to increase financial development in the short term, while in the absence of quality of institutions in the rent of resources, in the short term rent of resources leads to reduced financial development in the country. Accordingly, even if the quality of institutions in the country simultaneously improves, oil resources and the resulting increase in rents can not be considered a long-term factor for improving and growing financial development in the country.

With the advent of cryptocurrencies in international financial markets, the study of new issues around the portfolio of individuals and the risk relationship of different segments of the financial market with each other has become prominent among investors and researchers. In this study, with the help of monthly data from 2013-to 2021 and to investigate the interaction effects of bitcoin fluctuations (risk) and exchange rate on fluctuations in the Iranian stock market index in the portfolio of the M SBVAR approach, two behavioral regimes (consistent with before and After the corona epidemic) has been identified for bitcoin price fluctuations and then the effects of bitcoin innovation on the stock market index during the two behavioral regimes. The results of the study, while confirming the adaptation of bitcoin behavioral regimes to the prevalence of the corona epidemic, show that during the corona epidemic, the stock market index reacts more strongly to bitcoin shocks and exchange rates, and the overall risk Increases the portfolio.

The main purpose of this study was to investigate the impact of different information environments on the relationship between disclosure quality and idiosyncratic stock risk in listed companies in Tehran Stock Exchange. To investigate this issue, the research hypotheses were tested on the basis of a statistical sample of 86 companies over a 11 year period from 2009 to 2019 using multivariate regression models. For this purpose, management forecast error as a proxy for disclosure quality, capital asset pricing model for idiosyncratic risk measurement and company size criterion as representative of information environment are used. In order to calculate the management forecast errors, the absolute value of the composite measure of management forecast errors using the principal component analysis method was used. The results show that there is a negative and significant relationship between disclosure quality and idiosyncratic risk. Also, having a favorable (weak) information environment increases (decreases) the quality of disclosure and reduces (increases) idiosyncratic risk.

This study examines the relationship of the leader-follower relationship in determining the composition of the capital structure (CS) using the absolute cross-sectional dispersion (CSAD) method in terms of market growth and decline. The firms in each industry were divided into two categories of leaders and followers based on the criteria of profitability growth, profitability and market share. In the category of profitability growth in the machinery and equipment and cement lime gypsum industry in general, as well as in the basic metals and chemical industry (decline and growth period), pharmaceutical industry (decline period) and cement lime gypsum industry (growth period), The leader-follower relationship is not rejected. With the criterion of profitability in the same market conditions, non-metallic mineral and cement lime gypsum industry and taking into account the period of growth and decline in the food except sugar, chemical, non-metallic mineral and cement lime gypsum industry (growth and decline period), and automobiles and parts and electrical appliances (growth period) firms follow the industry leader. Based on the market share criterion, in the automobiles and parts industry in similar economic conditions and the basic metals and chemical industry (growth and decline period) and rubber and plastic industry and electrical appliances (growth period) in different economic market conditions, the hypothesis of a leader-follower relationship is not rejected. Therefore, firms' following to the industry leader can be considered as a determining factor in the capital structure of firms.

One of the most important issues in the field of financial management is that investors can distinguish favorable investment opportunities from unfavorable ones. One way to help investors is to anticipate the financial recovery (exit from helplessness) of companies with financial distress. Therefore, this study intends to provide a model for predicting financial recovery using the support vector machine algorithm for companies listed on the Tehran Stock Exchange. To achieve this goal, 54 financial variables were determined using the Lars feature selection algorithm and to test the accuracy of the results of the proposed model, the support vector learning algorithm was used. For this purpose, in the period of 2001-2018, the information of 167 helpless companies that were out of financial helplessness and revived was extracted. The research findings show that the research model accurately predicts the recovery time of the financially helpless company from financial distress with 74% accuracy.

 Institutional investors play a crucial role in monitoring and reducing asymmetric information. Institutional investors reduce asymmetric information by demanding high quality in corporate governance practices and information disclosure that leads to informed decisions on investment choices. In the present study, the impact of institutional investors, investment horizons on management efficiency and investment decisions of the companies is investigated. In thes study, the data of 138 companies listed in tehrak stock Exchange during 2018-2011 were investigated. In order to test the hypothesis, Regression models with panel data method and fixed effects approach areused. The results of the testing of research hypotheses indicated that the short-term investment horizon of institutional investors has a negative and significant impact on management efficiency. also, The research findings indicated that the long–term investment horizon of institutional investors has a negative impact on the investment decisions and expenditures of companies with high operating cash flows, by the method based on Bushee(1998)and these investors(with lng-term investment horizon) reduce theinvestment decisions and expenditures of companies with limited funding. This finding could be due to the fact that the majority of institutional investors withlong–term investment horizon in companies with limited fundinghave absolute control and exacerbate the company's problems in order to extract private benefits.

In this paper, some of the challenges of portfolio optimization are discussed simultaneously. So that the fuzzy multi-objective programming model was used to consider the multi-criteria nature of stock selection and the lack of correlation with the return on assets. Also, due to the weaknesses of traditional risk measures such as variance for risk assessment of the investor, the criterion of value at risk was replaced as a coherent risk criterion with the fuzzy credit theory approach. On the other hand, in this research, ZAPROS III method was used to interfere with investors' subjective opinions and judgments, and by considering the formal index of quality (FIQ), the views of investors in portfolio optimization were considered. In designing the model, in addition to the main constraints, limitations such as the minimum and maximum allocation of wealth to each asset, as well as the minimum and maximum number of stocks in the portfolio are considered. To demonstrate the applicability of the developed model, 10 of the more active companies accepted in Tehran Stock Exchange were used. The results of model implementation using the MOPSO algorithm showed that optimal pareto-optimized models of model execution compared to randomly assigned portfolios have better and more favorable performance in terms of achievement of predetermined goals.