نشریه چشم انداز مدیریت مالی
پیاپی 22 (تابستان 1397)

The purpose of this research is to study the relationship between dividend payments and stock price crash risk for the companies listed on Tehran Stock Exchange (TSE). Also, this research is to study the role of information asymmetry and free cash flow on the relationship between dividend payments and stock price crash risk. Hence, this research utilizes NCSKEW, EXTR_SIGMA, and DUVOL as proxies for stock price crash risk. The research population composes of total companies listed in the TSE and statistical sample composes of 68 companies listed in the TSE which have been studied during the years 2003 to 2013. The multivariate linear regression is used for testing hypothesis of the research. The results suggest that there is a negative relationship between dividend payments and stock price crash risk proxies (i.e. NCSKEW and DUVOL) but, there is no relationship between dividend payments and stock price crash risk proxies by EXTR_SIGMA. Also, the results showed that information asymmetry has not a moderating role in the relationship between dividend payments and stock price crash risk. In addition, the results showed that free cash flow has a moderating role in the relationship between dividend payments and stock price crash risk proxies by NCSKEW.

This paper aims to investigate the impacts of product market competition on the corporate investment. Firms increase their investment when there is competition. However, high growth sale increases the impacts of product market competition on the corporate investment. In this position also, industry leaders invest more than before. For empirical review, data related 83 firms in Tehran stock exchange, during 2006-2015 were analyzed. Overall, the results obtained indicate that with increase of product market competition, corporate investment will be increased. Overall, the results be obtained also, firms which have a good position in their industry, and the so-called are industry leaders, increase their investment, when there is competition.

Financial strength has always been, one of the influencing factors for the success of companies, and undoubtedly the company's capital structure, plays a key role in this context. Therefore, Capital structure and the influencing factors are the issues of interest for the researchers, especially in providing of finance. This study evaluated the impact of initial debt policy on Future debt policy, in the companies listed on Tehran Stock Exchange for the period of 2001 to 2015. In the following, along with the assessment the impact of particular Company’s factors on liabilities policy, this question is going to be answered that whether Chief Executive Office departure has effect on their liabilities policy? This research has the practical goal and the results of it are ex-post. With Systematic elimination, 115 cooperation companies have been chosen as a sample, and by the help of field study, the required data have been gathered.To analyze data and to assess relationships between variables, multivariate linear regression model and Statistical methods of panel data were used. The first hypothesis test results, show that the impact of initial liabilities policy on future liabilities policy is not significant. In the second hypothesis particular factors affecting on the company's liabilities policy examined and the results show the importance of the particular factors effects, on the company's liabilities policy. Based on test results in the third hypothesis, we come to this conclusion that the Chief Executive Office departure has no significant impact on corporate liabilities policy.

During the last decades several models have provided for predicting stock returns. The Capital Asset Pricing Model (CAPM), has attracted much attention, but also raised to it a lot of criticism. One of the criticisms is mentioned to this model is ignoring the intra-period volatility, structural breaks and duration of the calculations. Therefore, this study by using fuzzy regression has been tried to intra-period volatility to be included in this model. Then the stability of beta and accurately of the return prediction on the Capital Asset Pricing Model by using of Fuzzy logic system (FLS), Ordinary Least Squares regression model (OLS) and Generalized Method of Moments (GMM) are compared. The population of research is all companies listed in Tehran Stock Exchange and the research sample is consisted of 31 companies that in the period of 2011-2014, have been with the highest turnover. The results of research shows that the prediction error of stock returns in fuzzy model is more than OLS and GMM model, but the prediction error of stock returns in both of ordinary least squares regression model and Generalized method of moments doesn’t have any significant difference with each other. So the classical beta still has offered the more stable beta than fuzzy beta and thus is offering a better prediction of stock returns.

The goal of this research is to represent a Granger causal analyzing and its application in stock exchange. In order to do that, free float index of stock exchange, exchange rate(Rial in terms of Dollar), OPEC basket price(barrel per Dollar), gold price(Ounce in terms of Dollar) time series are selected to study Iran financial market interaction with domestic market(exchange rate) and international markets(oil and gold). Daily data cover the period that taking office by new presidency occured, also important domestic and international events such as efforts for realization of resistive economy, downfall oil price, middle east tension and Joint Comprehensive Plan of Action. Causal analyzing implements by using MSBVAR-DCC model and Bayesian approach. According to odds ratio, the variables are non-causal in conditional ratio, there is causal relation in conditional variance from the variables to financial variable. Therefore, oil, exchange rate and gold volatility includes exclusive information for stock index volatility. Consequently, shocks and past volatility of stock index lonely are not sufficient for volatility specification of the variable and using domestic market and international markets volatility information is strongly suggested.

After confirming the target leverage, estimating the speed of adjustment is the most important step in leverage behavior modeling. Each of the three main theories of Trade off, Market Timing and Pecking Order theories analysied with their own prespective the costs of deviations from target leverage and costs of adjustments to target leverage to predict speed of adjustment. Above or below target debt, financial deficit or surplus, equity overvalued or undervalued are main factors of estimating leverage adjustment speed in each of the theories. This study examined the speed of adjustment based on predictions of three theories and their interactions in listed firms of Tehran Stock Exchange over the period 1383–1395 with DPF.The results confirm the effects of three factors but not confirm the their interaction effects perfectly. The evidence showed that most of the adjustments occurred when firms have below-target leverage with a financial deficit and while etheir equity is undervalued in the market.

Capital markets’ return is influenced by multiple and various factors. These factors range from macro global factors to dependent variable historical behaviors. Each of many researchers has selected a segment or segments of this vast range of factors impacting capital market return in different countries and embarked on devising a model for projection of the respective capital market return. The present research has utilized self-explanatory and combined models for the purpose of modeling and projecting Tehran Exchange’s return using Autoregressive Moving Average (ARMA) and Autoregressive Moving Average with External Inputs (ARMAX) models. In this research, to utterly expound the model and to factor in maximum factors, following examination of return issues and the factors impacting return, the subject of projection and the common methodology thereof have been surveyed and different models of projecting capital market return have been examined in detail. Then, Classic Linear Regression Models, Autoregressive Moving Average (ARMA) and Autoregressive Moving Average with External Inputs (ARMAX) models were used to predict the return of Tehran Exchange. After estimating the above models, deploying 99-period data and confirming the power of expressing them via applying diagnostic tests, the return of Tehran Exchange for the next 4 periods was projected. These predictions by applying estimation models were compared with the real data and the optimum model was selected using Akaike Information Criterion (AIC), Schwartz-Bayesian Information Criterion, Hannan-Quinn and also MSE, MAE, MAPE criteria. The final result indicates superiority of ARMA over ARMAX