نشریه تحقیقات مالی
پیاپی 53 (بهار 1398)

Based on the existing theories, many variables may affect stored corporate cash. And on the other hand, occurrence of outliers in financial variables is common. This affects the performance of statistical methods, especially variable selection based on the ordinary least squares (OLS). In this article using robust variable selection, the most important factors affecting stored cash by firms accepted in Tehran stock exchange (TSE) are identified. To identify the most important predictors of stored cash, we apply robust variable selection technique based on the SCAD penalty function. For this purpose, the data from 196 stock firms in the period of 2009-2011 were analyzed. Findings showed that robust variable selection identifies variables affecting stored corporate cash which traditional regression methods such as OLS can't identify. Presence of outliers in financial variables may spoil the performance of the traditional ordinary least squares (OLS) method. This problem highlights the importance of using robust techniques in empirical finance.

Paired trading is among the most well-known and oldest algorithmic trading systems. The efficiency and profitability of this system have been demonstrated in many studies conducted so far in financial markets. Paired trading is principally based on long-run equilibrium relationships or reverting to the mean characteristic. In recent years, a large number of studies have been conducted on algorithmic trading using machine learning.
In this research, the reinforcement learning method - an appropriate method for modeling and optimizing problems involving different long-run relationships - was used in order to select appropriate trading thresholds and time windows for the purpose of maximizing efficiency and minimizing negative risks in paired trading through adopting the co-integration approach. Results are obtained by applying a combination of reinforcement learning method and co-integration approach in paired trading.
Empirical results based on the intraday dataof paired stocks showed that the reinforcement learning method used to design trading systems in paired trading had significant advantages over the other methods in previous works.
A pair trading strategy with the proposed algorithm can be used as a neutral market strategy in all market conditions, including prosperity and recession, by investors and individual and institutionaltraders.Also, for future research, it is possible to consider transaction costs in a pair trading strategy.

Pyramidal ownership structures and cross-ownership are common phenomena in developing economies. These structures result in a divergence between voting rights and cash flow rights on the one hand, and create internal capital markets through which firms can raise capital internally instead of going to the public market when they need additional financing. In the present study, the effects of firm interlock and stock price synchronicity in Iran Stock Market was investigated. We used a unique data set containing stock ownership and directors of all companies listed on the Tehran Stock Exchange considering three definitions of management, stockholding, and ownership and examined interlock through both equity ties and interlocking directors. We found that over 40 percent of all listed companies belong to networks of interconnected companies and there was a significantly positive relationship between being a member in each of these networks and the profitability of the firm alongside other colleagues. We also documented a complex set of networks among listed and unlisted companies, which have not been previously documented. We further find that pairwise interlocks through equity ties – either direct ownership or common owner – are correlated with higher stock price synchronicity, whereas common directorship is not linked to return co-movement. We can also claim that the effect is decreasing as firms become farther away from each other within a network. The results show that firm interlock, particularly through cross holdings and common ownership, can increase stock price synchronicity.

The effect of the contagion between financial assets is one of the most important challenges that investors are faced with. Financial markets have been severely affected by economic, political, and social events and they are subject to volatility and turmoil that causes financial investors and analysts to be disturbed. Therefore, the main objective of the present study is to investigate the effect of shocks on the return volatility of the selected indices. The main objective of this study is to use random simulation techniques for GARCH models considering multivariate skewed distributions using the Bayesian approach. For this purpose, we used the daily return data of selected Tehran Stock Exchange (TSE) indices during the period from December 13, 2009 to Jan 29, 2003 and also the flexible types of multivariate distributions that can model both skewedness and fat tail. The results of the Bayesian DCC GARCH (1.1) model showed that the effect of shocks on the return volatility is not the same  for the selected indices and this situation implies different risk conditions and uncertainty in the returns of these groups. The results also showed that the stock returns of the automotive group spread more volatility from the previous day to the current day in comparison to Banking group and Petroleum group respectively. Shocks on variables affects the correlations among them. Also, GED distribution was identified as an appropriate distribution in line with the characteristics of the skewedness of the studied indices. Therefore, in estimating the risk of assets and portfolio optimization, one can exploit the main idea used in this study.

The purpose of this paper is to investigate the effect of excessive CEO's self-confidence on the returns and unsystematic risk given the dual role of managing director in Tehran Stock Exchange. The statistical population of this research includes all the companies accepted in Tehran Stock Exchange and its statistical sample includes the data of 142 companies for the period of 8 years from 2009 to 2016.  Systematic method of elimination was used for sampling. The method used to estimate the pattern was multi-variable regression model based on the combination method. Results showed that, overconfident managers can lead to increasing stock return and unsystematic risk of companies. Other results showed that if the manager is overconfident and also is a member of Board of Directors, it has a significantly positive effect on the returns, yet a significantly negative impact on the company's unsystematic risk. Most self-assured managers are beginning to invest more, they are also thinking of more development and innovation, which brings more values to their company, so the company's returns are rising. Also, a moderately self-confident manager is bold enough to always make cash and securities available for sale as low as possible and maximize the level of short-term loans. So, these factors may increase the risk of the company. In the event that the over-confident CEO is also a member of the board, his understanding of the market and the demands of the shareholders and the financial reporting process will be enhanced and will enjoy higher power and control, and thus the company's returns will increase and the company will face less risk.

Capital asset pricing model (CAPM) has been among the most common models to estimate the expected return. In the standard CAPM model, a) the beta coefficient is fixed and b) the relationship between stock returns and market returns is assumed to be linear. While in financial markets, it is possible that the beta coefficient varies over time by changing the cost-benefit analysis on returns and risks, and also in a nonlinear environment, the beta coefficient estimate will be linearly inappropriate and oblique. Therefore, it seems necessary to use other models in estimating expected return. In this study, in addition to the standard CAPM model, the threshold regression and kernel regression models were used to estimate the CAPM model. Considering that the basis of each of these models is based on different assumptions; therefore, this research has tried to use a genetic algorithm in the time period from 2008 to 2017 to propose a hybrid model in order to estimate the expected return. Expected return was calculated using standard CAPM, threshold regression, kernel regression and the hybrid model of these three models, and the results were compared with the realized returns. The mean square error (MSE) index was used to measure the predictive power of research models. Using the paired t-test on the mean square error, the research models were compared with each other. The results show that applying the hybrid model increases the predictive power of realized return compared to other research models.

Systemic risk occurs when failure or crisis in a particular segment of a market propagates to other segments and develops into a pervasive crisis, so that the loss incurred by one or more influential major institutions spreads to others.The precise and timely identification of systemic risk is an indispensable necessity in each country's financial system to prevent a financial crisis. In this regard, this study seeks to evaluate the systemic risk in Iran financial system.
In the present study, we try to evaluate the systemic risk in the financial system of the country including banks, investment and insurance companies between 2011 and 2017 using Granger Causality Network method.
According to the results of this study, banking and insurance sectors have the highest and lowest systemic risk, respectively. In addition, the results showed that the systemic relationship between financial institutions changes over time. Finally, the Rank regression was used to validate the results of the research and it was found that the extracted results enjoy adequate validity.
The results showed that, among the three sectors, banking sector imposed the largest impacts on others indicating high level of systemic risk in this sector. In other words, as a part of financial system of the country, banking system is of higher systemic importance than the other sectors, so that a possible crisis in this sector may easily spread to other sectors given its relatively large influence on other sectors.