محمد احدزاده

This study investigates the impact of the board's social capital on the investment efficiency of listed companies in the Tehran Stock Exchange. Based on the theoretical foundations, the board social capital as a social-behavioral factor can affect the problem of over or under-investment (both of which are examples of the inefficiency of investment decisions). Therefore, when the board's social capital is at a high and favorable level, company managers show less opportunistic behavior and do their best to increase cooperation and interaction within the company, which leads to the strengthening of investment efficiency. In terms of purpose, the current research is the applied-developmental type and takes a descriptive-correlational manner. We measured board social capital using the Co-Working Experience index. Investment efficiency is also measured through under- and over-investment using the Richardson (2006) model. The control variables also include the size of the board of directors, the independence of the board of directors, the size of the company, the ratio of net profit to sales, the rate of return on assets, and the level of financial leverage. The statistical population of the research includes 183 companies admitted to the stock exchange from 2016 to 2022. In order to test the research hypothesis, a multivariate regression model has been fitted using the panel data method with the fixed effects approach. The results of the research indicate that the hypotheses of the research are confirmed, and there is a positive and significant relationship between the social capital of the board of directors and investment efficiency.

This research delves into the application of Chaos Theory and non-linear analysis to the stock market, utilizing empirical data from international markets to scrutinize the presence of chaotic trends and non-linear processes within the time series of 20 international stock price indices, spanning from January 1984 to January 2024. Through the employment of predictability and non-linearity tests, the study found evidence of a non-linear process in the stock price index. Further, correlation dimension tests were conducted to evaluate the correlation between observations, uncovering a significant correlation between variables. Cumulative periodic tests were subsequently applied to refine the analysis, taking into account the chaotic nature of the stock price index variable, thereby affirming the chaotic nature of this process. Following the validation of the stock price index's predictability, ARFIMA, FIGARCH, LSTAR, and ESTAR models were applied for forecasting future periods. Among the models that incorporate long-term memory in the stock price index variable, the FIGARCH model exhibited superior forecasting power by accounting for both the long-term memory and the variance and changes of the variable. Among the nonlinear models, the ESTAR model demonstrated the highest prediction capabilities. The implications of this study are of considerable significance for investors aiming to comprehend and analyze stock market trends, offering a novel perspective on the stock market's dynamics.

The modeling and forecasting of yield volatility in financial markets have become increasingly critical due to their pivotal role in key applications such as Value at Risk (VaR) assessment, optimal resource allocation in investment portfolios, effective investment management, and accurate pricing of derivatives. Among the key drivers of yield volatility, information asymmetry between market participants has emerged as a significant factor influencing market dynamics. Despite its importance, the empirical exploration of this phenomenon in emerging markets, particularly the Tehran Stock Exchange (TSE), remains scarce. This study examines the impact of information asymmetry on the volatility of the TSE's total index, employing the advanced Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity (FIGARCH) framework. Intraday trading data spanning 2023 and 2024, collected at five-minute intervals, form the basis of this analysis. Information asymmetry was quantified via the transaction volume-weighted price impact metric. The Akaike Information Criterion (AIC) and Schwartz Bayesian Criterion (SBC) determined the optimal model specification, leading to the application of FIGARCH, with exogenous information asymmetry incorporated into the GARCH (1,1) baseline model. Empirical results underscore a positive and statistically significant relationship between information asymmetry and return volatility in the TSE. These findings have profound implications for enhancing market efficiency, risk management, and derivative pricing strategies in emerging financial markets.

Herd behavior, the tendency of individuals to mimic the actions of a larger group, significantly impacts capital markets by influencing stock prices, market liquidity, and overall market stability. This phenomenon has garnered significant attention in financial studies due to its implications for both institutional and individual investors, contributing to increased market volatility and potential crashes. Various methodologies have been developed to assess herd behavior, revealing its presence across diverse market conditions, including periods of high distress and volatility. This study examines macro herding in the Tehran Stock Exchange from March 2016 to February 2024, using weekly asset returns to measure herd behavior among listed companies. For the first time in Iran, we employ the TV method to calculate herding. The TV method offers two primary advantages: it is adept at identifying macro herding because it captures the collective trading direction of investors, and it operates independently of asset pricing models, minimizing biases associated with those models. Focusing on the collective trading direction, we aim to detect significant deviations in stock price movements indicative of herd behavior. Our findings indicate that herd behavior is more pronounced during extreme market conditions, both positive and negative, with a particularly notable increase during periods of negative market returns. This study provides insights into the dynamics of investor behavior in the Tehran Stock Exchange, highlighting the importance of monitoring such behavior to mitigate its potential adverse effects on market stability.