جستجوی مقالات مرتبط با کلیدواژه « tehran stock exchange (tse) » در نشریات گروه « مدیریت »

The role of corporate governance in matching the interests of senior executives with those of the shareholders’ interests has been investigated in previous studies. If corporate governance can reduce agency costs, companies with good governance may have fewer unfavorable issues in their capital allocation. The main purpose of this study is to examine the relationship between the components of corporate governance and the efficiency of capital allocation in public joint stock companies.

In order to study and achieve the objectives of the research, 80 companies listed on the Tehran Stock Exchange (TSE), from 2015 to 2018, were selected via the purposive sampling method. The research hypotheses were tested using the panel data and fixed effects model.

According to research findings, corporate governance has a significant relationship with investment efficiency. The results also showed no significant relationship between investment efficiency and corporate governance variables including independence of the board of directors, leadership structure, the number of members of the board of directors, block-holder ownership, and the presence of outside directors in the audit committee. There is also a significant and positive relationship between institutional ownership and investment efficiency

The achieved results of the present study confirm the significant relationship between investment efficiency and corporate governance. Accordingly, investment efficiency is expected to improve as the quality of the corporate governance structure increases. Only a significant relationship between the existence of institutional investors as a corporate governance variable with investment efficiency was proved. The presence of institutional shareholders in the ownership structure and, consequently, their representative on the board of directors, can lead to better control and more efficient decision-making in capital allocation. On the other hand, there was found no empirical evidence for the relationship between other corporate governance variables such as board independence, leadership structure, board size, block ownership, and the presence of outside directors in the audit committee with investment efficiency as the agency theory proposes. The agency theory cannot explain the empirical findings; therefore, the behavioral aspect and legal limitations and requirements are required to be considered.

One of the efficiency assumptions of stock market is that investors act rationally, but the results of researchers' scholars show that in many cases, investors in the stock market have irrational behavior. The present study show irrational behavior. In the present article, two types of personalities are considered for shareholders in the stock market. The first group has an impulsivity personality and the second group has an oscillating personality. Traders who have an impulsivity personality buy stocks when prices increased and sold when prices decreased. On the other hand, there are oscillators who buy when prices dropped and sold when prices increased. The innovation of the present study is to provide a dynamic model for analyzing the behavior of investors by agent-based modeling and considering their various personalities of trader’s behavior in NetLogo software environment. Finally, after designed model, the validity of the model is assessed which results indicate high validity of the designed model in the simulation environment. The results show that the percentage of shareholders with oscillating and impulsivity personality in the price balance is distributed to 52% and 48% between impulsivity and oscillating shareholders.

Financial performance evaluation is very important in a highly competitive environment for companies, so an accurate and appropriate performance evaluation is critical. financial performance indicators must be carefully identified in the evaluation process because they reflect the competitiveness of a company. In this study, a hybrid approach of multicriteria decision making method is proposed for financial performance evaluation of automobile producer group companies of Tehran stock exchange (TSE). In this study for this purpose, with help of financial experts, a hierarchical financial performance evaluation model is structured based on the accounting based financial measures and value based financial measures (modern financial measures). In this quantitative approach fuzzy analytic hierarchy process (FAHP) was applied to determine the weight of the criteria and then the companies are ranked by using VIseKriterijumska KOmpromisno Rangiranje (VIKOR), Additive Ratio Assessment (ARAS) and Complex Proportional Assessment (COPRAS), simultaneously. The results represented the results represented the highest important of value based measures than accounting measures, also results of three outranking methods are almost identical and the results was combined to obtain final ranks.

This paper proposes a Soft Computing model for effective bankruptcy prediction, based on the integration of Group Method of Data Handling (GMDH) neural network and genetic algorithm which is called here as GA-GMDH. Genetic algorithm (GA) designs the whole architecture of the GMDH network and optimizes its topology. In order to demonstrate the effectiveness of our proposed GA-GMDH model, its performance was compared with performance of the commonly used statistical techniques of logistic regression (LR) and a relatively new artificial intelligent technique of Adaptive Neuro-Fuzzy Inference System (ANFIS). Performance of the designed prediction models depends on the utilized variable selection technique. Therefore, we constructed 12 prediction models through combining the four filtering feature selection methods and the three prediction models. The four feature selection methods of independent samples T-test, correlation matrix (CM), stepwise multiple discriminant analysis (SDA) and principal component analysis (PCA)are combined with prediction models to generate four optimal feature subsets. Empirical data were collected one year prior to failure from Tehran Stock Exchange (TSE) during 1997-2008. For robust assessing of prediction models’ performance, we applied Type-I and Type-II errors, and area under the receiver operative characteristics curve (AUC) measures. Experimental results indicate that our proposed GA-GMDH model has high ability in bankruptcy prediction problem and significantly outperforms ANFIS and LR models in all combinations with four feature selection methods. Meanwhile, the CM method has the best ability in selecting predictive variables in comparison with other feature selection methods. Therefore, CM-GA-GMDH model is determined as the best constructed model for bankruptcy prediction using our particular dataset from TSE.

This paper investigates the presence of long memory in the Tehran stock market, using the ARFIMA, GPH, GSP and FIGARCH models. The data set consists of daily returns, and long memory tests are carried out both for the returns and volatilities of TEPIX series. Results of the GPH, GSP and ARFIMA models indicate the existence of long memory in return series. Also, suggest that long memory dynamics in the returns and volatility might be modeled by using the ARFIMA–FIGARCH model. Furthermore, results of this model shoes the strong evidence of long memory, both in conditional mean and conditional variance. In addition, the assumption of non-normality is appropriate for capturing the asymmetry and tail fatness of estimated residuals. These findings suggest that the model based on the Gaussian normality assumption may be inappropriate for modeling the long memory property. Finally, it seems that the Tehran Stock Exchange (TSE) cannot be considered an efficient market in terms of the speed of information transmission. Hence, speculative earnings could be gained via predicting stock prices.