درخت حوزه‌های تخصصی

This study investigates the effects of macroeconomic variables on the stock market (stock price index).The effects of macroeconomic variables including global gold and oil price, exchange rate, interest rate, economic growth rate on the Iranian stock market has been investigated by using a non-linear distributed auto-regression model .The results indicated that the relationship between oil price and oil price index in the short term and long term is direct and inverse, respectively. The effect of the exchange rate on the stock price index is direct in the short and long term. In such a way that a long-term positive shock will lead to an increase of 0.87 percent and a negative shock of the exchange rate will lead to a decrease of 8.6 percent of the index. The effect of the positive interest rate shock in the short and long term on the stock price index is insignificant. Meanwhile, the negative shock of the mentioned variables will lead to a 0.12 percent decrease in the stock price index and in the short and long term on The positive shock of the gold price on the stock price index is insignificant.In terms of our results, economic growth has positive relationship with the stock price index. This result is in line with a one percent increase in economic growth, the stock price index will improve by 0.09 percent and with a one percent decrease in the economic growth rate, the stock price index will decrease by 0.1 percent.

The modelling of strategies for buying and selling in Stock Market Investment have been the object of numerous advances and uses in economic studies, both theoretically and empirically. One of the popular models in economic studies is applying the Semi-parametric Markov Switching models for forecasting the time series observations based on stock prices. The Semi-parametric Markov Switching models for these models are a class of popular methods that have been used extensively by researchers to increase the accuracy of fitting processes. The main part of these models is based on kernel and core functions. Despite of existence of many kernel and core functions that are capable in applications for forecasting the stock prices, there is a widely use of Gaussian kernel and exponential core function in these models. But there is a question if other types of kernel and core functions can be used in these models. This paper tries to introduce the other kernel and core functions can be offered for good fitting of the financial data. We first test three popular kernel and four core functions to find the best one and then offer the new strategy of buying and selling stocks by the best selection on these functions for real data.

As the main achievement of the modern portfolio theory, portfolio diversifica-tion based on risk and return has attracted the attention of many researchers. The Markowitz mean-variance problem is a convex quadratic problem turned into a mixed-integer quadratic programming problem when incorporating car-dinality constraints. Due to the high number of stocks in a market, this problem becomes an NP-hard problem. In this paper, a metaheuristic approach is pro-posed to solve the portfolio optimization problem with cardinality constraints using the differential evolution algorithm, while it is also intended to improve the solutions generated by the algorithm developed. In addition, variance, val-ue-at-risk, and conditional value-at-risk are assessed as risk measures. Candi-date models are solved for 50 top stocks introduced by the Tehran Stock Ex-change by considering the cardinality constraints of not more than five stocks within the portfolio and 24 trading periods. Finally, the obtained results are compared with the results of genetic algorithm. The results show that the pro-posed method has reached the optimal solution in a shorter time.

This study aims at investigating the effects of the change in the information environment from risk to ambiguity, disclosure dynamics, and the ambiguity aversion behavior of investors on the disclosure of integrated information for selected corporates operating in digital industry listed on the Tehran Stock Exchange during 2012-2022. The corporate voluntary disclosure lag, stock return volatility, and the probability of favorable returns incidence represent respectively the disclosure dynamics, firm risk, and investors' ambiguity aversion, within dynamic panel models. To investigate robustness of the estimates we have also included capital market ambiguity, firm size and stock liquidity indices in the model. The findings are consistent with existing theories. Firm risk and the capital market ambiguity have an increasing impact on the integrated information voluntary disclosure. However, the ambiguity at firm's level has decreased its voluntary disclosure. Likewise, a rise in ambiguity aversion (in-dependent of the ambiguity level), which makes investors more pessimistic about the firm's cash flows, makes the manager to increase the level of voluntary disclosure. Further, inertia in voluntary disclosure has been detected among the studied digital corporates. Depending on the magnitudes of the risk, ambiguity, ambiguity aversion, and the source of investors' ambiguity, the corporate managers decide on their voluntary disclosure policy appropriately.

Detecting financial fraud is an important process in the activities of compa-nies. In the last decade, much attention has been paid to fraud detection techniques. Financial fraud is a problem with far-reaching implications for shareholders. Today, financial fraud in companies has become a big prob-lem. Companies and regulatory agencies must continuously develop their mechanisms to detect fraud. Machine learning and data mining techniques are currently commonly used to solve this problem. However, these tech-niques still need to be improved in terms of computational cost, memory cost, and dealing with big data that is becoming a feature of current financial transactions. In this research, machine learning techniques including logistic regression, neural network, and Bayesian linear regression were used to de-tect financial frauds in the Iranian stock market. According to the obtained results, the support vector machine model with radial kernel has the lowest RMSE and the highest accuracy criterion, and the support vector machine model with linear kernel and Bayesian linear regression has the highest RMSE and the lowest accuracy criterion for modeling the financial fraud of companies in they were Tehran stock market. Also, the models of artificial neural network model, Bayesian linear regression and support vector ma-chine model with linear kernel respectively had the lowest characteristic values and did not perform relatively well in detecting the existence of fi-nancial fraud in the companies present in the Tehran stock market.