مطالب

The stock exchange is considered to be an important establishment to finance long term projects, on one hand, and to collect savings and finance of private section. The stock exchange can be a safe and secure place to invest surplus funds to purchase corporate stocks. As recession and prosperity in this market can have a great role in stockholders` decision-making, it becomes vital to predict these cycles. In this paper, using model MSMH(4)AR(2), we extract the financial cycles of the market. Then, using the ant colony algorithm, we determine the most significant predictors and predict the market financial cycles using neural networks. The results show that the PNN model performs better in predicting the future market with respect to the criteria of mean squared error, the root mean squared error, the model accuracy and kappa coefficient.

The Stock Exchange is a private sector savings and liquidity fund to fund long-term investment projects. The indicators of this market are influenced by several factors, one of which are economic variables. Banks are also one of the most important investors in the financial market. In this study, the effect of exchange rate, bank interest rate, liquidity and inflation rate on stock price index using time series data of 1978-1988 and using equation system model of four equations of the stock price index, inflation rate, profit rate Deposits, and facility interest rates were estimated using the three-stage least squares method. The results showed that the effect of the exchange rate, deposit interest rate and positive liquidity volume and the effect of inflation rate on stock price index was negative. Other equations have been estimated because of the relationship between the bank interest rate and inflation rate, and the results show that both deposit interest rate and facility interest rate have a positive relationship with the inflation rate

Behavioral finance is a new issue raised by some financial intellectuals over the past two decades and has been quickly addressed by professors, experts, and students throughout the world. Investigating the factors affecting investment decisions is carried out in the field of behavioral finance; in other words, the focus of behavioral finance is on the specific charac-teristics of human behavior and applying them in asset pricing. Empirically, pricing models rarely include psychological factors, but the noticeable point is that nowadays, researchers have found behavioral factors influencing empirical asset pricing models that can manipulate returns on asset mispricing. Behavioral asset pricing is the result of applying behavioral finance theories within traditional asset pricing theories. Thus, despite the existence of many asset pricing models, due to their weaknesses and lack of comprehensiveness, as well as the necessity of reviewing behavioral factors, this study aims to model asset pricing through behavioral models. Using the data from 141 listed firms in Tehran Stock Exchange over the years 2008 to 2017 and multivariate regression, this study is an attempts to model asset pric-ing through employing behavioral models and Fama-French approach. Using Fama-French approach, the results showed that accounting information risk, investors’ trading behavior, and investors' sentiment have a direct and significant impact on asset pricing.

Several financial soundness frameworks, such as CAMELS, are currently present in the banking industry, but some evidence suggests that the present frameworks have inefficiencies in an Islamic banking environment. This study is aimed at identifying and prioritizing the adjusted financial soundness indicators in Iranian banks. In this paper, the factors affecting financial soundness in banking industry were investigated and rated based on the viewpoints of 382 banking experts. Data gathering is done by designing a questionnaire. The research method is descriptive-correlation. For data analysis and the testing of the hypotheses, R-test software and confirmatory factor analysis have been used. TOPSIS method is used to rate the indicators from the points of view of senior banking managers. The findings showed capital adequacy, asset quality, profitability, liquidity, management quality, sensitivity to market risk, Islamic banking, corporate governance, and facilities with technical and economic backing affect the financial soundness of banks, while the liquidity and profitability indexes have the most impact.