درخت حوزه‌های تخصصی

This study aims to present a model for predicting corporate default among Tehran Stock Exchange’s selected industries. To do this, corporate default drivers were identified and selected by referring to previous research findings and using experts’ opinions. These drivers were divided into five categories: accounting ratios, market variables, macroeconomic indicators, nonfinancial factors, and earnings quality measures. Structural equation modeling (SEM) technique was used to derive the prediction model. In this technique, corporate default drivers were used as latent independent variables, and their constituent factors were considered as observable indicators of the above variables. In addition, corporate default, as the latent dependent variable, was calculated by a measure based on the Black-Scholes-Merton (BSM) option pricing model. After implementing structural equation modeling (SEM) technique by use of Smart PLS software, a prediction model that contains influential drivers of corporate default was derived and presented for each of the selected industries.

In this research, we use jump beta and continuous beta as indicators of financial sector companies systematic risk and study their determinants in banking, insurance and investment industry. In result, the value of jump beta is higher than continuous beta. Jump beta of Banking industry and Investment industry is considerably lower than average. We found some negative and positive effects of firm characteristics on jump beta and continuous beta. In insurance companies, the supremacy of jump beta is influenced by firm characteristics. Size has positive effect on aggressiveness of both continuous and jump betas in investment companies. Current ratio has positive effect and debt ratio has negative effect on aggressiveness of insurance companies. Firm characteristic has some positive and negative effects on continuous industry beta deviation, but no effect on jumpy one. Inflation has negative effect on continuous beta but has no considerable effect on jump beta. Inversely, exchange rate has negative effect on jump beta but has no sensible effect on continuous beta. Influence of growth rate is strong positive for all industries of financial sector but weak positive for banking and insurance companies

Classic finance believes that stock price changes are related to systematic changes in the company's intrinsic values. However, recent research shows that behavioral factors play a very important role in determining stock prices and returns of investors, one of these behavioral patterns is noise trading. The purpose of this study is to investigate the effect of unsystematic risk fluctuations on noise transactions.    For this study, we use the random variance of the capital asset pricing model-disrupted unit as a measure of unsystematic risk fluctuations and for measuring noise trading We used a comparison of  company market value with industry companies the average market value. The research sample included 92 companies listed in the Tehran Stock Exchange during the period of 2011-2016.       The result of the test the hypothesis of the research showed that the relationship between unsystematic risk fluctuations and noise trading using is positive and significant and thus unsystematic risk fluctuations can be used as a criterion for detecting noise trading.

The purpose of this paper is to examine the asymmetric effects of banking sector and stock market development on economic growth in Iran. For this purpose, Smooth Transition Regression (STR) model used based on seasonal time series data during 1989-2017. The results indicate that the impact of financial and banking development indices on economic growth is different for economic growth rates above and below 6%. Therefore, if the economic growth rate is higher than 6%, then we have a regression and when economic growth is lower than 6% will have another regression in order to effect of financial development of economic growth. In addition, results show that that the relationship between private sector credit and economic growth is much stronger than the relationship between stock market and economic growth.

Information is like a strategic decision-making tool in which the quality of decisions will merely depend on the information used at the time of making those decisions. The purpose of this research is to assist individual investors in Tehran Stock Exchange by providing them a logistic model enabling them to predict their trading behavior. The data in this research has been collected from the statistical population of the study through variables, believed to have effects on the investors' process of decision making. Therefore, in order to achieve the statistical data, 2400 transactions in the form of 100 transactions, including buy and sell of stock shares from March 2017 until February 2019 have respectively been collected as samples. Based on the results of the logistic regression test, the behavior of institutional investors, as well as the volume of stocks traded have a significant positive impact on the behavior of individual investors (the probability of buying shares by them) versus the Beta, earnings per share, and dividends per share that have a negative effect on the probability of purchasing shares by individual investors. The analysis of the results suggests that individual investors are mainly subject to collective behavior which in particular is the same behavior of institutional investors. On the other hand, they tend to invest in stocks with low beta (defensive stocks) along with factors such as earnings per share and dividends per share which have less impact on the probability of stock purchases by individual investors.

The use of technology acceptance models is a way to understand the users' attitude towards new technologies. The present study aimed to investigate drivers affecting the customers' behavioral intention to use blockchain capabilities as a financial instrument using a combination of several technology acceptance models to determine what factors encouraged users to use blockchain capabilities as a financial instrument. Therefore, determinants of the behavioral intention to use blockchain capabilities as a financial instrument were first explored by the literature review, and then the structural equation model was built according to this basis. Results of data analysis indicated that the personal propensity to trust and structural assurance beliefs directly affected the initial trust that in turn, affected the users' behavioral intention. On the other hand, task and technology characteristics directly affected the task-technology fit that in turn, affected the users' behavioral intention. Furthermore, the performance expectancy directly affected the users' behavioral intention. Results of the present research indicated that the intention to use blockchain capabilities as financial instrument came from a social need; and users were keen to use blockchain despite some limitations of this technology.