نشریه چشم انداز مدیریت مالی
پیاپی 29 (بهار 1399)

Behavioral finance explains the contradictory patterns with efficient market hypothesis with behavioral bias. One of the most common stock market price patterns is momentum, which can be due to the investor's under-reaction of reference prices resulting from two behavioral biases is adjustment and anchoring bias and disposition effect. This research studies the role of adjustment and anchoring bias and disposition effect in formation of momentum in Tehran Stock Exchange. Using portfolio method as research method and data over the period from 2007 to 2017 (1386-1395 in Solar Hijri-Iranian calendar), It was found that investors, in comparison with disposition effect, are more affected by adjustment and anchoring bias, and by under-reaction against reference points based on price extremes up to one year cause the formation of momentum pattern. Also, among price extremes, investors are more affected by high-26 weeks and robustness tests confirm these results by using the Fama-Macbeth regression and the Fama-French model.

The function and importance of banks in the country's economy is very high due to the nature of their work; and if banks do not play their role properly and cannot respond to investors and stakeholders, they will create a financial crisis. Determining and managing liquidity (liquidity creation) of banks and in particular recognizing the factors affecting this type of banks' liquidity, can be of great assistance to supervisory authorities to advance the objectives and prevent bank failures. The observers, investors, analysts and financial market activists can also use this index and its influential variables to make the right decisions in their investments. Therefore, this research tries to calculate the liquidity creation index and investigate the factors affecting the liquidity creation of banks listed in Tehran Stock Exchange during the years 2010-2016. For this purpose, a sample consisted of 8 banks listed in Stock Exchange were selected and the liquidity creation index was calculated. Then, using the multivariable regression model, the relationship between variables was investigated. The results of the research indicate a weak performance in generating liquidity of sample banks in the total study years. The results of the research hypotheses indicate that there is a meaningful and direct relationship between the ratio of the total loan to the total deposits and the liquidity creation index. Also, no significant relationship was found between the other research variables and the liquidity creation of the banks participating in statistical sample during the study period.

This paper investigates the determinants of liquidity constraints and corporate performance with emphasis on credit and business cycles based on a sample consists of 206 industrial firms listed on the Tehran Stock Exchange over a 10-year period (2006-2015) by using the GMM and dynamic panel data estimation technique. For this purpose, first the impact of credit cycles on the firms’ access to bank credits is studied. Then the impact of credits on the liquidity constraints of firms is estimated. Finally, the relationship between liquidity constraints and return on sales of firms, as a performance indicator, is considered during business boom and recession. According to the results, firms’ access to bank credit during the credit cycles is asymmetries as in time of credit bust the access to the credit is affected greater than in credit boom. Also, the business cycles influence on relationship between liquidity constraints and firms performance, because the performance is affected more in a recession period than a boom. That means an expansionary credit policy, in order to liquidity constraints reduction during recessions, is effective but it should be accompanied by improvement in macroeconomic conditions. The results based on the firm size and industry type show, that the negative impacts of credit crunch on liquidity and performance of smaller firms are more than larger firms. Also, in the credit boom, the most and least access to bank credit are the automotive and machinery industries firms, respectively. Whereas the liquidity constraints have the most impacts on non-metal mineral industry and the least on the automotive industry.

The purpose of this study was to provide a model for predict the volatility in Tehran Stock Exchange index, taking into account the behavioral and macroeconomic factors affected on financial and monetary markets of the country. The importance and necessity of this study is that, unlike other previous studies, in order to realize this importance, only macroeconomic dimensions have not been addressed, and in addition to this dimension, the behavior dimensions have been examined, Until to improve the explanatory power of the model than the other presented models in this regard. This matter has been accomplished by using the system dynamism method and the relationship between behavioral bias, alternative assets and macroeconomic factors. The simulated model is structured by using the Vensim DSS software and the results of the research prove this matter that according to the upcoming conditions until 2021, the value of all alternative assets of shares except the value of oil, will grow strongly and consequently, the Tehran Stock Exchange Index will follow this pattern. In this regard, it should be noted that the multi-fold growth of the value of each assets in the monetary and financial markets in a short period of time does not indicate the economic growth in the country, even this matter due to the attract unknowing investors to these markets and it can lead to economic crises.

To optimize the investment portfolio, conditional value at risk is a new approach. To amend the non-normal distribution of return on assets, and the non-linear correlation between return, the modification of Copula-CVaR compound method has a better performance in measuring portfolio risk. In this study, it has been attempted to present a more efficient model for portfolio optimization that provides greater returns for investors, given the uncertainty investment conditions. The VaR model was compared variance-covariance approach and the Copula-CVaR model for their efficiency frontier. The research area entails of 2014 up 2018; The statistical population was the top 50 companies of TSE.The variance-covariance approach was used to estimate the VaR of the portfolio. Moreover to estimate the CopulaCVaR model we have used the ARIMA-GARCH time series disruption component of the asset return distribution; then the marginal distributions of the assets were estimated using the CAPA-Student function; finally through Monte Carlo simulation the return on assets and their CVaR for the 10-day period were calculated. The optimal portfolio composition was determined at 95% and 99% confidence levels for different levels of risk. The results of this study showed that the optimized portfolio formation using the compound model, the Copula-CVaR model, has performed better

In current competitive environment, the risk of financial distress of companies has increased, in this situation, individuals and corporations and financial institutions make a lot of effort to learn about the status of Investee companies to protect their capital. The assessment and recognition of the firm's financial condition requires utilizing efficient measures with less error probability, Therefore, the purpose of this study is to compare the different criteria for identifying financially distressed companies. For this purpose, after reviewing and identifying the most important criteria and models for identifying distressed companies from non-distressed corporations, the distressed companies of Tehran Stock Exchange during the years 2006 to 2018 were separated from healthy firms and compared with the results of logistic regression and artificial intelligence methods, Article 141 of the trade law, Altman (1968), Altman (1995) and Asquith et al (1994). The results of this study showed that during the period under review and in the conditions of the Iranian companies based on Tehran Stock Exchange that Asquith et al (1994) criteria is the best way to identify distressed companies and to predict the financial position of companies and the Altman criteria (1995), Article 141 of the trade law and Altman (1968) are among the top priorities in identifying distressed enterprises.

The main purpose of present research is to investigate the effect of governmental ownership on the relationship between intellectual capital and financing decision. Empirical studies have identified other factors which are related to financing decisions and influence the intellectual capital efficiency. Therefore, in this research in addition to investigating the effect of intellectual capital and its dimensions on financing decisions, the moderating role of governmental ownership on the relationship between intellectual capital and financing decision has been considered as well. In order to attaining research goal, two main hypotheses and twelve sub-hypotheses have been provided. Research sample consisted of 107 listed companies in Tehran stock exchange during year 1389 to year 1397. Structural equation modeling based on variance approach and SmartPLS software were used for testing hypotheses and research conceptual model. Results show the positive effect of intellectual capital on using debts in capital structure. Also, results show that governmental ownership can enhance the positive relationship between intellectual capital and borrowing