محمد ندیری

Numerous studies have focused on developing theoretical frameworks to examine the relationship between stock returns and investor sentiment dynamics. In contrast to earlier studies, which treated investor sentiments as entirely irrational, Verma et al. (2008) added to the literature a new framework that divides sentiment into fundamentals-driven (rational sentiment) and irrational components (irrational sentiment). Furthermore, they explored the possibility of a dynamic relationship between individual and institutional investor sentiments, which had been ignored in previous research. This study aims to investigate the impact of rational and irrational investor sentiments, both individual and institutional, on the overall index returns of the Tehran Stock Exchange.

To test the research hypotheses, we first used Baker and Wurgler's (2007) method to extract the sentiment index of individual and institutional investors using Principal Component Analysis (PCA), and then we investigated the hypotheses using two methods and two steps. In the first step, we used an autoregressive distributed lag (ARDL) technique to regress macroeconomic fundamental variables on individual and institutional investor sentiments to decompose the sentiment variables into rational and irrational components. In the next step, the effect of rational and irrational investor sentiment on stock market returns was evaluated using the vector autoregressive (VAR) approach on monthly data from 2011 to 2020.

The Granger causality test and impulse response functions from VAR model estimation show that the rational component of individual and institutional investors positively and significantly affects market returns. For both individual and institutional investors, it was discovered that the impact of rational sentiments on stock market returns was greater than the impact of irrational sentiments. Furthermore, the findings show that past stock market performance has a significant unidirectional impact on irrational sentiments but has no effect on rational sentiments for individual and institutional investors. The research indicates that rational sentiments have pricing power in the Tehran Stock Exchange. Under such conditions, the rational expectations theory of stock returns is a valid argument for the Tehran Stock Exchange. Overall, the results indicate that sentiment-induced fundamental investing has a much more significant effect on individual and institutional investors than sentiment-induced noise trading in the long run.

The findings have important implications for investors and policymakers. Individuals, particularly inexperienced investors, are encouraged to trade in these markets with adequate information, expertise, and market analysis. They should avoid herding and emotional trading and maintain a long-term perspective on the stock market. Furthermore, the stock exchange organization and related financial institutions should assist in reducing emotional behaviors and increasing the efficiency of the stock market by enhancing the financial literacy of inexperienced retail investors and strengthening the investment culture. Today, the sentiment index is used as a financial market predictor, so it is essential for the institutions in charge of regulating the financial markets to be involved in the compilation, reporting, and analysis of this index. In the event of extreme price fluctuations or market deviations (even in the short term), these institutions should issue the necessary and timely warnings and support the effectiveness of the market by approving and implementing control mechanisms such as short selling.

This research investigates the relationship between oil price Volatility and investors' sentiments in the Tehran Stock Exchange. Oil is one of the most important sources of energy in the world and is necessary for many industries. Oil price Volatilities directly and indirectly impact the economy and financial markets. In this research, the monthly relationship between oil price Volatility and investors' sentiments was investigated using the Nonlinear Autoregressive Distributed Lag Model (NARDL) in the period from April 2011 to March 2023. The results showed that there is a significant relationship between oil price Volatility and investors' sentiment index, asymmetrically in the short term and symmetrically in the long term, that is, the price of oil in the ups and downs of the market has a different impact in the short term and a similar impact in the long term on sentiments. investors, and on the other hand, in the short and long term, oil price Volatilities impact investors' sentiment with different intensity, Therefore, stock market investors should have a different analysis in forming their portfolio for oil price Volatilities in the ups and downs of the market.

Since financial developments that are not driven by fundamental factors of monetary policy may have a large impact on the economy, policymakers have placed significant emphasis on the financial conditions index over the past recent years. Policymakers must maintain a vigilant watch over financial conditions, as their significance becomes increasingly significant. The construction and use of the financial condition index include three issues, which are: a) the selection of financial variables to enter the financial index, b) weights used to relate financial variables to the index, and finally c) the relationship between this index and the macroeconomy. There are many reasons for the changeability of these three cases over time, which can be discussed about the reasons for their occurrence and effect on the results. Many changes affect the way a financial index is made. Therefore, in this research, the goal is to design an index of financial conditions using time-varying dynamic models to improve the forecasting of macroeconomic variables.

The process of implementing the conceptual model can be explained in the following steps. First, extracting the desired variables to be used in the desired models (for this purpose, we have used monthly data during the years 2001 to 2021 for 19 financial variables and 5 macroeconomic variables). It should be noted that since all the variables must be in the form of rates - if necessary, all the variables were converted into growth rates. Also, all the variables were examined from the dimension of stationary, and the problem of their stationary has been solved. Second, the desired models were calculated to predict macroeconomic variables. Using time-varying models, dynamic averaging models, and dynamic selection models, the financial condition index was constructed in such a way that this index could include various variables to adopt different coefficients from its previous and subsequent periods and challenge constant parameter and variable models. Third, the predictive power of each of the proposed models in the estimation of macroeconomic variables (sum of squares of prediction error) was analyzed and estimated. Fourth, financial index extraction was done based on the model selected in the third step.

The findings suggest that employing models that solely consider variability in the coefficients (without accounting for variability in the variables) leads to enhanced predictions of the unemployment rate compared to vector autoregression models and vector autoregression models incorporating the basic component. In addition, by moving from the generalized factor vector autoregression models and the time-varying generalized factor vector autoregression and the generalized factor time-varying vector autoregression, in which only the variability in the parameters is included (without considering the variability in the variables) towards the models that consider the variability in the parameters both in a Bayesian and dynamic way, a reduction will occur in the forecast error in the variables of the consumer price index, liquidity, monetary base, and gross domestic product. However, the amount of improvement in each of the variables is different from each other.

This study employed time-varying models to derive the financial condition index, aiming to provide the most accurate estimation of macroeconomic variables. The findings demonstrate that the utilization of these models outperforms other approaches in forecasting a majority of the macroeconomic variables

The stock price crash risk is one of the main concerns of financial market participants. According to financial theories, Retail investor attention is one of the factors affecting the stock price crash risk. This study investigates the effect of retail- investors' attention based on the GSV index on the stock price crash risk in the Tehran Stock Exchange. Also, considering the moderating role of institutional ownership and the quality of companies 'audits in the relationship between investors' attention and the probability of stock price crashes, the role of these two variables has been investigated in the study. The research period was 2009-2020 and the sample included 164 companies. The study's results using the panel data regression approach indicate that investors' attention in the current period increases the risk of a stock price crash in the subsequent period. Also, there is a negative relationship between institutional shareholders and the stock price crash risk in the next period and there is no relationship between the quality of companies' audits and the stock price crash risk of companies in the next period. Also, institutional ownership and audit quality do not affect the relationship between retail-investor attention and stock crash risk.

Although a positive mean-variance relation is a cornerstone of traditional finance theory, empirical evidence supporting it is controversial and mixed. According to behavioral finance theory, the mixed risk-return tradeoffs attributes to investor sentiment in the financial market. In this paper, we investigated the effect of individual investor sentiment on the mean-variance relationship in 103 Tehran Stock Exchange firms using the BSI index. Meanwhile, we examined the relationship between small and large companies, high and low-priced firms, and growth and value stocks. The conditional volatility of stocks was calculated with GARCH models, and the research hypotheses were examined using a panel data method. The results show that the risk-return relationship in the total sample, growth stocks, and high-priced entities are less affected by sentiments, but sentiments strengthen the positive mean-variance relation in value stocks, low capitalization, and low-priced firms. However, sentiment does weaken the positive relation in high-capitalization firms. According to the research results, in constructing portfolios based on variance, investors should consider not only the sentiment of investors but also the features of the share in terms of value and growth, the market value of the company, and the stock price of the companies.

The credit risk and non-performing loans of banks are among the most important problems of the banking system in Iran. And according to available statistics, the average default of loans and non-performing loans of banks in Iran is much higher than the global average. The main purpose of this study is to identify and analyze the basic and important factors affecting credit risk in the Iranian banking system.

For this purpose, first, using the method of guided interviews with twenty experts and credit risk managers of the country's banking sector, who were selected by the snowball method, the most important factors affecting credit risk were identified; then, these factors were returned to the experts for ranking in the form of a pairwise comparison questionnaire, and finally, the important factors affecting this risk, along with their sub-factors were analyzed using denp technique (the dematel based analytic network process).

The results of the research show that the macroeconomic factors are the most important factor as well as instability in macroeconomic environment, failure to take timely and inappropriate actions with defaulters, poor and inadequate credit monitoring, ordered loans, and lengthy and time-consuming judicial procedures are the most important factors affecting risk credit in Iranian banking.

Originality/Value: 

The distinguishing feature of this study from other similar studies is the use of DANP technique to investigate the relationships between factors and sub-factors.

The capital asset pricing model provides an equilibrium model to show the relationship between risk and return on assets. One of the economic areas is herd behavior, which has attracted a lot of attention in recent decades. Therefore, the present study deals with the herd behavior in the Iranian economy on the efficiency criteria of the asset pricing model. The research method used in this research is correlational research that a regression method has been used to test the research questions. To test the hypotheses, Karhart's 4-factor model was used, to which the bulking factor was added. The statistical sample of the present study includes 115 companies active in the Iranian economy. The research period is for 10 years, which has been studied from 1389 to 1398. The results show that mass rotation is different in different degrees of risk and occurs more in high risk areas of the market and causes a return of beta in the market and the inefficiency of the capital asset pricing model to is following. It was also found that by removing high-risk portfolios from the sample, no bulkiness is observed and beta behavior is stabilized according to the theory of capital asset pricing model and beta return does not occur.

The estimation of an accurate measure of the Value-at-Risk is still a topic of interest in financial research and among risk management practitioners Despite the simple concept of VaR, measuring it is a very challenging statistical problem; because of normal distribution assumption, time-varying conditional quantiles, and the main limit of this approach consists into considering linearly conditioned quantiles. CAViaR model and its extent to the multivariate CAViaR approach (MCAViaR) have solved some of these shortcomings. To this end, Copula functions were introduced. This approach provides a flexible non-linear multivariate representation among quantiles. An important parameter of Copula functions is the degree of dependency between tail distributions, the incorrect estimation of which also leads to inaccurate interpretation. One way to estimate the dependency parameter is to use the optimization process; such as meta-heuristic algorithms due to their very high accuracy. Among the meta-heuristic algorithms, the Particle Swarm Optimization (PSO) algorithm is widely used in optimization research due to its high convergence speed. In this study, the MCAViaR model and the Clayton and student’s-t-type hybrid Copula model are used to estimate VaR dynamics in 10 large and active companies of the Tehran Stock Exchange from April 2009 to March 2020. The results showed that the tail dependency coefficients of the MCAViaR model for the studied stocks are zero, and therefore this model can be divided into two independent CAViaR equations. The results of estimating time-varying quantiles, which indicate the dynamics of value at risk, indicate that the time series of quantiles derived from the hybrid Copula model shows the dynamics well due to the higher time-frequency than the MCAViaR model. The results of the Kupiec back-test also confirm the better performance of the hybrid Copula model than the MCAViaR model.

Business environment has a significant impact on performance of all enterprises, and thus affects economic development. According to the view of the majority of economic experts, Iranchr('39')s business environment is not in a good condition. Most of actions for its improvement are implemented without considering their components interactions and hence no success is achieved.Therefore,before any action, it is crucial that the components of business environement should be identified.The aim of this research is vast analysis of business environment elements and their interaction, Hence the context and business environement improvement is obtained. This research has identified 481 components of the business environment by examining 1751 international and domestic researches. Interpetive- Structural Modeling as a tool of soft operation reseach (OR), makes people and groups map the complex relationships among many elements in a complex decision making situation. In this research, by using Interpretive Structural Modeling we analysed intensity of influence, effects, direct and indirect effects and their dependency. The results of the research show that, the most influential component of business environment is legal environment. Hence, for improvement of legal environment and consequently Iranchr('39')s business environement patology and procedures of law making are mostly important duty of policy makers.

One of the most important risks that the Islamic banking system, the same as conventional banking, faces is liquidity risk. The liquidity management in the Islamic banking system, however, is more complicated compared to conventional banking because of difference in the nature of the tools used there and the forbiddance of riba  . The aim of this study is to investigate the relationship between the qualities of assets - debts and ways of financing with liquidity risk in Islamic banking, and also to examine this issue by considering the role of time element in liquidity risk, by dividing liquidity needs into 2 categories, urgent and non-urgent, using financial ratios. To achieve this we gathered the data for 67 Islamic banks from ORBIS and Banker database during the 2016-2011. The method used to investigate the subject was Canonical correlation analysis approach. The results gained by Stata software indicate that the liquidity risk is highly correlated with the quality of assets and financing. They also show whenever we use three urgent and non-urgent risk measures simultaneously, 97% of variability of liquidity risk is explained by the variability of independent variables (i.e. quality of asset - financing) but when we use urgent and non-urgent risk ratios independently, the amount of variability of dependent variable is explained by the variability of independent variables (i.e. quality of asset -financing) at 0/531 and 0/382 respectively.