نشریه تحقیقات مالی
پیاپی 70 (تابستان 1402)

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 efficient market hypothesis is based on the assumption that investors behave rationally, but studies in behavioral finance have contradicted this notion. The irrationality of investor behavior can be attributed to risk in less efficient markets, which can lead them to make decisions based on emotions. These emotional decisions impact firm performance. This study is to determine the influence of two investor behaviors, "loss aversion" and "overconfidence," on the performance of companies listed on the Tehran Stock Exchange. Loss aversion serves as an indicator of negative emotions, while overconfidence serves as an indicator of positive emotions. This study, to the best of our knowledge, is among the initial research in Iran's financial field to utilize loss aversion and overconfidence as indicators of negative and positive emotions, respectively. Based on these objectives, two hypotheses were developed in this study. The statistical population of this research consists of companies listed on the Tehran Stock Exchange (TSE). Data required for the statistical analysis were obtained from various sources, including the Rahvard Novin software, the TSE Technology Management Company website, and the financial statements of the companies. To test these hypotheses, a sample of 156 companies, listed on the Tehran Stock Exchange between 2013 and 2020, was selected using the screening method. The research questions were answered using the panel data method, descriptive statistics, correlation analysis, and multivariate regression model. The findings shed light on the correlation between investors' behavior and the performance of companies on the TSE. The study reveals a strong negative correlation between investors' loss aversion and the economic performance of companies. This suggests that more loss-averse investors tend to contribute to the lower economic performance of companies. Furthermore, the study shows a positive correlation between investors' overconfidence and market performance. This suggests that companies with more confident investors tend to perform better in the market. Interestingly, the study also found that Iranian investors tend to be more overconfident than risk-averse, meaning that their overconfidence outweighs their tendency to be loss-averse. This generally optimistic attitude of investors in the TSE has a positive impact on the overall market performance by encouraging investment and boosting market confidence. Loss aversion bias can lead investors to hold onto loss-making investments due to their fear of realizing losses. Pessimism and unfavorable stock market conditions may cause investors to prioritize avoiding losses over making strategic investment choices, ultimately reducing the economic performance of the company. Additionally, the overconfidence bias can cause investors to underestimate the risks associated with assets, leading them to base their investment decisions solely on simple criteria they deem important in their minds, without exercising sufficient care and attention. The rise in demand may lead to an increase in the securities' price, ultimately enhancing the overall value of the company.

Most economic and financial theories, according to the "rational economic man" theory, assume that when investing, investors consider all aspects and make completely rational decisions. However, the concept of a rational person remains ambiguous since human decisions deviate from the standard assumptions of economics. Individual stocks are recommended for purchase or sale by financial experts and stock market analysts and are followed by a large number of individual and institutional investors. Analysts provide information of varying quality to various groups of investors, as a result of the differences in these groups' characteristics and the information available to them, each group behaves differently. Since each of these groups may uniquely rely on the market, understanding the basis of the collective behavior of different investor groups can lead to a better understanding of price fluctuations. This study investigates how investors in the Iranian province of Fars react to the recommendations of fundamental analysts when selling shares. In this study, the laboratory-field method was employed to design and execute two experiments involving a total of 315 investors. The participants were divided into control and experimental groups, and the study focused on investigating the presence or absence of the tendency effect in the Iranian stock market within the control group. The impact of analysts' reasoning and recommendations on investor decisions and behavior was evaluated in the experimental group. The first experiment showed that the Iranian stock market is influenced by a tendency effect. The findings of the second experiment revealed that under good market conditions, investors kept winning stocks with both strong and weak analyst recommendations. Under difficult market conditions, investors were less likely to sell losing stocks (with negative returns) with excellent expert recommendations but more eager to sell with weak analyst recommendations. The purpose of this study was to examine how the recommendations of fundamental analysts impact the decision-making of stock exchange investors in the Fars province of Iran regarding the sale of stocks. Accordingly, 315 Tehran Stock Exchange investors from various cities in Fars province were divided into two experimental groups (each with 130 participants) and a control group (including 55 participants). The research findings indicated that given favorable market circumstances and the lucrative share position, with both the analyst's strong and weak recommendation (based on keeping the share), people resist selling and begin to keep the share. Furthermore, despite the analyst's strong recommendation, people stop selling and risk keeping the shares due to unfavorable market conditions and the losing stock's position (with additional information to support the recommendation to sell the shares). Unlike earlier studies, this study investigated the impact of fundamental analysts' recommendations on investors' decisions to sell shares depending on probability.

This article explains different approaches for pricing life settlements in the secondary market to adopt them for pricing the same product in Iran’s insurance industry. This is a quantitative study with a focus on life settlements pricing in Iran’s secondary market. It uses deterministic, probabilistic, and stochastic approaches. For this purpose, the Iranian life table, applicable since March 21, 2021, and the French life table, which was previously used by Iran’s insurance companies, were used to calculate the mortality and survival probabilities. Also, these probabilities were adjusted based on the insured's health status and lifestyle. Furthermore, the research findings were compared with the surrender value of the life insurance policy. The results showed the insurance policy's surrender value to be lower than the price calculated based on the probabilistic and stochastic approaches. It was found to be higher than the price calculated based on the deterministic approach. Therefore, based on probabilistic and stochastic approaches, the exchange of life settlements in the secondary market will benefit the insured. In addition, the price of life insurance policies, based on deterministic and stochastic approaches using the Iranian life table, was lower than the French life table. This was even though the price of these life settlements using the probability approach for the Iranian life table was obtained more than the French life table. Therefore, using the Iranian life table in the pricing of life settlements, based on a probability approach, will benefit the policyholders, and based on a deterministic and stochastic approach; it will be detrimental to them. Furthermore, the life settlements price for Spain is higher than for Iran due to different assumptions. To increase the penetration of life insurance, insurance companies need to enter new fields, such as secondary markets. The life settlement in the secondary market means the sale of ownership or the transfer of benefits from the insured to the investor. In general, life settlement pricing requires the development of regulations to determine the necessary assumptions, such as the internal rate of return and the adjustment multiplier of the mortality probability.

The daily observations of the total index of the Tehran Stock Exchange show that in the last few years, stock prices have been very volatile. This volatility can harm the economic environment of Iran. Modeling and predicting price volatility in this market can provide important information about uncertainty and risk to actors and thus help with managing possible unwanted changes in the field of financial investments. This, along with the rise in the value of the stock market in recent years, has caused the investigation of the issue of volatility to become increasingly popular among academicians and financial policymakers. Volatility and risk measurements are essential parameters in risk management programs and can affect a country’s economic activity and public confidence. These are also key parameters in studies that examine the relationship between the stock market, economic growth, and other financial variables. Tehran Stock Exchange markets have been volatile in recent years. Controlling the negative effects caused by stock price volatility has made it necessary to predict and model price dynamics for participants in this market. In this paper, the class of Parameter-Driven volatility models (stochastic volatility models) is used to predict the price volatility and calculate the risk of the price index in the Tehran stock market. Therefore, four stochastic volatility models were used. To make a comprehensive review, the asymmetry in the volatility (leverage effect) and the heavy tail of the stock price return distribution (with t- student distribution and Skew normal) have been included in the models. To estimate the models, the Gibbs sampling method was used, and to accurately compare the models, the test based on the posterior distribution of the models and the Bayesian factor was used. The results indicated that the canonical stochastic volatility model with Skew normal distribution (SNSV) is more effective than other stochastic volatility models in predicting the price of stock market volatility based on the Bayesian factor. Therefore, to analyze stock market risks using stochastic volatility models, there is no need to include the leverage effect in the state space of the Volatility equation. The SNSV model makes it possible to observe volatility and make predictions related to it, thereby improving market transparency and ultimately making diversification and risk management easier to implement. Also, the backtests of VaR and CVaR market risk assessment using Kupiec and DQ tests do not show evidence that the estimation is over or under the risk limit. As a result, the calculation of volatility and pricing with this model will lead to more precision risk management for professionals, especially fund managers who intend to include Tehran Stock Exchange stocks for asset allocation.

Bank controllers and regulators have always focused on bank capital and capital adequacy rate as two important factors for sustaining and improving banking activities. In this research, we have tried to investigate the role of capital and the relations between the banking sector with the economic real variables. We also sought to study the impact of bank capital on the real sector of an economy, using the DSGE model. Accordingly, in this model, to comply with the facts of Iran's economy, we defined the capital adequacy rate based on Basel I and II standards and took into account the probability of default for the loans and facilities of the banks and firms endogenously. Meantime, since the banking sector plays a crucial role in the country’s economy, we tried to examine the relations between the banks and Iran Central Bank (CBI) as the regulator within Iran's economic ecosystem.

To examine the actions and reactions of key dynamics of the Iranian banking sector with the economic real variables, we used Dynamic Stochastics General Equilibrium (DSGE) model, by using quarterly modified data during 1996-2019.

The results of simulating the model in response to the bank capital increase and oil positive shock on the defined variables proved the designed model to be highly consistent with the theoretical expectations and the past experiences of Iran's economy. Furthermore, it was found that due to the increase in oil price and the positive balance of payment because of the increase in the oil income, bank loans could be increased. Therefore, by improving economic variables temporarily, oil positive shock would result in a decrease in the probability of default in banks, whilst increasing their profits. The results also indicated that the bank capital can be considered a key factor for evaluating real variables in Iran's economy by the CBI, so the increase in bank income would temporarily end in a decrease in GDP, because of different factors, including the cost of bank finance and the loan rate.

It can be concluded that capital plays an important role in the bank transition mechanism to society and the Central Bank can also play an important role in the process through applying the needed changes in the capital adequacy rate. Nevertheless, as the increase in the capital adequacy rate in the long term would decrease risk and improve banking activities, it would affect real variables during the transition period. Furthermore, any increase in liquidity and bank loans would decrease the real interest rates, affecting the economic variables positively. In addition, the effects of any increase in oil income can be shown in bank profit. Therefore, oil shock changes can be generalized to other economic real variables.

There are many perspectives on the interplay of profitability, Corporate social responsibility (CSR), and financial risk in different industries. The purpose of this paper is to examine the factors affecting the success of asset management companies (AMC) in Iran from a financial and social point of view. Since the asset management industry in Iran is emerging and the first AMC was founded in 2011, not much research has been conducted in this field. Therefore, this study tries to contribute to the extant literature by investigating the interactive effect of profitability, CSR, and financial risk. It also seeks to document empirical evidence in this industry. To fulfill the purposes, the researchers examined 45 AMCs licensed by the Stock Exchange and Securities Organization through cross-sectional regression models and correlation tests. Comparatively, the correlation between information related to social responsibility using KLD checklist scores and assets under management of 51 large foreign AMC was tested. The KLD checklist consisted of seven different concepts: environmental, products and services, human resources, customer ship, social, culture and belief, and energy. Previous studies used many different factors to score CSR but this study computed CSR scores following Kordestani & et al. (2018) The results proved the coefficients of mutual funds management fee (especially fixed funds fee) to be higher than the coefficients of the portfolio management fee in explaining profitability. It was also found that the number of employees and the average financial risk do not impact profitability. In addition, CSR does not relate to any of the following variables; the profitability, the fees and the number of managed portfolios, the fees and the number of mutual funds, the number of companies in the group, the financial risk as well as the number of assets under management. The comparative study of 51 large foreign AMCs also revealed that compliance with CSR disclosure requirements in the world has a significant relationship with profitability. AMCs could be more profitable by focusing on mutual funds development than on portfolio expansion, especially if they establish a fixed fund. As a result, we suggest newcomers constitute fixed mutual funds as the first action plan. Our observation showed that the average debt ratio in the asset management industry hovered around 32%, of which 28% belonged to short-term debts. This revealed that the active AMCs mostly preferred short-term debts to long-term ones. So, we concluded that these AMCs avoided financial risk in favor of the high business risk of the industry. To provide financial success, they should enhance their network via having a vast group community and robust their beneficial activities and their synergy among the whole group. Also, due to the voluntary disclosure of CSR in Iran, there is no sufficient information and the average related information is very low (about 11%).

Stock price informativeness refers to the extent to which the market price of a share is informative and contains information about the real and underlying economic conditions of that share. It can be one of the most prominent topics in the field of company investment in developed and developing markets. Several factors can affect stock price awareness. Companies proficient in effectively communicating and disseminating information to investors tend to achieve superior performance in the capital market, often resulting in an increase in their stock prices. Stock price informativeness enables companies and industries to be aware of the latest developments and events related to the capital market and thus, to make intelligent investment decisions. Stock price informativeness denotes the degree to which a share's market price conveys valuable information about its true underlying economic conditions. Accordingly, this study endeavors to enhance our understanding of this domain by evaluating the efficacy of factors that influence stock price informativeness. Accordingly, the purpose of this research is to investigate the effectiveness of factors affecting the informativeness of stock price announcements in companies listed on the Tehran Stock Exchange.

This research employs a mixed-methods approach, combining both qualitative and quantitative methods within an inductive-deductive framework. The research participants in the qualitative part were professors and financial managers. According to the purpose of the research, sampling was done in a purposeful way, using the snowball technique, with a number of 17 individuals. The research's statistical population comprised financial managers from companies listed on the stock exchange. Out of this population, 68 managers in this field participated in the study, selected through the stratified probability sampling method.

The data obtained from semi-structured interviews led to the extraction of 41 factors influencing stock price informativeness. In this research, the focus has been on factors that impact other variables, along with factors that are influenced by them. Additionally, it has identified factors that permeate and are receptive to permeation. Based on Dematel analysis, management stability factors, management strategies, board experience, and transaction volume are the most important factors that are both penetrable and penetrant. Among the factors examined, state ownership, political conditions, economic factors, and capital market laws exert the most significant influence. Additionally, factors such as managerial stability, strategic company management, the presence of a strategic plan, and growth expectations have been found to have the greatest impact among the other variables.

In this research, the factors affecting stock price informativeness have been identified. "Given that the decisions made by managers and investors hinge on their understanding of stock price information, it becomes imperative for researchers and financial industry executives to place significant emphasis on cultivating this awareness and comprehending the factors that influence it". "Therefore, the researchers of the present study suggest that diversifying ownership, delivering timely and precise financial information to investors, and fostering interaction with shareholders can be instrumental in bolstering investors' trust in the company's management and decision-making".