mohammad osoolian

Predicting the future trends in financial markets stands as a critical task for both investors and researchers, given its pivotal role in enabling well-informed decision-making processes and effective risk management strategies. Nevertheless, the realm of stock market dynamics is fraught with inherent complexities and uncertainties, posing a formidable challenge when it comes to achieving accurate predictions. A wide array of predictive modeling techniques have been meticulously investigated, spanning from conventional statistical methodologies to more sophisticated machine learning algorithms. The primary focus of this research endeavor revolves around the predictive analysis of the Tehran Stock Exchange (TSE) Composite Index, wherein a novel hybrid neural network framework is employed. This approach seamlessly integrates multiscale temporal features, with the ultimate objective of bolstering prediction precision and offering profound insights into prevailing market trends and dynamics.

The hybrid neural network architecture that has been put forward integrates the unique capabilities of convolutional neural networks (CNNs) in the realm of feature extraction with the effectiveness of long short-term memory (LSTM) networks in capturing temporal dependencies. The dataset used in this study consists of daily historical data pertaining to the TSE Composite Index, covering a substantial period from the year 1998 to 2022, which has been meticulously gathered, preprocessed, and subsequently partitioned into distinct sets for training and validation purposes. Within the framework of this hybrid neural network model, a sophisticated approach is adopted to harness multiscale temporal features derived from the input data, enabling the generation of highly accurate predictions regarding the future trends of the index. Moreover, to further enhance the performance and resilience of the model, sophisticated feature engineering methodologies are implemented to optimize its overall functionality.

The results of the study reveal that while the hybrid neural network model, integrating CNN and LSTM components, demonstrates promising capabilities in predicting the TSE Composite Index, its accuracy falls short compared to competing models, particularly at weekly and monthly time scales. Conversely, the standalone CNN model exhibits superior performance, yielding more accurate predictions of the index's movements. These findings challenge the prevailing notion regarding the efficacy of hybrid neural network models in financial market prediction, highlighting the importance of evaluating alternative modeling approaches based on their specific strengths and limitations.

Despite the potential of hybrid neural network models, as demonstrated in previous research, the findings of this study suggest that simpler neural network architectures, such as CNNs, may offer better prediction performance in certain scenarios. To address the limitations identified, future research endeavors could explore alternative model configurations, ensemble methods, or hybrid architectures that combine the strengths of different predictive models. Additionally, incorporating additional market indicators and exploring intraday data sources could further enhance prediction accuracy and robustness. This abstract encapsulates the key findings and implications of the research, providing valuable insights for investors, researchers, and practitioners in the field of financial market prediction.

The dynamics of the market and the stochastic behavior of index time series worldwide have made forecasting trends a primary concern for researchers and investors in the capital market. Therefore, various models are used for forecasting. Time series analysis and prediction methods based on networks, including the visibility graph model, have proven effective for time series analysis. In this research, we aimed to predict the TEDPIX index for the years 2013-2024 using three visibility graph-based methods. These methods include the most similar node model, the weighted similar node model, and our proposed method, the cross-visibility graph model. We converted the time series of the index and some auxiliary variables into a visibility graph and predicted the aforementioned time series using these three models. The results showed that the performance of the weighted similar node model and the most similar node model varied depending on different conditions. However, our proposed model, the cross-visibility graph model, and the weighted similar node model generally provided the best results in predicting the index and were more accurate compared to other models.

Examining herd behavior in the stock market is crucial due to its impact on market volatility and stability. Therefore, this study focuses on the effect of firm size and market returns by analyzing this phenomenon using the TV method. The use of the TV method is important for two reasons: first, it is more suitable for identifying macro-level herd behavior and better captures collective trading tendencies among investors; second, its independence from asset pricing models reduces computational biases. Findings indicate that herd behavior is more common in larger firms compared to smaller ones, which can be attributed to factors such as intrinsic value bias, media coverage, and investor preferences. Larger firms attract more attention, leading to increased media coverage, which in turn promotes herd behavior among investors. Additionally, this study examines herd behavior in conditions of both high and low market returns. Results show that, for both large and small firms, the level of herd behavior rises with an increase in positive returns as well as negative returns.

Financial distress refers to the situation where a firm’s cash flows are insufficient to meet contractually required payments. This has caused concern among capital owners and compelled financial analysts to employ a variety of methods to assess companies’ equity and analyze the firm’s financial status. Assessing and predicting financial distress in a timely and accurate manner can aid decision-makers in finding the optimal solution and preventing it. Numerous models have been developed thus far to predict and evaluate financial distress. The prediction accuracy has been improved through the use of various innovative methods. Using financial ratios and market data as independent variables and obtaining patterns for the financial forecast is one of the most important methods for evaluating the financial stability of businesses. Therefore, the primary objective of this study is to evaluate the performance of five models in this field, compare their accuracy of prediction, and ultimately select the best model to predict financial distress for a specified period in Iran. Specifically, the logit model, artificial neural network (ANN), support vector machine (SVM), partial least squares regression (PLS), and a hybrid model of SVM and PLS were chosen, analyzed, and compared. The results of the average accuracy of prediction indicate that the SVM has the highest accuracy one year before the onset of financial distress. In addition, findings from the two years preceding the failure indicate that the SVM-PLS model provides the most accurate classification of financially distressed and non-distressed firms.

Herding behavior is typically described as the inclination of investors to follow the actions of others in their investment decisions. Herding represents a behavioral tendency in which investors rely on collective rather than private information. Herd literature shows that return jumps can serve as a representation of information arrival, leading to significant price changes. This proposition is introduced due to its potential impact on investor sentiment, assuming greater awareness among other investors as a factor related to the occurrence of herding. Furthermore, it is believed that, in conditions of negative market returns, market participants are more inclined to mimic the behavior of others due to the stress induced by the risk incurred. In the background of previous research, evidence indicates the occurrence of herd behavior on days with return jumps and negative returns. In this study, we investigated herding behavior and its asymmetry through the utilization of return jumps, employing the CSAD method. Under circumstances in which there were no occurrences of return jumps and without taking into account negative market returns, our research was unable to verify the existence of herding at the market level. Nevertheless, when return jumps and negative market returns were present, the occurrence of herd behavior was proven, and the asymmetry of herd behavior was also verified.

In financial markets and behavioral finance studies, the herding behavior of shareholders is considered an important variable. This behavior refers to a process in which capital market participants make decisions without relying on their own information and analysis, instead imitating the actions of others. Analyzing herding behavior can enhance our understanding of how bubbles form in stock markets and contribute to increased capital market efficiency. Herding behavior can distort asset prices, leading to deviations from their fundamental values, which subsequently impacts overall market stability. By understanding the factors that drive herding behavior, policymakers and market regulators can implement strategies to mitigate its adverse effects.
Given the significance of collective behavior, this research examines the relationship between monetary policies, changes in exchange rates, and the herding behavior of shareholders. To measure herding behavior, we utilized the model proposed by Chang et al. (2000). According to this model, herding behavior is indicated when the deviation of stock returns from the market return decreases, suggesting that investors are following the market trend rather than making independent decisions. Furthermore, to measure monetary policy variables and exchange rate changes, we employed indicators of seasonal changes in liquidity volume and monthly changes in the exchange rate, respectively. Using time series data from 2011 to 2021, which includes information from 133 companies listed on the Tehran Stock Exchange, we applied multivariate regression analysis to test our hypotheses. This comprehensive dataset allowed us to capture various economic conditions and policy changes over a significant period.
The research results indicate that changes in the exchange rate lead to the formation of herding behavior in the Tehran Stock Exchange. This suggests that as the exchange rate fluctuates, investors tend to mimic the actions of others rather than rely on their own analysis. Additionally, while changes in liquidity volume also contribute to the emergence of herding behavior among shareholders, these results were not statistically significant. This implies that liquidity alone may not be a strong enough factor to drive herding behavior, or that other underlying factors may be influencing these outcomes. The findings highlight the importance of exchange rate stability in maintaining market discipline and preventing irrational collective behaviors.
The herding behavior of shareholders has increased the distance between the market price and the intrinsic value of the stock, leading to an increase in stock prices and impacting the efficiency of the capital market. This misalignment between market prices and intrinsic values can create conditions for market bubbles and subsequent crashes. Some studies show that herding behavior is more prevalent when markets are in a stable state (rising or falling) than during periods of chaos and uncertainty. This indicates that investors are more likely to follow the crowd during predictable market conditions, while in volatile times, individual decision-making becomes more pronounced. Policymakers aiming to influence mass behavior should make decisions and implement policies when markets are stable, before a financial crisis or bubble formation occurs. Proactive measures can include regulatory interventions to ensure market transparency and the dissemination of accurate information to all market participants. Considering the results of the research on the effect of exchange rate fluctuations on the formation of mass behavior, it is recommended that stock exchange organizations use this factor to improve investment behavior, guide the market towards intrinsic values, and prevent bubble formation and subsequent collapse. Market supervisors, lawmakers, and the central bank should consider the potential limitations of rational models when designing policies and making decisions related to monetary policies, including determining liquidity levels and controlling the exchange rate. Additionally, the behavioral elements of market attitudes and psychological and cognitive factors should be taken into account when making these decisions. By addressing these aspects, policymakers can better manage market dynamics and foster a more stable financial environment.

 The stock market is one of the most important and influential segments of the financial market in any economy [1]. This market reflects the economic situation of countries because one of its functions is to collect capital and transfer it to individuals and companies seeking investment. In addition, the capital market is one of the most important pillars for financing. The stock returns of listed companies can be affected by various factors. Changes in oil prices can be considered as one of the important factors affecting the stock market. Oil-exporting countries, of which oil is an important source of income, are affected by the fluctuation of the price of this black gold, their economy is affected more than other countries, and also their budgets, which often rely on oil prices, are affected in this regard. In countries such as Iran, where their main source of income comes from oil sales and whose budgets rely heavily on oil, the impact of oil price fluctuations on various sectors of the economy has been a concern for policymakers and economists. One of the most important sectors is the stock market, where economic developments and oil prices are increasingly being considered by researchers to make the relationship between the two more obvious over time. Accordingly, the purpose of this study is to investigate the relationship between the herding of the crude oil market and the Tehran Stock Exchange. In the study of herding behavior, financial literature is divided into two main branches: The first branch of measures of herding behavior based on efficiency dispersion [2]. The second proposal is a statistical criterion based on analysts' transactions [3]. In this study, based on the approaches of Christie and Huang (1995), the herding behavior between the stock market (with the index of the top 50 companies of the Tehran Stock Exchange) and the crude oil market has been investigated.Christie and Huang (1995) presented the standard deviation of the CSSD cross-sectional study as a model of herding behavior. Their model is as follows: where N is equal to the number of securities that make up the market index,  is equal to the return on securities i in period t,  is equal to the average yield of N securities from the market portfolio for day t. If there is herding behavior in securities, then the dispersion will be low. In fact, because herding behavior tends to follow market trends (ascending or descending), securities follow market trends and thus dispersion decreases. Christie and Huang (1995) model believes that herding behavior occurs more during intense market movements (ups and downs). The results show that the regression coefficient of the square market return is negative and statistically significant. The existence of a negative coefficient for the stock market and crude oil market returns indicates the reduction of deviations of companies' returns from market returns during the market turmoil (increasing or decreasing). Reducing the deviations of companies' returns from market returns means the existence of herding behavior in the market.Also, in examining the existence of herding behavior in a reciprocal manner, the results of examining the hypothesis of the existence of reciprocal herding behavior in the stock market and crude oil market in the stock market are confirmed. The existence of a negative square coefficient for the variable market return indicates a decrease in the deviations of companies' returns from market returns, when the cross-market, i.e. oil or stock market, is fluctuating. Thus, the hypothesis of the existence of reciprocal herding behavior in the stock market is confirmed. The results of this section are similar to the research of Balcilar, Demirer [4], Gong and Dai [5], and Mabrouk [6]. This study investigates the reciprocal herding behavior between the stock market and the crude oil market in Iran. The results of the herding behavior indicate the existence of herding behavior among the top 50 companies on the Tehran Stock Exchange and the OPEC oil market between 2011 and 2019. The results of the study based on two criteria explaining herding behavior, confirmed the existence of herding behavior between the top 50 listed companies and the OPEC crude oil market. The results also show that the relationship between the herding behavior of investors is reciprocal between these two markets.

This study examines the risk of the Tehran Stock Exchange and the S&P 500 index, as well as gold and silver. Each investigation was performed separately before the outbreak of Covid-19 and during it. To do so, we use the fractional cumulative residual entropy (FCRE) for risk analysis. Also, we investigated the effect of Covid-19 on the Hurst exponent of the aforementioned markets and discussed the dynamic of each time series as well. The results showed that the Hurst exponent increases to one after the Covid-19 pandemic for all investigated markets. Therefore, the pandemic led to higher persistence trends in all markets that were studied. The results of FCRE showed that for all markets, randomness and irregularity have increased since the outbreak, which indicates that the risk and uncertainty have generally increased after the outbreak. Gold, however, showed the lowest entropy level since the pandemic, indicating that information perceived by gold investors did not significantly affect their fears during the Covid-19 pandemic. The results provide insights into the response of financial markets and precious metals to investor expectations after the Covid-19 pandemic.

The present article compares the effectiveness of financing from the banking system and the stock market on the performance of listed companies in the Tehran Stock Exchange. Test cases have been studied. This study examined the statistical data of 55 companies during 7 years (3 years before entry, 3 years after entry and year of entry) and concluded that between the effects of financing from the banking system and the stock market on the performance of firms In this study, the productivity variables of total factors of production, investment and employment as performance variables, and long-term facilities, capital increase from cash flow and entry into the stock market as an independent variable and firm size, interest rate, Firm production, wages, public and private ownership of the company are considered as control variables. According to the research results, financing from the banking system has a positive and significant effect on the investment variable as a performance variable, while entering the stock market and increasing cash flow does not have a significant effect on the investment variable. Also, the effect of entering the stock market and increasing cash flow, such as the effect of financing from the banking system on the productivity variables of all factors of production and employment is meaningless.

The stock market index is one of the effective features in investment because it can well reflect the health status and macro change trend of a country’s economic development. Various features affect the stock index. The various combinations of these features create a wide state space. Hence, it is impractical to provide a data set containing all these combinations to train the stock index prediction model. in this research, an attempt has been made, after collecting a significant number of effective features on the index, to provide a method for selecting appropriate features for the stock index prediction model with aim of increasing prediction accuracy. For this purpose, the mRMR algorithm is used as the basic algorithm. Also, to select the appropriate model, a number of the most applicable artificial intelligence models for predicting the stock index were compared and according to the results, the LSTM network was selected to predict the stock index. The results of this study show that using the LSTM network and the proposed method in selecting features, with 8 selected features, high accuracy can be achieved in the daily prediction of the Tehran Stock Exchange Index. So that MPE is calculated to be about 2.66,

There have been many researches in the world about Post Earning Announcement Drift (PEAD). Most of the findings support the existence of excess abnormal returns in case of benefit from PEAD. In this research, the existence of PEAD in Tehran Stock Exchange companies during the period from 2009 to 2021 was investigated, and for the first time, the role of the herding behavior of institutional investors in the formation and intensification of PEAD was investigated. Different percentages of earning adjustment were divided into 6 groups, 3 groups were related to negative adjustment and 3 groups were related to positive earning adjustment. Also, the type of institutional behavior after adjusting earning was divided into four categories: strong herding behavior of selling, weak herding behavior of selling, strong herding behavior to buying and weak herding behavior of buying. The significance of the results was assessed using the Newey-West t test, and the results are as follows: PEAD exists for a short period on the Tehran Stock Exchange. Also, when the institutional investors are the buyer for positive earning adjustment and and sellers for negative earning adjustment, especially for groups of negative 40% to positive 40% earning adjustment, the possibility of obtaining positive abnormal returns is strengthened.

Examining the importance and influence of financial market companies is one of the main issues in the field of financial management because sometimes the collapse of a stock exchange company can affect an entire financial market. One systematic way to analyze the significance and impacts of companies is to use complex networks based on Interaction Graphs (IGs). There are different methods for quantifying the edge weight in an IG. In this method, the graph vertices represent the stock exchange companies that are connected by weighted edges (corresponding to the extent to which they relate to each other). In this paper, using the GARCH model (1,1) and the Clayton copula, we obtained the lower tail dependence interaction network of the first 52 companies of the Tehran Stock Exchange in terms of average market value, between June 2017 and October 2020. Then, based on the minimum spanning tree of the interaction network, we divided the companies into different communities. Using this classification, it was observed that the companies of the first group (Food Industry) and the second group (Oil Refinery) have the greatest impact on other companies. We also calculated the central indexes of the minimum spanning tree for each company. According to the results, the companies of the third group (Steel) have the highest average in the central indicators.

Capital market plays an important role in attracting and circulating market liquidity and directing it to efficient economic sectors. Management forecast is one of the most important sources of information in the stock market, while its misrepresentation leads to more idiosyncratic risks and consequently inappropriate investment decisions by investors. In this study, management forecast errors were considered as a proxy for disclosure quality to investigate the relationship between information disclosure quality and idiosyncratic risk, as well as the effects of Information environment on these two variables in Tehran Stock Exchange (TSE). To this goal, a sample of 160 listed firms in TSE was examined from 2009 to 2017. The results indicated that the management forecast errors were positively related to idiosyncratic risks, while they were less positively related in a good information environment.

The purpose of this research is to investigate two controversial cases about the momentum in the Tehran Stock Exchange. These two cases include risk theory and under-reaction theory. This research will be followed in two parts. The first part tries to explain the momentum with respect to risk. The second part describes the excess return in momentum portfolios with the under-reaction hypothesis. The research hypotheses have been examined using the data of 58 non-financial companies from the Tehran Stock Exchange between 1389 and 1398. The results show that the risk-adjusted momentum profit is statistically significant. In addition, the results show that the five-factor risk model is not able to explain the momentum effect. However, the momentum effect can be explained by using the under-reaction hypothesis. The under-reaction is asymmetric for first six month after the earning announcement date. In general, the findings also rejects the efficient market hypothesis in favor of the under-reaction theory.

Prediction is one of the most important premises in making investment decisions. Accordingly, investors are keen to be aware of market trends and price returns. For this purpose, several methods have been used in different fields; however, in the present study, the ability to predict the crisis by Cumulative Residual Entropy (CRE) and its generalized type, Fractional Cumulative Residual Entropy (FCRE), has been investigated. The data used in the research include the overall index, volume, trade value, and foreign exchange rate from October 2010 to July 2021. The results showed that both criteria could predict the crisis, but the FCRE is superior in crisis prediction. The identified periods of crisis are 2011-2012, 2014-2016, 2018-2019, and 2020. Each of the crises, including the recent Covid-19, was analyzed and investigated.

In this study, we intend to analyze the risk of investing in financial markets such as the Tehran Stock Exchange and the S&P500, as well as investing in precious metals such as gold and silver before the outbreak of the Corona pandemic and during it separately. Secondly, we investigate the correlations between the markets mentioned above. For this purpose, we use the fractional cumulative residual entropy for our analysis. The results of fractional cumulative residual entropy show that for all markets, randomness and irregularity have increased since the outbreak. This indicates that the risk has generally increased after the outbreak of the virus in the markets. Furthermore, correlation results showed that the information sharing network between markets changed during the Covid-19 pandemic. From a financial and managerial point of view, the results show that: 1- Investing in a basket consisting of silver and gold, silver and S&P500, silver and the overall index of the TSE can be riskier due to the increase in the level of correlation and mutual information between these pairs of markets during the period of the pandemic. 2- Gold showed the lowest level of information deviation after the pandemic, indicating that information perceived by gold investors did not significantly affect their fears during the Covid-19 pandemic. 3- It is possible to create a safer investment portfolio by investing in S&P500 and gold. The results provide insights into the reaction of financial markets and precious metals to investor expectations after the Covid-19 pandemic.

The main purpose of this study is to investigate the effects of investor sentiment on bubble formation in the stock market. This study aims to find a significant relationship between investors' sentiments and the creation and explosion of the bubble in the Tehran stock exchange. For this purpose, daily index data is used as well as investor sentiment index for the 2009 - 2019 period. By using Dicky – Fuller test, we identify bubbles in the Tehran stock market and calculate the indicator of investor sentiment. Afterwards, we study the relationship between investor sentiment and bubble formation by using Logistic regression. According to the research findings, the hypothesis of the influence of investor's feelings in the capital market on the formation of the financial bubble is rejected.Also according to the results of logistic regression and probit regression, The probability value of the variable EMSI (Equity Market Sentiment Index) at conventional statistical levels is meaningless. Further, the author observes that, Investor's sentiment have no effect on the formation of a bubble in the Stock market.

This study investigates the effect of cash flow volatility on capital structure decisions and the debt ratio of the companies listed on the Tehran stock exchange. To achieve this aim, we collect and present information attributed to eighty companies from 1390 to 1397.  In that case, our statistical sample with a frequency of six months would be 1280 observations in total. To test the hypotheses, we followed the Panel Data method running EViews 10. The empirical result from testing the first hypothesis revealed that the operating cash flow risk has a significant and positive effect on the sample companies’ debt ratio. Based on the statistical findings, this effect is not substantial, which shows that the majority of managers do not consider the impact of cash flow volatility in their decisions regarding the capital structure.  In addition, the findings obtained from testing the second hypothesis illustrate that this impact is relatively more considerable in top and bottom quintiles. In companies with lower or higher operative cash flow, the effect of cash flow volatility on the debt ratio was more intense

Stock liquidity is one of the most critical factors for investors in the capital markets and one of the main functions of the stock exchange. The tendency to investigate this issue stems from the fact that investors decide to trade in the capital market by comparing the risk and return of investment opportunities, and liquidity risk is one of the main risks. Managers are considered this issue, and various aspects of it have been studied so far. The purpose of this study is to determine the effect of excess cash on firm value and investigating the benefits of cash holding in firms with higher growth opportunities or financial constraints.

Liu's 2006 measure for liquidity risk is used, which instead of focusing on a specific aspect of liquidity risk, such as trading volume and the difference between the buy and sale price, covers all aspects of it. The present study is in the category of post-event research. The data used in the research is related to 189 firms of the Tehran Stock Exchange from 2008 to 2018. In this study, Jark-Bra, Arch-Engel, BDS, Box-Pierce, IPS unit root, cohesion test, F-Limer, Hausman tests were used to evaluate descriptive statistics and results.

Excess cash has recently been considered by researchers as one of the measures for estimating the liquidity risk of firms. The research variables were examined for reliability. For this purpose, IPS test were used. Based on the results of this test, all research variables except for the cost of Research and Development (R&D) which is scaled by sales and institutional shareholders are at a stable level, while the latter variables are stable with a one-time differentiation. Accordingly, due to the lack of significance of a number of variables in the level, the existence of a long-run relationship was tested by the co-integration test method. The results of the test show that despite the unreliability of some research variables at the level, there is a long-term relationship between research variables. The results indicate that excess cash is a valuable criterion for firms’ liquidity risk and its effect becomes more severe under the influence of growth opportunities or financial constraints.

The results indicate that the excess cash had a significant negative effect on the value of the firms. Additionally, in the study of firms with growth opportunities or financial constraints, it is found that for firms that have growth opportunities or financial constraints, the relationship between excess cash and liquidity of firms’ shares was more intense.

This study aims to explain the relationship between cognitive abilities and faith in intuition with behavioral biases and also to explain the relationship between behavioral biases and performance of professional investors in the Tehran Stock Exchange. We compare two different measures of impulsive or intuitive behavior, examining their effect on anchoring bias and conjunction fallacy, and finally examining the effect of behavioral biases on investors' performance.

The sample consists of 311 professional investors in the capital market whose data were collected via questionnaires. Chi-squared test, Mann WhitneyU test, ordinary least squares, and probit regression were used to analyze the data.

The results show that faith in intuition has a positive and significant effect on anchoring bias and conjunction fallacy. While cognitive ability has a negative and significant effect on the conjunction fallacy. The results also show that faith in intuition has a negative and significant effect on return. The results of regression analysis also show that gender reinforces the negative effect of cognitive ability and the positive effect of faith in intuition on the conjunction fallacy.

The results show that faith in intuition is more related to behavioral biases and performance than cognitive abilities, and is more informative overall.

Earnings management takes advantage of how accounting rules are applied and create financial statements that inflate earnings or revenue. In breaking down earnings management, companies use earnings management to smooth out fluctuations in earnings and present more consistent profits each month or year. A company's stock price often rises or falls after an earnings announcement, depending on whether the earnings meet or fall short of expectations. In this research, we study the effect of earnings management on the long-term investment productivities of selected companies in Tehran Stock Exchange. We also study these companies in two separated groups, companies with Decreasing Earnings Management and companies with Increasing Earnings Management.
The results showed that the earnings management is not common among selected companies listed on the Tehran Stock Exchange. Most cases are increasing earnings management (over 88%). Increasing earnings management does not have a negative impact on the long-term investment productivity of these companies. But, increasing earnings management and investment productivity is inversely correlated. Also, decreasing earnings management has a negative impact on the long-term investment productivity of these companies.

The importance of forecasting in investment discussions has led to the use of a wide range of methods in various sciences to predict future trends and prices. Crisis forecasting has also become more prominent in recent years. Further investigation of the dynamics of complex systems is required to determine what changes in system dynamics occur during a crisis. This research has investigated the crisis forecasting ability of cumulative residual entropy measure in Tehran Stock Exchange (as a complex system). In this study, crisis simulation data generated by logistic map have been used to examine the ability of the proposed measure theoretically. In the practical case, the data of the overall index of Tehran Stock Exchange from October 2010 to September 2019 has been used. The results showed that the proposed measure can forecast the crisis

During the last decade, studies on the factors affecting stock market returns have reached a peak with the advances of financial economics in the field of statistics and mathematics, and modeling is of great importance in this regard. Accordingly, this study seeks to present a new approach to modeling the nonlinear relationship between financial variables and stock returns. This paper employs Bayesian Markov chain Monte Carlo (MCMC) sampling methods for updating the estimates and quantile threshold regression with heteroscedasticity. To study and model this approach, we used returns of the Tehran Stock Exchange, Coin Price, Oil, and Gold Price from 2011 to 2019. The results show that these variables have different effects under low and upper quantile levels. Also, the impact of the financial variables on the stock returns is different under higher and lower threshold amount for each quantile levels. Based on the result, we can say that stock returns have a nonlinear relationship with other markets in the bullish and bearish market.

In this study, the effect of exchange rate fluctuations on abnormal returns of companies listed on the Stock Exchange were studied. In this study, Auto Regressive Distributed Lag model due to its ability to explain this connection was used for short-term and long-term. To evaluate the effect of exchange rate fluctuations , the banks' legal deposit at the central bank, GDP, inflation, current account and capital account on the stock market according to the designated filters, exporting companies that gained the research conditions, were determined. In this study, the company's export price index was calculated at the end of each quarter and abnormal returns were calculated for the group of companies. After calculating abnormal returns of firms, independent and control variables in the ARDL model imported and the effects of the explanatory variables investigation revealed abnormal returns. The results show that the exchange rate fluctuations variable has a positive and significant impact on exporting companies are abnormal returns.

The aim of this study is to explain the relationship between cognitive abilities and faith in intuition with trading strategies and performance of professional investors in Tehran Stock Exchange. To this end, we examine the components of cognitive ability and faith in intuition on active / passive (buy and hold) and portfolio diversification / concentration strategies as well as the performance of investors. The sample consists of 310 professional investors including 160 traders and 150 analysts in thecapital market whose data were collected via questionnaires.Logistic regression and ordinary least squares were used to analyze the data. The results show that for traders, highfaith in intuitionis associated with more active trading, and higher cognitive ability results in more concentrated portfolios. Moreover, higher cognitive ability has a positive and significant effect on traders’ performance, while higher faith in intuitionhas a negative and significant effect on their performance. The results are somewhat different for analysts, as their portfolios become more diverse with their age, while faith in intuition leads to a more concentrated portfolio. However, analysts’ intuition, like traders, is negatively and significantly correlated with performance