Iran Stock Market Research Articles

Impact of accrual and real earnings management on the stock liquidity: the month-of-the-year and Ramadan effects

Purpose The abnormality of the month-of-the-year and Ramadan effects has extensively existed in the stock and other markets. The commercial strategy pattern and the computation of such predictable patterns in the market allow investors to make money. By using anomalies such as the month-of-the-year and the Ramadan effects on earnings management (EM), it is possible to achieve such a goal. This study aims to investigate the month-of-the-year effect and the Ramadan effect on the relationship between accrual earnings management and real earnings management (AEM and REM, respectively) and liquidity in the Iranian capital market. Design/methodology/approach This empirical analysis comprises a panel data set of 80 listed firms (400 observations) on the Tehran Stock Exchange from 2016 to 2020. Findings The findings exhibit that when AEM and REM increase, information asymmetry also increases. The simultaneous increase of these variables leads to a decrease in stock liquidity. Furthermore, the results indicate that the month-of-the-year and Ramadan effects intensify the negative relationship between AEM and REM with stock liquidity. Therefore, EM is affected by the investor’s behavior in specific months. Practical implications Anomalies caused by the Ramadan effect and the month-of-the-year effect on reducing liquidity in the Iranian stock market were confirmed. Investors can use these anomalies to identify predictable patterns, exchange securities according to those patterns and earn abnormal returns. Originality/value To the best of the authors’ knowledge, this is the first study that empirically examined the simultaneous effect of Gregorian and Islamic calendar anomalies on the relationship between EM and liquidity, and while helping managers and other readers, it can be the basis for future research.

A novel algorithm for complete ranking of DMUs dealing with negative data using Data Envelopment Analysis and Principal Component Analysis: Pharmaceutical companies and another practical example.

When there is an extensive number of inputs and outputs compared to the number of DMUs, one of the drawbacks of Data Envelopment Analysis appears, which incorrectly classifies inefficient DMUs, as efficient ones. Accordingly, the DEA ranking power becomes further moderated. To improve the ranking power, this paper renders the details of an algorithm that presents a model combining the Principal Component Analysis and the Slacks-Based Measure (PCA-SBM) which reduces the number of the incorrectly determined efficient DMUs. Also to complete ranking of DMUs, the algorithm presents a Super-Efficiency model integrated with PCA (PCA-Super SBM) which can rank the efficient DMUs (extreme and non-extreme). Whereas the most important previous models for ranking efficient units cannot rank non-extreme ones. Additionally, in most previous studies, DEA models combined with PCA fail to handle negative data, while, the presented models can cover this data. Two case studies (pharmaceutical companies listed on the Iranian stock market and bank branches) are manipulated to demonstrate the applicability and performance of the algorithm. To show the superiority of the presented models, the SBM model without PCA and the Super SBM model without PCA have been implemented on the data of both cases. In comparing the two methods (PCA-SBM and SBM), the PCA-SBM model has higher ranking power (five efficient DMUs versus nineteen in the case of pharmaceutical companies and four efficient DMUs versus twenty-nine in the case of bank branches). Also in comparing the PCA-Super SBM and Super SBM, the PCA-Super SBM model works more powerfully in complete ranking. As the Super SBM model cannot rank non-extreme units unlike the PCA-Super SBM. Consequently, the presented algorithm works successfully in ranking the DMUs completely (inefficient, extreme, and non-extreme efficient) with low complexity.

Herding behavior and government policy responses: Evidence from COVID-19 effect

The purpose of this study is to investigate the impact of a sudden shock from the COVID-19 epidemic on the behavioral bias of investors in the stock market of Iran as a developing country. The study also examines whether the government response to the COVID-19 pandemic can reduce investor herding behavior. We have used the Cross-sectional absolute deviation (CSAD) to measure securities dispersion from market returns. The studied period includes the cross-sectional data of the top 50 companies listed on the stock exchange during 2381 working days of the market (from March 1, 2012, to March 1, 2022). Furthermore, we use the semi-parametric estimator of the quantile regression for the data on the Iranian government response during the COVID-19 epidemic taken from the Oxford COVID-19 Government Response Tracker (OxCGRT). The main findings are in order. First, results show that the COVID-19 pandemic caused the formation of herding behavior aggravated by market volatility. Second, we document that the government response stringency index is unsuccessful in reducing investor herding behavior in the Iranian stock market. Finally, given the evidence that herding behavior, as a form of behavioral distortion, can drive security prices away from equilibrium values supported by fundamentals and cause price bubbles, our findings have important implications for policymakers and investors to mitigate herding effects and mis valuations.

CEO compensation and money laundering risk

This paper investigates the connection between money laundering risk and Chief Executive Officer (CEO) compensation in the Iranian stock market. For this goal, we construct a firm-level measure of money laundering risk using the opinion of certified auditors. Our findings documented a positive relationship between CEO compensation and money laundering risk. Furthermore, our results suggest that firms’ characteristics like size and independence of the firms' board can reduce the risk of money laundering.

Sanctions and Iranian stock market: Does the institutional quality matter?

The effect of sanctions, oil revenues, exchange rate, and inflation on different regimes of the Iranian stock market is examined in two cases (with and without considering institutional quality) during the 1984–2020 period, using the threshold Structural Vector Autoregression model (TSVAR) model. The results show that the difference between the impulse-response functions in the bullish and bearish markets, with and without considering institutional quality, is only related to the effects of sanctions and inflation, respectively. Considering the institutional quality case, inflation brings about an improvement in the stock market in the medium-term; however, without institutional quality, there is only an improving short-term effect. Accordingly, within increasing the sanctions case, if the institutional quality has been also enhanced; an increase in the number of sanctions causes a long-term improvement in the bullish market. Also, in the bearish market, an increase in the sanctions and the depreciation of the rial, along with increasing institutional quality, leads to a long-term growth in the stock market.

Assessing the role of the Gold and US dollar as a safe haven and risk hedge of the Iran stock market during Covid-19 pandemic and earlier

Assessing the role of the Gold and US dollar as a safe haven and risk hedge of the Iran stock market during Covid-19 pandemic and earlier

A Hybrid Approach to Sentiment Analysis of Iranian Stock Market User’s Opinions

With the significant growth of social media, individuals and organizations are increasingly using public opinion in these media to make their own decisions. The purpose of sentiment analysis is to automatically extract people’s sentiments from those social networks. Social networks related to financial markets, including stock markets, have recently attracted the attention of many individuals and organizations. people in these networks share their opinions and ideas about each share in the form of a post or tweet. In fact, sentiment analysis in this field is the assessment of people’s attitude towards each share. There are different approaches in sentiment analysis, in this article, a hybrid approach is proposed for sentiment analysis. In this way the feature vector used in machine learning is obtained from a lexicon that is automatically extracted from user’s tweets. This lexicon is made by using stock price information related to user’s opinion. Also, by using the next day’s price information of each share, amendments were suggested to this lexicon. Therefore, the lexicon generated for the feature vector was constructed in three ways, and all three methods reported about an 8% improvement over the baseline method in terms of F-score. The baseline method that is considered for this work, is the Persian version of SentiStrength lexicon which is designed for general purpose.

Investigating the asymmetric relationship between housing prices and the stock market in Iran: quantile-on-quantile approach

PurposeThe purpose of this study, the nonlinear relationship between the real estate market and the stock market was investigated in Iran. For this intent, the monthly data from 2012:4 to 2022:5 is used.Design/methodology/approachIn this study, the quantile-on-quantile estimation method is used, which is a combination of the nonparametric estimation methods and the quantile regression.FindingsThe research results show that, in the low quantiles, the effect of stock market return on the housing market return is negative or zero. In fact, in this situation, the increasing returns in the stock market will shift part of the financial resources of the economy to the market and create stagnation or even negative returns in the housing market. This situation is seen more strongly in some other quantiles, including the 0.25 and 0.75 quantiles; in contrast, the effect of high quantiles of stock market returns is positive on the housing market.Originality/valueIt seems that the demand in the housing market increase in a situation where the returns of the stock market are growing, and the market is in a bullish condition, and this causes an increase in the price and returns in this market. In addition, the results show that the effect of stock market returns on capital market returns is asymmetric and nonlinear.

An Investigation of the Link between Major Shareholders’ Behavior and Corporate Governance Performance before and after the COVID-19 Pandemic: A Case Study of the Companies Listed on the Iranian Stock Market

One of the basic functions of establishing corporate governance (CG) in companies is improving performance and increasing value for shareholders. Expanding the company’s value will ultimately increase the shareholders’ wealth. Therefore, it is natural for shareholders to seek to improve their performance and increase the company’s value. If CG mechanisms cannot perform this function in companies, they do not have the necessary efficiency and effectiveness and, therefore, cannot improve the efficiency of companies. This article investigated the connection between the power of major shareholders and the modality of CG of companies listed on the Iranian capital market before and after the COVID-19 pandemic. The statistical sample of the research included 120 companies listed on the Tehran Stock Exchange for the selected period from 2011 to 2021. The results showed that the concentration of ownership is harmful to adopting corporate governance (GCG) practices. In particular, the high level of voter ownership concentration weakens the corporate governance system (CGS). The results of this study, which was conducted using panel analysis, revealed that the concentration of ownership impairs the quality of CGS, and major shareholders cannot challenge the power of the main shareholder; it alsonegatively affected the quality of business boards, both during and before the COVID-19 pandemic. The competitiveness and voting rights of the major shareholders negatively affected the quality of board composition before and after the COVID-19 pandemic. The concentration of voter ownership also negatively affected the quality of CGS, both during and before COVID-19, and the competitiveness and voting rights of major shareholders before COVID-19. This concentration positively affected the quality of CGS after the COVID-19 pandemic.

Detection of the Type of Left and Right Returns-to-Scales Using Envelopment DEA Models in Crisp and Fuzzy Environments: An Application for Predicting Changes in the Stock Market

This paper aims to analyze left and right returns-to-scales in crisp and fuzzy data envelopment analysis (DEA). Since all previous envelopment DEA models for assessing left and right returns-to-scales are parametric, they are prone to encountering infeasibility problems, producing incorrect or different solutions for determining the type of returns-to-scale because of the different choices of parameter values. This misdiagnosis will lead to poor management decisions. Due to the mentioned problems, the issue of one-sided returns-to-scale has also not been studied in inaccurate environments. The present paper first proposes an alternative method of left and right returns-to-scales determination with crisp data to address this problem. This approach develops two non-parametric envelopment DEA models for analyzing left and right returns-to-scales. Then, the proposed method is extended to the fuzzy environment where data are considered more realistic. Due to its major advantages, credibility measure is used for solving fuzzy DEA models built to determine left and right returns-to-scales. As an application of the proposed method, data of companies in the Iran stock market are collected for 2014–2019 as fuzzy data and frontier units are analyzed by one-sided returns-to-scale.

Modifying Hidden Layer in Neural Network Models to Improve Prediction Accuracy: A Combined Model for Estimating Stock Price

Investment experts, who deal with stock price estimation, commonly look for the most accurate and appropriate statistical techniques to make decisions on investment. The aim of this study is to improve the accuracy of stock price prediction models through modifying the structure of a combined neural network model with time-series data, in which the main contribution is to insert the time-series analysis prediction into the hidden layer of the neural network. The proposed structure is made up of neural networks and time-series analysis, with variable reduction used to remove attributes with inter-correlations. Data has been collected over six years (72 months) from the Iranian stock market, including the number of trades, new-coin price, gold-18 price, US Dollar and Euro equivalent currencies, oil-index price, Brent-oil price, industry index, and balanced stock index, followed by developing the prediction models. Comparing the performance criteria of the proposed structure to the traditional ones in terms of the mean square and mean absolute errors revealed that inserting time-series estimated variables into hidden layers would improve the performance of neural network models to estimate stock prices for making investment decisions. Doi: 10.28991/HIJ-2022-03-01-05 Full Text: PDF

Impact of global oil and gold prices on the Iran stock market returns during the Covid-19 pandemic using the quantile regression approach

The main aim of this paper is to investigate the influence of global oil and gold prices on the Iran stock market during the Covid-19 pandemic. The approach used in this study is the quantile regression method and the effective parameters on the stock price index in the deciles of 0.1–0.9 were studied. The daily data from February 20, 2020 until January 30, 2021 were used in order to achieve the purposes aforementioned. The results showed that there is not a significant relationship between the global gold price and the stock price Iran stock exchange index. In addition, in all studied deciles, there is a negative and significant relationship between OPEC oil prices and the Tehran Stock Exchange index. Therefore, it is necessary for investors and policymakers to consider the impact of changes in these variables on stock returns.

Analyzing the Investment Behavior in the Iranian Stock Exchange during the COVID-19 Pandemic Using Hybrid DEA and Data Mining Techniques

The main purpose of this paper is to investigate the effects of COVID-19 regarding the efficiency of industries based on data in the Tehran stock market. A hybrid model of Data Envelopment Analysis (DEA) and data mining techniques is used to analyze the investment behavior in Tehran stock market. Particularly during the COVID-19 pandemic, many companies face financial crises. That is why companies with inferior performance must be benchmarked with efficient companies. First, the financial data of investments on selective companies are analyzed using data mining approaches to recognize the behavioral patterns of investors and securities. Second, customers are clustered into 3 selling and 4 buying groups using data mining techniques. Then, the efficiency of active companies in stock exchange is evaluated using input-oriented DEA. The results indicate that, among 23 industries listed on the stock market in Iran, solely nine were efficient in 2019. Moreover, in 2020, the number of efficient industries further decreased to six industries. Comparing the obtained results with those of another study which was conducted in 2018 by other researchers revealed that COVID-19 strongly affects the performance of an industry and some industries which were efficient in the past such as the bank industry became inefficient in the following year.

Effect of financial literacy and risk aversion on the relationship between self-control and financial security of individual investors in Tehran Stock Exchange

Capital markets play an important role in the economy and naturally, the proper functioning of the capital market will play a key role in ensuring economic growth. So, if capital markets are efficient, economic development will be ensured. On the other hand, since the Iranian stock market is currently very popular among investors, it is undoubtedly important to examine the behavioral biases of individual investors. Therefore, in this study, the effect of positive self-control behavioral bias on financial security was investigated with the moderating role of financial literacy and risk aversion of individual investors. For this purpose, the statistical population consisted of individual investors, traders and brokers of Tehran Stock Exchange who are directly participating in the market. Also, the statistical sample was calculated to be 421 using Cochran’s formula. A structural equation model using PLS method was used to analyze the data. The results showed that the self-control variable has a significant positive effect on risk aversion and financial security. Also, by using the financial literacy variable as a moderating variable, the intensity of the relationship between self-control and financial security of individual investors increases; that is, the financial literacy of individual stock market investors intensifies the positive relationship between self-control and security of their financial behavior. In addition, by using risk aversion as moderating variable, the intensity of the relationship between self-control and financial security of individual investors is reduced. This means that the risk aversion of individual stock market investors reduces the positive relationship between self-control and the security of their financial behavior.

Investigating the Effect of Financial and Monetary Policy on the Iranian Stock Market by Using DSGE Model

This paper uses a dynamic stochastic general equilibrium model to investigate the effect of fiscal and monetary policy on the stock market in Iran. Results show that a positive money shock leads to a rise in output, stock price index, and inflation. In addition, the response of the stock demand to money supply shock is negative. We found that a positive government expenditure shock led to a rise in output and inflation. The response of stock demand and stock price index to the government expenditure shocks are negative. Furthermore, results show that a stock market shock leads to a rise in output and inflation.

Investigating the Financial Crisis of the Iranian Stock Exchange Using Transfer Entropy Method and comparing it with the Dow Jones Financial Market

The focus of the present study is on the financial markets of Iran. In fact, the purpose of the present study is to examine the critical state of the Iranian stock market and compare it with one of the famous American markets called Dow Jones. The purpose of the financial crisis is to examine the effect of the interaction of companies within a market. According to modern financial theory, crisis is a phenomenon of market orientation and finding a negative preferred direction among companies. The stock market data of 180 companies of this market that were active during the period of 2008 to 2019 were used to investigate the financial crisis of the Iranian Stock Exchange. The results show that the average transfer entropy for 180 firms in the Iranian stock market is more random. That is to say, the observed declines do not correspond to the time periods of the crisis in the stock market. Although transfer entropy seems to be a good indicator for determining the period of crisis in the US Dow Jones market, it cannot be an indicator for determining the financial crisis for the Iranian stock market.

Providing a Network for Measuring the Dynamics Volatility Connectedness of Oil and Financial Markets

Various studies have shown that markets are not separated and that fluctuations in different markets affect each other. Therefore, awareness of connectedness is needed for investors and policymakers for making appropriate decisions. The aim of this paper is to measure the dynamics connectedness of selected stock markets in the Middle East, oil markets, gold, the dollar index, and euro-dollar and pound-dollar exchange rates during the period February 2007 to August 2019 in networks with different weekly horizons. In this paper, we intend to evaluate the pairwise impact of crude oil and the Middle East stock markets, in particular on the Tehran Stock Exchange, and to analyze this variance using different time horizons. The results show that in all time horizons the variance of forecast error in most markets is due to the shocks themselves. The Saudi Arabian Stock Exchange has the most impact on other Middle Eastern stocks. The dynamics connectedness of the oil markets is remarkable, however, as the time horizon increases, dynamic connectedness between the two markets decreases and they are mostly affected by other markets, especially the Middle East stock exchanges except for Iran. Moreover, Iran stock market is an isolated market. About the gold market, there is a significant connectedness with the pound-dollar exchange rate and gold market; however, the dynamics connectedness of this market with other markets are not significant. Therefore, this market and Iran stock exchange can be used as a tool to hedge risk for investors.

An Uncertain Allocation Models in Data Envelopment Analysis: A Case in the Iranian Stock Market

Data envelopment analysis (DEA) can be employed for investigating operation and evaluation of units as one of the most important concerns of managers. DEA is a linear programming technique for calculating relative performance of decision-making units (DMUs) with multiple inputs and outputs. Although all inputs and outputs are considered as certain items in these models, there are uncertain items in the real word and existing interference between these two concepts will result in uncertain models. Allocation models were studied in an uncertain environment with belief degree based uncertain input costs and output prices. Belief degree based uncertainty is useful for cases where there is no historical information on an uncertain event. Utilizing the uncertain entropy model as a second objective function, the cost and revenue models showed an optimal performance with a maximum dispersion rate in their constituent components. As a solution methodology, the uncertain allocation models were separately converted into crisp models by expect value (EV) and expected value and chance-constrained (EVCC) methods. A practical example from the Iranian Stock Market was also presented to evaluate the performance of the new model.

The Linkage between Sentiments and Stock Market Dynamics New Evidence from Iran

In recent years, the issue of financial behaviour and the impact of investors’ sentiments on their decision making have become such a popular issue. The sentiments of financial activists affect the market price of financial assets and particularly stocks, and therefore it is included in the new pricing models of capital assets. In this article, we seek the effect of investors’ sentiments on the dynamics of the Iranian stock market (TSE). To do this, among the companies accepted in the stock market we select 120, considering the research criteria and screening method, we examined TSE specifics throughout 2010-2018 using regression analysis and causality test. Our results show that firstly investors’ sentiments have a direct effect on the stock returns and there is a bilateral relationship between them. Secondly, inflation has the opposite effect and economic growth has a direct and positive effect on the relationship between investor sentiment and stock returns. Finally, government spending has no significant effect on the relationship between investor sentiment and stock returns.

The effect of the ratio of lending to deposits with capital adequacy ratio the banks listed on the stock market in Iran

Objective: Capital is one of important and essential factors in evaluating healthy and sustainability in banking system and in order that adequate capital basis can address wide range of risking which every bank faces. Methodology: A brief review of banking theory implies empirically that all different aspects of banking organization were influenced by available capital and expected possible return directly or indirectly. Capital operates as a shield against decreasing of asset value or increasing bank debts. The relation between capital adequacy and banking and also economic factors has a considerable importance. A plenty of universities and financial institution have tried to identify the main factor of determining capital adequate ratio.in this term, in recent studies these questions have been issued to determine as is there any positive and meaning relation between fund ratio to depositing on legitimate banking capital adequacy in Tehran commercial paper and Iranian over the counter OTC)? And also any positive or meaning relation between banking asset return on capital adequacy ratio in commercial paper exchange market? Therefore in terms of finding a solution for issued matter, some assumptions were implied based on determined relations. Results: Considering static population was included all acceptable bank in exchange market in during of 1388 to 1392 and any sampling haven’t been determined. So 15 acceptable bank were selected as a sample in this article.it is better to mention a cross multi – factors model was designed to be examined codified assumption by using synthetic data. Conclusion: And achievable results shows there aren’t any positive or meaning relation between paid fund on depositing and acceptable banking capital adequacy in Tehran exchange commercial paper and OTC.