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Changes in economic markets have made it necessary to understand the psychology of individual investors. Conducting effective studies on the decision of investors to buy stock in the stock market can be useful. Therefore, it is necessary to identify and prioritize the factors affecting the decision-making of investors to purchase shares of the stock exchange. The purpose of this study was to analyze causal relationships and to weight effective factors on individual investment to purchase shares of Tehran Stock Exchange.

The present study is applied research in the term of its purposes and a descriptive-survey one in the term of data gathering methods. The data required in this study was collected through library and field studies. The study population included 35 investment experts. In present study, multi-criteria decision-making techniques in type-2 fuzzy environments have been used to analyze the causal relationships and weighing the factors affecting individual investment in purchasing stock in the stock market.

In the study, 4 indicators and 20 sub-indicators influencing individual investors’ decision to purchase shares of Tehran Stock Exchange were selected based on the literature review in the field of investment in the stock exchange, as well as interviews with experts. Analyzing the opinions of experts showed that they have much paid attention to financial index compare to the economic, political and psychological indicators of the market in determining the priority of indicators. In analyzing sub-indicators, it was identified that Iranian investors pay special attention to economic and political developments, political news and international economic developments.

The present study has been carried out in Iran, and therefore, is geographically limited to Iran. In thematic terms, it is limited to effective factors of individual investments in Tehran Stock Exchange. The statistical population of present study was limited to investing experts in Tehran Stock Exchange. The difference in financial, economic, social and political conditions of individuals was another limitation of present study. The main consequences of research were the explanation of causes of investors’ higher attention to financial factors than economic, political and mental factors of market in buying stocks.

Given the uncertainty in the market status, using multi-criteria decision-making techniques in financial analysis can help decision-makers to make better decisions. In addition, it would be possible to take into account many variables that do not have a mathematical aspect but are important in decision-making and lead to increased decision-making satisfaction. The research initially analyzed causal relationships of determinants of individual investment on stock exchange for buying stocks through a type-2 fuzzy approach.

US sanctions have been a major feature of US Iran policy since Iran’s 1979 Islamic revolution, but the imposition of UN and worldwide bilateral sanctions on Iran that began in 2006 and increased dramatically as of 2010 is recent by comparison. The objectives of US sanctions have evolved over time. Broad international sanctions imposed on Iran harmed Iran’s economy and contributed to Iran’s acceptance of agreements that exchange constraints on its nuclear program for sanctions relief. The subject of this study is important because both Iran and the international communities are demanding for information about the effect of sanctions on Iran. In an international and regional perspective, it seems that sanctions have a negative impact on economic, social and even political status of Iran. Therefore, this paper aims to examine the impact of Iran Central Bank sanction on Tehran Stock Exchange as on December 31, 2011.

Variables of model are consisted by exchange rate, oil prices and Tehran Stock Exchange Price Index (TEPIX) from October 2, 2011 to March 29, 2012, which is offered daily. To analyze the model, the authors used Johansen–Juselius and Autoregressive Distributed Lag (ARDL) methods.

The results indicate that there is a long-run equilibrium relationship between selected variables as oil prices, and exchange rates have a positive effect on the TEPIX. In other words, the results of the econometric estimation show the positive effect of the Iran Central Bank sanction on the TEPIX. Thus, because of economic sanctions imposed by the Western countries, Tehran Stock Exchange has been growing.

No empirical research exists that examines the impact of sanctions on stock price in developing countries. This study fills this gap by examining the links between sanctions and stock price in Iran.

This paper presents a combined method of ensemble learning and genetics to rebalance the corporate portfolio. The primary purpose of this paper is to determine the amount of investment in each of the shares of the listed company and the time of purchase, holding or sale of shares to maximize total return and reduce investment risk.

To achieve the goals of the problem, a two-level combined intelligent method, such as a support vector machine, decision tree, network Bayesian, k-nearest neighbors and multilayer perceptron neural network as heterogeneous basic models of ensemble learning in the first level, was applied. Then, the majority vote method (weighted average) in the second stage as the final model of learning was collectively used. Therefore, the data collected from 208 listed companies active in the Tehran stock exchange (http://tsetmc.com) from 2011 to 2015 have been used to teach the data. For testing and analysis, the data of the same companies between 2016 and 2020 have been used.

The results showed that the method of combined ensemble learning and genetics has the highest total stock portfolio yield of 114.12%, with a risk of 0.905%. Also, by examining the rate of return on capital, it was observed that the proposed method has the highest average rate of return on investment of 110.64%. As a result, the proposed method leads to higher returns with lower risk than the purchase and maintenance method for fund managers and companies and predicts market trends.

In the forthcoming research, there were no limitations to obtain research data were easily extracted from the site of Tehran Stock Exchange Technology Management Company and Rahvard Novin software, and simulation was performed in MATLAB software.

In this paper, using combined machine learning methods, companies’ stock prices are predicted and stock portfolio optimization is optimized. As companies and private organizations are trying to increase their rate of return, so they need a way to predict stock prices based on specific indicators. It turned out that this algorithm has the highest stock portfolio return with reasonable investment risk, and therefore, investors, portfolio managers and market timers can be used this method to optimize the stock portfolio.

The homogeneous and heterogeneous two-level hybrid model presented in the research can be used to predict market trends by market timers and fund managers. Also, adjusting the portfolio with this method has a much higher return than the return on buying and holding, and with controlled risk, it increases the security of investors’ capital, and investors invest their capital in the funds more safely. And will achieve their expected returns. As a result, the psychological security gained from using this method for portfolio arrangement will eventually lead to the growth of the capital market.

This paper tries to present the best combination of stock portfolios of active companies of the Tehran Stock Exchange by using the two-level combined intelligent method and genetic algorithm.

The aim of this research is to predict the total stock market index of the Tehran Stock Exchange, using the compound method of fuzzy genetics and neural network, in order for the active participants of the finance market as well as macro decision makers to be able to predict the market trend.

First, the prediction was done by neural network, then the output weight of optimum neural network was taken as standard to repeat this prediction using the genetic algorithm, and then the extracted pattern from the neural network was stated through discernible rules using fuzzy theory.

The main attention of this paper is investors and traders to achieve a method for predicting the stock market. Concerning the results of previous research, which confirms the relative superiority of non-linear models in price index prediction, an appropriate model has been offered in this research by compounding the non-linear method such as fuzzy genetics and neural network. The results indicate superiority of the designed system in predicting price index of the Tehran Stock Exchange.

This paper states its originality and value by compounding the non-linear method issues pattern to predict stock market, to encourage further investigation by academics and practitioners in the field.

This paper aims to evaluate the financial performance of companies listed on Tehran Stock Exchange by using negative data envelopment analysis (DEA) approach.

First, the financial metrics for performance evaluation were extracted and then filtered based on the experts’ opinions. Upon choosing the appropriate financial measures, the financial information of 72 companies selected from four automotive, pharmaceutical, petrochemical and cement industries were collected, and the criteria values were also measured. The financial performance of selected companies was assessed using negative data bounded adjusted measure in the DEA, and efficient and inefficient companies were identified. Finally, the efficient companies were ranked using Andersen and Petersen model.

The required analysis was conducted, and the financial performance of selected companies listed on Tehran Stock Exchange was evaluated. There were 58 efficient companies with a performance value of 1; 14 companies became inefficient because the efficiency size was less than 1; therefore, reference units were also introduced to the managers for efficiency of inefficient companies.

The aim of this study was to identify the required financial criteria and to determine an appropriate model for performance evaluation based on negative DEA. The findings can help shareholders to identify efficient companies and make the optimal portfolio accordingly; the managers of inefficient companies can also take the proper reforming actions to improve efficiency.

In this study, to maximize returns and minimize investment risk, an attempt was made to form an optimal portfolio under conditions where the capital market has a price bubble. According to the purpose, the research was of the applied type, in terms of data, quantitative and postevent, and in terms of the type of analysis, it was of the descriptive-correlation type. Sequence, skewness and kurtosis tests were used to identify the months with bubbles from 2015 to 2021 in the Tehran Stock Exchange. After identifying the bubble courses, artificial bee colony meta-heuristic and invasive weed algorithms were used to optimize the portfolio. The purpose of this paper is to address these issues.

The existence of bubbles in the market, especially in the capital market, can prevent the participation of investors in the capital market process and the correct allocation of financial resources for the economic development of the country. However, due to the goal of investors to achieve a portfolio of high returns with the least amount of risk, there is need to pay attention to these markets increases.

The results identify 14 periods of price bubbles during the study period. Additionally, stock portfolios with maximum returns and minimum risk were selected for portfolio optimization. According to the results of using meta-heuristic algorithms to optimize the portfolio, in relation to the obtained returns and risk, no significant difference was observed between the returns and risk of periods with price bubbles in each of the two meta-heuristic algorithms. This study can guide investors in identifying bubble courses and forming an optimal portfolio under these conditions.

One of the limitations of this research is the non-generalizability of the findings to stock exchanges of other countries and other time periods due to the condition of the price bubble, as well as other companies in the stock market due to the restrictions considered for selecting the statistical sample.

This study intends to form an optimal stock portfolio in a situation where the capital market suffers from a price bubble. This study provides an effective and practical solution for investors in the field of stock portfolio optimization.

The purpose of the present paper is to examine the trade-off relationship between managerial ownership and corporate debts and whether this relationship is moderated by ownership structure and corporate tax rates, particularly in a transition and emerging market whose unique institutional characteristics considerably differ from those prevailing both in the West and East markets.

This research is semi-empirical in terms of method and practical in terms of purpose. The authors test their hypotheses by using simultaneous equations system methodology with two- and three-stages least squares regression (2SLS and 3SLS) and panel data technics on a sample of 952 listed companies on the Tehran Stock Exchange during 2011-2018.

The findings indicate that, contrary to the current line of research, there is no trade-off relationship between managerial ownership and debt concerning the reduction of agency costs. Likewise, the study finds no convincing evidence that either the controlling shareholder or the corporate tax rate could influence or moderate this interrelationship. The conjecture lies in the fact that the fundamental environmental variations between the Tehran Stock Exchange and the institutional assumptions underpinning the Western models have led to the formation of such unexpected results.

The implications drawn from this study are constrained by two primary limitations. First, the present study is conducted in an Iranian setting; therefore, the data used for the study only contain companies listed on the Tehran Stock Exchange. The utilization of listed companies on the Tehran Stock Exchange is likely to affect the generalizability of the study in an international context. Second, in this study, we were unable to extend the sample time period because of some major deficiencies in the Tehran Stock Exchange library and its supplementary software. The usage of an extended time period could have provided more generalizable results. However, extended time period, per se, may impair the validity of the results as well.

Because the fundamental institutional assumptions underpinning the Western and even East Asia capital structure models are not valid in the institutional environment of Iran, the findings of this study could provide substantial implications for the understanding of agency costs and capital structure literature. These significant institutional and ownership differences are the factors affecting firms’ leverage and capital choice decisions. Indeed, this study has laid some groundwork upon which a more detailed evaluation of the Iranian firms’ capital structure could be based. In addition, the examination of such relations may provide the ground for sound decision-making by various interested users of financial and accounting information.

The purpose of this paper is to investigate the relationship between financial reporting and cost stickiness in companies listed on the Tehran Stock Exchange.

Data of all Iranian manufacturing listed companies gathered for testing hypotheses during 2010–2016 and R statistical software are employed in order to analyzing data.

The results of this study indicate that there is a significant relationship between administrative, sale, material, labor and overhead costs and the financial reporting qualities of the companies under study.

The study focuses on relationship between financial reporting and cost stickiness in companies listed on the Tehran Stock Exchange, which is the first study of its type in Iran.

The main aim of this study is to investigate the effects of marketing and costs and research and development (R&D) investments on profitability of pharmaceutical companies of Iran.

In this study, pharmaceutical companies that have been accepted in Tehran Stock Exchange until March 19, 2013 were investigated. Random-effect panel data estimator was used for this purpose.

The findings indicate that variables such as company size, capital-to-total asset ratio and debt-to-asset ratio have an effect on profitability. But, company life, advertising cost and R&D investment are ineffective on profitability.

Legal issues like not having patent law and pricing mechanism are reasons for the ineffective relationship between R&D and marketing costs and its effect on profitability of the Iran pharmaceutical industry.

The purpose of this research paper is to provide a model for reporting quality of financial information based on behavior of listed companies in Tehran Stock Exchange which is based on structural equation modeling approaches.

This study uses applied research and postsemi experimental method of data collection in the field of proofing accounting research with deductive–inductive approach. The statistical population of this study includes the sample of 128 listed companies in the Tehran Stock Exchange between 2007 and 2017. The behavioral characteristics of managers (hidden variables) are measured by observable variables of myopia, opportunistic behavior and overconfidence of managers. Reporting quality of financial information is also investigated based on the scores accrued to each company and the announcement published by the Tehran Stock Exchange based on the companies' rating in terms of the quality of reporting and proper notification.

After insuring the acceptable fitness of the measurement pattern and the structure of research in both approaches, structural equations modeling and regression, the results indicate that there is a significant negative relationship between the behavioral characteristics of managers and the reporting quality of financial information.

Accountants have a critical and difficult responsibility of dealing with transactions and presenting them in the form of financial reports that can be used by interest groups to assess the performance of companies. This critical responsibility becomes meaningful when professional and ethical behaviors are the basis for disclosure of financial reporting. Based on the behavioral characteristics of disclosing financial reporting in emerging capital markets such as Iran, this study can be successful in developing new and theoretical literature in this field.