Fraudulent reporting by managers in financial reports is a major threat to investors. However, in practice; there is no method for immediately detecting fraudulent reporting by managers. Therefore, it is very necessary to pay attention to the direct criteria affecting the possibility of fraud in financial reporting. Therefore, this research was conducted with the aim of explaining the prevention of fraud in banks' financial statements based on organizational culture and professional ethics, emphasizing the role of whistleblowing. This research is descriptive-causal in terms of research approach. The statistical population of the study was all certified public accountants and managers of public and private banks. Based on the Morgan table, 384 questionnaires were distributed among the statistical population as a sample and 293 responses were received. The information collected by the questionnaires was analyzed using SPSS24 and AMOS24 software using structural equation modeling. The results of the analysis of the research hypotheses showed that the variables of organizational culture, professional ethics, and whistleblowing have a positive and significant effect on preventing fraud in banks' financial statements, the variables of organizational culture and professional ethics have a positive and significant effect on whistleblowing; whistleblowing has a positive and mediating role on the relationship between organizational culture and preventing fraud in banks' financial statements, and whistleblowing has a positive and mediating role in the relationship between professional ethics and preventing fraud in banks' financial statements.
Given that sudden events and the influx of new information into the market such as announcements of capital increases lead to heightened return volatility and the emergence of abnormal returns, the present study investigates investors' reactions to capital increases via asset revaluation. The statistical sample comprises 147 companies listed on the Tehran Stock Exchange, selected through systematic random sampling for the period 1390–1397 (2011–2018). Cumulative abnormal returns (CAR) were calculated over a six-month window (three months before and after the capital increase announcement) using the market model and served as a measure of investor reaction to capital increases from asset revaluation. The findings indicate that capital increases through asset revaluation have a positive and significant effect on investor reactions. Moreover, the results reveal that the market reacts more strongly to capital increases from asset revaluation compared to those from cash contributions and retained earnings. These findings can assist company managers in financial decision-making and help investors evaluate investment opportunities.
Purpose:In the present study, has been investigated, the relationship between the tone of disseminated earnings news by companies via Twitter social media and the reaction of the capital market.Research Method: For this purpose, has been extracted, data from US S&P 500 companies, for 2016-2019 and has been analyzed.Results: The results showed that there was significant relationship between the tone of disseminated earnings news on Twitter and the stock abnormal bid-ask spreads. Also, the results showed that there is a significant relationship between earnings tweets containing original and existing news and the stock abnormal bid-ask spread. Conclusion: Disseminated additional news through social media by companies, expands market participants' access to information and be used as a complementary source of awareness in capital markets. Contribution: Despite the role of social media as complementary awareness resources, this environment can be prone to strategic news dissemination by companies, and vigilance by users of this environment should be considered.
Although compensation for losses resulting from securities fraud, including market manipulation, has received the attention of legislators and courts in the US and Iranian legal systems, it has always faced challenges. The difficulty of establishing a causal relationship between the harmful act and the damage suffered and determining the appropriate method of calculating the amount of recoverable damages has led the American legislator and courts to take the initiative and establish specific rules for this area. In this article, the challenges of civil liability resulting from capital market manipulation have been examined through a comparative study and an analytical-descriptive method and it has been concluded that it is possible to adopt the American legal system in the field of separating transaction causation from loss causation in establishing the causal relationship between market manipulation and the loss incurred and to choose the "Out of Pocket" method in calculating the amount of recoverable damage, taking into account the standards of the Iranian legal system. However, the assumption of a causal relationship based on the "Fraud on the Market" theory, especially considering "Market Efficiency" as the basis of the aforementioned theory, is not consistent with the legal system governing the Iranian capital market and cannot be applied in claims for damages resulting from market manipulation.
This study aims to present an optimization model for a multi-objective stock portfolio in the Iranian capital market under conditions of deep uncertainty. The research adopts a descriptive-analytical approach to optimize stock portfolio selection in the Iranian capital market under deep uncertainty. This study employs a mixed-methods approach, combining both quantitative and qualitative methods. In the quantitative section, mathematical modeling and simulation techniques are used to optimize the portfolio based on three objectives: return, risk and liquidity. The two-stage approach employed allows for initial decision-making followed by alignment with real market scenarios. Data is analyzed using Monte Carlo simulation and a generalized goal programming method is utilized for optimization. This model assists investors in selecting a portfolio that is suitable under conditions of uncertainty. Overall, the findings of this study indicate that the scenario-based multi-objective approach in stock portfolio selection, especially under deep uncertainty, can be an effective tool for investors and portfolio managers. By providing a comprehensive and flexible framework, this model facilitates improved risk management and portfolio performance optimization. However, the effectiveness of this model is contingent on the accuracy of scenario determination.
Given the importance of risk in financial markets, the accurate estimation of it has always been a primary concern for participants in these markets. The recurrent financial crises resulting from financial risk over the past two decades globally have underscored the necessity of precise financial risk estimation, with a focus on market risk. In this study, portfolio optimization is initially performed using the Mean Absolute Deviation (MAD) criterion, followed by portfolio loss measurement using the copula functions to account for the dependencies between the examined components. The CVaR is employed to estimate the loss of the optimal portfolio. The size of the optimal portfolio risk is calculated based on historical data, and then Monte Carlo simulation is used to forecast portfolio risk for the next period. Finally, to achieve the most accurate estimate of the optimal portfolio loss, the simulated loss using various distributions is compared with the loss derived from historical data. The data examined pertain to six stocks from the Tehran Stock Exchange, collected from April 2017 to June 2021. The results indicate that the normal copula for asset returns shows a lower estimate of the optimal portfolio loss, while using the Student's t-copula with lower degrees of freedom provides a more accurate risk estimation for the optimal portfolio.
In this study, we explored stock market bubbles in the Tehran Stock Exchange (TSE) by applying the Dynamical Systems Log-Periodic Power Law Singularity (DS-LPPLS) model to data from 2009 to 2024. What stands out about this model is how it picks up on super-exponential price surges and those distinctive log-periodic oscillations, allowing us to spot both positive bubbles (sharp upward rallies) and negative ones (sudden crashes). We used the Confidence and Trust indices as early warning tools.Our empirical findings highlighted several key bubble periods. The most striking was the massive positive bubble in 1399 (2020), where the index jumped nearly 500%, perfectly aligning with heavy monetary injections, a huge influx of retail investors, and the disruptions from the COVID-19 pandemic. Other bubbles tied closely to geopolitical events, like sanctions and the JCPOA negotiations. Remarkably, the model achieved over 80% ex-ante accuracy in forecasting critical turning points in this highly volatile emerging market.Overall, the results show DS-LPPLS outperforms traditional methods, clearly illustrating how macroeconomic shocks fuel bubble formation. This work provides practical insights for better regulatory monitoring and helping investors manage risks in turbulent conditions.
IntroductionAt the same time as the speed of financial exchanges increases, many complications are also added to it, and the amount of attention paid to the role of human actors in it has faded, and the method of high-frequency trading is over time. In this research, a model for HFT has been presented in order to dynamically adapt to environmental changes in addition to cost reduction.MethodologyThe method of conducting the research is to use the algorithm of colleagues' cellular automata and extermal optimization, and from fundamental analysis books, technical and economic analysis indicators and daily stock information as used and Statistical community of the symbols available in the Tehran Stock Exchange market in August 2016 to August 2021. The confusion matrix model has been used to evaluate the model.ResultsThe results of the research led to the modification of the effect range of technical analysis variables as well as economic variables for trading symbols, and a dynamic model for stock market trading was presented, which is able to adapt to changing environmental conditions in order to reduce the trading risk.
Institutional investors' behavior, as a fundamental link in the chain of macroeconomic decision-making, plays a decisive role. These behaviors sometimes transcend the boundaries of economic rationality and enter the realm of destructive behaviors. A systematic understanding of the drivers and consequences of these behaviors is essential for unlocking the complex puzzle of economic interactions.This study, considering its exploratory research approach and qualitative methodology, employs thematic analysis.The literature on destructive behavior among institutional investors was reviewed. Then, qualitative data were collected through interviews with 15 experts and university professors with professional experience in the capital market in 2023. After analyzing the interviews, the collected qualitative data were coded and analyzed using the Attride-Stirling thematic analysis method and MAXQDA software. In the quantitative section, systemic representation modeling was used to determine the drivers and consequences of destructive behaviors among institutional investors.The research findings identified two overarching themes (judgment-based destructive behaviors and perception-based destructive behaviors), 10 organizing themes (reference point, overconfidence, reliance on analysis in stock market transactions, locus of control, one-dimensional analysis and adverse effects, limited attention, structured corruption, short-term psychological shocks, stereotypical behaviors, and self-serving behaviors), and 33 basic themes.The most influential driver of destructive behavior among institutional investors is structured corruption, which stems from the pattern of perception-based destructive behaviors and can lead to one-dimensional analysis and adverse effects. Institutional investors can utilize the findings of this study to systematically redefine their behaviors within a structured framework.
In recent years, Telegram groups and channels become considerable parameter in user’s opinion. Previous researches show the importance of sentiments in forecasting models and researches find significant relation between sentimental analysis and stock price prediction. They consider buy, sell and hold contents as independent variable. But in reality, there is a difference between people who publish their opinion in social media. Means people consider the speaker’s background and also for the viral messages there are same situation. In this research we consider importance of the publisher and message in social media. Then we separate content into different variables. We analyse 15.736.204 Telegram messages and according to our sentimental dictionary, we tag them then put merge them with 19.312 records related to symbols in different days. Selected symbols are the stocks in 30 company index portfolio and the duration of survey is from march 2019 to august 2021. Six variable selection approach was considered in this survey, for each symbol (among 36 selected companies) we train different artificial neural network which the number of neurons in hidden layer varies from 6 to 10 and the lag of data for input variable differs from 1 to 5. In order to remove initial values effect in neural network, each network runs 10 times. Results showed binary prediction in a model which separates messages by importance of people and amount of view has better performance than other models and RMSE error in that model for return forecasting significantly lower than other models.
In the present study, the effect of the unusual tone of managers' disclosure with the unusual transactions of small shareholders is investigated. The relationship between the unusual tone of managers' disclosure and the volume of unusual transactions of minor and major shareholders indicates the information asymmetry between minor and major shareholders. Research tests have been conducted on 162 companies admitted to the Iran Stock Exchange during the years 2014 to 2023, and a total of 1,585 annual reports were reviewed by removing outliers. For testing of models using the multiple regression method, taking into account the fixed effects of year and industry. The results showed that the abnormal tone of managers has a positive relationship with the volume of abnormal transactions of minor shareholders and has no significant relationship with the volume of abnormal transactions of major shareholders. From the findings of the research, it can be concluded that small shareholders do not have the ability to process the biased tone of the managers, and with the unusual tone of the managers, they make unfavorable transactions and are so called misled.
Working in financial institutions and the capital market is among the most stressful and sensitive occupations in the world. Considering the critical role of human resources in the performance and events of the capital market, this study aims to develop a model of quality of work life (QWL) based on reducing human resource risks in capital market organizations. The research was conducted using a descriptive–survey method. The statistical population consisted of 128 employees from firms active in the capital market. Data were collected using three questionnaires, all of which were validated in terms of reliability and validity. Data analysis was performed through confirmatory factor analysis and path modeling using structural equation modeling (SEM) via SPSS and Smart PLS software. The findings indicate an inverse and significant relationship between QWL and human resource risks. Furthermore, results confirm that all identified QWL factors contribute to reducing at least one type of human resource risk within the capital market.
The main purpose of this study is to measure the impact of non-systematic risk on the financial characteristics of the company during a 14-year period from 2006 to 1398 with 1904 observations in 136 companies listed on the Tehran Stock Exchange. In this paper, firstly we estimate firm - specific (unsystematic) risk through using Fama & French three factor model and also with incorporating of GARCH and EGARCH models. Next, we expand Fama - MacBeth regression to analyze how firm characteristics such as return, book value, financial leverage, size , Earnings per share (EPS) and share turnover could discriminate firms in terms of idiosyncratic risk. By using Tehran stock Exchange (TSE) which includes a sample of 136 listed companies, we came into this conclusion that different firm financial characteristics have impact on idiosyncratic risk. Firm size, return and EPS have negative relation with idiosyncratic risk. In contrast, our findings show that share turnover and financial leverage could increase the idiosyncratic risk level.
AbstractPurpose: The purpose of this paper is to examine whether the banking business model affects earning smoothing or not. In addition, the current research deals with the role of auditors' participation in independent monitoring. Method: Profit smoothing with loan loss reserves is measured on unmanaged profit and corrected standard errors are used. Also, the sample includes Iranian banks that were observed from 1390 to 1401.Findings: The current research showed that despite the effect of the diversification business model, the retail and market-oriented business model does not have a significant effect on earning smoothing. But the retail business model and diversification has a significant effect on the predictability of cash flows. Also, the emphasis on audit quality has no significant relationship with the smoothing of market-oriented banks.Conclusion: Based on the results of the research, it can be stated that the expected loss approach for estimating credit losses cannot increase the information of the accounting numbers of other business models except the information of the accounting numbers of the diversification business model. Also, the present paper contributes to the earning smoothing literature by addressing the role of the business model as a whole in explaining the smoothing propensity, rather than limiting the observation to particular features of the balance sheet.
The purpose of this research is to achieve a suitable model for forecasting volatility of a broad market index. In this paper the CCARR model is proposed for forecasting volatility and its estimation results are compared with the popular GARCH, CGARCH and CARR models. The model is intuitive and convenient to implement by using the maximum likelihood estimation method. GARCH and CGARCH models use price returns and CARR and CCARR models use price ranges to predict volatility. CCARR and CGARCH models assume that the price range comprises both a long run (trend) component and a short run (transitory) component, which has the capacity to capture the long memory property of volatility. Daily data of The Tehran Stock Exchange index from 2009 to 2022 including high, low and close prices are used. The results show that range-based models such as CARR and CCARR models fit the data better than return-based models. Also two-component models fit the data better than one-component models. In general, the CCARR model generates more accurate out of sample volatility forecasts than the popular GARCH, component GARCH and CARR models.
In order to make best decision, investors need information about accounting earnings. Earnings management can lead to information asymmetry among shareholders. Classification shifting is a type of earnings management where items in the income statement are reclassified without changing the net income, misleading users of financial statements. The purpose of this study is to examine the relationship between classification shifting and information asymmetry, as well as the moderating role of disclosure quality, earnings forecasts frequency, and audit quality on this relationship. The research sample includes 117 companies listed on the Tehran Stock Exchange from 2011 to 2020. Multiple regression method was used to test the research hypotheses. The results shows that there is a relationship between classification shifting and information asymmetry, and that disclosure quality and earnings forecasts frequency weaken this relationship. Since this method is used by managers as a complement to other earnings management methods, there is a essential need to examine and understand the effects of this method alongside other earnings management techniques, which has rarely been addressed in domestic researches.
Introduction: This study examines the use of private information by informed traders and the impact of block trades on stock price volatility in the Iranian Capital Market. Additionally, it explores whether insights from these trades can be used to predict future market volatility.Methodology: To achieve these objectives, the Lee-Ready algorithm was applied to classify block trades into buys and sells, and the Probability of Informed Trading (PIN) model was employed to estimate the likelihood of informed trading. The sample included 34 companies among the top 50 on the Tehran Stock Exchange, analyzed between April 2022 and April 2023 on trading days with a minimum of 10 transactions. Intraday data during this period allowed for a more precise analysis of price movements.Findings: Findings indicate that block purchases, especially by informed traders, exert a greater impact on stock price increases than block sales. Additionally, the correlation between permanent price effects and the probability of informed trading is stronger than with temporary or overall price effects, suggesting that private information has lasting price impacts. The study also reveals that price impacts are more significant during early trading hours, likely due to the release of new information.Conclusion: Private information significantly influences price volatility, with informed traders capitalizing on this information for profit. These findings are important for market analysts and financial regulators, who may consider implementing regulatory measures to control the misuse of private information and to manage the effects of block trading on market volatility.
Iran's housing crisis, driven by high inflation and inefficiencies in traditional financing systems, necessitates redefined financial mechanisms and innovative models. While advancements in financial knowledge and technology have introduced diverse tools in financial markets—each addressing specific challenges—their effective use requires oversight from a centralized institution to determine the timing, resource allocation, and implementation stages of housing projects. This institution would coordinate these tools to optimize resource distribution and enhance financing system dynamism. This research examines existing financial instruments through content analysis and expert interviews, concluding that current tools lack efficiency in Iran’s economic climate due to inflation and the absence of an integrated institutional framework. The proposed model centers on the **National Housing Fund** (established under the 2021 Housing Production Leap Law) as a pivotal institution. It integrates multi-market financial tools, such as public-private partnerships, lease-to-own contracts, and foreign investment participation, within a unified framework. Key findings indicate that this hybrid approach—combining institution-building with simultaneous use of diverse financial instruments—not only mitigates the housing crisis but also incentivizes private sector engagement through profit-driven participation. Mechanisms like the "finance supply chain" enable households to address housing needs through future income streams rather than relying solely on past savings. Additionally, reducing fiscal pressure on the government allows concurrent execution of large-scale developmental projects. By fostering coordination between public and private stakeholders, the model aims to balance supply-demand dynamics and establish a sustainable housing finance ecosystem.
Investment funds have an essential role in collecting small funds. They help to minimize risk and obtain the highest return on investment through the diverse composition of their portfolio. Therefore, it is crucial to understand the factors affecting these funds. Investing in stocks is one of the most critical assets of funds, highlighting the need to address the reporting quality of companies in their portfolio. This study aimed to investigate the relationship between the quality of financial reporting of investment portfolio companies and the fund's performance. Earnings liquidity, accruals quality, and earnings timeliness were used to measure the quality of financial reporting. In addition, fund performance was measured using Alpha Jensen's index. The research population included 50 investment funds selected by quota method from 2016-2020. The research data were analyzed using the generalized least squares method. The findings showed that the earnings liquidity and timeliness of companies have a negative and positive relationship with fund performance, respectively. These results indicate that timely financial report publication is crucial and interest liquidity does not affect fund performance. This issue should be considered when determining financial reporting policies.
Temperature Changes & The Cost of Equity Capital; Evidence from Tracking Portfolio Approach AbstractTemperature fluctuations resulting from climate change can be considered as one of the factors causing uncertainty, causing investors to worry about their assets and leading to an increase in their expected returns. This study aims to investigate the effect of temperature changes on the cost of equity. For this purpose, the risk premium of temperature changes has been calculated we employ the economic tracking portfolio approach and its pricing is examined using Fama -McBeth regression (1973). We chose a sample with about 170 firms in Tehran Stock exchange in the period of 2000 to 2020 . The results show that risk premium of temperature changes is positive and significant and caused a 35% change in the cost of equity, however, this effect did not show an increasing trend over the research period. The results of quantile regression also confirm the mentioned findings- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
