مجله مدیریت دارایی و تامین مالی
سال دوازدهم شماره 1 (پیاپی 44، بهار 1403)

Idiosyncratic volatility of stock returns is caused by idiosyncratic risk. While proper stock portfolio diversification can mitigate idiosyncratic risk, the precise relationship between idiosyncratic volatility and stock returns remains ambiguous. The present study aims to shed light on this relationship by examining the effect of investor attention. It analyzed a sample of 176 companies listed on the Tehran Stock Exchange from 2016 to 2022. Employing the market model, the authors measured idiosyncratic volatility and abnormal trading volume to proxy for investor attention. The findings revealed that idiosyncratic volatility exerts a negative and significant impact on future stock returns. Moreover, it was found that investor attention amplified this negative effect, possibly due to behavioral biases arising from limited attention. Notably, this research diverges from prior studies by emphasizing the role of investor attention, an understudied variable, in shaping the idiosyncratic volatility-stock returns nexus. 

The investigation of risk-return relationships has a rich history in financial literature. According to the traditional asset pricing model, only systematic risk significantly impacts asset returns, while idiosyncratic or unsystematic risk that can be eliminated by diversification by the investor does not affect asset returns. However, empirical evidence concerning the link between idiosyncratic volatility and stock returns is ambiguous. Some studies such as Malkiel and Xu (1997), and Goyal and Santa-Clara (2003) find a positive relationship while some other studies such as Ang et al. (2006) and Jiang et al. (2009) document a negative association. Based on previous studies, investor attention can play an important role in shaping stock prices and affect the relationship between idiosyncratic volatility and future stock returns. Therefore, this study aims to investigate the effect of investor attention on the relationship between idiosyncratic volatility and future stock returns.

To test the research hypotheses, we utilize multivariate regression models and panel data methods. The sample consists of 176 companies listed on the Tehran Stock Exchange from 2016 to 2022. The independent variable is the idiosyncratic volatility of stocks, which is calculated through the market model. In addition, the moderating variable, the investor's attention, has been measured by the abnormal volume.

The empirical findings indicate that idiosyncratic volatility has a negative and significant effect on future stock returns. In other words, the higher the idiosyncratic volatility of the stock return, the lower the future monthly return. Moreover, the investor's attention exacerbates the negative effect of idiosyncratic volatility on future stock returns. That is, the greater investor attention causes the idiosyncratic volatility to have a stronger negative effect on future monthly stock returns.

The results of the first hypothesis show that idiosyncratic volatility has a negative and significant effect on future returns. In other words, as idiosyncratic volatility increases, stock returns will decrease in the next month. This result is consistent with Ang et al. (2006), Jiang et al. (2009), and Hur and Singh (2022). In the second hypothesis, the effect of investor attention on the negative relationship between idiosyncratic volatility and stock returns was investigated, and it was expected that investor attention would mitigate the negative effect of idiosyncratic volatility on future stock returns. However, the results indicate investor's attention amplifies the negative effect of idiosyncratic volatility on future stock returns. This result diverges from the conclusions drawn by Hur and Singh (2022). This unexpected outcome may be attributed to the behavioral bias associated with limited attention among investors. When attention is constrained, investors may react inaccurately to available information or evaluate it incorrectly, leading to exacerbated negative effects of idiosyncratic volatility on future stock returns.

The importance of the uncertainty of economic policies and its role in financial decisions and companies in today's complex world is evident more than ever. On the other hand, credit transactions are one of the most important sources of short-term financing for companies, which play an effective role in managers’ decisions and determining a firm value. Emphasizing the moderating role of credit transactions, in this study, the effect of economic policy uncertainty on credit transactions and firm value has been investigated. The data of the research were gathered from 170 companies in the Tehran Stock Exchange during the period of 2011 to 2022. Regression analysis was used to analyze the data and test the hypotheses. The results show that the uncertainty of economic policies has a significant positive relationship with the payment period, the difference between the collection and payment period, and the firm value. However, it has no significant relationship with a company’s collection period. On the other hand, the collection period and the payment period moderate the relationship between the uncertainty of economic policies and the firm value, and the difference between the collection and payment periods does not have a moderating role in this relationship. By examining the moderating role of credit transactions in the relationship between economic policy uncertainty and firm value in Iran’s emerging economy, this study can contribute to the expansion of the literature in this field.

The uncertainty of the economic policy affects the micro aspects at the company level, the financial markets, and the behavior of companies (Huayu et al., 2021). It increases information asymmetry between internal managers and external users (Shen et al., 2021). Studies have shown that the uncertainty of economic policies has had a negative effect on the financing and investment of companies (Hussain et al., 2023), but there are few studies about its relationship with credit transactions of companies. Theoretically, the effect of economic policy uncertainty on credit transactions may lead to two opposite effects. On the one hand, suppliers bear the risk that their customers will not repay their debts in the future, which is exacerbated by increasing economic policy uncertainty. Thus, companies are reluctant to offer credit deals, which is a negative effect. On the other hand, companies may consider credit transactions as a shield to maintain a long-term relationship in times of economic policy uncertainty, which is a positive effect (Liu and Dong, 2020). This study investigates the effect of economic policy uncertainty on firm value. It also deals with the moderating role of credit transactions in the relationship between the uncertainty of economic policy and the firm value. Considering the few studies in this field, and the lack of investigation of this issue in domestic research, it can help the development of research literature in this field.  

 The sample of this study includes 170 companies in the Tehran Stock Exchange over 12 years. To test hypotheses and analyze research data, regression analysis was used using Stata14 and SPSS16 software. To gather the data, the Tehran Stock Exchange library, Codal website (https://www.codal.ir) and Tehran Securities Exchange Technology Management Company (https://www.tsetmc.com)were used. To calculate the uncertainty of the economic policy, the central bank website (https://www.cbi.ir) was used. FindingsThis study investigated the effect of uncertainty of economic policies on credit transactions and firm value with an emphasis on the moderating role of credit transactions in the period of 2011-2022. Credit transactions include the collection period, payment period, and the difference between the collection and payment period. The results showed that economic policy uncertainty has no significant relationship with a company’s collection period, but it has a significant positive relationship with a company’s payment period and the difference between the collection and payment periods. Another result of the study is that economic policy uncertainty has a significant negative relationship with firm value. In addition, the collection period of a company as well as the payment period moderates the relationship between the uncertainty of economic policies and the firm value of, but the difference in the period of collection and payment does not moderate the relationship between the uncertainty of economic policies and the firm value. 

The results based on the positive and significant relationship between the uncertainty of economic policies and the payment period indicates that managers act conservatively in economic uncertainty situations and try to reduce and control the risk of bankruptcy and uncertainty and make payments in a longer time. Another finding is that economic uncertainty has a significant negative relationship with firm value and shows that it is in accordance with the theory of information asymmetry. Regarding the moderating role of credit transactions on the relationship between economic uncertainty and firm value, the results show that in the period of high economic policy uncertainty, with the increase in the debt collection period, the firm value increases, and in time of uncertainty, the manager tries to revise the collection period of a company and act more reasonably, and the collection period of a company is meaningful considering the policies of economic uncertainty. The study also shows that during a period of high economic policy uncertainty, the firm value increases with an increase in the payment period. In periods when a company operates in a situation with high uncertainty in economic policies, this situation has a negative effect on the firm value, and when the factor of lengthening the payment period enters into the relationship between the two variables, it moderates this relationship. That is, in times of high uncertainty, increasing the payment period increases the firm value.

This study adopted a qualitative approach to investigate the obstacles faced by Small and Medium-sized Enterprises (SMEs) in financing for their production activities. Through the utilization of the meta-synthesis method and a comprehensive review of 23 relevant articles, this research identified 57 factors categorized into 10 key dimensions that acted as barriers to financing. These dimensions encompassed aspects, such as financial infrastructure and accessibility, legal framework and political implications, information asymmetry and transparency, collateral requirements, asset-based financing, loan terms and conditions, credit assessment and risk perception, market dynamics and competition, technology advancements and digitalization, social and economic factors, regional inequalities, financing mechanisms, and innovations. By presenting a structured framework, this research aimed to enhance the comprehension of these challenges and provide potential solutions. 

In today's intricate global economic landscape, Small and Medium-sized Enterprises (SMEs) represent thriving sectors characterized by their innovation, dynamism, and growth potential. These enterprises often hailed as the backbone of economies worldwide play a pivotal role in driving economic development, generating employment opportunities, and fostering technological advancements. However, despite their significant contributions, SMEs encounter a myriad of formidable challenges that impede their prosperity and hinder their optimal contribution to economic progress. From legal obligations and market competition to resource constraints and technological barriers, these businesses navigate a complex environment fraught with obstacles. Among the various hurdles they face, one of the most prominent one pertains to production financing, wherein SMEs encounter difficulties in accessing capital and securing adequate financial resources. Such barriers manifest in the form of stringent lending criteria imposed by financial institutions, information asymmetry and lack of transparency, restrictions on collateral, and exorbitant transaction costs. Consequently, these enterprises grapple with limited budgetary options, thereby curtailing their growth prospects and impeding their capacity to innovate and compete in the market. This study endeavored to bridge the existing research gaps by conducting an in-depth examination of the barriers faced by SMEs in securing financing for production activities. The primary objective was to develop a comprehensive model for analyzing production financing barriers in small and medium-sized enterprises. Such a model can serve as a valuable tool for policymakers, financial institutions, industry associations, and entrepreneurs alike, empowering them to formulate effective strategies aimed at overcoming these obstacles.

This study adopted a qualitative approach following the methodology proposed by Sandelowski and Barso (2007) for data collection. The methodology consisted of 7 steps, which involved formulating research questions, conducting a systematic literature review, selecting relevant studies, extracting information from the chosen studies, analyzing and synthesizing the findings, ensuring quality control, and presenting the outcomes. To conduct a comprehensive search, reliable databases were systematically queried using relevant keywords and phrases, such as "small and medium businesses", "production financing", "financing barriers", and related terms. The search covered titles, abstracts, and keywords. Inclusion criteria were applied, including the selection of non-Persian qualitative papers published between 2014 and 2023 indexed in ISI or Scopus, as well as valid Persian articles published between 2013 and 2014. Initially, 282 articles were identified and after a rigorous selection process, 23 articles were chosen for the final analysis. To ensure validity, entry criteria, team meetings, expert opinions, and EndNote software were employed. Reliability was determined by critically evaluating and scoring the articles. Parameters, such as authors, publication year, research purpose, methodology, and findings, were considered during the evaluation process.

Culmination of the meta-synthesis section yielded an analysis model illustrating the barriers to production financing in small and medium-sized businesses. Being presented in Figure 1, this model encompassed the key findings of the research.

This study explored the financing challenges faced by small and medium-sized enterprises using a hybrid approach and identified 57 indicators across 10 key dimensions that acted as barriers to financing. The dimension of "financial infrastructure and access" encompassed challenges related to accessing financial institutions, banking services, and venture capital. Additionally, the study examined microfinance as an alternative avenue with its own possibilities and challenges. In the dimension of "legal environment and political consequences", barriers, such as legal transparency, compliance, administrative ease, and government support programs were discussed as they impacted the performance of SMEs. The dimension of "information asymmetry and transparency" focused on challenges related to accessing information, transparency in lending practices, and disclosure requirements. The dimension of "collateral requirements and asset-based financing" addressed the complexities and challenges of evaluating and accepting collateral from SMEs, including collateral policies, asset valuation methods, loan-to-value ratios, and non-traditional collateral acceptance criteria, which often posed difficulties for businesses with limited assets. The dimension of "loan terms and conditions" highlighted the challenges that SMEs encountered when seeking financing, including issues related to interest rates, loan duration, flexibility of repayment terms, and collateral requirements. The dimension of "credit assessment and risk understanding" encompassed challenges, such as credit score assessment, financial statement analysis, business viability assessment, and understanding market and economic conditions. The dimension of "market dynamics and competition" explored the diverse effects of market dynamics on the access of SMEs to financing, including factors, such as market structure, competitive landscape, industry concentration, market entry barriers, and impact of new technologies. The dimension of "technology developments and digitization" examined how technology and digital advancements were reshaping the financing landscape for SMEs, including advancements in digital infrastructure, adoption of new financial technologies, and cybersecurity measures. The dimension of "economic and social factors and regional inequalities" addressed various challenges, including levels of economic development, income inequality, access to education, and infrastructure development. Lastly, the dimension of "alternative financing mechanisms and innovations" explored diverse avenues for SMEs to obtain financing. Peer-to-peer lending, crowdfunding platforms, and other innovative financing approaches provided these companies with new opportunities to raise capital and offer more flexible terms. In conclusion, this research shed light on the multifaceted barriers that SMEs faced in accessing financing. By comprehensively examining these dimensions, policymakers, financial institutions, and entrepreneurs could develop effective strategies to overcome these challenges and foster a more conducive environment for SMEs to thrive and contribute to economic growth.
This study proposed recommendations for addressing the barriers that hindered the access of small and medium-sized businesses to production financing. The key recommendations included enhancing transparency and access to financial information, simplifying laws and improving tax transparency, implementing financial education programs to enhance financial literacy, fostering collaboration among the government, financial institutions, and industries to develop innovative financing solutions, investing in digital infrastructure and cybersecurity measures, and implementing supportive policies to reduce regional disparities. It is important to acknowledge the limitations of this research, such as its reliance on the existing literature, which may have implications for the quality and comprehensiveness of the studies reviewed. To gain a deeper understanding of financing trends and dynamics and devise appropriate interventions, it is recommended to undertake longitudinal, comparative, exploratory, and interdisciplinary research.

Due to the high costs of financial distress and bankruptcy and the importance of timely prediction of financial distress to take corrective actions, this study examines the research related to the prediction models of financial distress and bankruptcy in Iran and other countries. This study is a review of research that examines 102 research in Iran and 298 research in other countries during the years 1930-2023, describing theories and analyzing the nature, trend, composition, and periods of using models and presenting practical and research recommendations. The results indicate an upward trend of research in Iran, while it is declining in other countries. The noteworthy point is the use of various combinations of models and techniques in other countries is more diverse than in Iran, and the prominence of intelligent models that are based on evolutionary calculations. The research concludes by providing executive and research recommendations. The recommendations include diversifying predicting techniques, emphasizing models based on evolutionary computing algorithms, paying attention to the predictions after the crisis, developing special models for small and medium-sized companies, separating the criteria for recognizing financial distress and bankruptcy in the Tehran Stock Exchange, making dynamic predictions, and developing more focused and specialized models to increase accuracy. This study addresses the shortcomings of past research, provides more up-to-date information and insights on the dynamic predictions, compares these studies in Iran to other countries, and provides some executive recommendations and research topics based on the results.

Financial distress indicates that a company is approaching bankruptcy and the vast and profound negative effects of bankruptcy on society, necessitate reviewing bankruptcy prediction models. Therefore, predicting financial distress in its early stages can inform the stakeholders of companies about their future possible losses (Zhou et al, 2023). Global crises like the 2008 financial crisis and the COVID-19 pandemic have forced even very strong international companies to continuously monitor their financial situation (Woodlock & Dangol, 2014; Hassan, 2022; Papik & Papikova, 2023). These environmental dynamics require reforms and more accurate predicting methods for the financial health of enterprises, (Brygala, 2022) especially for small and medium-sized companies, which have weaker financial resources (Ciampi et al. 2021; Mirza et al., 2023). While existing literature reviews are often outdated or limited in scope, this study addresses these gaps by comprehensively analyzing the models and techniques used for predicting financial distress and bankruptcy in Iran and other countries from 1930 to 2023. Furthermore, the study compares the nature, trends, composition, and periods of use of predicting models in Iran and other countries and offers insights for future research.

Systematic review refers to examining, criticizing, and evaluating a specific research topic, extracting and interpreting data from published articles, and analyzing, and describing results based on clear evidence (Lasserson et al., 2020). It generates highly credible, low-bias, and quality scientific research documents (Yetley et al., 2016). This meta-analysis research systematically evaluates financial distress and bankruptcy prediction models by analyzing 102 studies from Iran and 298 from other countries from 1932 to 2023. Data were sourced from reputable Iranian and international databases and journals, including Emerald, Science Direct, and ProQuest. The study aims to rectify previous shortcomings, update information, and offer new research avenues based on the findings of this review.

In Iran, financial distress prediction studies that started in 2001 shows a rising trend, encompassing 13 studies conducted during 1968-2007 and 89 studies during 2007-2023. Financial distress prediction research in other countries started in 1932 and has shown a declining trend in recent years including 10 studies conducted during 1932-1968, 217 studies in 1968-2007, and 71 studies during 2007-2023. The 102 predicting techniques in Iran during 2001-2023 consisted of 43 statistical models, 40 intelligent models, 17 combined models, and 2 other models such as Data Envelopment Analysis and judgmental, etc. However, 298 studies in other countries include 117 statistical models, 125 intelligent models, 25 combined models, 21 theoretical models, and 10 other models such as Data Envelopment Analysis and judgmental, etc. Among the 102 papers from Iran, 61.76% introduced modified or new models, while 87.5% of global research presented innovative models. Techniques used globally from 1930 to 2023 include statistical, intelligent, hybrid, judgmental, and other models. Statistical models dominated earlier, however, intelligent models, particularly those inspired by animal collective intelligence, gained prominence and attention for their high accuracy.

Iranian research on predicting financial distress and bankruptcy exhibits an upward trend, while global research has been declining recently. The results verify that intelligent models like neural networks and genetic algorithms, notably those inspired by animal collective intelligence such as the ant algorithm, and the firefly algorithm, the bird algorithm, demonstrated higher prediction accuracy than statistical models such as multiple discriminant analysis, logistic regression, probit, and theoretical models. Successful intelligent models, which widely used recently and exhibited a higher accuracy, include gradient boosting models (Jones, 2017), machine learning methods (Chen et al., 2023), and models based on collective intelligence such as the firefly algorithm (Bayat et al., 1997). Among the statistical techniques, the nonlinear logistic regression techniques demonstrated a high level of accuracy (Lohmann et al., 2022; Lohmann & Mollenhoff, 2023). In addition, 61.76% of 102 models from Iran and 87.5% of 298 articles abroad were innovative models. Recommendations include diversifying predicting techniques, emphasizing models that are based on evolutionary computing algorithms, attention to the necessity of post-crisis predictions, developing tailoring models for SMEs, separating criteria for financial distress and bankruptcy recognition in the Tehran Stock Exchange, the necessity of adopting dynamic predictions and (Lohmann & Molenoff, 2023) developing focused and industry-focused models for enhanced accuracy (Nazmi Ardakani et al., 2017).

The ultimate goal of corporate governance is to achieve the promotion of fairness, transparency, accountability, and compliance with the rights of shareholders in companies and economic enterprises. However, there are doubts about its success rate in achieving the aforementioned goals. The disclosure of more and periodic information will reduce the cost of capital and the cost of transactions, reduce the error in profit forecasts, or demand more than usual for a company's bonds. Managers can pursue the goal of maximizing shareholders' wealth by managing variables (such as transparency) in the financial structure. The purpose of this study is to investigate the effect of corporate governance on the financial performance of companies using the mediating variable of transparency. In this study, using a meta-analysis approach, the impact of transparency on corporate governance and its effect on financial performance is investigated. A hypothesis about the effect of corporate governance on the mediating variable of transparency and a hypothesis about the effect of the mentioned variable on financial performance were proposed and 20 articles including a total of 81 effect sizes (of type r) whose publication dates were between 2003 and 2018 were studied. If the effect size was divergent, the random effects method was used, and for convergent cases, the fixed effects method was used to calculate the effect size. In addition to testing each hypothesis, after determining the size of the cumulative effect, a robustness test was performed and analyzed for each hypothesis. The results indicated that corporate governance has no effect on transparency, but the robustness test showed that the mechanisms of corporate governance, including the independence of the board of directors, and the dual role of the CEO and the audit committee, have a significant effect on transparency. In addition, the results show the significant impact of transparency on financial performance.

In recent years, the discussion of corporate governance has become one of the topics of professional and scientific research. In previous studies, the direct impact of corporate governance on the company's financial performance has been measured (Che Haat et al., 2008; Mardnly et al., 2018; Saidat et al., 2019). Corporate governance is expected to improve financial performance not only directly but also through the mediating effect of transparency. Corporate governance may affect the level of corporate disclosure, especially when the board of directors manages the information disclosed in annual reports (Gibbins et al., 1990). Centralized decision-making power (such as the same role of the CEO and the chairman of the board of directors) hinders the possibility of monitoring various aspects, including disclosure policies, due to the threat to the independence of the board of directors (Carver, 1990; Fama & Jensen, 1983). A good corporate governance system requires periodic, transparent, and comparable information reporting, especially information related to financial, managerial, and ownership issues of the company. Transparency, in terms of its role in reducing information asymmetry, is the preferred area for shareholders to control managers because it reduces risk and consequently improves performance. Therefore, the first hypothesis was formulated as follows:Hypothesis 1: Corporate governance affects the transparency of firms.
Reducing information asymmetry from the perspective of agency theory reduces the tendency to selfishness, and from the perspective of investment, it reduces risk and expected return. Both of these aspects make analysts expect that information disclosure can improve the firm's financial performance. Therefore, a high level of transparency has a positive effect on the company's performance. Accordingly, information disclosure such as periodical reports can reduce capital costs and information asymmetry (Lang & Lundholm, 1999). Therefore, the second hypothesis was formulated as follows:Hypothesis 2: Transparency has an effect on the financial performance of economic enterprises.

After reviewing various scientific sources, seven steps were selected for the meta-analysis (Glass et al., 1981; Hunter & Schmidt, 2004; Kohli & Devaraj, 2003; Rosenthal, 2001). The first step is to determine the thematic framework. The second step is to specify the keywords, determine the databases and search for studies, the third step is to screen the searched articles according to one of the research hypotheses, to report the effect size and the type of correlational study. The fourth step is to collect the data from the searched articles. The fifth step was to calculate the effect size for each member of the sample (each test). The sixth step was to calculate the common effect size for each hypothesis. Finally, in the seventh step, the homogeneity of the effect size was calculated for each hypothesis (each group of effect sizes).
If the within-group effect sizes are convergent (zero variance hypothesis is not rejected), it means that there is a common effect size that all sample tests have measured; and the fixed effects method is used to calculate the cumulative effect size. If the effect sizes within the group are divergent (zero variance hypothesis is rejected), it means that there are different effect sizes, one of which has been calculated in each sample of the tests; and the random effects method was used to calculate the cumulative effect size.

In this section, the robustness tests performed to test the hypotheses and comment on the confirmation or disconfirmation of each of the hypotheses. In both hypotheses, the common effect size is divergent, that is, it is related to a group of different effect sizes with high deviation, and the random effects method is the basis for calculating the common effect size. The results indicated the rejection of the null hypothesis related to the second hypothesis. In other words, the effect of transparency on financial performance is confirmed, but the effect of corporate governance on transparency is rejected. The robustness test in the first hypothesis shows the lack of influence of corporate governance on transparency in developed and developing countries. There is no significant relationship between corporate governance and transparency in different time periods, except for the years 2005-2009. The results show that there is a significant relationship between the independent variable of the board of directors and transparency. The CEO duality variable has a negative and significant effect, board size has a positive and significant effect at the 5% level, and the audit committee has a positive and significant effect on transparency. The indicators of concentration of ownership, the number of board meetings, and gender diversity in the board of directors have not been tested because the sample number is less than 5.
In the validation of the second hypothesis, the results show no significant effect between transparency and financial performance in developed countries. In the time interval of 2015-2019, there is a significant and positive effect between transparency and financial performance, and the results of other periods have not been tested because the number of samples is less than 5. In the financial performance section, a significant and positive effect was observed between transparency and ROA and ROE. No significant effect was found between transparency and Tobin's Q. The results of other indicators were not tested because the number of samples was less than 5.

The main purpose of this study is to meta-analyze the indirect effect of corporate governance on the financial performance of companies using the mediating variable of transparency. In the first hypothesis, the relationship between transparency and corporate governance was tested. The results of the current study did not show a significant effect between corporate governance and transparency due to the overlap of various corporate governance indicators. The study of various indicators of corporate governance shows a significant and positive relationship between the independence of the board of directors and transparency, the audit committee and transparency, and a significant and negative relationship between the duality of the CEO and transparency. In the second hypothesis, the relationship between transparency and financial performance has been tested. The results show a significant and positive impact on the company's financial performance. The robustness test shows that this effect is quite evident in the studies studied in recent years. The high level of information disclosure helps the stakeholders in improving the quality of their decision-making.
The above results, like any other research, may be criticized due to the researchers’ limited access to all published empirical studies, the time limit of studying past articles and looking at them retrospectively, the meta-analytic nature of this study, and the dependence on meta-analysis results on past experimental studies. However, due to the wide range of companies that support the results, the validity of the empirical studies collected and the robustness check performed are robust enough to be the basis for decision-making and policy-making for managers, policymakers, and investors. Therefore, it is suggested to the shareholders to emphasize the independence of the board of directors and the audit committee to improve the financial performance, through the index of the audit committee, control the level of transparency, and provide the basis for the improvement of the financial performance, through the indicators of the independence of the board of directors and the duality of the CEO to create the context of increasing the transparency of the company and thus improving the financial performance, pay enough attention to the concentration of ownership, the duality of the CEO, the size of the board of directors, and the number of board meetings in the development of the corporate governance system because it is a good basis for optimizing transparency and improving financial performance.

This study focused on measuring investors' sentiments in the Tehran Stock Exchange (TSE) and evaluating their effectiveness in predicting future market trends. The study utilized two indices, ARMS and BSI, to gauge investors' sentiments and their performance in predicting stock market behavior was assessed over different (monthly, three-month, and six-month) time periods. Back-testing was employed to determine the accuracy of these indices. The findings revealed that the success rate of the indices in predicting stock prices was higher over a six-month period compared to shorter durations. Moreover, the BSI indicated that institutional investors tended to have a more accurate prediction capability than individual investors. Furthermore, the back-testing results demonstrated that the ARMS index outperformed the BSI index in forecasting stock prices. Therefore, the ARMS index, particularly over a six-month timeframe, served as a reliable indicator for predicting investors' sentiments. This research contributes by exploring the application of back-testing on investors' sentiment indices in the stock market, providing an optimized index and recommending an appropriate time period to enhance its predictive power. 

Investor sentiment holds significant importance in financial markets as recognized in the financial literature (Sun et al., 2016). The presence of sentiment and the potential risks and costs associated with arbitraging against sentiment-driven investors (Brunnermeier & Pedersen, 2005) may lead rational investors to deviate from standard pricing principles. Behavioral finance, in contrast, provides a fresh perspective for studying the influence of investor sentiment on stock market returns (Baker et al., 2007; Hribar & McInnis, 2012). The reality is that investor sentiment can impact asset prices, allowing swing traders to generate profits and sustain their presence in the market over the long term (Palomino, 2016).
In Iran, several studies have examined investor sentiment and its correlation with financial and economic variables. Noteworthy studies include those conducted by Nadiri and Panahian (2023), Jalilvand and Rostami Nowroozabad (2018), Gholami Jamkarani et al. (2019), Muridipour et al. (2020), Gol-Arzi and Piri (2022), Dadger et al. (2023), and Jalilvand et al. (2016). Furthermore, numerous studies have demonstrated the predictive value of investor sentiment in forecasting stock returns (Brown & Cliff, 2004; Finter et al., 2012; Kim et al., 2019; Lee et al., 2002). In this context, the present study aimed to introduce, for the first time, a more suitable index for measuring sentiment in the Tehran Stock Exchange (TSE), utilizing the ARMS and BSI indices. Furthermore, through back-testing, the study evaluated the appropriate timeframe for calculating this index.

The research was conducted from 2019 to 2023, encompassing a 5-year period. The sample comprised 380 companies listed on the TSE, of which 279 companies were selected for data analysis and measuring sentiment indicators. For sample selection in subsequent years, all companies admitted to the TSE before 2019 were considered, unless they were delisted from the exchange or had ceased operations for an extended period by the end of the research period in June 2023.
The research methodology involved calculating investor sentiment indices, specifically the ARMS and BSI indices, on a monthly basis, as well as using simple and moving average calculations over quarterly and 6-month periods. Subsequently, a backtest was conducted to estimate the success rate of the indices for different timeframes: monthly, quarterly, and 6 months. A success rate exceeding 50% was interpreted as a measure of success, while a rate below 50% indicated failure. In other words, when the sentiment indicators correctly predicted the future price direction of the stocks (based on both the number and market value of the desired shares) in the upcoming period with an accuracy of over 50%, it was considered successful; otherwise, it was deemed a failure. Finally, based on the performance of the most effective index, a market forecast for the upcoming period was provided.

The findings indicated that the ARMS sentiment measurement index exhibited a higher success rate in predicting stock price trends on the TSE over a 6-month period compared to 3-month and monthly intervals. This suggested that the ARMS index provided a more accurate forecast of the stock prices of listed companies over a longer timeframe. Similar observations were made for the BSI sentiment measurement index. Furthermore, the BSI index highlighted that institutional investors offered more reliable predictions of market and stock price trends than individual investors.
Table 1 presents the results of back-testing the investors' sentiment indices for stocks listed on the TSE. The table demonstrates that the ARMS sentiment measurement index outperforms the BSI in predicting stock exchange price trends. Additionally, the success rate of institutional investors' BSI index surpasses that of individual investors' BSI index. Based on these findings, the paper recommends adopting the ARMS sentiment measurement index as a representative measure of investors' sentiment for stocks listed on the TSE, particularly over a 6-month period.

To date, no study has addressed the issue of identifying the appropriate index, either ARMS or BSI, for measuring investor sentiment and predicting stock price behavior. Thus, the objective of this study was to propose a suitable index for measuring investor sentiment and market behavior based on the past approach and behavioral finance theory. Our findings revealed that the ARMS index exhibited a higher level of accuracy in predicting stock price trends over a 6-month period. This finding supported the hypothesis that investor sentiment has a more pronounced impact on stock prices in an inefficient or weakly efficient market, such as the Tehran Stock Exchange (TSE). The implications of this study are twofold. Firstly, shareholders and portfolio managers can utilize the results to construct optimal stock portfolios by considering the future price behavior of stocks listed on the TSE. Secondly, regulatory bodies, such as the Securities and Exchange Organization and the supervision departments of the exchange, can employ these findings, particularly the recommended index for measuring investors' sentiments over a 6-month period in conjunction with other control tools to monitor suspicious behavior by various market participants.