Iranian Journal of Finance

Two predominant methods for analyzing financial markets have been technical and fundamental analysis. However, the emergence of the Internet has altered the trading landscape. The availability of Internet and social media access plays a moderating role in information asymmetry, resulting in investors making informed decisions. Social media has turned into a source of information for investors. Through diverse communication channels on social media, investors articulate their perspectives on whether to buy or sell a stock. According to Surowiecki, the collective opinions gathered through social media frequently offer better predictions than individual opinions, a phenomenon referred to as the Wisdom of the Crowd. The wisdom of the crowd stands as an essential measure within social networks, with its potential to reduce errors and lessen information-gathering costs. In this study, we tried to evaluate the wisdom of the crowd's potential to improve stock price prediction accuracy. So, we developed a prediction model by Long Short-Term Memory based on the wisdom of the crowd. Users’ opinions in Persian about the Tehran Stock Exchange (TSE) stocks were collected from SAHMETO for eight months. The Support Vector Machine classified them into buy, sell, and neutral classes. During the research period, people mentioned 823 stocks, and 52 stocks with over 100 signals were chosen. The results of the study show that although the model presented has achieved an acceptable level of accuracy, correlations between the actual and predicted values exceeded 90%. The accuracy metrics of the proposed model compared to the base model were not improved.

Making decisions regarding capital structure is among the most challenging issues ahead for firms and the most critical decisions for their survival. On the other hand, several significant aspects, such as behavioral factors, have been overlooked in this field. Thus, the present study mainly seeks to identify the factors affecting capital structure in Iranian firms, emphasizing the role of behavioral factors. The present study employs mixed qualitative and quantitative research methods. From the qualitative point of view, capital market experts were inquired, and theoretical saturation was achieved using the snowball method. After the interviews, research components were extracted through coding. The opinions of a group of experts and managers of firms listed on the Tehran Stock Exchange were used in the quantitative section, and a structural equation form was used to perform confirmatory factor analysis on the research model. A total of 63 concepts in the form of six categories were identified at the first stage, which was reduced to 58 in the form of six categories and was confirmed after the concepts were sent back to the experts. The principal components included behavioral factors, macroeconomic factors, political factors, socio-cultural factors, firm features, and corporate governance. Results were validated through factor analysis in the quantitative portion of the study. The present study can be considered among the comprehensive studies at the construct level with an integrated approach to firms' capital structure. The emergence of behavioral finance resulted from understanding the importance of measuring human behavior as a factor with transcendent consequences for financial decisions. Hence, most behavioral finance studies are focused on observable behaviors. However, the item response theory presents an integrated method for disciplines that work with cognitive variables. Accepting opportunities for new knowledge is essential for firm decisions to respond to the mental views of financial managers. The present study sought to identify the factors influencing firms' capital structure in Iran. The tool used in the present study reflected the elements making up the capital structure. In this regard, the notable point is how the classic criterion of structural capital components can explain financial managers' perception of decision-making. The research results in this area are interesting since we have confirmed a capital structure theory at the construct level. The conformity of the results and the obtained reliability levels indicate that this theory fits the given dimensions well. Moreover, relevant evidence indicates that senior financial managers adopt various states considering internal and external factors at the structural level, which can cause cognitive bias in decision-making.

Portfolio allocation with Deep Reinforcement Learning (DRL) has been the focus of many researchers. In investing, a portfolio optimization strategy is selecting assets that maximize return on investment while minimizing the risk. Asset optimization involves balancing risk and return, where stock returns are profits over time, and risk is the standard deviation value of the asset's return. Many of the existing methods for portfolio optimization are essentially the expansion of diversification methods for assets in the investment. Signiant drawdowns and early entry into the share are still challenging in portfolio construction. The idea is that having a portfolio based on net money flow is less risky than allocating a portfolio based on historical data only and turbulence as risk aversion. This paper proposes a profitable stock recommendation framework for portfolio construction using the DRL model based on the net money flow (MNF) indicator. We develop a new risk indicator based on the intelligent net-flow behavior of smart money to help determine the optimal market timing for buying and selling. The experimental results of real-world trading scenario validation show that the model outperforms all the considered baselines and even the conventional Buy-and-Hold strategy. Moreover, in this paper, the effect of defining different environments made of various information with hyper parameter optimization on the performance of models has been investigated, and the performance of DRL-driven models in different markets and asset positions has been investigated. The empirical results show the dominance of DRL models based on MNF indicators.

Corporate integrity is considered as part of the company's development strategies, which in the long run can lead to the increased firms' financial transparency to stakeholders. The purpose of our study is to present a corporate integrity model and, then to investigate its effect on firms' information asymmetry. In this study, to measure the corporate integrity, we use Meta-synthesis and Delphi analysis in the qualitative part. Then, in the quantitative section, the corporate integrity questionnaires were sent to the managers of the sample firms. Subsequently, a total of 138 questionnaires were completed and sent back, which were used as the final samples for analysis. In addition, information asymmetry is measured using the three different proxies, namely bid-ask spread, turnover, and Amihud illiquidity measure. Our findings show a significant and negative effect of corporate integrity on information asymmetry. This results suggest that corporate integrity, by promoting behavioral values based on truthfulness and commitment, the structures will enhance the corporate governance mechanisms and, thereby, firstly motivate the managers to reduce the agency gap and, secondly, implement a more effective level of the supervisions on the firm's performances in front of the stakeholders by accelerating the circulation of information and giving timely and reliable feedback to the stakeholders. This is the first study that presents a corporate integrity model through qualitative analysis and then, investigates the effect of corporate integrity on firms' information asymmetry. Therefore, our study can contribute to the extant literature of this context.

This research sought to investigate the assumption that commodities operate as hedging and safe-haven for stocks -during various periods of stock market volatility. In this regard, market test regression models and daily data from 21/03/2009 to 19/03/2020 were used. The researcher was able to test both hypotheses of commodities as hedging and safe-haven simultaneously using these three models. According to the market test model results, "periods of relatively high and low volatility," gold coin futures contracts are viewed as a strong safe-haven for Changes in Tehran stock exchange returns, yet they lack the property of hedging. According to the results of the market test model “Low return periods," gold commodities and other petroleum products serve as safe-haven. Furthermore, “during times of crisis," commodities such as polymer, copper, and gold (cash and futures) had a consistent relationship with the stock market returns. They can be regarded as a strong safe haven for Changes in stock returns. Gold, in general, provided a safe-haven property for the stock index returns in all market test models, and it can serve as a stabilizing force for financial systems by reducing the casualties caused by extreme negative market shocks. The findings indicated that commodities can be used as risk management tools during economic and financial crises. Regarding hedging, the commodity market performed poorly compared to the stock market. Hedging does not always represent a safe haven for the stock market return, and vice versa.

Today, choosing the suitable model for determining the portfolio of investment in financial assets is one of the critical issues of the attention of analysts and capital market activists, and investing in a portfolio consisting of mutual investment funds is the same. With this statement, the purpose of the article is to evaluate and compare the net assets value (return) of the Federation of Asian and European Stock Exchanges (FEAS) member countries by using support machine models in comparison with statistical models. The statistical and sample population included the data of 39 selected traded funds and FEAS members from 12 selected countries (including Iran) between 2014 and 2021. The data related to the mentioned funds were classified and analyzed using spss-modeler, rapid miner, and Weka software. They were tested with 24 support machine methods and 11 statistical methods, and the results showed that the prediction accuracy of statistical models is lower than that of support machine models. The Mann-Whitney test was used to determine the significance of this difference. Also, the results show that at the 95% confidence level, it can be claimed that the prediction accuracy of machine learning models is higher than statistical models. The average rating of machine learning models was (20.86) much higher than statistical models (10.85).