نشریه پیشرفت های مالی و سرمایه گذاری
پیاپی 11 (تابستان 1402)

The aim of this research is to investigate the capability of artificial neural networks and machine learning algorithms, including Support Vector Machine and Random Forest, in predicting the information content of accounting profits before its announcement in accepted companies on the Tehran Stock Exchange during the period from 2015 to 2020. Daily data required for the research were collected using Rahnaward-e-Novin software, and a systematic random sampling method was used to select 88 companies. MATLAB was used for modeling artificial neural networks and machine learning algorithms, and Python code was employed to calculate abnormal returns in neural networks and machine learning algorithms. The information content of profits was measured through the test of the relationship between profits and abnormal returns, based on the model by Porti et al. (2018). The input variables for artificial neural networks and machine learning algorithms are technical indicators. Accuracy, precision, recall, and F-score metrics were used for performance evaluation. The results of predicting with three models of artificial neural networks, Support Vector Machine, and Random Forest showed that Support Vector Machine and Random Forest had higher accuracy than artificial neural networks in predicting buy, sell, and hold strategies, and only Support Vector Machine had the ability to predict the information content of profits among the three models.Originality / Value: Designing a predictive model for stock price movements in the next trading day using artificial neural networks, Support Vector Machine, and Random Forest as the main innovation of the research. The research findings can increase the speed of information dissemination to the market and attract it, which will reduce the impact of informational asymmetry and information-based trading and ultimately enhance market efficiency.

One of the key roles of futures markets is to provide risk hedging tools. The optimal strategy for risk hedging is determined by estimating the risk hedging ratio. Calculating the risk hedging ratio and the effectiveness of hedging explicitly depend on the relationship between futures prices and spot prices. Therefore, the aim of this study is to estimate the optimal risk hedging ratio in various timeframes under low and high volatility conditions using a Markov regime-switching multivariate regression model.

The slope obtained from the Markov regime-switching multivariate regression, representing the optimal risk hedging ratio, is chosen, which is dependent on the choice of timeframes and two cases for the multivariate regression model are adopted according to the level of volatility considered.

The research results on 5 futures contracts in the period from 2014 to 2018 indicate that in three markets, normal (composite), low volatility, and high volatility, risk hedging has been able to reduce risk by at least 20%. In the high volatility market, the optimal risk hedging ratio has reduced volatility by at least 23% in all timeframes (with the mean square error criterion), and the 0/95 timeframe performs the best in terms of the highest reduction in volatility and the lowest risk hedging ratio. In the low volatility market, the optimal risk hedging ratio has reduced volatility by at least 58% in all timeframes, and the 0/05 timeframe performs the best in terms of the highest reduction in volatility and the lowest risk hedging ratio. In the composite market, the optimal risk hedging ratio has also reduced volatility by 21%.Originality / Value: The results of this study not only contribute to the literature on risk hedging but also assist all stakeholders and users in evaluating the level of attention to the risk hedging topic.

In order to explain stock price crash risk as an index to measure asymmetry in risk, despite its importance in portfolio analysis and pricing of capital assets, no model has been designed. At the same time, it is very necessary to identify effective factors on stock price crash risk in Tehran Stock Exchange, which is a nascent, inefficient and developing market, because this risk is an inhibiting factor in attracting financial resources in capital market. Therefore, in present research, it has been investigated effective specific corporate characteristics on stock price crash risk. In this research, data of 127 companies during period of 2011 to 2021 was used and for analysis, structural equation modeling was used in PLS 3 software. Research variables (except dummy variables) have been entered into model in decimal form. Specific corporate characteristics include growth opportunity, agency cost, size, performance and risk of the company, and two measures of the negative skewness coefficient of the stock return and fluctuation of the specific weekly return of the company have been used to measure stock price crash risk. findings indicate that effect of growth opportunity, size and performance of company on stock price crash risk was negative and significant, and effect of agency costs was positive and significant. Meanwhile, stock price crash risk has been independent of company's risk.Originality / Value: findings of the research showed that the stock price crash risk was independent of other company risks. Therefore, this issue should be considered by investors.

The present study aims to conduct future research of the role of the determinants of managers' fraud, focusing on the diamond model of fraud. To measure the types of fraud and indicators related to the diamond model of fraud from the Likert scale questionnaire which contains three parts (demography, types of fraud, and elements of the fraud diamond model) and 74 declarative sentences were used. Furthermore, SmartPLS Software and the structural equation modeling technique with the partial least squares approach were used to test the hypotheses. The opportunity side is considered as an influential component on different types of fraud. Moreover, the 'capability' side has an effect on managers' fraud through social engineering, misuse of resources (assets), and earning management with opportunistic purposes, and 'rationalization' is the side that influences on the components of non-application of methods in accounting standards by managers and misuse of resources. Additionally, 'pressure' (motivation) is the side that affects the component of fraud management through social engineering. the sides of the fraud diamond model are only efficient when wich is a matter of separation of management and ownership and no effective processes for the supervision of managers.Originality / Value: In the current research, it was noted that the reason for the occurrence of four types of fraud indicators in Iran What was it based on. The added value of this research is that in most of the articles conducted by the researchers, attention was paid to the fraud triangle model.

The common feature of the theoretical approaches adopted to study business groups is the presence of internal capital markets in these groups. The accounting literature shows that the efficiency of these markets in allocating resources and performance can be affected by self-interested motives resulting from the agency problem. Therefore, the purpose of this research is to investigate the effect of the ownership-control wedge on the efficiency of internal capital markets of business groups in resource allocation and performance with regard to product market competition. This descriptive-correlation research has been done from the perspective of practical purpose and using the post-event approach. . In order to achieve the goal of the research, the data of 18 business groups whose parent companies are listed in the Tehran Stock Exchange or OTC were collected during the years 2015 to 2022 and the study hypotheses were tested using multiple regression with panel data. the findings of the study show that the ownership-control wedge has a negative and significant effect on the efficiency of resource allocation and the efficiency of the internal capital markets of business groups. Also, the findings only show that the relationship between the ownership-control wedge and the efficiency of the internal capital markets of business groups weakens with the increase in product market competition. However, there was no evidence of the effect of product market competition on the relationship between ownership-control wedge and resource allocation efficiency.Originality / Value: It is important to pay attention to the internal capital markets of business groups as a potential mechanism to facilitate the efficient allocation of resources and achieve strategic advantages. Therefore, by knowing more about the phenomenon of ownership-control gap and its effect on the efficiency of internal capital markets of business groups in the allocation of resources and performance, basic steps can be taken to achieve strategic benefits (such as the development of the domestic economy).

This research aims to examine the relationship between market competition, CEO extraversion, and the speed of capital structure adjustment in the capital market.Research

This study is applied in terms of its objective, quantitative in terms of data, deductive in terms of logic, and descriptive-correlational in terms of execution. The preferred method for gathering evidence is post-event. Relationships between variables have been examined using correlation and multiple regression models with a panel data approach. The study employs data from 143 publicly traded companies on the Tehran Stock Exchange during the period from 2012 to 2021. Company data is analyzed and processed using the R software, and multiple regression is used to estimate the research model.

Data analysis results, using multiple regression with a panel data approach at an expected error level of five percent, reveal a significant relationship between market competition and the speed of capital structure adjustment. Furthermore, CEO extraversion strengthens the relationship between market competition and the speed of capital structure adjustment.Originality/Scientific Contribution: The findings of this research not only contribute to the literature on capital structure adjustment speed and market competition but also assist all stakeholders and users in assessing companies' attention to the impact of market competition and capital structure adjustment speed using the theories of limited liability and opportunistic behavior. Additionally, this relationship is analyzed and dissected based on the personality type of the CEO (extraversion). No previous domestic studies have investigated the effect of CEO extraversion on the speed of capital structure adjustment. Therefore, this research is innovative within the country.

The present study examines the effect of dividend policy on the financial stability of banks. For this purpose, the relationship between the dividend policy and the stability of the dividend policy with the financial stability of banks in the long and short term was tested. In this research, 23 banks admitted to the Tehran stock exchange and over-the-counter stock exchange in the period between 2011 and 2019 were examined. The data of the research was collected with the approach of mixed data and using the estimation method of the vector autoregression model by the method of co-accumulation of Johansson-Josilius and the vector error correction model and it was statistically analyzed with the help of Eviews software. The results of the research show that the ratio of dividends paid has a positive and significant effect on the financial stability of banks in the long term, and the ratio of accumulated profits also has a negative and significant effect on the financial stability of banks in the long term, and the findings showed that stability in the distribution policy Dividends have a positive and significant effect on the financial stability of banks in the long term.Originality/scientific added value: The importance of the financial stability of banks in the monetary system has made it necessary to study the effect of profit sharing policies on the financial stability of banks. Dividends can have a great impact on the cost of capital, financial risks and the future price of banks' stocks.