نشریه مهندسی مالی و مدیریت اوراق بهادار
پیاپی 60 (پاییز 1403)

In the last few decades, market participants have given considerable attention to the of liquidity in financial markets. Commonality liquidity and shared movements in transaction costs associated with such a phenomenon have significant consequences in the microstructure of the market. The analysis and identification of such commonalities enables the investor and the policy maker to discover the evidence related to inventory risk and asymmetric information in increasing the liquidity of the stock market. From the non-linear autoregression method with NARDL distribution breaks between the seasonal periods of 2008:01 to 2020:12. The asymmetric long-term results show a negative and significant relationship between the positive rate of return shock and Commonality liquidity and a positive and significant relationship between the negative market return shock and commonality liquidity. The variables of monthly investment by institutional investors, commonality liquidity and exchange rate of return have a positive and significant relationship with commonality liquidity. Export logarithm has a negative and significant relationship with, uncommonality liquidity.

One of the most important issues in modern financial markets is finding efficient ways to summarize and visualize stock market information. The purpose of this paper is to discover a method to reduce risk and increase investment returns. By analyzing the mass volume of Tehran stock market data as a case study, and finding the relationships between the data and the discovery of their hidden information that has a significant impact on investors' decisions; an algorithm was designed. Moreover, the data from the automobile industry and oil products and the index of various industries were utilized from 2018 to 2022, and modeling was done by twenty technical indicators. The results of this research showed that mentioned model has a significant performance in identifying and predicting the sales signals issued at the maximum points and the prediction is done with acceptable accuracy. Portfolio management and capital supply companies can use this trading algorithm to make decisions regarding the sale, purchase or holding of securities.

This study, using "MyBiocycle" software, the sinusoidal fluctuations of the emotional and illuminative periods of active investors are evaluated in order to influence thick decision in the capital market. In this study, in order to evaluate the sinusoidal fluctuations, time period of 2 months were considered in the form of 4 time periods of 15 days in order to determine the difference between the emotional and radiant orientations of active investors. In this study, 48 real investors were selected based on the ratio of "12/4" (four groups of twelve people) in order to be placed in two emotional and illuminative categories and based on variance analysis tests, their effectiveness in the formation of crowd decision-making in the capital market was investigated. The results of the study indicated that the real active investors who are in the negative area in the evaluation of the sinusoidal fluctuations of the emotional period have higher thick decision compared to the active real investors who are in the positive area in the evaluation of the sinusoidal fluctuations of the emotional period.

this research is aimed to provide a model of default risk of default ( default ) of banking customers ( case study : legal customers of mellat bank ) .for this purpose , the statistical population of this research in the qualitative section includes experts and experts in the field of banking , senior managers of mellat bank , managers of the region of each province . .the analysis of data was carried out based on strauss - kahn 's instructions .this method consists of three main stages of open coding , axial coding and selective coding which finally the qualitative research model is expressed .results with a high confidence of 95 % can be shown that the deposit behavior and credit behaviors affect the financial behavior of customers on the risk of default .the overall situation of the macro - economy , the customer 's own personality traits , the customer 's financial position on the risk of default ( default ) of the banking customers .the situation in which the customer is operating , the banking system status , the company 's strategy on the risk of default ( default ) of the banking customers .

Investment portfolio theory is an important foundation for portfolio management, which is a well-studied but not saturated topic in the academic community. Integrating return forecasting in investment portfolio formation can improve the performance of portfolio optimization model. Since machine learning models have shown a superiority over statistical models, in this research, a approach of forming the stock portfolio in two stages is presented. first step, by implementing neural network, suitable stocks are selected for purchase, in the second step, using the (MV) model, the optimal weight in investment portfolio is determined for them. In particular, the stages of selecting suitable stocks and forming a stock portfolio are the two main stages of the model developed in this research. first step, a convolutional neural network model is proposed to predict stock buy and sell points for the next period.second step, stocks that are labeled as buys are selected as stocks suitable for buying, and MV model is used to determine their optimal weight in the stock portfolio. The results obtained using 5 shares of Tehran stock market as a study sample show that the efficiency and Sharpe ratio of proposed method is significantly better than traditional methods (without filtering suitable stocks)

According to the 22 selected features (which are checked during the research) with machine learning methods, this study predicts the cash holding of companies admitted to the Tehran Stock Exchange. 201 companies were investigated from 1396 to 1400. Multiple linear regression, K-nearest neighbor, support vector regression, decision tree, random forest, extreme gradient boosting algorithm and multilayer neural networks are used for prediction. The results show that the multiple linear regression methods provide the k-nearest neighbor of the root mean square error (RMSE) and the mean absolute error (MAE) of the high error. Meanwhile, more complex algorithms, especially support vector regression, achieve higher accuracy; The findings indicated that by reducing to 15 variables, machine learning methods, especially K-nearest neighbor, provided better results. Based on the paired sample t-test, support vector regression has a better performance than other supervised machine learning algorithms except decision tree. Also, the most important variables were company size and capital expenditures (CapEx). The World Uncertainty Index and inflation were also relatively important variables; Therefore, by using the support vector regression algorithm, we may predict the amount of cash to a significant extent.

The main purpose of the current research is to investigate the effect of voluntary disclosure of information on the company's risk-taking, emphasizing the role of managerial ownership in companies listed on the Tehran Stock Exchange during the years 2011 to 2019. The statistical sample of the research is 159 companies. The research method is descriptive-correlation with applied approach. The method of collecting information in the theoretical foundations section was the library method, and in the hypothesis testing section, the document analysis method of financial statements was used. In general, the hypothesis testing method is correlation and multiple regression. The results of the research showed that the voluntary disclosure of information has a direct and significant effect on the company's risk-taking, and increasing the percentage of managerial ownership strengthens this effect. The results related to the control variables showed that the financial leverage and the ratio of accruals have an inverse and significant effect on the company's risk-taking, and the effect of the company's size and operating cash flow on the company's risk-taking; it is direct and meaningful

The current research was conducted with the aim of investigating the effect of tunneling Incentive, intangible assets and profitability on transfer pricing. The statistical population of this research is the companies listed to the stock exchange, and sampling among these companies was done based on the systematic elimination method. According to the conditions considered for the sample, the sample size is equal to 84 companies. The research period is between 2016-2023 and this period includes all companies that were listed to the stock exchange before 2016 and were present until the end of 2022. The tests were conducted using Eviews 10 software and Stata 14 software based on the panel data statistical technique. The results based on the generalized regression model (GLS) indicate that the variable of tunneling incentive has an effect on transfer pricing, and on the other hand, the variable of intangible assets also has an effect on transfer pricing. Finally, the profitability variable has an effect on transfer pricing so that the hypotheses raised in this regard are confirmed.

Paying attention to the relationship between the CEO and CFO as two influential members in determining and implementing the company's operation strategy is very important. Therefore, the aim of the this research is to empirically test the role of the auditor's fee moderator in the relationship between the consistency of the tenure of the CEO and the CFO and Earnings quality. For this purpose, information related to 109 companies listed in Tehran Stock Exchange was collected and analyzed between 2016 and 2021. The findings of the research confirm the negative relationship between the consistency of the tenure of the CEO and the financial manager and the earnings quality. Also, the findings of the research show that the audit fee plays a moderating role in the negative relationship between CEO-CFO tenure consistency and the quality of profit. Based on this, it can be claimed that in companies where the tenure of the CEO and the financial manager are the same, the financial manager cooperates and participates with the CEO in earnings management, and as a result, auditors increase the level of handling and audit fees to cover the audit risk.

Abstract This article analyzes and illustrates the state of the scientific production of financial literacy, knowledge, and customer experience in the context of the capital market based on scientometric techniques, articles indexed in the Web of Science database between the period of 1977-2022 and the discovery of research gaps. The current research is of a descriptive and applied type, which was conducted using co-occurrence analysis of words. The statistical population of this study comprises scientific articles in the fields of financial literacy and customer experience. A total of 789 articles were selected from two areas close to business management and business finance, and the data were analyzed using VOSviewer software. The most and most frequent words "financial literacy" and "behavior" which are the results of co-occurrence of words and are related to the word "investment"; Seven thematic clusters were also identified: the experience of the financial crisis of COVID-19, financial literacy and investor behavior, asset management, customer satisfaction and experience of services, financial education and knowledge, retirement economy, strategic plan and development of financial education. Keywords: Financial literacy, financial knowledge, customer experience, capital market, scientometrics