مجله راهبرد مدیریت مالی
سال دوازدهم شماره 3 (پیاپی 46، پاییز 1403)

It is important for investors and traders to be aware of the stock price in listed companies, so it is important to identify the factors affecting Stock price informativeness. The purpose of the current research is to interpret the structural modeling of factors influencing Stock price informativeness in companies listed on the Iranian Stock Exchange.The identification of factors affecting informativeness through research background and semi-structured interviews with qualitative content analysis method based on theoretical saturation technique was done with 17 professors and financial managers. The validity of the qualitative part of the research was confirmed by the method of relative content validity and reliability using Cohen's kappa index. Coding of the interviews using MAXQDA software led to the identification of 41 effective factors, after referring to experts and drawing a pie chart, 34 factors were determined as the most important factors affecting Stock price informativeness. In order to stratify and model the structural interpretation of the identified factors, the opinions of 41 financial managers of listed companies were used with the available non-probability sampling method and with the help of a paired comparison questionnaire. The validity of the questionnaire was confirmed using the content validity and its reliability using the test-post-test method.Interpretive structural modeling of the factors affecting Stock price informativeness led to the formation of thirteen levels, where economic factors and financial macroeconomic policies were the most effective, and transparency of financial information, auditors' management letter, and the type of auditor's comments were the most effective factors. Conclusion Identifying factors affecting awareness and information disclosure by companies in the financial sector is essential, it helps investors and other stakeholders to make more informed decisions.

The purpose of this study is to investigate the effect of managerial myopia on corporate social responsibility (CSR) and also the moderating effect of investors' behavioral biases (emotional behavior, investor myopia and herding behavior) on the relationship between managerial myopia and corporate social responsibility. By reducing research and development and marketing activities, short-sighted managers sacrifice long-term profitability for short-term gains. Since social obligations usually have long-term benefits; Managerial myopia may affect corporate social performance. Furthermore, since management decisions are influenced by investor behavior, investors' behavioral biases are expected to influence the relationship between managerial myopia and corporate social responsibility. The research hypotheses were tested for a sample of 143 companies admitted to the Tehran Stock Exchange during the years 1391 to 1400 using a multivariate regression model, considering the fixed effects of year and industry. The results show that managerial short-sightedness has a negative effect on the corporate social responsibility score, and the behavioral patterns of investors, including emotional behavior, market short-sightedness, and herding behavior strengthen this negative relationship.

 Todays one of the most important factor for dicision making process by market participants at Tehran Stock Exchange listed companice is the information published by the companies listed in If the information is not properly disclosed, without a doubt, the price discovery mechanism in the market will not work properly and the pricing of the bonds will not be done in a favorable way. Therefore, according to the importance of the level of disclosure, it is important to identify the factors affecting the quality of disclosure, therefore, in this research, the impact of credit rating changes on the quality of disclosure has been investigated. In order to carry out the research, a sample consisting of 126 companies listed in Tehran Stock Exchange during the period of 2012 to 2021 were examined. In order to perform the analysis, the data related to the credit rating, disclosure quality rating and control variables were extracted from Rahvard Navin software and the accompanying notes of the financial statements. Based on the results of the research, there is a negative relationship between credit rating changes and disclosure quality. In other words, credit rating changes are effective on the voluntary disclosure level of companies, but this effect was not statistically significant. While there was a negative and significant relationship between credit rating and disclosure quality. In other words, the higher the credit rating, the lower the company's disclosure quality. The higher the credit rating, because investors evaluate the risk as low, their demand for more information disclosure also decreases. In other words, increasing the credit rating through reducing the demand for information can lead to a reduction in disclosure.

One of the key factors for the development and stability of countries' economies is the creation of a suitable financing system for companies. The financing system of the companies should be updated and optimized should provide an appropriate response in face of various opportunities and threats. In the last decade, companies in the country faced two major crises, the intensification of sanctions and Covid-19, which seems to have had a profound effect on their financing. In this regard, the present study identified the dimensions affecting the development of the financing system of companies by comprehensively studying the research literature and then experimentally tested the proposed model under the influence of sanctions and covid-19 using the structural equation method. In other words, this study sought to answer the challenge of how the effectiveness of the financing model is influenced by the sanctions and covid-19.For this purpose, the data of 159 companies during the years 2013 to 2022 (1590 companies-years) were compiled to examine 14 hypotheses and were tested using the structural equation method using SmartPLS4 software in the form of three separate models.The financing model was divided into three parts: internal, short-term external and long-term external.The results of the research showed that the sanctions and covid-19 crisis have an effect on audit characteristics, financial performance, market performance, investment efficiency, political Communication on internal financing through retained earnings, as well as the effect of financial performance, market performance, investment efficiency, political Communication and Corporate characteristics significantly reduce short-term and long-term external financing and increase the impact of economic uncertainty on internal financing through retained earnings and short-term and long-term external financing.Finally, based on the determination coefficients of all three research models, it was determined that the selected structures have more explanatory power for long-term financing than internal financing and short-term financing.

Non-fungible tokens are known as unique digital assets that are stored in the blockchain. Their non-fungibility makes them different from other crypto assets. Therefore, their pricing is different from the pricing of similar tokens The purpose of this research is to identify the basic characteristics and better understanding of the factors affecting the pricing of these tokens in meta synthesis method Which is done by using Sandelowski's seven steps and by searching the title, abstract and main text of the sources in the databases that were provided until September 2023. In total, among the 86 sources found, 35 researches reached the content acceptance stage. This research is one of the first researches that has identified the factors affecting the pricing of non- fungible tokens, while so far these factors have not been presented using the meta synthesis method. The results indicate that 30 factors are effective in the pricing of Non-fungible tokens, which are categorized into six components and two main groups. The validity of this research has been explained by explaining the steps and using the critical assessment skills program method, and the reliability was measured and confirmed using the intraclass correlation method.

The purpose of this research is to provide a model to determine the Stock Block Trading Premium in different industries of Iran's capital market. By using the background of past researches, the influencing factors on Stock Block Trading Premium were selected and analyzed in the proposed model. In this paper the companies admitted to the stock exchange of Iran were considered and the time period of the research were from 2010 to 2019. The relationships of the research variables were measured throughh Eviews and using multivariate regression analysis, and based on the output results of the models, research hypotheses were tested and the coefficients affecting the Stock Block Trading Premium were estimated. Finally, in order to investigate the difference in the effect of variables on Stock Block Trading Premium in different industries and to increase the accuracy of the findings, the variables have been investigated in Python. The results showed that the coefficient of each variable on Stock Block Trading Premium is different, and it has a direct relationship with the size of the block, cash, assets, and revenue, and has an inverse relationship with the debt ratio, profit, and company size. Further, the findings indicated that in the industries of petroleum products, coke and nuclear fuel and metal products; Profit; in the chemical industry; company size, in automobile and parts manufacturing ,banks and credit institutions; block size and in investment industry; Asset amount has the biggest impact on Stock Block Trading Premium.

In this research, a new model for explaining the relationship between corporate governance and ownership structure with innovation is presented. This research, by deleting individual variables and adding new variables and determining the importance of the Delphi group, is located and They were selected for use in the model after two steps. The research was correlational and the statistical population of the study included companies listed in Tehran Stock Exchange, and the sample was screened and after the removal of Pert observations by 104 Tehran Stock Exchange companies during the period of 2011-2018. In this study, the variables of corporate governance and ownership structure are considered as independent variables. And using qualitative and quantitative combination data, multivariate regression and software spss version 20 and Eviese 7 their relationship with company innovation as dependent variable has been investigated. The results show that among the variables of corporate governance and ownership structure, ownership concentration, state ownership, and institutional ownership reduce innovation. However, the variables of the proportion of non-executive members and remuneration of the board improve the status of innovation in bourse companies. According to this model, other corporate governance variables did not have a significant effect on innovation.

The present study aim is to empirically investigate the impact of comparability and readability of financial statements on avoiding incorrect pricing of company shares. In order to achieve the objectives of the research, a sample of 127 companies listed in the Stock Exchange, which were selected according to the systematic elimination pattern, was collected for a period of 10 years from 2013 to 2022, and since the data of the period 2022 in the calculations It was needed for 9 years and was used for statistical work. A multivariable linear regression model was used to test the research hypotheses. For incorrect pricing of shares, the model previously developed by Pantzalis and Park (2013) and for measuring the readability and comparability of financial statements, the fog index and the profit-return index were used, respectively. The results of the research hypotheses test showed that the increase of both the readability of financial statements and the ability to compare accounting information separately leads to a decrease in the volume of incorrect stock pricing. In addition to that, further investigations revealed the interaction of the above two variables and led to the exchange and formation of stock prices in figures close to their intrinsic values, and the volume of incorrect stock pricing has decreased due to the reduction of information inequality

Forecasting the stock price due to its attractiveness has always been on the focus of experts and capital market activists. In such a way that various prediction models such as technical and fundamental analysis and data mining are increasingly used to predict stock prices. Past studies indicate the possibility of stock price prediction by machine learning models, but the prediction accuracy of these models has not been evaluated. The purpose of this research is to accurately compare the intelligence of five commonly used data mining algorithms, including neural network, Logestic regression, k-nearest neighbors, support vector machines and cross validation. Among the 385 active companies in Tehran Stock Exchange, 72 companies have been selected by the method of systematic elimination and the above models have been implemented to predict stock prices on the daily prices of selected stocks for the years 2009 to 2020. The stock price is used as a dependent variable and changes in the opening price, closing price, highest price, lowest price and volume of trade, daily price of forign currency, gold and oil price are used as independent variables. Three indicators R2, MSE and RMSE are used, to evaluate the accuracy of models, and analysis of variance using F statistics is used to fit the accuracy of the models, and t-student statistic is used to compare two models. The results are showed that among the smart algorithms used, the support vector machine algorithm has the highest accuracy. Matlab software is used to analyze the data and compare the models.

The purpose of this study is to explain the role of financing sources mergers and acquisitions on corporate performance based on Pecking Order Theory. What distinguishes this research from other mergers and acquisitions is the variable financing methods that are measured using a Pecking Order Theory of debt ratios, accumulated profits, and financial leverage. And to measure the firm's performance, variables such as equity returns, asset returns and the Kyotubin ratio are used.This study, using regression data on a sample of companies listed in the Tehran Stock Exchange that were acquired by other companies during the years 1391 to 1395, It shows that the source of financing of mergers and acquisitions has a different impact on the performance of the company. Whatever the company uses its debt ratio to fund, the company's performance has changed, increasing equity returns and decreasing returns on corporate assets. Also, increasing Retained earnings and Leverage as a source of financing , has increased the rate of return on assets and corporate performance, but the return on equity and value of the company has not changed.