درخت حوزه‌های تخصصی

The main objective of this study is to investigate the relationship between monetary policies, capital structure and risk-taking of listed banks in Tehran Stock Exchange. In this study, to measure the monetary policies, the indicator of legal reserve rate have been used. The sample consists of 21 banks listed in Tehran Stock Exchange in the period from 2012 to 2018. In this study, the data were extracted from the Rahavard Novin 3 software and then classified by Excel software, and after calculating the variables, finally, through Eviews10 software, the multivariable regression model and panel data was used to test the hypotheses. The results showed that there is significant negative relationship between monetary policies and banks' risk-taking with in 95% confidence level. In addition, other findings indicated existence of a significant positive relationship between monetary policies and bank capital structure. However, the research results show that there is no significant relationship between bank capital structure and banks' risk-taking.

This paper examines the credit risk in the Iranian banks during 2008 to 2018 through the Z-score (Accounting based data) and the KMV-Merton (Market based information) models. In the Merton model, equity is equal to call option on underlying value of the bank’s asset. The market value of assets is estimated by share price. The value of assets is then compared to the value of liabilities. Therefore, default when occurs that the market value of assets is less than the book value of debts. so, value of equity becomes negative. In the Z-score model, Return on Assets and Equity to Assets as the numerator and standard deviation of ROA as the denominator are applied. If the mentioned ratios of numerator increase and the denominator decrease, the probability of default decline. As well as, Independent variables are divided into five groups: leverage, management efficiency, profitability quality, financial health, and liquidity. As a result, capital adequacy and profitability have a greater impact on both models. Also, the ANOVA table proves the validity of two models. The value of ROC test in both models is above average (0.5) which are efficient and their efficiency is 99.48% and 92.68%, respectively. Also, in terms of Voung’s test, the KMV is more efficient than the Z-score.

Cluster fluctuations and fractal structures are the two important features of space-time correlation in complex financial systems. However, the microscopic mechanism of creating and expanding these two features in financial markets remains challenging. In the present study, using factor-based model design and considering a new interactive mechanism called multilevel convergence, the process of forming cluster fluctuations according to the fractal structure of financial markets is investigated. Virtual agents' trade in different groups according to market performance and their mass behavior is measured at three levels of stock, segment and market. The results, in addition to providing new insights into the space-time correlations of financial markets, show that multilevel convergence is one of the microscopic mechanisms of microstructure of such markets. In other words, multilevel collective behavior is an important factor in the occurrence of cluster fluctuations, price bubbles and market fractals and therefore should be considered in interpreting the concept of risk and defining risk management strategies from this perspective.

EXTENDED ABSTRACT INTRODUCTION The purpose of this study is to analyze the concept of "developed man" and determine the degree to which attention is paid in the Fundamental Transformation Document (FTD) in education in Iran approved in December 2011. This concept has been developed based on the theoretical basics of classical economics on description of "economic man" and also the Engels’s theory on the description of "modern man" and with regard to some of adapted and localized national and Islamic ideas and beliefs. What is meant by "Developed man" in this research is a person whose attitude and opinion has been developed and has the ability to turn the country's resources into wealth and prosperity for the people of society.  METHODOLOGY The instrument by which one can measure it, is a researcher-made content analysis checklist that involves 25 characteristics for developed human, and its face reliability and validity have been also verified and ranked by subject matter experts. Shannon entropy indices have been used to determine the importance and ranking of developed human components. In order to achieve a valid explanation and transparent results in this study, some important and fundamental concepts and components that were analyzed in the literature reviewing this document and their frequency was determined, were selected, and their frequency was compared with the total frequency of developed human components in the transformation document text. FINDINGS By use of word-counting the whole text of the Fundamental Transformation Document of education system, it also became evident that only about 1% (113 words out of 10914 words available) of this 64-page document are dedicated to the components of developed human and as a result it can be said that with regard to the weight and importance of the economy and its related issues in the country (such as unemployment, weak attitudes and skills of wealth creation and lack of efforts to increase production and improve public living and poverty alleviation) from the leaders and policymakers viewpoint, especially. CONCLUSION In recent decades, little emphasis and attention has been paid to developed human, and accordingly, it is worthwhile to pay close attention to these components in revision of the Transformation Document text.

The accurate imputation of missing values in time series data is paramount for maintaining the integrity and reliability of analyses and predictions. This article investigates the effica-cy of various missing values imputation methods, encom-passing well-known machine learning and statistical tech-niques. Moreover, for a better understanding, they imple-mented two financial data time series: S&P 500 and Bitcoin markets spanning from 2016 to 2023 on a daily frequency. Initially utilizing complete datasets, controlled missingness was introduced by randomly removing 45 data points. Then, these methods applied multiple imputation strategies for estimating and substituting these missing values. Experi-mental evaluation yielded insightful findings regarding the performance of the different methods. The examined ma-chine learning methods, including k-Nearest Neighbors (k-NN), Random Forest, Deep Learning, and Decision Trees, consistently outperformed their statistical counterparts, such as Mean Imputation, Regression Imputation, Hot-Deck Im-putation, and Expectation-Maximization Imputation. Nota-bly, Random Forest emerged as the most effective method, showcasing superior performance in terms of accuracy and robustness. Conversely, the Mean Imputation method exhibited com-paratively inferior outcomes, suggesting its limited suitabil-ity for financial time series data. This research contributes to the ongoing discourse on data integrity within finance ana-lytics and serves as a comprehensive guide for practitioners seeking optimal missing values imputation methods. The empirical evidence provided herein advances the under-standing of imputation techniques' relative performance and their application in financial data, facilitating enhanced de-cision-making processes and yielding more reliable predic-tions.

The flow of information in the capital market is strategically important because it determines the path of investors' decisions. In this decision-making process, the managers of the companies can disclose timely and reliable information based on their cognitive and perceptual characteristics of capital market situations. This article aims to contribute to the capital market knowledge literature by presenting the framework of managers' inertia drivers in response to reliable disclosure of information. This study adopted mixed, both inductive and deductive approaches to develop an integrated framework, validate its practicability, and verify its effectiveness in selected firms listed on the Tehran Stock Exchange, respectively. In developing the framework and implementation procedure, the study employed a systematic screening data collection (qualitative) approach to review the managers' inertia drivers. Then, in this study's second phase, the Interpretive Rating Process (IRP) and Fuzzy Reference System are used to develop the framework of managers' inertia drivers in response to reliable disclosure of information. The study's results in the qualitative part indicate the determination of 8 driving areas of managers' inertia in the reliable disclosure of information. On the other hand, the quantitative section results showed that managers' overconfidence and excitability are the most influential fields in stimulating managers' inertia in the timely disclosure of information. Based on the results, it was determined that the excitability of managers' overconfidence in creating inertia causes managers' subjective estimates to cause exclusivity in information disclosure.