درخت حوزه‌های تخصصی

We consider a class of fuzzy stochastic differential equations (FSDE), in which the integrands of the stochastic integrals are not adapted to the duration generated by a Wiener process. Such equations with randomness, fuzziness, and non-adapted processes can be applied in financial models. We discuss the existence and uniqueness of strong solutions. We consider a class of fuzzy stochastic differential equations (FSDE), in which the integrands of the stochastic integrals are not adapted to the duration generated by a Wiener process. Such equations with randomness, fuzziness, and non-adapted processes can be applied in financial models. We discuss the existence and uniqueness of strong solutions.

So far, readabilityof disclosure concepts and the behavioral features of investors have not been addressed appropriatedly in Iran despite their importance and there is not even a suitable tool to measure it in the country. Therefore, it seems necessary to conduct research in Iran to introduce the concept of disclosure readability, its measurement and the impact of business strategy on the readabilityof disclosure of financial statements. Therefore, the current paper evaluated the effect of the type of business strategy on the readability of financial statements of companies listed on the Tehran Stock Exchange. This is an applied causal (post-event) correlational paper. The statistical population of the study is all companies listed on the Tehran Stock Exchange. Using the systematic elimination sampling method, 129 companies were selected as the research sample and were surveyed in a period of 5 years from 2015 to 2019. EViews 9 and regression were used to test the research hypotheses. The results show that the type of business strategy affects the readability of financial statements of companies listed on the Tehran Stock Exchange.

The financial sector plays a central role in economic development and growth, and due to playing an intermediary role in allocating resources to all sectors of the economy, by reducing financing costs and encouraging savings and their efficient use, a major contribution. In the long-term economic growth of the government in oil-exporting countries, relying on oil revenues, it is possible to enter the financial markets extensively and make various changes in it. The main goal of policymakers from such changes is to stimulate economic growth. But studies in this area show that fiscal development does not necessarily lead to economic growth. However, in recent decades, the role of financial development in economic growth has been forgotten. Therefore, this study examines the impact of the development of financial institutions on economic growth. The statistical population of the present study consists of MENA member countries in the period 1980 to 2019. In order to conduct this research, due to the nonlinear relationship between the research variables, the PSTR model and Markov switching time series pattern have been used. Financial depth, accessibility and efficiency are also variables in the development of financial institutions that have been considered in this study. The results indicate that all three components of the financial institutions development index have a significant effect on the economic growth variable.

This study focuses on providing an intelligent credit risk management system of the bank in the presence of the macroeconomic indicators using a combined methodology of econometrics and artificial intelligence. In addition to the use of scientific documents and reports, the panel data related to the annual reports and datasets of stock exchange banks are analyzed by using the MATLAB programming environment. One of the most important results of the this paper is that the proposed approach has been based on the calculations made with the GARCH economic model in which the input values of the component "Inflation rate factor (A4)” have a weight of 0.943734 (equivalent to the membership function "High H"); the component "rate factor Bank deposit (B4)” has a weight of 0.959346 (equivalent to the "High H" membership function); the component “Unemployment rate factor (A3)” has a weight of 0.990343 (equivalent to the "High H" membership function); the component "Exchange Rate Factor (B2)" has a weight of 0.990413 (equivalent to the membership function "High H"); And the component "GDP growth rate factor (A1)” has a weight of 0.959256 (equivalent to the membership function of "high H"); This means that, 5.46 is within a range of 6, i.e. the target variable is exactly in the 91st position (the fifth level of the system output is excellent).

The increasing progress of technology has affected all aspects of human life. One of the new technologies for which various applications have been created today is virtual reality technology. Virtual reality seeks to create a sense of reality in an unreal environment. The aim of this research is the effect of virtual reality technology on the creation of customers' attitudes and understanding of property and service providers in the real estate consultant department in Shiraz. The statistical population includes all real estate clients and those who refer to the real estate sector in Shiraz, and since the population is considered, it is unlimited; The number of statistical sample is 384 people. In this research, the standard questionnair of Players and Ponsin (2020) were used to collect research data. Structural equation modeling approach and PLS3 software were used for data analysis. The results showed that virtual reality technology has a significant effect on creating the attitude and understanding of customers towards the property and the service provider in the real estate consultant sector. The combination of virtual reality technology with augmented reality is one of the measures that can significantly improve the attractiveness and efficiency of using virtual reality in the field of real estate.

The financial anomalies, which have brought about challenges for the nature of efficient-market hypothesis, provide ample experimental evidence on the deviation of market from rationality. According to the efficient-market hy-pothesis, investors avail themselves of all the information available in the market, and then attempt a thorough and painstaking interpretation of it. The empirical evidence, however, reveals that most investors fail to imple-ment rigorous information processing, thereby rendering to a number of behavioral theories concerning market inefficiency and biased information processing. The current research sets out to identify and scrutinize the im-pact of the CEO’s perceptual biases in judgment and economic decision-making on the reporting quality of the firms listed on the Tehran Stock Ex-change. The Purpose of this study CEO Overconfidence and Overinvestment by Role of Economic moderators Tehran Stock Exchange (TSE). One hun-dred five companies were selected as the sample size by systematic removal sampling reviewed in 2012-2019. Due to its dichotomous dependent variable, this study uses probit regression to test the research hypotheses. The results indicated the significant positive effect of CEO overconfidence on overin-vestment. It was also noted that Exchange Rate strengthens the positive effect of CEO overconfidence on overinvestment. Based on these results, the CEO's decisions as a decision-maker in charge of any company, especially under inflationary conditions, can play a substantial role in future corporate investment levels. The findings reveal that the indices of the CEO’s percep-tual biases in judgment and economic decision-making exert a significantly negative effect on financial reporting reliability and competitiveness.

Criteria weighting is a crucial step in the entire decision-making process. Determining the appropriate weights will lead to more reliable results. This study aims to use a coalitional game method for calculating proper criteria weights in multi-criteria decision-making (MCDM). In this paper, the Shapley value method is used to determine the weight of criteria. A numerical case study of 65 banks has been used to explain the efficiency of the proposed method. To this end, using the TOPSIS technique, the alternatives are ranked once in Shapley value and again in the Shannon entropy weighted matrix. Then the results are obtained applying Spearman rank correlation coefficient are compared to efficiency-based ranking using data envelopment analysis (DEA) as a powerful benchmarking method. In the proposed method, unlike many conventional weighting methods, the selection of criteria weights is made in a coalitional game with the participation of all criteria; the obtained weights are both intuitively and objectively fairer, and more reliable rankings are provided. According to the logical and fair calculation of weights, having a simple and understandable mathematical method, and no need for experts’ judgment, the proposed method can be used in real problems. Especially where realistic ranking has a significant impact on the equitable allocation and absorption of resources.

Due to the rapid advancements in computer technology, researchers are attracted to solving challenging problems in many different fields. The price of rainbow options is an interesting problem in financial fields and risk management. When there is no closed-form solution to some options, numerical methods must be used. Choosing a suitable numerical method involves the most appropriate combination of criteria for speed, accuracy, simplicity and generality. Monte Carlo simulation methods and traditional numerical methods have expensive repetitive computations and unrealistic assumptions on the model. Deep learning provides an effective and efficient method for options pricing. In this paper, the closed-form formula or Monte-Carlo simulation are used to generate data in European and Asian rainbow option prices for the deep learning model. The results confirm that the deep learning model can price the rainbow options more accurately with less computation time than Monte-Carlo simulation.

After closing, the multi-period portfolio can be corrected and revised at regular intervals. The philosophy behind using multi-period portfolio models is that investors often have a multi-period view of future changes in assets, which can be the result of technical and fundamental analysis or statistical model analysis. In conventional multi-period portfolio models, it is assumed that the forecast and correction time horizons are the same for all assets. However, one asset may be forecasted over a one-month horizon while another may be forecasted over a two-month horizon, and both may be revised in the future. The purpose of this study is to present a multi-period portfolio model in which assets have different time horizons for corrections or an asset may not be traded for the first few periods and then enter the correction stage. In this model, fuzzy variables defined in a credibility space are used to describe the return, and the credibility measure controls the risk. The model's objective function is to maximize the portfolio's ultimate wealth, and a constraint is used to control portfolio risk, in which the validity of the portfolio's ultimate wealth below a certain threshold is controlled at a certain level of confidence. A combination of particle swarm optimization and simulation is used to find the best solution. Finally, using a numerical example, the model is implemented on a portfolio with 6 assets and 4 monthly time steps on the Tehran Stock Exchange.