gholamreza zomorodian

Investigation of the evolutions of the stocks, gold, and oil markets in Iran shows clearly that prices of these assets have experienced abrupt fluctuations during the recent past. This indicates the great importance of investigating these fluctuations to see how they are transferred from one financial market to another. When it comes to economic policy setting, such an investigation serves as an efficient economic instrument for realizing increased production and employment, because a proper understanding of the mentioned price fluctuations in the markets contributes to setting appropriate controlling policies. Oil and gold, as two strategic and irreplaceable commodities, have attracted much attention recently. These two goods in particular cause an increase in prices and an increase in economic costs.Therefore, the present research investigates the relationship between fluctuations in the gold and oil markets as well as Iran’s stock exchange market (SEM) during 2012 – 2022. Data analysis was performed by the SVAR-asymmetric-BEKK-GARCH model. Findings indicated a one-way relationship amongst fluctuations overflown across the considered variables.

In this paper the price bubble contagion in two currency market and the stocks market in a six year period (2015-2021) is investigated. For this purpose, the price bubble of both markets was examined and the dates of their formation and collapse were determined by using (RADF), (SADF),(GSADF) test. Then the contagion of the bubble in the financial market of Iran was investigated using a regression model. The findings of this research showed that there was a bubble in the foreign exchange market during five periods of 2016:10:31-2017:01:21, 2017:10:03-2018:12:16,2018:12:23-2019:07:14, 2020:02:22-2021:01:17 and 2021:01:23-2021:03:19.There were four bubble periods in the stock exchange for the total price index in periods 2016:02:06-2016:04:28,2017:09:04-2017:10:10,2017:10:17-2018:04:19 and 2018:06:10-2021:03:19. Also, the results indicate that the contagion of the price bubble from the currency market to the stock exchange market is statistically significant and the contagion of the bubble has occurred between the currency market and the stock exchange market .

Nowadays, financial markets and especially the stock market are important and undeniable sources of financing for investment toward the economic growth and development of countries. These markets also have a tangible role as a basis for implementing monetary policy. This study aims to investigate the effect of monetary volatility on the seasonal performance of the Iranian stock market from April 2001 to March 2021.The TEDPIX index of the Tehran Stock Exchange was used for designing and explaining the research model for measuring monetary policy uncertainty in terms of the debt of banks to the Central Bank and to measure the Iranian stock market’s performance. With portfolio theory as the theoretical basis for the study, the housing price index and the exchange rate were added to the research model as other independent variables due to their importance to the portfolio of individuals. In this regard, monetary policy uncertainty was first calculated using the exponential general autoregressive conditional heteroskedastic (EGARCH) method. Then, the effect of uncertainty on the TEDPIX index was calculated using the vector auto regression (VAR) statistical method in EVIEWS 12. The findings indicate a significant negative correlation between monetary policy uncertainty and short and long term TEDPIX index. Moreover, exchange rate and housing price index has a significant positive effect on the TEDPIX index.

This study aimed to present an explanatory model of stock price using deep learning algorithm for companies listed in the Tehran Stock Exchange. In this study, a deep learning network was used to predict stock prices. The study was applied-developmental research in terms of purpose. To test the research questions, accounting data were prepared from 2011 to 2020 and input variables were calculated based on it for the model. The method of systematic elimination sampling has been used in this study. The results indicated that the precisions of prediction has a high precisions in the deep learning model. The proposed algorithm was reviewed according to its prediction accuracy and modeling cost. According to the data volume, it was found that the prediction accuracy in the deep learning model has a relative superiority and the diagram of performance characteristic and AUC criteria also showed this superiority in detection power.

the purpose of this study is to develop a multilevel model for the impact of macroeconomic variables on the money and capital markets with a future research and multi-criteria decision making (MCDM) approach.

this is an applied research conducted based on a descriptive survey design and a mixed method approach. The research was conducted in the course of 2020-2022 through two main phases using VIKO method and ISM technique. The statistical population included professors and PhD students of accounting, financial management, economics, and business administration in Iran. Using purposive (selective) sampling, 15 people were selected as the respondents to an online survey using a questionnaire, which took place in the winter of 2020.

applying VIKOR method, 18 macroeconomic variables were selected. Next, based on the obtained data from the questionnair, the components were ranked using ISM technique. This ranking took place at six levels of the endvariables.

The results of the interpretive structural modeling showed that the eighteen variables, identified in the VIKOR phase, could be ranked into six levels where government spending, active population, and investment were at the first level, and oil revenues, granted banking facilities, and GDP at the sixth level.

From the consequences of business intelligence, we can mention the realization of organizational goals, having a systematic and standard structure on the consequences of business intelligence in terms of ethical decision-making, and increasing the ability to process data. The purpose of this research is to provide a comprehensive model regarding the process of business intelligence in accounting businesses, including ethical decision-making, so that in this way it can help to spread business and ethical intelligence more, of course, with importance in the said accounting business. In this research, using the contextual theory method, the parameters of business intelligence were identified in terms of ethical decision-making. For this purpose, causal, background, intervening factors, business intelligence action strategies, and the consequences of business intelligence were first identified. And then, using the structural model, the relationships between the factors were identified. The results show that organizations are different from other organizations in terms of structure, culture, and type of workforce. In terms of ethical decision-making, they can be effective in the consequences and response of business intelligence. Although so far none of the internal research has addressed the issue of business intelligence model design in terms of ethical decision-making in accounting businesses, but the mixed approach to modelling applied using the data theory method of the primary model foundation shows that the design of the business intelligence model in terms of ethical decision-making in accounting businesses can measure the effect of auditors' ethical decision-making in the organization and its effect on the design of business intelligence.

Designing a comprehensive financial stress index that includes both domestic and foreign financial indicators and a comprehensive perspective of systemic risks facing an economy in the period from 10-31-2012 to 10-21-2012 2022 is the subject of this study. In this article, 5 important global indices of Brent crude oil price, price per ounce of gold, US dollar index, Nasdaq stock index and the price of the euro-dollar currency pair have been used to construct the financial stress index. This index combines domestic and Externality represents the mutual and spillover effects in a globalized financial landscape. Then, with appropriate weighting to each index, various methods such as principal component analysis and statistical regression models have been used to determine optimal weights. Therefore, in this research, the open source software R 4.2.1 and Eviews13 were used to perform the final analysis. From the results, we find that there is a significant relationship between the designed financial stress index and the volatility of the Iranian stock market. Also, there is no significant relationship between the designed financial stress index and the volatility of the foreign exchange market in Iran. Also, the result of weighting the research variables based on the hierarchical technique shows that the gold ounce has the highest weight with 0.369, followed by the dollar index with 0.222 in the third place, the euro/dollar currency pair rate with 0.174 and the Brent oil index with 0.170 and the Nasdaq stock exchange. With 0.065, they are ranked fourth and fifth.

In this research, asset and liability management, which is one of the most important things done by banks, is examined. The methods and complexity of the process used to manage assets and liabilities depend on the size and complexity of the portfolio. Bank Mellat, as one of the most central banks in the country, in line with its organizational goals and missions, needs accurate economic planning and high-yield investments in order to maintain the value of depositors' reserves and provide the necessary resources to fulfill its long-term and short-term obligations. According to the stated content, the main problem of this research consists in the fact that due to the importance of asset and liability management with the approach of liquidity risks, credit, balance sheet, capital structure as a comprehensive approach that includes different types of important risks and It includes the important, it should be noted. In this research, we consider asset and debt management with more important factors in this matter, so that the model is more efficient than the previous models presented, which has been taken into account with the approach of goal programming.

Investment decisions are examined based on financial concepts that are based on the efficient market hypothesis. Considering the confirmation of the complete inefficiency of Iran's capital market, there is an expectation of obtaining more returns. In this research, we seek to introduce investment portfolios based on the momentum investment strategy (which has been confirmed in various studies) and its combination with other factors affecting the company's value and profitability. Based on this, 119 companies were investigated in the period of 1388 to 1400 in three short-term periods of three and six months and medium-term of one year. According to the findings, the average returns obtained from the formation of different combined portfolios are positive in all periods and the returns of the combined portfolios are higher than the single-criteria portfolios. The investment strategy based on the asset growth rate and momentum in the short-term periods of three and six months and the medium-term of one year has the highest average return, then the strategy based on over-leverage and under-leverage and momentum and finally the strategy based on leverage

The purpose of this study is to investigate the ability of mutual fund investors to predict the performance of mutual funds and select a fund with higher returns. To predict whether investors and the cash they enter into investment funds will be able to select a more efficient fund, first, the information of 34 funds in the period 2012 to 2020 was Collected to examine the intelligence of cash. The economic prosperity periods of the market were then examined separately to determine whether investors had acted differently in different market periods. Also, real and legal investors are separated from each other to examine the performance of each separately. Using Carhart's four-factor model, the adjusted return on risk of the funds was calculated to assess the intelligence of investors, which showed that group of real and legal investors, at the time of entry during market blooming and at the time of exit money during market stagnation act wisely in mutual funds.

Choosing the right portfolio is a crucial goal for investors. Two key factors to consider when selecting a portfolio are return and portfolio risk. This problem can be written as a mathematical programming equation and can be solved quickly. For instance, some investors choose stocks based on past performance, while others consider factors like liquidity when making their selections. Additionally, fundamental and technical analysis are often used in stock and portfolio selection. However, the strategies and methods used to select stocks and portfolios can vary depending on the current market conditions and the investor's level of knowledge. This article focuses on designing and explaining a portfolio optimization model using the cuckoo optimization algorithm. Results show that the best value of the utility function increases as the number of iterations increases. The growth value of the best value of the utility function is higher in the initial iterations and gradually decreases until it reaches zero, indicating that the algorithm has converged to the optimal solution. This research fills a gap in the study of investment portfolio optimization using nested optimization models. Additionally, the study finds that shares selected from industries with better performance make up a higher proportion of the portfolio.

The main goal of investing in the stock market is to get the highest return at the desired time. Successful trading in financial markets should be done close to key reversal points.In this research, we are trying to select the widely used technical rules by studying the previous researches and improving the decision parameters in the mentioned technical rules for each stock by using the firefly and gray wolf algorithm and converting contradictory signals issued from the optimized indicators to the unit. And finally, through the LSTM neural network, we will try to predict the entry and exit positions of the stock market.This research was conducted from 1390 to September 1401 on Tehran Stock Exchange companies. The proposed model has been able to correctly identify buy, sell and hold points for a future trading day for long-term investors with an error of about thirty-six percent.Keywords: Technical indicators, firefly algorithm, GWO and LSTM

A model for determining the oil market transferability turmoil on the financial markets of the Iranian economy using the dynamic systems approach. At first, data related to oil, gold, stock exchange and foreign exchange were extracted from statistics related to the World Bank, Central Bank and Statistics Center of Iran and were analyzed with statistical analysis and simulation software. Then the research model was constructed using simulation methods and system analysis and the results were analyzed. The oil market in supply and demand for price determination is based on global systemic behavior. this simulation has used the factors affecting oil supply, oil demand, the expectations that shape this supply and demand, as well as macro factors such as macroeconomic indicators of the US economy, sanctions on the oil sector in Iran, the rate of world industry development and the available knowledge on oil substitution. Hidden mechanisms are the main reason for some oil price behaviors. The results of the research have led to the forecast of oil prices in the baseline scenario until 2025. The presence of political problems due to the interconnectedness of parallel markets in Iran causes widespread fluctuations in the currency and gold sectors in the Iranian economy

The aim is to investigate three methods to measure the bubble and categorize the methods of its contagion .The price bubbles of the capital market were tested with three methods (RADF), (SADF) and (GSADF) and the dates of their formation and collapse were determined. Two models of contagion using DCC-GARCH and BEKK GARCH methods were expressed and compared.The results indicated four bubble periods as follows 2015:11:17-2016:02:09, 2017:06:13-2017:07:18, 2017:07:25-2018:01:30 and 2018:03:20-2020:12:16. The results showed that in all three methods, the existence of a price bubble in stock exchange companies was confirmed

As the main achievement of the modern portfolio theory, portfolio diversifica-tion based on risk and return has attracted the attention of many researchers. The Markowitz mean-variance problem is a convex quadratic problem turned into a mixed-integer quadratic programming problem when incorporating car-dinality constraints. Due to the high number of stocks in a market, this problem becomes an NP-hard problem. In this paper, a metaheuristic approach is pro-posed to solve the portfolio optimization problem with cardinality constraints using the differential evolution algorithm, while it is also intended to improve the solutions generated by the algorithm developed. In addition, variance, val-ue-at-risk, and conditional value-at-risk are assessed as risk measures. Candi-date models are solved for 50 top stocks introduced by the Tehran Stock Ex-change by considering the cardinality constraints of not more than five stocks within the portfolio and 24 trading periods. Finally, the obtained results are compared with the results of genetic algorithm. The results show that the pro-posed method has reached the optimal solution in a shorter time.

One of the important topics discussed in the stock market, which should be considered by both natural and legal investors, is choosing an optimal investment portfolio. In this regard, investors are studied in order to select the best portfolio based on risk and return. However, traditional investment methods do not focus on portfolio optimization and only consider the highest return and lowest risk. This research addresses the gap in solving the problem of wide portfolio optimization by comparing answers using more effective and efficient metaheuristic optimization algorithms, thus reducing the probability of error. During this research, metaheuristic optimization methods are well-designed and studied, and then used to optimize the portfolio despite real market limitations. The developed algorithms are all implemented to solve the extended portfolio optimization problem. In this research, more effective and efficient metaheuristic optimization algorithms are used to solve the problem of wide portfolio optimization and by comparing the answers, the probability of error can be almost zero. The stock portfolios formed by the model based on censoring models have more returns and less risk (variance) than the invasive weed algorithm, showing the superiority of the proposed model in comparison to the invasive weed algorithm. The findings of the research have filled the research gap in investment portfolio valuation and demonstrate that the proposed model has effectively considered investment portfolio selection conditions and determined an optimal investment portfolio.

The present study presents the revised systemic risk model with the Changing conditional value-at-risk (∆CoVaR) approach in banking network with an emphasis on bank indicators. Systemic risk investigates the potential capacity of financial crisis spread among banks and ultimately the real sector of the economy through simultaneously increasing the fat tail of loss distribution. This is a descriptive-analytical research in terms of method and a developmental/applicative study in terms of purpose. The research time zone is 2009/03/21-2021/01/19. The research data includes the weekly average stock price of seven banks (Mellat, Tejarat, Saderat, EN Bank, Parsian, Karafarin, and Sina) listed in stock exchange and the weekly average of the general stock market index from Rahavardnovin system, and data related to the banks’ financial metrics are extracted from the financial statements of the banks in the Codal website.To measure each bank’s share in systemic risk, the measure (∆COVaR) is employed. We show the better fit of ∆CoVaR for measuring risk compared to VaR and CoVaR models. The ratings of the investigated banks are tested by means of two criteria (RMSE) and (MAE) and it is concluded that in some banks, the crisis has higher destructive effects on the entire financial system than that in other banks. Finally, the association between systemic risk and the financial parameters of the investigated banks is reviewed and it is concluded that the improvement of the capital adequacy ratio (CAR) has an inverse and significant relationship with systemic risk.

Gold has always been considered as a strategic commodity in crises. When markets are down, gold acts as a safe haven. Because the price of gold usually does not move with the market prices. The purpose of this research is to investigate the gold market bubble and the possibility of its contagion to the stock exchange. For this purpose, in order to check the existence of a price bubble in the gold market, using (RADF), (SADF) and (GSADF) tests, the price bubble of the gold market was tested and the date of their creation and collapse was determined. To investigate the contagion of the gold market bubble to the stock exchange, a time variable regression model was used. The results indicate the existence of 4 periods of explosive behavior and the existence of multiple price bubbles in the price of gold. Based on this, the bubble periods in the Iranian gold market are related to the periods 1395:07:13-1395:04:13, 1397:09:25-1396:10:17, 1398:06:04-1397:10 09:09 and 1399:12:30–1398:09:19 and the contagion of the price bubble from the gold market to the stock market has also occurred.

The main purpose of this study is to investigate the impact of economic uncertainty and government policies on the price volatility of the banking sector in the Tehran Stock Exchange, considering the structural instability in the model parameters. In this regard, in order to estimate the uncertainty of the variables, a variety of symmetric, asymmetric and non-linear GARCH models were estimated, and finally, based on the information criteria and the significance of the asymmetry coefficients, the EGARCH model was selected as the optimal model. In the following, the effect of uncertainty Indicators on the instability of the banking sector index was investigated by the time-varying parameter autoregressive (TVP-VAR). In this research, monthly data were used in the period of 2010:7-2020:9. The results of the final estimation of the research model indicate that the impact coefficients of uncertainty indicators on the instability of the banking sector are variable. In such a way that the effect of the uncertainty of government policies (uncertainty of tax revenues) on the instability of the banking sector estimated negative at the beginning and positive at the end of the period. Based on the impulse response function (IRF), the effect of inflation uncertainty as the indicator of economic uncertainty on the instability of the banking sector is positive. Also, the impact of government policy uncertainty through the exchange rate uncertainty channel has had a positive impact on the volatility of the banking sector in the stock exchange. However, the uncertainty of government policies based on the uncertainty index of tax revenues has had a negative impact on the price volatility of the banking sector, and this impact has gradually decreased.

The purpose of this research study is to predict the default probability of automobile and food industries in the corporate banking portfolio of banks, (not) considering the correlation of default. In this study, 30 companies from the automobile industry and 30 companies from the food industry whose stock market information is available were randomly selected. The market information of these companies was used daily from August 1, 2018 to August 31, 2019 for modeling purposes. The modeling of default probability of companies was done based on estimation of asset value, asset volatility, and drift rate. Multivariable GARCH (MGARCH) model was used to estimate the parameters of CAPM model, which is necessary to predict the drift rate. Through calculating the average default probability of companies in each industry, the probability of default of that industry (without considering the correlation of default) was obtained. Also, the asset value approach was used to calculate the default probability of each industry by considering the default correlation. In the asset value approach, maximum likelihood method was used to estimate default correlation parameters. The results showed that the prediction of the probability of default of the selected industries by considering the correlation of default is more consistent with the actual default.

The purpose of this research is to investigate the impact of meta-heuristic hybrid algorithm analysis on portfolio diversification and excess returns of investment funds. In terms of method, the current research is a part of correlation research. In correlation research, the researcher's effort is focused on discovering or determining the relationship between one or more variables. In fact, the purpose of this method is to study the limits of changes of one or more variables with the limits of changes of one or more variables, and from the point of view of the purpose of this research, it is an applied research, the results of which can be useful for shareholders, stock exchange officials, and researchers. It is useful and in terms of the type of post-event studies that examines hypotheses based on past financial data. The statistical population of this research includes all the companies admitted to the Tehran Stock Exchange during the period of 36 months in the period from April 2019 to March 2011, which number is 591 companies based on the Rahevard software. According to the conditions and application of the aforementioned restrictions, 150 companies were selected as a sample in the 36 months ending in March 1401. By observing the results of the stock portfolio selection models with single and combined measures, we find that in all three models, the amount of risk increases with the increase in return. This shows that investors, in order to obtain more return, They are forced to accept

Today, the risk-based audit method is emphasized in modern tax systems, so explaining a comprehensive model for rating the risk of taxpayers is one of the basic steps of implementing a comprehensive tax plan. Therefore, in this article, we aim to measure the performance of decision tree algorithms and support vector machine in the validation of taxpayers. The statistical population of this research is the companies accepted in the Tehran Stock Exchange, which were active during the years 2012-2017 and for the selection of the sample was made using the screening method (elimination). In this research, first, using Delphi technique and meta synthesis, 164 effective components in the validation of taxpayers were identified, then the data needed to measure the variables of the research were extracted from the Kodal website and by examining tax files, and finally by using the collected data, we investigated the accuracy of the decision tree (C5.0 algorithm and random forest) and support vector machine in validating taxpayers. The findings showed that based on the results of the AUC value, the C5.0 algorithm and the random forest have a better fit, however, the research hypothesis that it is possible to predict the risk of taxpayers using the SVM algorithm is not rejected.

The aim of this research is to compare the performance of the value-at-risk model with the liquidity-t-copula approach with dynamic conditional correlation (t-copula-GARCH-LVaR) with the value-at-risk (VaR) model to optimize the portfolio in the Tehran Stock Exchange. In the current research, in order to test the desired hypotheses, the period is between 2001 and 2021. All the variables used in this research on a quantitative scale and observations in the form of time series are the daily logarithmic returns of 40 stock market indices, including 39 industry indices and one index of fixed-income bonds from the beginning of September 2011 to the end of July 2021. In this research, to perform the final analysis, all the calculations required for this research were done using the open-source software R 4.2.1. Our results showed that the t-copula-GARCH-LVaR optimization model performs better according to the Sharpe criterion based on Mann–Whitney U test at the 95% test level.

The main purpose of this study is to investigate the impact of economic and policy uncertainty on the financial stability of the banking sector. In this regard, firstly, using Generalized AutoRegressive Conditional Heteroskedasticity (GARCH), inflation uncertainty as an indicator of economic uncertainty, government tax revenue uncertainty and exchange rate uncertainty as indicators of government policy uncertainty are calculated. Then, the impact of these indicators on the financial stability of the banking sector (calculated using the ZSCORE approach) has been examined. In this study, the monthly data of 19 banks including Melli, Sepah, Post Bank, Maskan, Tejarat, Mellat, Karafarin, Eghtesad Novin, Parsian, Pasargad, Mellal, Sina, Ayandeh, Day, Tourism, Shahr, Iran Zamin, Qarz Al-Hasaneh Resalat and the Middle East has been used in the period 2015 to 2020. The results of this study show a negative and significant relationship between inflation uncertainty and exchange rate uncertainty with the financial stability of the banking sector. However, the coefficient of tax revenues uncertainty is not statistically significant. In addition, the results of the final model indicate a positive relationship between the bank deposit ratio and a negative relationship between the size and ratio of foreign debt with the financial stability of the banking sector.

This study is aimed to investigate the effect of the 5 personality phases of managers of mutual funds in dealing with smart money by using the structural equation model. Meanwhile, managers' financial intelligence has also been examined as a moderating variable. This survey is carried out by collecting statistics from all mutual funds. This analysis was carried out in 1399 and all data have been collected from mutual funds during that time. Five studied phases of personality are: Hypomania, Psychopathic Deviate, Psychasthenia, Paranoia, Depression.The results show that out of the five personality phases studied, four of them have a significant relationship with smart money and there is no significant relationship between only one personality phase and smart money. Also, the moderating role of financial intelligence was accepted only concerning one type of personality phase( Hypomania,) and concerning the other four personality phases(Psychopathic Deviate, Psychasthenia , Paranoia, Depression), the effect of the moderating variable was not confirmed.