میرفیض فلاح شمس

The purpose of this article was to investigate the issue of price bubbles based on financial reporting in companies listed on the Tehran Stock Exchange in the period before and after the takeover. One of the strategies that is increasing in Iran's capital market during the recent periods due to the privatization program of state-owned industries and the expansion of investment companies as a result, is the takeover strategy. This strategy is among the strategies of companies to increase their competitive position in order to maximize the wealth of shareholders.The purchase and acquisition of companies can be considered as a trading opportunity, and the buyer prevents the decrease of the market share and prevents the entry of a new competitor. In this study, the information related to the transferred companies for the period of 2011-2021 was used. The obtained results have shown that there is a positive relationship between the increase in stock price and takeover, and in the estimated logit regression model, it was observed that a one percent increase in takeover can lead to a 6.5% increase in price and price bubbles. Companies react differently to positive and negative news. Based on this, the results of the test indicate that there is a relationship between takeover and the occurrence of price bubbles, and in companies that have takeovers and changes in ownership, the probability of occurrence of price bubbles is higher.

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.

Deep learning is a subset of the broader class of neural network based machine learning methods that has recently gained much attention in many fields, including time series prediction in financial markets. In this research, first, using deep learning models based on LSTM and CNN networks, the movement of Tehran Stock Exchange index is predicted. Then, by combining the two models, the hybrid CNN-LSTM deep learning model is used to predict the Tehran Stock Exchange index. In the next step, in order to evaluate the performance of the aforementioned forecasting models, three performance measures symmetric mean absolute error percentage (SMAPE), mean absolute error percentage (MAPE) and root mean square error (RMSE) were used. In this research, the daily data of the Tehran Stock Exchange Index was used in the period of 2016-07-13 until 2021-01-26. The estimation results of the models in predicting the Tehran Stock Exchange index with a one day step and comparing the efficiency measurement criteria indicate the superiority of the proposed CNN-LSTM model compared to the other two models. The LSTM model ranks next in accuracy and forecasting efficiency. According to the results that be presented in this research, financial market participants in Iran are suggested to pay attention to integrated deep learning models in order to increase the efficiency and accuracy of their predictions.

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

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.

With the expansion of globalization, financial markets in both developed and developing countries have become interconnected. In financial discourse, the interconnection between financial markets is referred to as financial contagion. Financial contagion can transmit the volatility of one market to another, potentially leading to economic booms, recessions, or varying levels of risk and return. Consequently, given the significance of volatility contagion in markets for investors and decision-makers, the purpose of this study is to examine the effect of dynamic contagion in the volatility cycle between the futures market of gold and the foreign exchange rate in financial markets and the Tehran Stock Exchange.

The data were collected daily over the period from 2009 to 2018. To test the research hypotheses, GARCH-BEKK models, Markov Switching, and Vector Autoregression were employed.

The results of the study indicate that the contagion effect of volatility is from the foreign exchange market to the gold futures market. Moreover, the contagion effect of volatility from the foreign exchange market to the gold futures market varies across different regimes.

Originality / Value: 

The findings of this research could provide new insights that may guide policymaking and decision-making in the financial industry.

The 2008 U.S.A. financial crisis, which subsequently spread around the world, showed that financial distress in the banking network could spread to the network of all financial institutions and the entire economy of a country. For this reason, the development of accurate measurements of the risk of transmission of financial distress by  each bank in the banking network is very important in controlling the risk of financial intermediation in every country. However, an issue that has not received much attention in previous research is the impact of macroeconomic factors on the risk of spreading financial distress. This is important because governments can anticipate the possibility of an economic crisis by anticipating an increased risk of spreading financial helplessness and adopting monetary and fiscal policies to reduce such risk. The present study, in three stages, estimates the risk of financial distress using the KMV method, estimates the risk of transmission of financial distress using the VAR method, and models the effects of macroeconomic variables such as inflation, exchange rate, and GDP on the risk of contagion. Based on the results, it is clear that GDP has a statistically significant inverse relationship with the risk of transmission of financial distress, meaning that with increasing GDP, the risk of transmission of financial distress is expected to decrease. Also, the relationship between the exchange rate and the risk of transmission is direct and significant, and an increase in the exchange rate leads to an increase in the interdependence of financial institutions and, consequently, an increase in the risk of transmission of financial distress.

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 purpose of this research is to provide a model for evaluating the performance of bank branches using the network coverage analysis model using fuzzy data. In this research, an attempt was made to use a model based on the traditional model of data envelopment analysis, taking into account factors such as the network nature and internal relationships of each decision-making unit, undesirable output, non-optional input, and the variables having a fuzzy nature. which is more consistent with the actual conditions related to the decision-making units and calculates the efficiency in a more accurate way.After presenting the research model, first, based on the studies conducted in the past researches and field studies and obtaining opinions from experts in this industry, indicators were considered as input and output. After identifying the indicators, fuzzy Delphi method was used for initial screening of the indicators. In the following, confirmatory factor analysis technique was used to finalize the screened indicators. At the end of the research, the developed model was solved using the data collected from the studied branches, by Gams software and with the alpha cutting approach. The results indicate that among the 34 investigated branches, 8 are efficient and 26 are ineffective.

The purpose of the research is to examine special financial institutions; In the financing of tourism industry projects in Iran, it is based on the capital market. Based on the philosophical premise, this research is interpretive. Also, its paradigm is pragmatism and its approach in the field of mixed research. Here, there is a tendency towards the applicability of "what works" and solutions to problems. Therefore, the important dimensions of the research and its questions are emphasized, so no hypotheses are examined and its purpose is to answer the research questions. In this method, the researcher has compared and summarized the qualitative and quantitative results with the triangulation approach by reviewing the documents and asking the experts' opinions through preliminary semi-structured interviews and questionnaires using the Delphi method. The results have shown that real estate investment funds, project joint stock company, risk investment fund, tourism infrastructure investment fund, bond guarantee syndicate by the tourism development fund with banks, playing the role of reinsurer by the fund The development of tourism, the formation of a joint insurance association for the compensation of tourism projects has been agreed upon.

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.

The issue of efficiency evaluation is one of the most important challenges facing managers in the dynamic and vital banking industry. Due to the very important and fundamental role of banks in most economic activities, the study of bank efficiency has a special place. Models based on data envelopment analysis had wide applications in the field of measuring and evaluating the efficiency of banks. In this study, we tried to add other assumptions to the traditional model of data envelopment analysis, using a model that is more consistent with the real conditions related to decision-making units and calculates the efficiency more accurately. In designing the data envelopment analysis model in the present study, attention has been paid to the networking and internal relations of each decision-making unit, undesirable output, involuntary input and the fuzzy nature of the variables. After developing the model with the mentioned assumptions, first, based on reviewing previous studies as well as field studies and obtaining opinions from industry experts, indicators considered as input and output. After identifying the indicators, fuzzy Delphi method used for initial screening of the identified indicators from the research literature. After the initial approval of the indices, the indices analyzed using the confirmatory factor analysis technique. Finally, the research model solved using data collected by GAMS software and with alpha-cutting approach. The results show that out of 38 branches examined, 8 branches are efficient and another 30 branches are inefficient.

The present study has investigated the contagious of turbulence systemic risk. In this study, the contagious effect of real and virtual currency (Bitcoin) fluctuations has been measured. In this regard, the method of self-regression vector analysis (VAR) and the conditional autoregressive model on the heterogeneity of multivariate generalized variances (MGARCH) have been used.The data used in this study, including the exchange rate of the dollar based on the euro and the price of bitcoin in the period 01/2015 and 2020/01, these data were collected and examined and analyzed by regarding the approach of the generalized multivariate conditional variance heterogeneity (CCC) method.The present study is based on the classification of research considering the, nature and direction, respectively descriptive survey, applied and post-event. The results of this study confirm the relationship between the contagins of volatility of real currency and virtual currency. In other words, the main hypothesis of the research based on the contagion of virtual and real exchange rate fluctuations has been confirmed unilaterally from virtual exchange rate to real exchange rate.

Blockchain technology is a relatively new technology, and due to its decentralization, consensus and encryption, it is one of the safest technologies in the world. Considering the importance of the security and accuracy of taxpayer information for the tax administration on the one hand and the capabilities of blockchain technology on the other hand, the present study examined the possibility of using this technology in the country's Tax Affairs Organization. In this research, since the possibility of using blockchain technology in the design of the value added tax information exchange system in the country's tax affairs organization should be investigated first, a model was presented by examining the existing theoretical and empirical foundations based on the technology acceptance model in the organization (TOE). Opinions on the factors affecting the adoption of technology in the organization were investigated. Then, using the qualitative method (content analysis), a semi-structured interview was conducted with 27 participants and the factors affecting the adoption of blockchain in the organization were identified. Lastly, the final model was presented by analyzing 15 questionnaires using the fuzzy Delphi method. The results of the present research showed that the perceptions of the employees of the Tax Affairs Organization regarding the three components of technology, organization and environment confirm the possibility of using this technology which was considered acceptable by them.

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.

 The current research has been designed to design the overflow model of probability of financial helplessness in Iran's banking system with the approach of multivariate GARCH models.The statistical population of the includes the banks admitted to the Tehran Stock Exchange, which have been analyzed in the period of 2015 to 2019. To calculate them, time series data of banks' stock returns, equity value, book value of liabilities and daily value of assets have been used. The current research has investigated the probability of financial helplessness spillover to other banks by applying the KMV method and the concept of distance to default and by using the VAR model and the multivariate GARCH method (DCC-GARCH).The results of the research have shown that there is a significant relationship between the financial helplessness risk of banks with each other; Mellat Bank is exposed to the highest risk of helplessness contagion and Parsian Bank shows the least effectiveness. Based on the results of the model, the increase in operational risks of banks, including credit risk and market risk, has a significant effect on increasing the risk of financial helplessness, and this risk can spread to other banks in the banks' communication network and then to the entire economy.

Background & New and innovative ideas are breathed into the organization's body like a spirit and saved from non-existence and destruction. The emergence of knowledge innovation not only enables organizations to gain a competitive advantage over competitors but also provides a useful tool for improving organizational performance. Knowledge is a major source of organizational innovation and productivity, and it is extremely important. This is why knowledge management is often known as the main source and reference of innovation and is considered one of the basic requirements of the innovation process in the organization. New knowledge allows the organization to improve its performance, develop its capabilities, and use the available resources more appropriately. Theoretically, knowledge management is a process during which the organization produces wealth from its knowledge or intellectual capital. Knowledge management means the creative, effective, and efficient use of all the knowledge and information available to the organization for the benefit of the customer and thus improving the organization. Knowledge management is the use of individual and collective experience and knowledge through the process of knowledge production, knowledge sharing, and its application with the help of technology itoachieve the organization's goals. Knowledge management includes theories, processes, technologies, and models that support the support, development, and exploitation of knowledge assets. In fact, intellectual capital includes all employees, organizational knowledge, and abilities to create added value and causes continuous competitive benefits. Intellectual capital is an intangible asset that is considered a competitive advantage for organizations and can increase the company's market share through knowledge and information. Intellectual capital is defined as intellectual action and something beyond the concept of pure thinking. The economy of the past depended on the use of natural resources, equipment, and capital to create value, while today's economy depends on the use of knowledge and information. On the other hand, organizational innovation is a management system that emphasizes the organization's mission looks for exceptional and new opportunities, and determines the success criteria. Innovation success requires hard, focused, and purposeful work. Innovation is a fundamental factor in creating competition at the global level, which leads to organizational growth, includes future success, and is like an engine that allows organizations to enjoy continuous efficiency in the global economy. Different definitions of innovation have been given by different authors. These studies are placed in the three fields of innovation outputs, inputs, and far end. The studies that have been carried out in these companies so far have been focused on identifying the implementation factors effective factors or key success factors in the knowledge management system, while the current research deals with this issue and answers these questions. - What effect does knowledge management have on the organizational performance and innovation of software development companies? What effect does intellectual capital have on the organizational performance of software development companies? In this research, by examining previous research and existing scientific documents and using the opinions of subject matter experts, the researcher also answers the question of what mediating variables affect these relationships. A suitable model will be presented to show the relationships of these variables.
The present study is a meta-combination type that was approved by the code of ethics IR.IAU.SARI.REC.1403.202 in the Code of Ethics Commission of the Islamic Azad University of Sari Branch. Meta-synthesis is a type of qualitative study that examines the information and findings of other qualitative studies with a related and similar topic. To achieve the goal of the research, the ultracomposition method was used according to Sandlovski and Barso (2007). By applying the qualitative meta-combination and systematic review approach, he analyzed the results and findings of previous researchers, and by performing the seven steps of the Sandlovski and Barroso method, the conceptual model of the factors affecting the organization's performance and organization's innovation has been categorized.
Based on three-stage coding, these factors were analyzed and categorized into 6 main categories and 49 core codes. The Kappa method was used to measure the reliability and quality control of the present study. The calculated Kappa coefficient is equal to 0.76, which is at the level of a valid agreement. The results showed that the factors affecting the organization's performance and innovation are: individual factors, organizational factors, technology and smart technology factors, knowledge, dynamism and agility, education, and learning.
To answer the research question, the meta-combination method - which is a suitable method to obtain a comprehensive combination of a subject based on the findings of internal and external articles - has been used. After performing the meta-synthesis steps, the conceptual model of the factors affecting the organization's performance and innovation about the role of knowledge management and intellectual capital, consisting of 6 factors and 49 criteria, was obtained: Individual factors: formal and informal relationships, self-learning and personal development, trust, perception, individual feelings and attitudes, communication skills, communication skills, motivation, spirit of cooperation and interaction. Technology and smart technology factors: social software (smart site, messenger, dash management software, information and communication navigation (Internet, software, and hardware according to the type of company), technological infrastructure, web Semantics (virtual discussion, types of sharing in social networks), knowledge management systems, ability to use new technology in massive data analysis, use of artificial intelligence, use of data mining. Knowledge: types of knowledge (specialized knowledge, practical, qualitative, general, and management of all types of knowledge and...)- Knowledge management processes (knowledge planning, knowledge acquisition, knowledge organization, knowledge retrieval, knowledge application, knowledge maintenance, knowledge evaluation, knowledge dimensions (tracking, representation), flow, map, audit, ontology, classification, clustering, etc.). While complying with regulations, and using smart knowledge in decision-making (dynamics).Education and learning: creativity and innovation, oral and experiential education and learning, distance and virtual education, exchange of ideas and educational storytelling, cooperative learning.

The purpose of this article was to provide a dynamic and dynamic model to explain how to transfer the pervasive risk of cryptocurrencies in the world markets. In this regard, the statistical information of the cryptocurrency market index and the data of the Nasdaq, New York, Toronto, London, Frankfort, Madrid, Shanghai, Hong Kong, Tokyo, and Mumbai stock market indices were used. In this research, the data related to the cryptocurrency market and financial markets from July 2012 to July 2022 have been used. In the first part of this study, using the information of the period 2012-2022, based on the frequency of monthly data for the financial markets, the comprehensive risk criterion has been calculated using the method of value at risk, conditional interval and expected loss. In the second part, using multivariate conditional heteroscedastic variance autocorrelation method (MGARCH), the external effects related to pervasive risk related to cryptocurrency were estimated on financial markets. The obtained results indicate that there are spillover effects between financial markets and an increase in pervasive risk in each of the financial markets leads to an increase in pervasive risk in other financial markets. In the second part, using multivariate conditional heteroscedastic variance autocorrelation method (MGARCH), the external effects related to pervasive risk related to cryptocurrency were estimated on financial markets. The obtained results indicate that there are spillover effects between financial markets and an increase in pervasive risk in each of the financial markets leads to an increase in pervasive risk in other financial

Contagion in financial markets takes place both because of fundamental or non-fundamental reasons like herd behaviors that can increase market risk levels and even end in inefficient allocation of financial resources. Thus, understanding the contagion and its dynamics will be critical for the participants of financial market. Hence, using network-based epidemic modeling, the study examined the dynamics of contagion in the Iranian stock market from 2011 to 2019 and short-term and long-term scales. To this end, first the correlation network of 46 Iranian stock market groups was developed and then analyzed using short-term and long-term contagion dynamics simulations. The results showed that the extent and speed of contagion is much higher in the short-term than in long-term and in long-term a significant number of groups can be immune to the contagion. However, in long-term the rate of return to pre-contagion status is shorter than in short-term

Financial markets play an influential role in economic development and most developed countries have more dynamic and stronger financial markets. Due to this market’s crucial role in the process of economic development, several studies have evaluated the role of financial markets in economy and Its effects on performance. These studies have often confirmed the positive impact of financial markets on the economic growth of countries. One of the most important components of financial markets is the stock market which in most countries, along with the banking sector, plays an effective role in financial resources allocation. Several factors can affect stock market returns. One of these is the government effectiveness, the regulatory quality, and the rule of law. Therefore, the aim of the present study is to study the role of government effectiveness, Regulatory quality and rule of law on stock returns in Iran and selected developing countries. For this purpose, the data of the period 2021-2000 and the quantile regression have been used.Based on the model estimation results, at a significant level of 5%, government effectiveness, regulatory quality and rule of law have a positive and significant effect on stock market returns. Also, the effects of inflation, per capita income, exchange rate and growth of industrial production on stock market returns have been positive and significant. In contrast, the impact of crude oil prices and interest rates on stock market returns is negative and is not statistically significant at the 5% level.

Algorithms based on a Convolutional Neural Network (CNN), which is a branch of Deep Learning (DL), have seen significant progress in picture and video analyses in recent years. Success of these new models has led to widespread use of them in various fields, including text mining and time series data. DL is part of a broader family of machine learning methods that attempts to model high-level concepts using learning at multiple levels and layers and extract features of higher levels from the raw input. This survey investigated the abilities of different CNN architectures to predict stock prices. Upon running the model with various architectures and parameters for the stock price of Esfahan Steel Company, the results showed that a CNN with max-pooling layers (a combination of Batch size=64, filters=256, and ReLU Activation Function) and Mean Absolute Percentage Error (MAPE) of 1.79% and Normalized Root Mean Square Error (NRMSE) of 2.71% had a higher prediction accuracy than other CNN architectures and Recurrent Neural Network (RNN).

The main goal of investors in the stock market is to get the highest return at the desired time, therefore introducing the most suitable method for conducting transactions is of special importance for investors. Successful trading in financial markets should be done close to key reversal points. In recent years, various systems have been developed to identify these return points. Technical analysis tries to identify the time to enter and exit trades.In this article, we are trying to select the one with a higher success rate by using the technical rules according to the previous researches, and by using soft calculations, the decision parameters in the technical rules are improved using the firefly algorithm.The results of this model are compared with the results of using the standard parameters of the indicators and the results of the purchase and maintenance strategy. In order to validate the introduced trading system, we compared it with the results of the optimized intelligent system based on optics and genetic algorithm. The results of the research show that by optimizing the parameters of technical analysis indicators, the investment efficiency can be increased compared to the usual methods in the stock market and previous researches.

Banks are the most important institutions in terms of mobilization and allocation of financial resources and savings.The risk of banks along with their performance has always been considered. Many factors affect the bank risks which have been investigated in various studies. The concept of social capital has been considered in recent years, but little research has been conducted about the relationship between social capital and the bank risks. Managers' social capital can be defined as the number of social relationships between company managers and their counterparts in other companies and other groups. the main objective of this research was to examine the impact of managers' social capital on bank risks. The research hypotheses testing were conducted using the correlation statistical method of panel data by the data on 30 banks of the I. R. Iran. Conclusively, 260 data-years were extracted to test the hypotheses. The results of the research showed that social capital had a reversal and significant effect on a variety of bank risks, including credit risk, liquidity risk and operational risk.

The purpose of this article was to measure systemic risk and the impact of fundamental variables on it in the country's banking system. In this regard, information from the period of 2010-2019 was used. In the first part, systemic risk indicators were estimated, then the impact of the fundamental variables of the country's banking system and financial stability was evaluated. In order to estimate the model, the final expected deficit method (MES) and panel data were used. The systemic risk index in this research is obtained from the calculation of the degree of leverage (debt size), market size and expected final deficit (MES) in order to finally be able to model the factors affecting it.In this research, first of all, different types of systemic risk assessment models have been evaluated according to the forecast error, and then, with the better selected model, the relationship between systemic risk and the important ratios of the country's banking system has been evaluated. The results showed that there is a significant positive relationship between independent variables such as inflation rate, external debt, government debt, liquidity growth, non-current facility rate, debt ratio and the ratio of book value of equity to market value, and independent variables such as profitability There is a significant negative relationship between the total index of the stock exchange, the growth rate of GDP and return on assets with the systemic risk index among the country's banking system.