نشریه مهندسی مالی و مدیریت اوراق بهادار
پیاپی 49 (زمستان 1400)

In this study, we examined Dynamic Conditional Correlation (DCC) between important monetary and financial markets of Iran using multivariate DCC-FIAPARCH model and daily market returns during eleven years, (from 2007(1386) to 2018(1396)), and The existence of hidden characteristics in financial data, i.e. the ability to record long-term memory in data, power or conversion power (unconditional variance to conditional variance due to the addition of observation to the time series) and asymmetry of market reaction to good and bad news have been studied. The results show that OPEC oil market fluctuations haven't effect on the domestic markets of Iran, very high and importance dynamic conditional correlation between the coin market (gold) and exchange rate, the existence of leverage, power and long-term memory with strong ARCH/GARCH characteristics. We also found that market data have clustering characteristics and the assumption of t-student distribution is more appropriate than normal distribution for market return distributions.

Risk has always been associated with the lives of people and organizations, and all decision-making situations face a variety or variety of risks. In classifying the risks that a bank or credit institution faces during its lifetime, credit risk or risk of default in payment has a special place, because it is related to the first role of the bank in the economy, namely collecting deposits and lending. . Risk management is important in monetary institutions because the resources used for allocation are in fact the debt of the monetary institution to shareholders, people and banks, which in the absence of turnover, both the creditworthiness and the ability to pay the debt of the monetary institution to Undermines the title of lender. The method used in this research is field and correlation. The statistical population of this research includes people working in Sarmayeh Bank and the statistical sample used in this research is 302 people. Therefore, in this research, a risk management model and cost reduction are presented. It was paid in Capital.

The present study tries to investigate the impact of the exchange rate fluctuations on the car stock index in Tehran Stock Exchange using the monthly data of the period of 1387:10-1398:12 and using the nonlinear Markov switching approach. For this purpose, among the various modes of Markov switching model, MSIAH(3) -VAR(2) has been selected. The empirical findings of the study show that only in a regime with high fluctuations, the exchange rate is the causal relationship of the car stock index and the increase in the exchange rate has increased the car stock index while the car stock index has no impact on the exchange rate. In addition, the results indicate that the sustainability of the car stock index in the regime with the very low fluctuations (first regime) was more than that of the regime with the low fluctuations (second regime) and that of the regime with the high fluctuations (third regime).

Banks and credit institutions, as one of the main pillars of financial markets, have special vulnerabilities in this field and make them fragile in the face of crises. Depending on their situation, banks offer different interest rates on interest on deposits and facilities, which leads to interest rate divergence. This study investigates the mediating effect of banking system fragility on the relationship between the sensitivity of controlling shareholders and interest rate divergence in companies listed on the Tehran Stock Exchange. In this study, 31 banks and financial institutions listed on the stock exchange were reviewed annually during the years 2011 to 2020. The results of testing the hypotheses using multivariate regression method show that 1- There is a negative and significant relationship between the sensitivity of controlling stakeholders and interest rate divergence. 2- There is a negative and significant relationship between the sensitivity of controlling shareholders and the fragility of the banking system. 3. There is a negative and significant relationship between the fragility of the banking system and interest rate divergence. 4. The fragility of the banking system explains the relationship between the sensitivity of controlling shareholders and interest rate divergence.

In recent years, different studies, using heuristic approaches for portfolio optimization, have been done to maximize the return and minimize the risk. In this study, we employed nine useful indicators, including SMA, EMA, ROC, OBV, RSI, MACD, TSI, HMA, Fibonacci Retracement, and genetic algorithm (GA), to construct an expert system that optimizes the portfolio automatically. In this system, Buy, Sell or Hold signals are produced based on the weighted combination of mentioned indicators and under the specified thresholds for each one. After that, signals (Buy/Sell/Hold) feed to the GA to optimize portfolio return/risk by changing indicator weights. This algorithm repeats continuously to optimize indicators weights for system and qualified stocks are selected according to optimized weights. Outputs of this expert system have been compared with Tehran Stock Exchange Index (Value-weighted and Equal-weighted Indices) from March 2013 until June 2021.The empirical results show that this expert system outperforms the Buy-and-Hold strategy (TSE indices) in terms of risk and return.

Micro-Businesses have resources constraints to start and develop a business and need access to resources beyond the business boundaries through relationships with Macro-Corporate. Macro-Corporate face a lack of flexibility despite access to financial resources. The solution to this problem is to use the capacity of development intermediary organizations. Accordingly, the present study has presented a model for accelerating the cooperation of Macro-Corporate with Micro-Businesses. The research method is based on the strategy of data foundation theory method with coding method and paradigm model of strauss and corbin. The statistical population of the research is business development experts and the sample of targeted research by snowball method includes 12managers, experts, professors aware of business development in the period of2021. The data collection tool was a structured interview and the focus group continued with the subject matter experts until the theoretical level was reached. The results showed that the basic category of cooperation in the pre-acceleration phase, the agile environment of cooperation in the acceleration phase and the gradual partnership of cooperation in the post-acceleration phase on the cooperation of Macro-Corporate with businesses. Wisdom has a significant effect.

Making a decision on providing financial facilities involves the analysis of information sources. Using credit indices, a suitable model for validating the bank's customers can be presented. Many of these models are classical and do not fully and reliably validate. Due to the increasing uncertainty of current markets, customer lending requires the design of a new model that can address environmental uncertainty in addition to validating indicators. Consider reducing credit risk. The use of fuzzy approaches provides such a possibility for creditors. The present study also aims to design a new system for validating bank customers based on hierarchical fuzzy rules. Validation indices were considered based on research literature and fuzzy rules were collected and analyzed according to experts' opinions. Finally, the final validation model of customers based on hierarchical fuzzy rules of design and as a new model for assessing the status of customer validation in one of the private banks was analyzed. The results of this research showed that, in the case of customer credit analysis, the minimum requirements offered in the proposed model, the customer will have a moderate credit standing, and the credit risk arising from non-repayment of facilities will be reduced as much as possible.

The purpose of this research is to investigate two controversial cases about the momentum in the Tehran Stock Exchange. These two cases include risk theory and under-reaction theory. This research will be followed in two parts. The first part tries to explain the momentum with respect to risk. The second part describes the excess return in momentum portfolios with the under-reaction hypothesis. The research hypotheses have been examined using the data of 58 non-financial companies from the Tehran Stock Exchange between 1389 and 1398. The results show that the risk-adjusted momentum profit is statistically significant. In addition, the results show that the five-factor risk model is not able to explain the momentum effect. However, the momentum effect can be explained by using the under-reaction hypothesis. The under-reaction is asymmetric for first six month after the earning announcement date. In general, the findings also rejects the efficient market hypothesis in favor of the under-reaction theory.

Fluctuations in the financial markets are accompanied by signals and noise. In this paper, in addition to Singular Spectrum Analysis (SSA), a Genetic Algorithm (GA) is used to find the optimal window length and cut-off point, the objective of which is to find the minimum value for the correlation function between signal and noise components. Therefore, first, ten-year data of the overall index of Tehran Stock Exchange during 2009 to 2018 were implemented in three using the SSA method. Then it was solved in the form of an optimization problem by a genetic algorithm. The results of the first hypothesis showed that signal and noise resolution is possible in the SSA method. Also, according to the results of the research, Singular spectrum analysis based on genetic algorithm with an absolute value of less than the average value showed an improvement in prediction accuracy. Finally, considering the lowest weight correlation between time series components for signal and noise separation (finding the cut-off point) and then obtaining the optimal window length in the SSA based on GA, indicates the fact that the amount of parameters can be changed. Improve the performance of the SSA method to be useful.

Identifying the factors affecting stock returns has become very important in recent years. Therefore, the purpose of this study is to investigate the effect of two behavioral factors of peak and end based on prospect theory on the explanatory power of Fama and French three-factor and five-factor models. The statistical population of the present study is the companies listed on the Tehran Stock Exchange during the years 2006 to 2020. In this study, multivariate regression method has been used to analyze the data. The results show that the five-factor model of Fama and French has more explanatory power than the three-factor model, in other words, adding two factors of profitability and investment to the three-factor model increases its explanatory power. Findings also indicate that peak and end behavioral factors have very little effect on increasing the three-factor and five-factor models of Fama and French. In other words, the addition of peak and end factors to the two three-factor models and the five-factor Fama and French models does not increase the explanatory power of these models. Although the results show that the peak power is slightly higher than the end, they have little effect on the models as a whole.

merge Bank is one of the ways to reform the structure of the banking network in the yearsThe latter has come to the attention of Iranian banking policymakers. Merger of banks in Iran with the aim of improving the health and Risk management of banks was done in 2017 one of the most important Risks، In the absence of proper management can cause a crisis in the bank. in this article the effect of bank mergers on credit risk in the years 1996-2018 has been studied. The virtual variable is used to measure the merger of banks.In this article, to create a bank merger variable, a virtual variable is defined, according to which, if the banks is merged, it is considered number one and otherwise it is considered a zero number. To measure credit risk, two methods of univariate and definition of a combined variable have been used. To estimate the effect of bank mergers on credit risk of the model ARDL Used. The results of the model indicate a positive relationship between return on assets, the ratio of income-generating assets to credit risk and a negative relationship between economic growth and credit risk

Due to the importance of transmission in financial markets, in the present study, using network-based epidemic modeling, the Iranian stock market in the period from 2011 to 2020 has been analyzed in three scales: daily, seasonal and annual. For this purpose, the correlation network of 46 Iranian stock market groups has been constructed and by creating daily, seasonal and annual graphs and to identify the topological properties and structure of the Iranian stock market network, the minimum spanning tree has been calculated and transmission dynamics have been analyzed using simulations. The results show that in the daily period, the minimum cover tree has 13 groups on the main branch and in the seasonal period has 19 groups and in the annual period 28 groups are on the main branch of the minimum cover tree. Also, network-based epidemic modeling (with a thousand repetitions) showed that in the short term, the market spread is faster and the changes (for example, due to an information shock) spread to more groups. And almost all market groups are affected by the changes

The aim of current study was to provide a model for predicting stock price reaction delay based on grounded theory. In the present study,semi-structured interviews have been used as data collection tools and snowball or chain sampling methods and purposeful sampling has been used to select the sample which based on the principle of theoretical adequacy The research data were analyzed using open, axial and selective coding. Results In this study, based on 42 conducted interviews, a total of 607 interview codes, 101 sub-categories (concepts) and 11 main categories were extracted. Then the qualitative model of the research is designed and based on the analysis of data (interviews) the link between the categories in the form of causal conditions, contextual conditions, intervening conditions, strategies and consequences has been conducted. The results indicated that macro factors and market shareholders are effective in predicting the stock price reaction delay.On the other hand, according to these affecting factors, strategies to improve the stock price reaction delay prediction, including the establishment of corporate information and financial statements, corporate information, market performance criteria, management and corporate control which are aroused in the context of affecting factors and interferers, are presented.

Real estate investment are characterized by low liquidity and irreversible cost. Real Estate industry is one of the most important industry in occupation. Moreover, its production has the biggest weight in family portfolio.These are important characteristics of real estate markets. Real estate industry has cyclical trend. In recession, investor delay their investment. Traditional capital Budgeting models such as Net Present Value are base on fix assumption and condition. They ignore management flexibility. In this paper residential Projects are evaluated with real options (option to delay) by Black-Scholes and Binomial Lattice Model.These model values managerial flexibility. Experimental results show that project Evaluation with real options outperforms the traditional models such as NPV. This paper studies the optimal timing of investment in an irreversible project where the benefits from the project and the investment cost follow continuous- time stochastic processes. According to optimal investment timing proposed by MC Donlad and Siegel, time to start this Project is 9/5 years from now.

This research investigates stock market liquidity, ownership, and choices of capital structure using the Panel VAR model for 50 companies listed in the stock exchange over 2013-2019. According to the results of Panel VAR estimates, a one standard deviation shock to ownership structure caused the capital structure to increase in terms of companies’ debts, and this effect continued by increasing debts. Also, after some periods, the impact of the shock to corporate ownership structure on capital structure in terms of debts achieved its highest point and was followed by a decreasing trend..Analysis of variance showed that 0.03% of the impacts were caused by shocks in ownership structure, 0.5% was related to liquidity shocks, 0.09% was related to shocks in the corporate size, 0.005% was related to shocks in sale growth rate, 0.02% was related to shocks in fair value of assets, 0.08% was related to shocks in profitability, 0.51% was due to shocks in the consumer price index, and 3.93% was related to shocks in exchange rate volatility. Among the variables considered, exchange rate volatility, inflation, liquidity, and ownership structure, respectively, had the most impact on the capital structure for companies listed in the stock exchange over annual periods.

The main reason that people invest in the stock market is to earn profits that require having accurate market information and stock changes and predicting its future trend. Therefore, the investor needs the powerful and reliable tools needed to predict stock prices. In this regard, the present study investigates stock price forecasts based on MSE mean square error, mean absolute deviation MAE and root mean square error RMSE. Finally, the methods investigated in this study are compared and identify the top method to predict stock prices. For this purpose, the data of the top 50 stock exchange companies, which are quarterly presented by the stock exchange organization, were used during the period 2012-2018. In order to test the research hypotheses, linear regression method, Brownian method and ARIMA method were used. The research findings show that the Brownian model predicts stock prices more accurately than the ARIMA method. It was also observed that linear statistical ARIMA models are less efficient in the financial markets than the brownian methods.

Financial technology has drastically changed the financial services industry andinstitutions in areas such as banking and insurance. Given the increasing role of FinTech in the future of the financial services industry, important understand the affecting drivers on future of financial technology. The present study seeks to identify and prioritize the drivers affect the future of financial technology in the country. This study has positive philosophical basis due to the use quantitative methods and its orientation is practical. The theoretical population the research is financial technology experts in the country and the samples were selected using judgmental sampling method. Two tools of interview and questionnaire were used to collect data. To analyze the data, the drivers extracted from the literature review and interviews with experts were first screened using fuzzy Delphi and expert opinions. the 14 key drivers identified, 7 drivers were excluded using fuzzy Delphi analysis and remaining factors were analyzed using the best-worst fuzzy typetechnique. Given the weights gained, the drivers of international exchanges and transactions, models cooperation between fintech institutions and traditional financial institutions and paying attention the interests and views of stakeholders in developing laws and regulations have the greatest impact on the future of financial technology.

DIS are designed to protect depositors by guaranteeing the repayment of funds owed by depositors of banks and other member credit institutions in the event of bankruptcy. This paper uses the SYMBOL and the Monte Carlo simulation method to calculate the systemic risk of the Iranian banking network. For this purpose, first, the probability of default of banks, using their balance sheet information, is estimated independently, and then, with the entry of the interbank market, the probability of default of banks in the presence of the spread of effects between Banking is measured and by obtaining the distribution of banks' losses, in both cases, the amount of coverage of these losses by the Deposit Guarantee Fund is examined and the capital adequacy of the Deposit Guarantee Fund is evaluated. The sample includes 15 Iranian banks and the period of 1397. The results show that the target size of the Deposit Guarantee Fund, in cases without and despite interbank effects, covers 91.5% and 87.5% of the losses, respectively. Failure of one or more banks can lead to Banking crisis and the collapse of the entire banking system, and it is necessary for regulators to take measures to prevent possible banking crises.

In this research, a model for analyzing and predicting the noisy financial time series of stock prices using singular spectrum analysis and support vector regression along with particle swarm optimization is presented. Thus, the time series of closed prices of 140 shares of companies in different industries per minute per day for the period from 28 May to 11 June for the years 1392 to 1398 was examined separately from the Tehran Stock Exchange. Also, the performance of the proposed model was compared with the performance of four wavelet transform models with neural network, moving average regression process, polynomial regression and naïve model. Mean absolute error, mean absolute error percentage, and mean square root of error were used as the main performance criteria. The results show that the performance of the proposed model for analyzing and predicting noisy financial time series based on mean absolute error, mean absolute error percentage and mean square root of error is better than other models (including: wavelet transform, moving average self-regression, regression Polynomial is the naïve model).

The main objective of this paper is to optimize and manage the portfolio by using copula functions. Copula function has been using as a powerful and flexible tool for the determination of dependency structure. Research data include the Iran stock market index and the Turkey stock market index. The present study seeks to find the effect of long memory on the structure of dependence between returns and optimal portfolio structure. In the first step, we compare the dependence structure between the net returns and the filter generated from the ARFIMA-GARCH process returns to investigate the impact of long memory on them. In the second step, the influence of the dependence structure between net returns and filtered returns on portfolio optimization has been investigated. The results indicated that the model can be fitted to the return of time series and the best pattern is the frank pattern. The results also indicated the existence of long memory in the mean and variance of stock return on the Iran stock market and the existence of long memory in the variance of the Turkey stock market. All models allocate more percentage of capital to Iran stock market and lower percent to Turkey stock market.

One of the most important components of financial systems is derivative financial instruments (option to trade and option to sell). The first role of derivatives is to provide a cheap way to get higher returns and reduce risk. Therefore, the purpose of this study is to investigate the relationship between derivative financial instruments (option trading and subordinate selling options) and stock return synchronicity.

This research is applied in terms of purpose and descriptive in terms of nature and method and correlation studies in terms of relationships between variables. In the present study, a total of 112 companies listed on the Tehran Stock Exchange that have offered derivative financial instruments (option and transaction option) have been examined for a period of 1392 to 1397. In order to test the research hypotheses, the multivariate regression model method - data panel method has been used.

It is shown that the issuance of subordinated stock options and the issuance of stock options at stock return synchronicity

Multi-asset portfolio management and optimization have always been of interest to investors. Due to the inflation in Iran market, different performance of the asset classes in different market conditions and the ability to earn more profit along with less risk by diversifying the types of assets, it seems necessary to select a portfolio consisting of stocks, foreign currency and commodities. In this paper, assets of the above categories, including Emami coins, American dollar, and 11 sector indices, are considered in the portfolio composition. Due to the importance of the risk measure in multi-asset portfolio optimization, a model with conditional value at risk, the historical simulation approach has been extended and its efficiency has been compared with the mean-variance model. The models have been solved using the artificial bee colony and imperialist competitive algorithms. The daily asset prices in the period 2013 to 2020 have been used to evaluate the models in Iran market. Results show that the mean-conditional value at risk model performs better than the mean-variance in the training and testing periods. Furthermore, optimized portfolios with the artificial bee colony algorithm could outperform the imperialist competitive algorithm based on the Sharpe ratio, conditional Sharpe ratio, and return on risk.

The purpose of this study is to model the spread of risks in the financial network. In this study, using MATLAB software, the interdependencies of claims and liabilities of companies listed on the Tehran Stock Exchange as a financial network are modeled and the spread of default in it is simulated. This research is among the applied researches. The statistical population of the studied information is 407 companies listed on the Tehran Stock Exchange in the period of 1393-1397. The results of this study emphasize the role of "contagious links" and show that the organizations that have the greatest impact on network instability have more contact with network members or a large proportion of contagious links. In this study, we consider a directional graph with a sequence of degrees and an optional weight distribution. Asymptotic results show that there is good agreement with simulation for networks of realistic size. In this study, we consider a directional graph with a sequence of degrees and an optional weight distribution. Asymptotic results show that there is good agreement with simulation for networks of realistic size.