نشریه مهندسی مالی و مدیریت اوراق بهادار
پیاپی 47 (تابستان 1400)

Financing to implement macroeconomic policies in the public and private sectors is one of the most important factors in achieving economic goals in any country. On the one hand, easy access and on the other hand, amount of financing costs of these funds via the financial intermediaries whose role is to provide these resources, can lead to change in the dynamics of macroeconomic variables and Consider as an important factor in volatilities in business cycle. Given the importance of role of financing costs in economic modeling, this paper seeks to investigate the role of borrowing and financing costs in economic models by designing a dynamic stochastic general equilibrium model and Compare the impact of such costs on the behavior of macroeconomic variables. Comparison of the results of the impulse-response functions of the variables shows that the entering of financing costs into the model led to reduce of the impact of shocks on the variables. In fact, the existence of these costs can change household investment decisions and cause investment to be delayed until the financing costs of financial intermediates are at their lowest and the investment horizon changed.

The present study was conducted with the aim of localizing technical analysis indicators in the Tehran Stock Exchange in order to predict stock price movement trends in implementing high frequency trading by the momentum method. In this study, calibration of exponential moving average, Bollinger Bands, Relative Strength Index (RSI), degree of difference, stochastic indicator and cash flow index was performed using cellular automata algorithm. The statistical population of this research consists of data obtained from transactions performed in the Tehran Stock Exchange during a three-year period from 7/23/2019 to 7/22/2019. According to the obtained results, there is a need to change the periodicity of the indicators calculations, so that the indicators of exponential moving average, Bollinger Bands, relative strength index, degree of difference, the stochastic indicator, the cash flow index have been changed from 14 to 2 days, 20 to 2 days, 14 to 13 days, 12 to 2 days, 14 to 4 days, and 14 to 20 days, respectively. As a result of the changes made, the increases in predictive power of the above indicators were achieved up to 1.35%, 36.90%, 1.89%, 30.52%, 64.20%, 0.24%, respectively.

Any factor that reduces the positive effects of rising stock prices creates a kind of financial friction for stocks. The role of financial friction in justifying the slowdown in stock price convergence is significant because it interferes with financial transactions and stock pricing, and investors are unable to completely reduce firm-specific risk through diversification if there is financial friction. The above issue is one of the new issues in the capital market that due to the novelty of the areas of financial friction and the speed of stock price convergence, little research has been done nationally and internationally; Less attention has been paid to this dimension of research variables. Therefore, the purpose of this study is to investigate the effect of financial friction on the speed of stock price convergence. The data of this study consisting of 89 companies listed on the Tehran Stock Exchange during the years 2010 to 2019 were reviewed. To test the research hypotheses, the generalized least squares regression model has been used. The results of the study indicate that there is a negative and significant relationship between financial friction and the speed of stock price convergence.

Investigating customer relationship based on customer portfolio management (CPM) in the insurance industry is a phenomenon that is associated with growth and development in this area, customer satisfaction, and consequently, the success of the program in this area, and recognizing the components and using tools in this area are very important. This study was conducted to provide a tool to investigate the components affecting customer portfolio management (CPM). This was a descriptive study whose content validity was confirmed through a pilot study and a survey of respondents and ten experts in the field studied. The reliability of the study was confirmed by internal consistency and Cronbach's alpha. After this stage, the questionnaires were distributed among the study sample of 384 insurance customers in Tehran who were selected by simple random sampling. 6 main components in the questionnaires were obtained based on factor analysis. The results indicated that Cronbach's alpha was 0.79 for the whole questionnaire and 0.72, 0.79, 0.77, 0.71, 0.78, and 0.70, respectively, for each of the subscales of customer attitude, customer value, organizational factors, customer experience, customer loyalty, and mental image. The obtained Cronbach's alpha had a good value.

In the present study the accuracy of predicting cryptocurrencies return was compared through two approaches of Geometric Broanian Motion (GBM) and Wavelet Transforms (WT). In order to do that, 5 cryptocurrencies of BTC, ETH, XRP, BCH and EOS as representatives of risky assets were studied with daily frequency during the one year period of 2018 to 2019. Two measures of RMSE and MAE were employed to compare the accuracy of approaches in prediction of returns. In geometric Brownian modeling, the Brownian process-based stochastic differential model for asset prices leads to the fact that the logarithmic return of an asset has a normal distribution with time-dependent parameters. The results of logarithmic returns prediction by both of methods showed that WTs have less error than GBM in returns prediction of BTC, ETH, XRP and BCH cryptocurrencies and for each of accuracy measures, an specific approach has desirable performance for prediction of EOS returns. citing these results it can be concluded that WT in prediction of risky assts returns has less error than GBM method.

Financial stress, both in the financial markets and in society, harms economic growth and social welfare by spreading financial instability and disrupting the functioning of the financial system. meta-synthesis is one of the types of meta- Study methods that using Sandolowski and Barso's seven-step method. Kappa index method and Spss and Maxqda softwares were used to evaluate the reliability and quality control of the qualitative method. The kappa index value was calculated to be %1/95, which is in excellent agreement.The research findings include the conceptual research model and 111 factors affecting the financial stress of individual investors and 26 factors as the consequences of financial stress in the Tehran Stock Exchange. Determining the factors and designing the final research model can improve the understanding of the concept of financial stress and become a suitable tool to help investment trustees and capital market policymakers to build trust and attract potential investors. This study identifies a comprehensive list of factors affecting the financial stress of individual investors in the Tehran Stock Exchange and its consequences and presents the relevant conceptual model.To date, these factors have not been mentioned in Internal and external studies of the capital market and related fields.

Cost and asset retrenchment strategy is one of the most common financial turnaround strategies followed by the firms facing performance decline and financial difficulties. In cost retrenchment, the main goal is to improve liquidity by reducing the cash outflow rate. In asset retrenchment, the first goal is to increase productivity through redeployment of assets and the second goal is to finance through the divestment of low-yield assets. Although this strategy has been considered by many researchers as a necessary and effective strategy in financial turnaround of the firms, the results of several empirical studies have shown in some cases this strategy is the cause of further decline and a barrier to financial turnaround. In this paper, we intend to show how the cost and asset retrenchment strategy affects Schumpeterian and Ricardian rent creation mechanisms in the firm, and to find the conditions under which it improves or deteriorates the effectiveness of financial turnaround, using the system dynamics modeling method.

Finding the best way to optimize the portfolio is one of the concerns of activists in the investment management industry. In recent years, the introduction of economic and mathematical models in the prediction of Bitcoin has helped many investors to optimize portfolios. Therefore, in this study, we introduce models of GARCH family composition and recurrent and convolutional neural network to predict the daily yield of Bitcoin will be paid during the period of 1398-1392. In this study, the Bitcoin is examined using GARCH and EGARCH short-term memory models. Of the two variables, the price of crude oil and the Gold as factors that their shocks and fluctuations have a major impact on Bitcoin are used as control variables. In addition to using long-term memory models, considering the better performance of combined models (compared to individual models) In anticipation In this study, all models of the GARCH family (both short and long run) with the recurrent and convolutional neural network were combined and using the combined models, the efficiency of the Bitcoin for the next 10 days were predicted step by step and its accuracy Based on the evaluation criteria.

The study aimed to review the effect of corporate governance on financial performance considering the mediating role of corporate sustainability in Iranian private banks. The current study was applied in terms of purpose. Also, it was a field study in terms of the method of collecting and receiving information. Also, this study was descriptive and correlational in terms of research method. The population was consisted of Iranian private banks' staff. The number of 384 people were selected and examined as a sample using Cochran's formula. Ultimately, we analyzed the required data for testing the research hypotheses using structural equation modeling and PLS3 software. The results indicated that corporate governance influences the financial performance and the corporate sustainability. Also, the corporate sustainability influences the financial performance. The results also indicated that corporate governance influences the financial performance according to the mediating role of the corporate sustainability. The mediating role of environmental efficiency and health and safety management system was confirmed in the relationship between corporate governance and financial performance. But, community development and environmental management, commitment and social development capacity do not mediate this relationship.

The Impact of Observing the Principles and Rules of Correct Communication on Project Management (Case Study: Karaj City) In recent years, many efforts have been made to guide investors in proper investment and numerous models have been offered. The concepts of portfolio optimization and diversification have become tools for developing and understanding financial markets and financial decisions. In most optimization methods, the optimal answer and its accuracy are highly dependent on inputs to the extent that a more appropriate and accurate selection of input variables will be very important in stock portfolio optimization. In this research, through a regular and logical process based on the judgment method in a survey of 14 experts in the field of capital market investment and a quantitative and multivariate model of fuzzy network analysis, to assess the level of importance, ranking and refining the effective factors. Portfolio optimization was undertaken. Based on the analysis, the variables of profit volatility, return on capital, company value, market risk, stock profitability, financial structure, liquidity and survival index can be introduced as the most important factors affecting the optimization of the stock portfolio.

Businesses are constantly changing, and changes are so vast and unpredictable that every day we see or integrate businesses based on new models, the banking system is no exception. To prevent failure, adapt itself to existing models to consolidate its presence. In this research, the researcher attempts to design a model based on the principles of collaborative marketing when merging between banks. That is, at the quantitative stage of the initial conceptual model based on mature causal variables b 23 variables including cobanking's topic and six dimensions and 16 design components and then in the second (quantitative) questionnaire consisting of 67 design questions that its validity through academic experts and its reliability using structural equation software with mean 75 % Confirmed that the relationships between variables were analyzed using Structural Equation Method in two parts: Model fit analysis (Examination of measurement models - Structural model investigation and General model) and Testing hypotheses. The template (one concept - six dimensions and ten components) was developed.

The present research aims to make a model for product design and development process (PDDP) based on the smart economy paradigm in the banking industry.The qualitative section approach is based on Grounded Theory.The data-collection instrument consists of semi-structured interview with twelve research and innovation experts and managers in banking industry applying purposeful as well as snowball sampling.Findings show that the final model consists of 6 main dimensions36 components and 236 indicators.The central category is the stages of the product design and development process in the field of banking which in six dimensions determine the goals and strategies of the PDDP ideas management,PDDP analysis, product design and processes, business development acquisition, performance management and upgrade during the product life cycle and according to causal conditions, intervening conditions, contextual conditions, strategies and results were. Among the research results is the application of smart economy strategies in the PDDP in the country's banking industry.In quantitative section,the data was collected by a researcher-made questionnaire consisting of 94 items and the descriptive-survey method was used for analysis and explanation of proposed model. The quantitative results indicate the verification of the model relations with an appropriate impact factor as well as its consistency with qualitative-section results

The ratio of investment can have a significant impact on various criteria such as the ratio of cash flows and dividends. The long-term policies of managers are such that the ratio of cash flows of companies increases, and in the same way dividend profits will increase, and with increasing dividends, the amount of investment in research and development spending will also increase. The purpose of this study is to investigate the relationship between financial development, financial constraints and investment. This research is library and analytical-Ali research and is based on the analysis of panel data (data panel). In this research financial information of 104 companies accepted in Tehran Stock Exchange during the period 2012 to 2017 (624 companies - year) was investigated. The results of the research show that according to the analyzes carried out in relation to the confirmation of the first hypothesis of the research, it can be concluded that financial development has a significant and direct effect on the investment rate of firms, and finally, according to the analysis of the relationship By confirming the second hypothesis of the research, we conclude that financial constraints have a significant and inverse effect on the investment rate of firms..

One of the human resources, which plays an important role in converting the company's resources into increase income and creating wealth for shareholders, are the managers of commercial companies. Information related to the ability of company managers, such as their ability to use investment opportunities, resource provision, allocation, resource optimization, and their knowledge and experience, is one of the important and valuable dimensions of intangible assets of commercial companies. The results of recent research show that the ability to manage is one of the determinants of companies' performance. This study investigates the two-side relationship between management ability and economic value added using the method of simultaneous equations. For this purpose, the required information for the period of 2014-2018 was collected for 105 companies and the research hypothesis was tested using the method of simultaneous equations (granger-causality). The results showed significant two-side relationship between management ability and EVA, also; two side granger-causality between management ability and economic value added: p-value (0.000), with 95 percent statistical significance.

The main purpose of This research ptovide asuitable model for financial risk management in the automotive industry using fuzzynetwork analysis process. In this study 13 variables from the identified variables were approvedby experts and specialists. The statistical population of the present study is the managers and specialists of irankhodro and saipa companies. The tools used in this study to collect data and in formation through a questionnaire and the opinions of experts and experts in the automotive industry have been used. The community of experts consists of 25 experts and the technique of fuzzy network analysis process has been used to analyze the data. Bby analyzing the data in the fuzzy network analysis process technique, it was found thet among the 13 identified variables affecting financial risk in the automotive in dusstry,liquidity risk is the criterion . which is the most effective among financial risks and is in the first place and has the highest priority. And bel0w the criterion of forecasting market demand and structural changes in the econpmy and the complex competition of domestic markets, respective ,have the highest priority.

Asset quality of banks is a considerable indicator of bankruptcy signals, affecting efficiency and continuity of bank activities. Increasing the volume of non-current receivables increases the risk and adversely affects the efficiency of the banking network. planning to collect such receivables timely as well as prevention of mis management in the lending sector result in increased income, profitability, increased resources of banks, decreased credit risks, and low likelihood of bankruptcy. The aim of present research is to provide a mathematical model of fuzzy multi-objective optimization to optimize exchange and participatory contracts of banks. In present research, optimization is implemented by increasing current loans and minimizing credit and bankruptcy risks. After programming, research data is analyzed by using GAMS software. Results of the optimization of each category of loans include a 2% increase in current loans returns, a 2.1% decrease in risks the passed due dates, a 3.3% reduction of outstanding loans, and a 10.95% decrease in doubtful debts.

Different types of risks threaten financial and credit institutions. Therefore, managers of organizations must identify and manage the existing risks. The risk that directly affects the profitability of financial and credit institutions is called financial risk. Financial risks include: balance sheet structure risks, income and profitability structure, capital adequacy, credit risk, liquidity risk, interest rate risk, market risk and exchange rate risk. Banking industry is one of the most sifnificant and critical section of economics which faces many sorts of risk. Financial structur risk is the most threating one that in case of non centrolling will lead to bankruptcy . the purpose of study is modeling risk in compaliance with finance structure in money market based of probabilistic decition theory. The populational of the research is experts and bank fanciancial statements after reviewing the literature all the aspects of risks and also financial ratios are being identifined. After gathering data ANP techinques are being app;ied . resuls show that the srquence of risks are, market , credit, liquidity, capital. The significance of risks are as follow: credit, capital, liquidity, income disturbution, market and systematic.

Discussions about forecasting Stock returns in developed countries has long been regarded as one of the most interesting scientific topics.However,due to many problems,the correct prediction of stock returns has remained a matter of strengthTtherefore,the researcher seeks to provide an accurate,practical and effective model for predicting stock returns for investors.The statistics sampel of research is consist of 138 active companies in Tehran Stock Exchange from 2008 to 2017 wich are selected by the systematic removal method . ANFIS,MGGP, regresion and neural network and different statistics tests are used for data analysis. For impelement of these techniques MATLAB and GenXproTools software are used respectively.The result of the study showed that in oreder to predict stock returns.the use of a meta –heuristic Hybrid models is more accurate and faster than other meta huristic models.Because ,first the most optimal input variables are selected through the ANFIS technique and then predicted using theMGG meta heuristic model.Therefore,due to the correct choice of input variables,predicting stock returns is both more accurate and faster.In addition ,the mathematical model is used to predict.

Portfolio optimization and selection is one of the most important issues in the financial world, so investors are trying to make decisions that are most in line with the real world. But the uncertainty in data and parameters, and the contradiction in the investor's goals, adds to the complexity of the stock portfolio optimization problem, and the other hand because of the efficient market, it is necessary to use multi-period models that, unlike single-period models, allow the investor to review their wealth at the beginning of each period. This paper introduces a new approach to optimizing a multi-period portfolio optimization based on fuzzy general theory and using scenario tree to deal with uncertainties. In addition to considering all of the above constraints, It has made it possible for the investor to be able to apply his manner by changing the parameter to optimistic-pessimistic, and there is no need to model in credibility, necessity or possibility mode. Then the proposed model is solved by the Epsilon constraint method. Finally, using the data of 17 companies from different industries operating in the Tehran Stock Exchange Market in 1398, we examine the validity of the model and its efficiency.

This article, had been analyzed the persistence in cryptocurrency market. At the beginning of the process, the autocorrelation of the time series data of four cryptocurrencies Bitcoin, Light Coin, Ripple and Ethereum during the period 2017 to 2020 has been studied. The data of this research are obtained daily from the investing database showed significant autocorrelation among cryptocurrencies. Long-term memory methods such as R/S analysis and fraction integration are used for analysis of market persistence. The results of this study showed that this market is persistence. That means there is a positive correlation between past and future values, and its level and value change over time. These results provide evidence for market inefficiency. Using the pair trading algorithm and redefining it, profit has been made and the results show a return of 1463% in 2 years from 2018 to 2020. The use of trading algorithms based on market memory and cointegration has the potential to generate profits, and the development of models and algorithms can help investors to generate returns and on the other hand lead to more efficient market in the long term.

The effect economic variables at investment markets is the important subject in financial theory. Tehran stock exchange to have special position in country financial system and efficiency development investment market is dependent being active this constitution in country. Two important function Tehran exchange market are gathering small savings and available liquidity in society and guide them to production process in country. In this way presentation optimization portfolio model from market index prediction model and exchange return rate is impact. One of the tools with high accuracy and applicable for predicting was neural network why so accuracy isnot decrease with increasing thesis data and its accuracy was very higher than regeression, linear and non linear for prediction. After some tests from artificial neural network and adaptive neuro fuzzy inference system and support vector regression with matlab software has been done. We design a model with high accurancy for predicting rate of liquidity index and total return index and then we design Ideal optimization portfolio.

Risk spillover between financial assets indicates the process of information transfer between markets. Financial markets are related, information created in one market can affect other markets. Risk modeling in different markets and the relationship between these markets are important for forecasting. The purpose of this paper was to investigate the spillover and dynamics of risk between commodity markets, financial markets and digital currencies using the multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) method in the period 2020-2014 with the frequency of daily data. The results of this study indicate the spillover of fluctuations between financial markets and the ratio of dollar to euro and bitcoin had a negative and significant relationship with each other, but other financial assets had a positive and significant relationship in terms of returns and fluctuations. Additionally the stability, the trend of changes in oil and gold prices leads to an important relationship between returns and strengthens the transfer of risk between the foreign exchange market, virtual money, oil and gold. Finally, the research model shows the intensity of contagion between financial markets in the context of small and large shocks, which indicates the existence of asymmetric effects on risk overflow between important financial markets.

The data used in this study, the daily cash and future prices with 71 contracts from 25 November 2008 to 23 Septamber 2018,and and the price and future of saffron, with 29 contracts from 22 May 2018 to the end of 19 March 2020, is in the statistical community of iranian stock exchange. In this study, using the regression coefficient model to investigate the changes in the spot price and the future price, from time to maturity of the futurt contract and to evaluate the point of convergence of price, from the approach of accumulation and paired sample mean comparisons are used, also from the Granger causality test, the existence or nonexistence of causal relation between spot and future price was investigated. The results showed that there is a convergence of price in the coin, but in the case of saffron contracts, prices don 't go to convergence. also in the point convergence , by using of the integrated approach and paired average tests show that convergence in both cargo has been studied. Also, the results of causality tests, The assumption of the cause - effect relationship in saffron has not been confirmed.

Choosing the optimal stock portfolio is one of the main goals of capital management. There are several criteria for choosing the optimal portfolio. In this paper, using data of 10 stocks which randomly selected from the Tehran Stock Exchange including Vanovin, Vakharazm, Seghrab, Shepna, Vapetro, Dana, Khasapa, Shekarbon, Shadous and Khahen, first the returns of these stocks are calculated and their portfolio risk is calculated using the models of absolute deviation risk and risk value, and these two criteria are compared by the classical solution method. The portfolio optimization output with each of these risks represents a different weight per share. In the optimization with the risk criterion of absolute deviation, the Dana has the highest weight and in the optimization with the value at risk criterion, the stocks of Segharb, Shepna and Shekarbon have the most weight. In the following, the deviation-absolute risk model and value at risk model of metaheuristic method are compared. The results show that the NSGA2 model of metaheuristic method compared to the classical method in solving portfolio optimization problem showed more risk in both MAD and CVaR criteria and therefore it is a better method to solve such portfolio optimization problems.

The present research examines the Financial Contagion or Volatility Spillover by financial assets such as exchange rates, gold and global variables on the stock market index. The correlation and Contagion between variables of global prices of gold, oil, and the dollar exchange rate on the index of 8 selected Tehran stock exchange industries over a period of 10 years (2008-2018) was examined. Method of the research is applied in terms of purpose and analytical-descriptive in terms of the nature. To test the research hypotheses using econometric approach based on Copula models, programming was performed in MATLAB software. The results of the show that the effects of volatility spillover of these variables on the index of selected industries are significant but different. The different models of the Copula method show that the Clayton and Gumbel models are most suitable for transmitting spillover effects in the upper and lower distribution of the range. The t-student model is in the next rank. In other words, the overflow effects of macro variables mostly affect one of the high (positive return) and low (negative return) domains, which indicates the existence of asymmetric effects on the return behavior of the selected industries of the stock exchange.

Portfolio selection problem which is one of the most important issues in finance, using a model that considers conditions of the real world is important. In financial markets, severe and frequent fluctuations cause frequent changes in the portfolio selection models outputs, which increases the number of times to change the weight of portfolio's assets, and so that incurs high management and transaction costs. In the literature of portfolio selection models, one of the approaches to prevent this kind of high costs is robust optimization approach. In this study, in order to optimize the portfolio, genetic algorithm and shuffled frog-leaping algorithm are used to solve robust probablistic planning model presented by Amiri and Heidari (1399) in higher dimensions. To this end, 15 specific problems with different dimensions (number of companies and time periods) are designed and processed. The results of the implementation of two algorithms on the above 15 problems were compared using T-test, which shows no significant difference between two algorithms in portfolio selection problem, but the combined approach of TOPSIS and entropy weighting selects the genetic algorithm as superior algorithm.

Modeling and predicting stock returns has always been one of the challenges for researchers and investors. Hence, different methods and models have been proposed, most of which have been based on assumptions such as the distribution of returns. The kernel distribution and mixture of normal distributions were examined to predict stock return in the present study. To this end, kernel functions and mixtures of normal distributions and related parameters have been estimated using maximization of likelihood function and quartiles 99%, 95% and 90% were computed for each of distributions and for 30 superior enterprises listed in Tehran Security and Exchange (TSE) at first quarter in 2019 as predictor values of stock return. In order to determine precision of prediction methods, MSE and PRED error criteria were employed and the findings showed that mixture of normal distributions and kernel approximation might propose favorable predictions for 5-day stock returns in quartiles 90% of return distribution. Comparison of precision between two methods indicated that kernel approximation, as a non parametric method for prediction of returns, leads to higher precision than mixture of normal distributions.

We live in an age characterized by the very rapid rate of financial innovation. The study of the historical evolution of progress and economic development of developed and industrialized countries shows that one of the main factors in the emergence of rapid and massive growth has been the existence of financial reforms in these countries. There are different incentives for individuals and active enterprises in the financial system to perform financial innovation, which is one of the most important incentives, the introduction of tools and methods to reduce, eliminate or manage existing risks. One of the most important tools the current situation that can help banks in the optimal management of consumption and prevention of claims. Designing and applying credit risk assessment models in granting facilities. The purpose of this study is to provide a suitable model for financial innovation based on credit risk measurement of SMEs in commercial banks. In this regard, effective indicators on the credit risk of SMEs were identified by using the genetic algorithm method and logit, neural network and fuzzy expert system were evaluated. The results show that the using the hybrid model has more accurate results in the assessment the credit risk of SMEs.