نشریه مهندسی مالی و مدیریت اوراق بهادار
پیاپی 43 (تابستان 1399)

Financial and economic decisions are always at risk due to future uncertainties. Therefore, one of the ways to help investors is to provide investment risk forecasting patterns. The more predictions are closer to reality, the decisions made on the basis of such predictions will be correct. In this research, the goal of predicting the systematic risk of companies admitted to Tehran Stock Exchange using artificial neural network software and three night-worm algorithms, decision tree algorithm and backup vector machine regression algorithm. For this research, a sample of 92 companies from listed companies in Tehran Stock Exchange during the period 2013 to 2018 has been used. The results obtained from the research hypothesis test showed that the predictive power of systematic risk in the night cream algorithm is more than the decision tree algorithm and the support vector machine regression algorithm, as well as the predictive power of the decision tree algorithm in relation to the backup vector machine regression algorithm It is higher for systematic risk prediction

In this study, we investigated of contagion unanticipated shocks of oil price, exchange rate and gold price on the stock market in Iran using DFGM model, aiming to explain the dependence of financial markets in times of fluctuation financial markets in times of volatility. The data includes weekly Tehran Price Index, gold price, oil price and exchange rate from November 2008 to August 2019. The results show that about 99% of the exchange rate fluctuations and the total stock price index in the pre-crisis period are explained by the specific factor. But after the currency shock in January 2018, it was observed that at the end of the year 2018, 88 and 63 percent of the exchange rate fluctuations and the total price index of Tehran Stock, were explained by contagion factor, respectively. The results also show that the currency crisis (currency fluctuations), it has contagioned to the stock market. In addition, evidence of the impact of oil and gold price shocks on the Tehran Stock Total Price Index and the exchange rate was obtained.

Choosing the right model to correctly forecasting volatility in these markets is very important. In this research, with a new approach in order to test the asymmetric volatility effects, the asymmetric conditional variance section (asymmetric volatility) was added to Bollerslev GARCH (1.1) model (1986). Then, using the weekly index information of the Iran and UAE stock market from December 15, 2008 until April 10, 2017 were estimated separately. Based on the results, the existence of asymmetric effects of increasing and decreasing volatility of the previous period on the current volatility of stock returns was confirmed in the UAE stock market and these asymmetric effects were not accepted in Iran stock market. In order to evaluate and compare the forecasting accuracy of the asymmetric GARCH model in volatility and a the symmetric GARCH model in the UAE stock market, "Root Mean Square Error Index" and "Theil Inequality Index" were calculated for these two models. Both indicators confirm the relative superiority of the asymmetric GARCH model over the symmetric model in this market. Therefore, the asymmetric model is the appropriate model to forecast volatility in the UAE stock market.

This study is an attempt to introduce a suitable model to evaluate the efficiency of capital market in Iran. Optimal allocation of resources in micro and macro investments is essential to the capital market. The main task of the capital market is to streamline capital and allocate resources effectively. Investigating what factor or factors influence the market price of a share in market and whether there is a systematic pattern of pricing a share is In terms of market efficiency .In this regard, the daily data of the Tehran Stock Exchange Index and Total Price Index during 2008-2018 have been used. ARCH, GARCH and Kalman filters were used to perform the test. The results of the tests show that there is no weak form of market efficiency in the Tehran Stock Exchange. And this inefficiency has not changed significantly since the first period. In other words, it is possible to predict returns in the Tehran stock market.

Economic shocks like sanctions and its influences against capital market, manifested the vulnerability of this market and its requirement to find a resolution for reinforcing it against external shocks. In this research, a conceptual framework is provided. Using structural equations modeling framework, hypothesis test and goodness of fit of the proposed model are evaluated. The evaluation results show that the relation of resilience of the capital market with “financial institutions”, “financial instruments”, “regulatory, rules and regulation” and “markets and publishers” is significant, which positive effect of financial institution and negative effect of financial instruments on resilience is considerable. On the other hand, market resilience does not have a significant relation with the external environment of the market. The research also discusses the executive and policy implications of resilience, using numerical outputs. If the capital market of the Iran including the financial institutions, instruments, the regulatory entity, rules and regulation, and its markets and publisher hold the resiliency characteristics, it can be concluded that the whole capital market is strong enough against environmental shocks and can recover and adapt itself to longterm trends in the case of the occurrence of a crisis.

Investors often aim to reduce the risks associated with traditional assets such as investing in equities. The risk of stock price volatility is one of the concerns that always active investors are trying to manage it. Therefore, this research tries to investigate the effectiveness of the hedging stock price volatility methods and its diversification using the gold coin futures. In this study, using monthly data on stock index returns and gold coin future returns between 2008 and 2018, we compare the result’s effectiveness of dynamic hedge ratios using multivariate generalized auto- regressive heteroskedasticity (BEKK-GARCH) and recursive ordinary least square (OLS) methods. The results indicate that the use of the gold coin future is suitable instrument to hedging stock portfolio and the multivariate GARCH method is more effective than the rolling regression method in 5 conditional effectiveness indices, including percentage reduction in variance, percentage reduction in semi-variance, percentage reduction in hedged losses, percentage increase in portfolio excess returns and percentage increase in positive returns.

Financial literacy is the possession of skills and knowledge that allows an individual to make informed and effective decisions. In this regard, the purpose of the present study is to develop a model for financial literacy education in Iran using the Grounded Theory approach. The dominant paradigm of the present research is the interpretive and its required data were collected using semi-structured interviews and snowball sampling method. The research interviewees include accounting and financial experts and professors. The open coding method which is specific to the grounded theory approach was used to analyze the research data and conceptual, main and macro categories were extracted. Finally, financial literacy education model were presented including causal, structural, intervener factors as well as strategies for financial literacy education and the consequences of implementing the correct financial literacy education system. The research results shows the main topics that can be used for designing, developing and implementing financial literacy education program in Iran include general headlines, income and savings management, risk management and cost management.

Issues related to environment are one of the most important issues the communities are facing with in the recent decades. On the other hand, the resource scarcity and the necessity of development for Iran and other similar countries makes the issue of carbon dioxide as a solution of the environmental crisis important. In this study, the effects of the variable carbon dioxide, Investment, Financial rate, Density of insurance, and Depth of insurance use are examined on the carbon dioxide emission with the method of panel data. In this study, we modeled the carbon dioxide flow over the period from 2005 to 2014 for the Financial depth and Financial growth using a dynamic spatial regression model. versus fixed effect specification, a spatial Hausman test was fixed effect. Also, we modified Wald test was not fixed effect regression and Durbin-Watson test was the Residual autocorrelation detection The SAR results support the hypothesis of the impact of spatial effects. Depth of insurance in provinces has led to an increasing relationship with CO2 emission in each province, investment had no significant relationship with CO2 emission in each province as well as insurance density and financial relationship of decline with CO2 emission in each province.

Today, because of the increasing competition in the global market, organizations need to change their product assets or create new products, business tactics and upgrades. The success of an organization in developing new products depends on the development of capabilities and relationships that are unique to the organization and secure in the medium to long term. On the other hand, a new product is a combination of its benefits, functions and characteristics from the customer's perspective (customer capital) and also requires the knowledge, skills, abilities and experiences of the employees (human capital) and the effective process and system for managing the flow of materials, Information and communication (structural capital). The purpose of this paper is to investigate the relationship between business strategy and intellectual capital disclosure with the influence of Tehran corporate governance. The statistical population of the present study was selected companies listed in Tehran Stock Exchange for the period 1391-1396 and model estimation using panel data model, the companies surveyed. The model estimation results show that business strategy and corporate governance on the disclosure of intellectual capital in the selected research firms in the 95% confidence interval is statistically significant.

 The purpose of this research is Modeling of exchange rate fluctuations with systems dynamics approach. In this research, all the factors affecting the exchange rate have been identified and evaluated their systemic relationships. First, the trend and the exchange rate price were predicted using the system dynamics from 2004 to 2022 , and the results were compared with the trend and real exchange rate of the exchange rate. The results show that the exchange rate trend over the 19-year period has always been with a slight upward slope, but during the years 2012 -2013 and 2018-2017 there was a sharp rise in price and slope that is exactly the same as the real exchange rate trend. Also, comparing the exchange rate data to the market price and the expected exchange rate until 2018 shows that this model has been able to accurately assess the trend and predict possible failures. Key word: Exchange rate, system dynamics, forecast, trend.

THE TORQUE COMPARISON OF THE EXTREME DISTRIBUTION MODELS AND THE DIFFERENCE IN THE RATIO OF FAILURE PATTERNS OF DIFFERENT TIME MODELS OF TEHRAN STOCK EXCHANGEExtreme risk assessment and the use of more efficient risk estimation models in today's financial world are of secondary importance. In this paper, appropriate partial statistical model and the optimal time pattern for estimating a constant and variable periodic risk index in time were selected using the new method of L-Moment torque index for positive and negative extreme values of Tehran Stock Exchange index (maximum block approach) and Estimation of the conditional Extreme risk in different time patterns. The results of the torque study of Extreme distribution models in both positive and negative (minimum and maximum) series showed that the optimal matching model with logarithm of return efficiency of Tehran Stock Exchange are often the GEV model and sometimes the GL model and from among the different time patterns, the daily and weekly pattern at a 90% confidence level, have less estimated errors.Key words: Torque modeling, Extreme risk models, Error Ratio, Block maximum.

The purpose of this study is predict and design Credit risk model for debut crowdfunding .According, the complexity of the risk assessment the best neural network architecture with Customize hidden layer neurons selected Multilayer perceptron algorithm for simulation. The statistical population of this study is the financial information of credit / loan file of all customer (506 cases) one of the banks of the country for the year 1997-98. In order to show the significant relationship the extracted indices of the sample and the model output variables (non-default and default), the sample member tested by regression.Thus, thirteen indices entered to the model neural network input vector with three hidden layers in non-default and default groups. In the simulation results, the proposed model was able to optimize the weights of each of the inputs to the network with lower prediction error and 94.1% efficiency .also the average error absolute value obtained for training data (0.88), test data (0.94) and evaluation data (0.84) indicating high capability of the proposed model. According to the research Results, among the indices, income, 0.163 weight, Current Account weight 0.123 are more important, but “degree of education of education” 0.053 are less important in the non-defaulted group.

The purpose of this study is to investigate the role of noise risk factors and market depth in explaining future stock returns in listed companies in Tehran Stock Exchange. The locative domain, the companies listed in the Stock Exchange and the time period, is between 2013 and 2017. noisy risk factors and the depth of the market are independent variables and future stock return is dependent variable. The research is descriptive (correlation) and applied. Data collection was done by library method and by referring to financial statements, explanatory notes and stock exchange monthly of sample companies. Descriptive and inferential data analysis (pre-test variance analysis, F Limer, Hassman & Jark-Bra test and multivariate regression test) were performed using Eviews software. The results showed that the changes of noisy risk factor indicate itself as a risk premium in future returns and risky factors of market depth changes, including the average variation in the volume of orders and the balancing changes between sales orders indicate itself as a risk premium in the future returns. The results obtained in this study are consistent with the Market theory of Noise.

 Based on Markowitz theory of portfolio optimization, capital market is not predictable by any methods and the risk can only be diversified through portfolio formation and optimization. Recent works made huge developments in the basic model from modeling and risk measures perspectives. Spectral risk measures such as expected shortfall and value at risk are being used frequently as risk measures. In addition, researchers tend to consider uncertainty in risk and return evaluation via fuzzy, stochastic and robust modeling. However, a matter that has been neglected in many researches is portfolio management under uncertainty conditions. This paper propose a method for robust modeling of portfolio optimization and management using expected shortfall as risk measure and Bertsimas modeling as robust programming. The proposed model solved with artificial bee colony algorithm and results show a better performance of proposed model compared to classic methods in both the optimal portfolio formation and its management phase.

Logical investment decisions require attention to different factors and different criteria at the same time. This goal can be achieved using various methods and algorithms. The purpose of this study is to develop an optimal stock portfolio model using a combination of data envelopment analysis methods, anomaly clustering algorithm and MLP neural networks.The statistical population of the research is the accepted companies in Tehran Stock Exchange during the period of 1386 to 1396. To create an optimal stock portfolio, all available criteria were grouped to reach the optimal stock portfolio.Then, the results were compared in different approaches based on the Sharp ratio. The results of the research indicate that using the combination of data envelopment analysis, anomaly clustering, MLP neural networks and accounting metrics in the provision of an optimal portfolio of stocks led to Increasing Sharp's ratio compared to other approaches (Risk and Efficiency, Value-Based, and Balanced Scorecard). In general, the simultaneous use of hybrid optimization techniques and comprehensive criteria derived from accounting reports can provide a more efficient basket of portfolios and more desirability for the investors.

Considering the inverse relationship between the value and momentum factors and the lack of simultaneous use of them in capital asset pricing models such as CAPM and Fama and French's three-factor model, the basis of this study is to provide a new functional model has been replacing pricing models of investing in stocks based on value, momentum with market risk and explain the anomalies of accruals and financial constraints through it. By imposing restrictions during the period from 1386 to 1395, 120 companies were sampled and tested. The results of the test of models and assumptions indicate the existence of convergence between the value and momentum factors with the excess return on stocks based on value / momentum, value / size, accruals / size, financial constraints / size, and these factors lead to Risk taking in the embedding portfolios based on the property of value (momentum), accruals (size) and financial constraints (size). Also, the three-factor model of value, momentum with market risk, has less GRS test statistic among competing models, and has the best performance compared to capital asset pricing models such as the CAPM and the Fama and French three-factor model.

The purpose of this research was to design a five-level supply chain network by simultaneously taking into account the financial and operational dimensions as a systematic perspective. The innovativeness of the present research was in that it presented an integrated financial-operational approach in the resilience supply chain. This initiation was mathematically modelled by considering five levels of suppliers, factories, distributors, retailers, and customers in strategic and technical decision-making phases. Given the importance of equity versus accounting profit, the modeling aimed at maximizing the shareholders’ wealth. The suggested model was designed and tested by GAMS24 and CPLEX solver. The results of modeling and solving the numerical model indicated the importance of the role of financial dimension and simultaneous consideration of financial-operational dimensions in the mathematical model in obtaining sustainable competitive advantage. Moreover, the ability of the five-level supply chain was explained by the suitability of several criteria such as cost, capital structure, further increase of equity versus profit. In order to analyze the supply chain resilience, Iran Khodro Company, as the studied case, was tested and validated using the designed model. The results showed that applying the present research model can improve the supply chain resilience of Iran Khodro Company.

Developing and growing markets and financial tools, markets financial complexity and specialization of investment, made investors and financial markets employees to have tools, methods and modes to help them choose the best way of investment. Momentum method is based on company profit movement assessment (rate tenor and amount of profit movement). Momentum has multifarious essence and it has traditionally relation with physical condition. This method acclaims that, market will response to all of profit movement, specially by buying or selling stock at best time. It also suggests that the growing profit will continue steady in the future. At this survey, we tried to study and define the determinant elements of momentum stock prices in Iran. Therefore, companies profit investments in stock markets has been evaluated. At this study, profit of Trades methods has been assessed according to current and last inputs and prices. We also tried to explain more about these standards and to explain, if we can explain the increase of stock input by this method or not?  The findings of this study revealed that trade methods based on stock price momentum are the main effective things. So, momentum strategy trade could be a great statistic for stock price. The findings determined that coefficient variant of stock price was 0/00171 and it revealed the negative effect of coefficient variant on momentum strategy. According to T statistic, the coefficient variant of stock is certainly 95%, there is also a great and meaningful relation between momentum strategy trade and input, on the other hand, by applying momentum strategy trade, stock input will be increased. Finally, by proving amount of trades and stock input relation by momentum strategy, the lookout of investors and beneficiaries to financial market and structures will be cleared.

Modeling and predicting stock market volatility using neural network and conditional variance patterns The fluctuation forecast is one of the most important issues in the financial markets, which attracted the attention of many academic researchers and experts in the field over the past few decades. In this study, considering this necessity, we examine the modeling and prediction of stock market volatility using the combination of artificial neural networks and conditional variance patterns. In this research, multi-layer perceptron nerve networks (MLP), conditional variance heterogeneity models (ARCH) and self-regression model and conditional variance (GARCH) (P, Q) have been used. The statistical population of the study is the Tehran Stock Exchange index for the period of April 2008 to April 2018 . The research seeks to reject or confirm the hypothesis that "the use of an artificial neural network and conditional variance models increases the accuracy of the forecast of stock market fluctuations in the Tehran Stock Exchange relative to the conditional variance model" . The results, confirm the validity of the above hypothesis.

In today's highly competitive world, the condition of survival and participation in the field of activity, functioning and efficient and effective, is not achieved except through continuous planning, monitoring, control and evaluation. In this regard, we tried to present a mathematical hybrid model for selecting and planning an optimal composition of the shares according to the goals and priorities, in order to obtain the highest compatibility between the final selection and the initial ranking of each share. The proposed model consists of three steps and several steps, the SBM method of data envelopment analysis (DEA) (for initial stock revisions, multi-factor decision-making (TOPSIS)) in uncertainty conditions, for the assessment and ranking of shares in two individual steps and categorized and integer linear programming (IP) for choosing the best stock portfolio with increased scores according to the organization's priorities and constraints. Collect information from reputable sites of five industries active automotive, pharmacy, petrochemical, cement and food industries to the best stock portfolio for investment, due to the impact of algorithms and methods.

The purpose of this study was to investigate irregular behavior in stock prices, investors' expectations and stock returns using the Liponov index and the Kolmogorov index. It also analyzes the related behavior of stock prices, investors' expectations and stock returns during the period of 2012-2018 in Tehran Stock Exchange. The BDS method was used to detect and measure irregular behavior. Copula Garch and Copula T-Gearch were used to study the joint motion among selected variables. The results showed that stock prices and stock returns are irregular according to the Liponov index and the Kolmogorov index. There is a significant evidence of a nonlinear sequence dependent between stock price fluctuations, investor expectations and stock returns. In addition, the high and low dependence sequence and the mobility between the analyzed series were confirmed. Also, fluctuations in stock prices have a huge impact on stock returns and long-term investor expectations of Capoula Garch and Kapila T-GARCH.