نشریه مهندسی مالی و مدیریت اوراق بهادار
پیاپی 40 (پاییز 1398)

DEA-based methods for assessing the effectiveness of data envelopment analysis (DEA), in which the input and output weights are limited, has the ability of bank branches to use unlimited weights that are consistent with exaggerated judgments, adjusted prices, taking into account the risk associated with operating costs and costs Non-operational. In this paper, we evaluate the performance of first-class branches of a commercial bank in Iran using the weight limitation model in data envelopment analysis. Firstly, the important indicators for assessing the performance of bank branches were identified according to previous studies and then, according to the opinion of the banking experts and data envelopment analysis, to evaluate the efficiency of the first grade bank branches, which include two input indicators and four output indicators, will be finalized. After collecting information about 40 first-class branches of the bank in two half-yearly years in 1395, the DEA model will be evaluated without any restrictions on the first grade bank branches. This will also be done with limited DEA models. Finally, using descriptive statistics and statistical tests, we will show that the applied weight limit improves the performance of the performance of first-class bank branches.

In the investment culture, there is a consistent principle that capital is risky and risky and tends towards profitability. This study examines the efficiency and risk taking into account liquidity variables and liquidity risk based on the function of debt management model for the period 1391-1396. The purpose of this research is to use the target and the dimension of the correlation of the time series and the information gathering using library studies. Using the secondary data extracted from the financial statements of the company's investment financing specialist holdings Analyzes the correlation relationship. The statistical population of this research is the specialized housings of social security investment company (SHASTA). The researcher selected all members of the community for sampling, and the collected observations. Data analysis was performed using the multivariate regression model presented in the study using Eviews software. The findings showed that liquidity had a negative and significant effect on returns. Also, liquidity risk of the company and liquidity risk of the market have a significant relationship with the return on debt assets.

Due to the complexity of the stock market and the high volume of processable information, often using a simple system to predict cannot release appropriate results. Therefore, researchers have been trying to provide a system with less complexity and more efficiency and accuracy using hybrid models. nowadays various patters are used including statistical technique (discriminate analysis , logistic , analysis factors) and artificial intelligent techniques ( neural networks(NN) , decision trees , case based reasoning , genetic algorithm , rough sets , support vector machine , fuzzy logic ) and the combination of these two technique for predicating stock prices. For most predictive models, the system uses only one indicator to predict, but in the proposed model in this study, a two-level system of multilayered perceptron neural networks is presented which uses several indicators to predict. To do this, required information of Tehran Stock Exchange price indicators, for fiscal years 2012 - 2017 was collected. We also used the Grasshopper Optimization Algorithm to select the best samples for better nerve network training and thus to improve the results.  The results show that the proposed model can operate with lower prediction error than other models.

The purpose of this paper is to solve the stock selection problem for a portfolio with the least undesirable risk by solving the mathematical programming model in random fuzzy conditions. For considering the vague and uncertain environment of stock price rate of return, a mixed uncertainty procedure was applied. Also MAD was aaplied as the risk measure for optimizing the portfolio model. This research is based on the Mean-Absolute Deviation (MAD) model and assuming that the stock return is a fuzzy random variable, it considers the attitude of investors to risk in the form of risk-averse and risk-seeking investors and an efficient mathematical model for selecting Optimal portfolio are provided. Eventually, with the help of the genetic algorithm, an attempt was made to generate a quest for solving a given model. In the following, the Pareto optimal solutions are obtained according to the desired criteria, the optimal answer is obtained.

According to development theories, achieving higher economic growth and creating economically viable opportunities for low income groups is the focus of development strategies; but over time, it has become clear that, despite high economic growth in some The low income and poor groups have not benefited from its benefits; in other words, for proper distribution of income, in addition to continuous economic growth, other instruments and policies are also needed. The forthcoming study examines the effect of liquidity of the stock market on the amount of inequality of income during Period 1376-1396 Payments. The results obtained by maintaining certain factors, including the traditional financial development criteria, indicate that there is a negative correlation between stock market liquidity and Gini coefficient as a measure of income inequality. In addition, the findings indicate that there is a negative correlation between stock liquidity and the poverty rate. In the final set of quizzes, the division of wage increases into two parts (the increase due to stock liquidity and the vertical increase in liquidity) resulted in less evidence of reducing inequality and poverty due to the increase in wages due to liquidity.

Among macroeconomic variables, interest rates are the most important ones; however, the impact of interest rates on prices and returns on the stock exchange is not so clear. In the other words, the relationship between interest rates and stock prices over time is not constant.
The purpose of this study is to investigate the relationship between the money market (interbank loan market) interest rates and stock market performance, as well as to investigate the predictability of price behaviour in the Tehran Stock Exchange. To determine effects as well as prediction, GARCH model is applied, also the Brock- Dechert - Scheinkman test (BDS) was used to determine the nonlinear behavior of the series for the stock market risk premium. The research findings confirmed the predictability and nonlinearity relationship between examined variables. Also, the research findings indicated that there is a meaningful relationship between the interbank loan rate with the price to income ratio of the Tehran Stock Exchange

We evaluate risk and return of bitcoin in comparison with other competitive markets such as currency (Dollar and Euro), stock exchange, and gold (futures contract and cash coin). We gather different data relevant to bitcoin prices, Tehran stock exchange index, Dollar and Euro currency rates, gold futures, and gold coins during 5 years (9/19/2013 until 9/18/2018). We apply Dickey-Fuller unit root test, GJR-GARCH(1,1), TGARCH, and Spearman correlation coefficient to examine the research hypotheses. Findings show that the risk and return of bitcoin is significantly more than other investment opportunities like currency, gold, and stock exchange in the country, however, there is no relation between its risk/return and other competitive markets. Unlike other assets, good news effect on bitcoin trading market is greater than bad news effect. Finally, Dyhrberg’s (2016) hypothesis is unacceptable which implies to bitcoin is a stuff between gold and currency.

Long-term memory, also referred to as long-range dependence, explains the correlation structure of values of a time series at long intervals. According to the efficient market hypothesis, prices follow a randomized step process, so returns on assets can not be predicted based on past price changes. Long-term memory is a weak point of the business-market hypothesis. Long-term processes have important implications for asset yields and play a crucial role in time series analysis. This study examines the existence of long-term memory in the price index of crypto currencies equals $ 1 and lower for the period from September 1, 2015 to September 1, 2018. To estimate the parameter d, the OLS method is used in the EVIEWS software package. The ARFIMA model is used to test hypotheses. The results indicate that long-term memory is in the currencies of DIGIBYTE, Dodge Coin, EMER Coin, BITSHARES, MAIDSAFE COIN, XEM, Redd Coin, NXT, Verge and Ripple, and on the other hand, three currencies of Byte Coin, SIA Coin, STELLAR lacks long-term memory, and therefore these currencies are among the most efficient market products.

Creating a balance between risk and return has always been the major criterion in investment decisions in stock exchange. All investors look for an optimized compromise for their investments so that the utility function of their investment is maximized. This study aims at arriving at a more efficient model for optimizing investment portfolio. This model is to consider uncertainty and investment risk on the way to create a bigger return for the investors. In this way, Extreme value theory for assessing investment risk, as one of the newest assessors of value at risk (VaR) was utilized. The time of this study covers the period from 2013 to 2018. The sample includes top 50 companies in Tehran Stock Exchange. Using GARCH method and maximizing likelihood function, the type of return distribution of top companies of Tehran Stock Exchange was determined at the first step. Next, efficiency frontier for investment risk in Tehran Exchange was compared to Markovitz model`s efficient frontier, using a quadric planning model through a Extreme value theory approach. The results of this study signify that forming an optimized investment portfolio through a Extreme value model would not make any significant difference with Morkovitz Variance-Mean model.

One of the most important factors to evaluate how successful a R&D process could be is, exposures in the raising level of the technological capabilities. In other words, the higher the technological capabilities and growth rates, the higher chances of successfulness in an organization. Considering this, the essentiality of measuring technological capabilities of an organization by evaluation of the R&D effectiveness or calculation of the ultimate costs, which has been spent on the R&D procedures, seems obvious and vital. Therefore, in this study, based on the previous studies and the extraction of factors affecting technological capabilities as well as identification of cause and effect loop, a dynamic model has been developed. Then, using DeMetal technique, the relationships between them and the effective coefficients are determined and included in the model. Finally, in order to examine the accuracy of the modelling and determination of both state and rate variable’s behavior, data was gathered from eight science-based companies located at for knowledge-based companies applying for admission to the Securities and Exchange in a 24 months period. Analysis of the behavior of variables in these Informations not only showed the accuracy of the proposed model, but also confirmed its fluidity.

The Development and Assessment of a four factor model inprospering & declining markets, Evidence From the Tehran stock ExchangeThe Development and Assessment of a four factor model inprospering declining markets, Evidence From the Tehran stock ExchangeThe Development and Assessment of a four factor model inprospering & declining markets, Evidence From the Tehran stock ExchangeThe velopment and Assessment of a four factor model inprospering & declining markets, Evidence From the Tehran stock ExchangeThe Development and Assessment of a four factor model inprospering & declining markets, Evidence From the Tehran stock ExchangeThe Development and Assessment of a four factormodel inprospering & declining markets, Evidence From the Tehran stock ExchangeThe Development and Assessment of a four factor model inprospering & declining markets, Evidence From the Tehran stock ExchangeThe Development and Assessment of a four factor model in prospering & declining markets, Evidence From the Tehran stock ExchangeThe Development and Assessment of a four fator model in prospering & declining markets, Evidence From the Tehran stock ExchangeThe Development and Assessment of a four factor model in prospering & declining markets, Evidence From the Tehran stock ExchangeThe Development and Assessment of a four factor model inprospering & declining markets, Evidence From the Tehran stock Exchange

There is a potential for crowdsourcing in different fields. rowdsourcing has been built on the basis of general intelligence and it is able to collect data and also reduce time and cost.crowdsourcing is possible only through IT technology and requires active and ongoing participation, stakeholders’ interaction and transparent feedback. Therefore, this research aims to propose a model for crowdsourcing science-based companies’ in banks.

Considering the method of data collecting and data required for analysis, the present study is a combination of quantitative and qualitative research, in other word, mixed research. It is applied In terms of objective type, samples are collected in descriptive method and it is a survey research in terms of data collection.

findings show that the contributing factors of crowdsourcing directly explain 88.6% of the changes related to the sources of crowdsourcing, 91.5% of the changes related to crowdsourcing phenomenon and 96% of the changes related to the accelerating or slowing factors..

According to the results of the research, the effectiveness of all dimensional components is confirmed. The component of external tissue factors has the highest effect on other factors.Also, factors such as smart malking financial resources, relation factors and psychological and motivational factors have the greatest effect.

Decision making in humans is based on a combination of reason and emotion, so in financial decisions, a simple modeling of financial models based on a fully functional market and a completely rational person would not be useful. As a result, behavioral models in financial decisions are needed so we could have a realistic analysis of personal financial behavior to provide financial advice.  The present paper aims to provide a new strategy for personal financial counseling under the title "Financial Mind Map". Considering the need for personal financial management in the life of each person and the impact of financial behavior and personal financial intelligence on financial decisions, we tried to incorporate these points into key Financial Mind Maps. Also, because the Financial Mind Map of the person has come out of his mind, reviewing it out for financial advice can be very helpful in improving and increasing the impact of financial counseling on financial mentality and continuing financial behavior of the applicant. After performing the research, it was determined that several factors such as: financial intelligence, financial behavior, Behavioral Bias, Personality bridges of investment, Financial knowledge, decision making, Personal finance management, Resources and financial expenses, They are effective on Financial Mind Map.

The purpose of the present study is to provide a fuzzy window data envelopment analysis (FWDEA) model in order to financial performance evaluation of stocks over different time periods under the uncertainty of the data. In other words, in this research, we will try to present a new approach to assess the stock's performance with the ability to be implemented in the presence of uncertain panel data. Because the use of information about several time periods rather than a time period, as well as taking into account the uncertainty in the data, can lead to more reliable results in the process of stock evaluation. It is necessary to explain that in modeling and presenting the mentioned approach, data envelopment analysis, window analysis and possibilistic programming have been used. Finally, the fuzzy window data envelopment analysis model was implemented on 5 stocks of the chemical industry in Tehran stock exchange for four periods from 2013 to 2016, and the results indicate that the FWDEA method is effective.

The occurrence of financial crises in recent decades has caused a lot of damage to the economy as well as economic enterprises in many countries. The Extreme Value Approach is a new approach to the phenomenon of financial crisis, which has been able to analyze the events that are less likely to occur but the damage caused by them is significant. In this study, we use the Extreme Value theory and Stochastic differential equations to find a new method for estimating the more precisely the value at risk. For this purpose, after estimating the parameters of the Stochastic differential equations, which includes the geometric Brownian motion, the geometric Brownian motion with the jump, the nonlinear GARCH model, and the Heston model, simulate the Monte Carlo simulations of future paths and then use peak over threshold approach, to estimate the value We at risk. The results of the simultaneous use of Stochastic differential equations and Extreme value theory ​​are compared with historical simulations and variance-covariance approaches for value at risk. The results of Back-test techniques on value at risk indicate the superiority of the Heston model in estimation of value at risk.

In this research, the statistical analysis of random forest effects has been done. Also, to evaluate the performance of the random forest algorithm in the field of statistical arbitrage compared to other models presented in the previous research, the comparison of the results from the application of this algorithm with deep neural network algorithm has been done. The models are taught with stock price information and the output from this technique categorizes stocks according to the position of buying and selling. Using this strategy, profitable positions are identified in market shares for profit. The results showed that the model of random forest with less error classification than deep neural network model. Using this strategy, profitable positions are identified in market shares for profit. The results showed that the model of random forest with less error classification than deep neural network model.

One of the main factors behind the growth of various countries is the development of financial markets, which has facilitated and accelerated the role of information and communication technology (ICT) in recent years for countries. The purpose of this study is to investigate the effect of stock exchanges on economic growth with the moderation of ICT in developed and developing countries during the period 2011-2016. Data and information about the sample countries are gathered from the reports the global innovation index. The method of fitting the models in the present study is the panel data method using Eviews software. The results showed that ICT affects the relationship between volume of stock exchanges and economic growth only in developed countries, which is due to the development of these types of technologies in developed countries. It seems that developing countries need to be equipped with ICTs as well as developed countries to achieve greater economic growth.