نشریه مهندسی مالی و مدیریت اوراق بهادار
پیاپی 36 (پاییز 1397)

In choosing the optimal portfolio, we must consider various criteria, some of which are determined by the nature of the optimization and some are determined by the investor's desire. Therefore, in this paper, multi-objective optimization models are designed and solved in MATLAB software environment. These models are designed in such a way that both the nature of the portfolio optimization, the considerations of the investor and the uncertain nature of the future return on assets, are taken into account. After designing the models in fuzzy and non-fuzzy (simple) conditions, due to their NP-HARD nature, a dedicated NSGA-II algorithm was used to solve it. After solving the models, the best portfolio from attained Pareto frontier, based on the Sortino ratio, be chosen. After that all of the obtained portfolios are compared according to the Treyner ratio. The results of statistical tests clearly show that the proposed models have a high power in choosing portfolios with maximum returns and a minimum risk. The results also indicate that that the designed models, with use of fuzzy logic in quadratic models creates more favorable results than simple models without using possibility theory and fuzzy logic.

In this paper, the sensitivity of the Greeks to the capital market will be examined. The purpose of this research is to make the use of the Greeks formula in the Tehran Stock Exchange. This research has been carried out on the applied research and financial information of Iran Khodro from September 9th to March 2010. In order to study and interpret the concepts of the Greek sensitivity formula, statistical and mathematical software such as MATLAB is used and calculations are performed with the European law (according to Tehran Stock Exchange). The outputs show that the mean and dispersion obtained in the selected sample is not normal distribution and it is proved with the help of the Lyon test that the mean and dispersion of the sample obtained can be replaced by the mean and dispersion of the society with a confidence coefficient of 99%, and the equation The Black-Scholes method is used to introduce the Greeks and examines the impact of each of the variables. Outputs show that the parameters of stock price, dispersion, maturity, and interest rate influence on the purchase price and have the effect of reducing market risk The stock introduces a mathematical model and Greek sensitivity coefficients such as d Leta, Rho, Vega, Theta and Gamma are necessary and it is necessary to use the Greek sensitivity coefficients in the Tehran Stock Exchange and acquaint investors with the concepts of mathematical finance.

The study of dynamics and relations between markets has been one of the research subjects. This paper use state space in vector autoregressive moving average model (VARMA) to investigate the effect of gold and crude oil’s price uncertainty on stock returns of the bank. In space-state equation system, the state variable is estimated by the Kalman filter and the specified parameters of the model by the maximum likelihood method. The results showed that gold and crude oil’s price uncertainty has a negative and significant effect on stock returns of the bank and the gold price uncertainty has a major effect on the stock returns of the bank. And furthermore, crude oil’s price uncertainty has a positive and significant effect on gold price uncertainty. In this research, daily OPEC crude oil prices, gold price (Bahar Azadi Coin- Old design) and banks stock index during the period 1390 to 1396-Shahrivar were used.

The next financial strategy is the strategy of the company that determines the nature and direction of the organization's finances. The ultimate goal of each trading company is to increase the shareholder's wealth, and desirable performance will increase the value of the company and ultimately increase the shareholder's wealth. Companies can not achieve their goals without having the right strategy, and strategy development results from the evaluation of performance from a variety of dimensions. The dimensions of corporate governance (adequacy and ability) are a set of internal and external control mechanisms that establish the right balance between equity on the one hand and the needs and authority of the board of directors on the other. The results-based research, A development based on purpose, descriptive-explanatory and method-based, is therefore an event. The statistical population of this research is pharmaceutical companies accepted in Tehran Stock Exchange which 25 companies were selected by judgment method. The data gathering tool is an analysis of audited financial statements of corporations, and T, Loonce, Kolmogroft-Smirnov and Uonom-Whitney tests, Chow and Hausman tests and regression models have been used. The results show that there is a higher and more significant difference between tuneal behavior of stockholders on firm performance (average return on equity, return on equity, Qobin ratio, and margin of profit) with a coherent financial strategy with corporate governance.

In the recent years, high frequency trading is becoming more popular among financial analysts in Iran. To analyze these data need special methods because of microstructure noise effect. In this study, the goal is assessment of realized volatilities according to the BARJAM effect from May to October in 2014 and 2016. This study uses the information about crucial Iran indices: Total index, Bank, Oil Industry Investment, Petrochemical Industry Investment and Automobile Industry indices. To estimate Integrated Matrix using “Pre-averaging” because this approach is able to control the effect of microstructure noise and, “Hayashi-Yoshida” is used to synchronization among indices. Moreover, the price jumps are removed to get more accurate estimator. The study of daily volatilities and their comparison show a positive effect of BARJAM on Total index, Bank and Oil Industry Investment, but there is not noticeable influence in Automobile Industry and Petrochemical Industry Investment. Finally, time series is modeled to forecast the volatility of indices in a short term.

Undrestanding the earning management for the users of accounting information due to performance evaluation, profitability forecast and detrmining the value of the company is very important.The purpose of this research is to estimate the a model for earning management using neural network model and then the use of Genetic Algorithm, and Particle Swarm Optimization to find a better combination of input data, so that it can optimize the initial model. For this purpose, 28 effective variables were used in the from of four groups (Financial, managerial, corporative and audit) during the years 2010 to 2016 in the companies admitted to the Tehran stock Exchange. The results showed that application of this algorithm has increased the efficiency of the model.Also, the evaluation of the performance of neural network patterns suggests the absolute superiority of this pattern compared to the time linear method (LR).Combined method (A-PSO) and (A-GA)by identifying four optimal variables respectively precision forecast, shareholding of major shareholders, company size and the ratio of the quality of earning management are carefully predicted respectively (%95/59) and (%94/75). In addition to the above mentioned intelligent methods, by improving correlation coefficient and error squares mean criterion compared to linear methods (LR) and neural network method (ANN) in predicting group results, management and corporate features are more efficient.

In the process of evolving portfolio theory, In order to eliminate the defects and basic assumptions limitation of the traditional model, the concept of downside risk and the Mean-LPM model has been introduced.
The Lower Partial Moment (LPM) has been the downside risk measure that is most commonly used in portfolio analysis. Its major disadvantage is that its underlying utility functions are linear above some target return. As a result, the upper partial moment (UPM)/lower partial moment (LPM) analysis has been suggested in the recent researches. The UPM-LPM framework is powerful because it implements the full richness of economic utility theory such as Morgenstern economic utility function and prospect theory.
In this study, using by stock market Sector indexes over 3 years period since 2010 to 2012, the mean-variance and UPM/LPM optimal portfolio has been calculated in different degrees of potential and risk aversion. In the next step, the optimal portfolio performance of both model has been measured over second period from 2013 to 2015. This research used MATLAB software for optimizing and analyzing of portfolio selection models.
The Jobson-Korkie test has been used to measure the portfolio performance difference between Mean-Variance and UPM-LPM model. It was found that there is significant difference between results of Sharp ratio in Markowitz portfolio and UPM-LPM portfolio, and in the different risk/potential aversion approaches the UPM-LPM portfolio are significantly better than the traditional Markowitz model

The insurance industry in Iran is growing, despite its long history of its presence in the Iranian economic market, has not yet been able to find a suitable penetration factor in the market.On the other hand, the increase of private companies and the increase of competition along with the unfavorable economic conditions of private sector actors in the industry and services sectors and the reduction of the share of insurance from the Iranian family of portfolios, due to other existing costs, made it harder for the insurance industry to work.In this paper, we will use the cross-over efficiency method in DEA based on the Shannon entropy, recently proposed by Sung and Liu (2016), to evaluate insurance companies in Iran. First, through interviews with experts in this field, the most important indicators of financial efficiency assessment were identified, which included 6 indicators. In the next step, in order to evaluate performance and ranking in the statistical society, 20 private and public insurance companies use cross-efficiency integration model and Shannon entropy. The ranking of insurance companies in Iran for the three consecutive years of 1393 to 1395. The results of the research indicate a complete ranking of insurance companies in Iran using the model presented in this paper

The purpose of this study is design of Intelligent System with using of artificial intelligence techniques to discover and classified the pivot price. The pivot price is a point of price that changes the stock price trend. Use of gann-square and candlestick patterns In this research with other variables. The number of data firm used in this study is related to Automotive manufacturing during five years from September 2012 to September 2017. Pivot price is recognized with ZigZag indicator and coded in SQLServer. The result of the research indicates the high ability of the system designed to discover and classify pivot price with a focus on the gann-square and candlestick patterns. the significance of the variables gann-square and candlestick patterns in correct classify pivot price was grater than of other variabls.

The purpose of this study is to investigate the clustering of fluctuations in financial markets, including the stock market, with the underlying model of simulation. Time series of financial asset returns show the clustering of volatility, which shows that large changes in prices tend to form clusters together And these clusters will last for a long time. Time series of financial asset returns often exhibit the volatility clustering property: large changes in prices tend to cluster together, resulting in persistence of the amplitudes of price changes. After recalling various methods for quantifying and modeling this phenomenon, we discuss several economic mechanisms which have been proposed to explain the origin of this volatility clustering in terms of behavior of market participants and the news arrival process. A common feature of these models seems to be a switching between low and high activity regimes with heavy-tailed durations of regimes. Finally, we discuss a simple agent-based model which links such variations in market activity to threshold behavior of market participants and suggests a link between volatility clustering and investor inertia.

The Crash, which indicates how much specific stock prices are at risk of collapse. Accordingly, the purpose of this research is to model the risk of falling stock price of listed companies in Tehran Stock Exchange using a multivariate optimization algorithm for particle cumulative movement and comparing results with logistic regression. For this purpose, a hypothesis was developed for the study of this issue and the data for 106 members of the Tehran Stock Exchange for the period of 2010-2010 were analyzed. First, 14 independent variables were introduced as inputs of the combined genetic algorithm and artificial neural network, which was considered as a feature selection method, and 7 optimal variables were selected. Then, using particle cumulative algorithm and logistic regression, predicted The risk of falling stock prices. The stock price collapse criterion has been used to calculate the risk of falling stock prices. The research findings show that the particle agglomeration algorithm is more likely than traditional logistic regression to predict the risk of falling stock prices. These findings underscore the need for managers to use meta-metric methods for forecasting.

By changing decision-making criteria and more competitive capital markets, stress and investment pressures for shareholders and investors have increased significantly, and it is necessary to pay more attention to stronger oversight mechanisms to improve the quality level of financial statements items It pays attention. This study examines the relationship between Earnings quality and Financial Stress in one hypothesis. The research method is descriptive and correlational and to analyze the relation between independent and dependent variables, multiple regression statistical methods were used. Then, to test the research hypothesis, using data on firms listed in Tehran Stock Exchange, through the sampling cup, 64 companies were selected as samples. The present results confirm the hypothesis that show the significant negative relationship between Earnings quality and financial stress in the capital markets in Iran.

According to efficiency market hypothesis security prices respond quickly to new information and accurately reflect their fundamental values. More recent work indicates that market frictions and the psychological limitations of traders can cause asset prices to deviate from their fundamental values for a considerable length of time. To investigate theoretical concepts, the composite error model and event study approach and for specification model Particular Swarm Optimization were used in this study. The results from Coelli one-sided likelihood ratio test in the event period shows that there are the biases in IKCO’s returns. This study develops an empirical method that tests for and estimates the degree of valuation bias. Being better able to detect valuation bias reveals profit opportunities and may improve the efficiency of financial markets if it sufficiently changes trader behavior.

Predicting the future stock price has always been considered as an important issue by both buyers and sellers. Hence, Artificial Neural Network (ANN) was used in this study to develop a model pertaining to artificial intelligence in order to predict stock price in Iran Stock Market. Since artificial neural networks should consist of the best network topology to achieve the highest performance, Firefly Algorithm (FA), a meta-heuristic Algorithm, was used to find the optimal structure of network. Finally, Bayesian regulation technique, rather than the conventional teaching techniques, was applied to maintain the more generalized network. In general, Data from three big companies: Iran Khodro Company, Shiraz Petrochemical Company, and Isfahan Steel Companywere gathered in span of three years. This paper profited from some parameters, including high price, low price, the opening price, closing price, EMA (5) ،EMA (10) ،RSI ،William R% ،Stochastic k% ،Stochastic D% و ،ROCas network inputs and benefited from the closing stock price in the next days as the neural network as well. After developing a model associated with each company, some parameters such as the root-mean-square error (RMSE), Standard Deviation of error (SD), Absolute average relative deviation (AARD), the regression coefficient (R2) as well as the graphical analysis of relative deviation have been used to examine the accuracy of the developed network. The outcomes of the analysis of the developed neural networks revealed that the mentioned models with great accuracy are able to predict stock price in the subsequent day for the corporations mentioned above.

Nowadays the energy is considered as driving sector of economy. Forecast of 150 billion dollar in energy sector during the fifth development program and banking and financial system requires a dynamic and modern economy and financial tools. However, this approach requires removing legal barriers and modification of the contract. Financing in the oil industry in recent years has faced with serious challenges. The joint investment in oil and gas fields is indispensable. Thus designing of a new contract with the Ministry of Petroleum oil bonds known parallel forward security, tries to raise funds needed. In this article we look at a proposal of the Ministry of Petroleum pattern for optimum pricing for securities offers based on Black and Scholes option pricing model. To estimate the prices, a proposal is presented based on empirical research and statistical models. Finally, we recommend that according to this model other researchers work on pricing the oil parallel forward securities.

The aim of this study was to evaluate the effect of corporate strategies on the market response to earnings announcements of listed companies of Tehran stock and exchange .The aim of this study was to evaluate the effect of corporate strategies on the market response to earnings announcements of listed companies of Tehran stock and exchange. This study was a descriptive-correlation and an applied research . The statistical l population of research consists of all companies listed in Tehran stock exchange market during 2010 to 2015 that a number of 118 companies were active in this period . Data of research was extracted from financial reports of companies and analyzed by regression models in panel data method . Findings showed that implementing the strategy of differentiation in products has direct effect on the market reaction to earnings announcements . Also the findings showed that increasing the level of cost leadership strategy, the market reaction to earnings announcement will increase.