نشریه مهندسی مالی و مدیریت اوراق بهادار
پیاپی 31 (تابستان 1396)

Value-at-Risk and Average Value-at-Risk are two important risk measures that used to measure the market's risk with quantity structure. However, both of these risk measures have defects in measuring risk. For this reason, the new risk measure GlueVaR has been introduced in 2014. In this paper the new measure is describe and the advantaes of this mesure are explained. A method for estimating the new mesure is provided using the composite quantile regression model. Finally, the efficiency of GlueVaR will be compared with two other mentioned risk measures for log-return data from the American’s stock market and Iran’s stock market.

One of the important features of industrialized and developing countries is the presence of money, dynamic market and capital. In other Words, if the saving of individuals will be directed by appropriate mechanism to the manufacturing sector it brings efficiency not only to the owners of capital but also it can be considered as the most important funding for launching economic projects of society.
In present study, three stock selection and optimization algorithms including genetic algorithm, particle swarm algorithm, and cultural algorithm has been studied. So, 106 listed companies in Tehran Stock Exchange, since 2007 to 2014 were tested in order to investigate this.
In this study, for plotting the efficient frontier and comprising of the optimal portfolio half of the variance is considered as the main factor of risk. This research investigates the significant difference between the averages of investment output in selected baskets based on three methods. The statistical analysis of the results shows that there is no difference between the three algorithms. However, in order to compare the two algorithms and analysis of superiority of algorithms, these two methods of optimization have been compared from two aspects of objective function, output ratio and risk.
Since the objective function of genetic algorithms was less, in other word, it has the least error and gain the best result so in comparing to other algorithms it has been performed better which shows the relative superiority of these algorithms in the selection of the optimal portfolio.

Looking for financial markets development in developed countries and emerging markets, the different financial instruments have been introuduced to meet the wide and variety demand of investors. These instruments include a wide range of stocks, fixed income securities, derivatives contracts and a combination of them. In this regard, the stock deposit certificates as a financial engineering product, not only pleys as a financing Instrument in the capital markets, but according to the having the unique features, but increases the stock market efficiency by increasing stock liquidity. It also maintains the management control of major shareholders. Therefore regarding to the low liquidity of Iran stock market and lack of the companies willingness for financing through the issue of new shares (despite the serious need) due to loss of managerial control, in this paper we introduced the operating model of the stock deposit certificates that it was localized for Iran stock market. This instrument is considered as a new instrument in corporate financing (without loss of management control) and increase the efficiency of capital markets (through increased liquidity).

Firms must follow a business strategy for success in the competition. There are two proposed strategies of Prospector and Defender firms. Defenders aims in decreasing costs and prices, however, prospectors conduct innovation and high level of R&D expenditures and try to differentiate their product from competitors. But this approach increase future Stock Price Crash, because one of causes can lead the firm to crash risk is bad news hoarding within the firm. With issuing the bad news, stock price experiences crash suddenly.
This paper examines empirically the effect of firm-level business strategies on future stock price crash risk. In the mean time, it is expected in the higher asymmetry information condition, prospect strategy increase crash risk more. To achieve the research’s goals, we selected 117 firms listed in Tehran Stock Exchange as a sample during 2011-2015. To examine research hypotheses we employed multivariate regression with panel data. The finding indicate prospect strategy increase crash risk, however, defend strategy will decrease it. We also found that in the higher asymmetry information condition, prospectors are more prone to increases future crash risk.

Forecasting stock return is an important financial subject that has attracted researchers’ attention for many years. Investors have been trying to find a way to predict stock prices and to find the right stocks and right timing to buy or sell. Recently, data mining techniques and artificial intelligence techniques have been applied to this area. Association discovery is one of the most common Data Mining techniques used to extract interesting knowledge from large datasets. In this paper, we propose a new multi-objective evolutionary model which maximizes the omprehensibility, interestingness and performance of the objectives in order to mine a set of quantitative association rules from financial datasets, including 10 common indicators of technical analysis. To accomplish this, the model extends the two well-known Multi-objective Evolutionary Algorithms, Non-dominated Sorting Genetic Algorithm II and Non-dominated Ranked Genetic Algorithm, to perform an evolutionary learning of the intervals of the attributes and a condition selection for each rule. Moreover, this proposal introduces an external population and a restarting process to the evolutionary model in order to store all the nondominated rules found and improve the diversity of the rule set obtained. The results obtained over real-world stock datasets demonstrate the effectiveness of the proposed approach.

The present study aims to explore the effect of factors beyond financial and accounting variables on sales outsourcing in ZamZam Company. The purpose of study is to investigate the level of compatibility and allegiance of product's outsourcing pattern under a chaotic situation. The data were collected by a questionnaire which was distributed after assessing its reliability and validity (each with a value of higher than 0.70). The sample under study included all managers and employees working in Zamzam Company, Tehran Province, Iran, who were basically involved in sales outsourcing. The results of data analysis showed that variables such as nonlinearity, self-similarity, strange attractors, butterfly effect, dynamic adaptation, and fractal affect product sales outsourcing in the company under study. In addition, hemostasis and bifurcation were found to have no affect product sales outsourcing and there was no significant correlation between these variables. Based on these findings, more extensive studies are suggested to be conducted on companies listed in Tehran Stock Exchanges.

This paper is trying to investigate comovement of the stock market, foreign exchange and gold in Iran. For this purpose, comovement and relationship mutually of these markets in Iran’s economy during the period 30.09.1997- 21.07.2015 with the weekly frequency have been investigated by the wavelet coherence analysis and Econophysics approach. The results of coherence analysis show that in the short time horizon during 2005-2008 and medium-term horizons during 2003-2006, the relationship between stock return and the exchange rate has been in the opposite direction (opposite phase). But in the longer-term horizonsduring2007-2010, the stock return moves after the moving exchange rate that the stock return is a lagging variable. Also, the coherence between gold and the exchange rate in the short term during1998-2002 was high with the same phase. After these years and during 2002-2012, the correlation between gold and the exchange rate had not been high particularly at high long-term horizons but after the year of 2012 with the intensification of sanctions, the coherency of these two markets has been high with the same phase until the year of 2014. Moreover, the coherency between stock return and the gold coin rates show that the intensity of correlation between these two variables during the period of study was low but the correlation of these two variables in 16-64 weeks’ horizons during 2001-2004 was high that of course, the direction of this relationship has been reversed.

Artificial neural networks (ANN) are mathematical models inspired by human’s neural and brain system. This research deals with the next day price forecasting in Tehran’s stock market by MLP, and attempts, by various methods, to reduce the prediction error. High pricing of stocks may lead to low demand for negotiable stocks and the failure of privatization. Raising various doubts in the negotiation of public properties, low pricing results in the long-term failure of negotiation policies. With respect to the importance of this issue, the newness of stock market and the lack of financing institutes and investment banks in Iran, prediction of stock price trend and its ascending and descending order can influence the decisions and strategies of managers. Various variables affect stock prices among which the role of economic indices, such as exchange rate / oil price and gold price is significant.
The purpose of the present study is to predict the final prices of stocks by utilizing daily data through neural networks. The results indicate that the ANN model has low error and high explanatory and thus considerable forecasting power.

In this research, five major bankruptcy predictions model to study and among the components of the five models, redesigned bankruptcy prediction is provided that consists of eight variables. The main issue in this research is that by examining the financial statements of listed companies in Tehran Stock Exchange we can offer a model to predict corporate bankruptcy. In order to design data from two groups of companies accepted in the Tehran Stock Exchange use the first group consists companies surveyed non-bankrupt company and second group included bankrupt company. The study period financial statements of exchange data during the years have been 2005 to 2014. The study results in relation to the ability to predict model reflects the fact that the model could be two years before the bankruptcy of companies provide accurate predictions about the crisis and bankruptcy. The results show that the predictive power of the model for one year before bankruptcy 91% and two years before the bankruptcy 83%.

The main goal of present study is to investigate the relation between co-movement and momentum in Tehran Exchange`s Listed companies during 1384-1393.For this purpose with following Wahal and Yavuz (2013) study, the style is constructed and the co-movement of each stock with its style was accounted and then the standard approach in the momentum literature is used. Following Jeegadeesh and Titman (1973) method for identifying winner and loser portfolio at the end of each month, the stock is ranked based on their 6 prior period return and the relation between co-movement and momentum is examined. Findings show that there is a meaningful relation between co-movement and (3, 6, 12) prior return in winner high co-movement and loser low co-movement portfolio.
Furthermore, in winner high co movement portfolios, with increasing the range of past return, the effect of co-movement on past return will expand. On the other hand, in loser low co movement portfolios with increasing the range of past return the effect of co-movement on past return will decrease.

One of the most important applications of futures, hedging is that this application is also evident in the futures coins and various stakeholders can use it. In this paper, using time series dollar in free market and price of futures contracts coin during the period 1390 to 1393 to assess the risk of cross hedging exchange rate using futures contracts coin. First, the correlation between the exchange rate and price time series econometric model for future Coin vector regression (VAR) found. After the confirmation of residual autocorrelation and heteroscedasticity conditional on the VAR, the model BEEK (which is a multivariate GARCH model), conditional variance Currency and coins was estimated future prices and then by minimum variance hedge ratio was calculated for different maturities and the profit or loss resulting from currency risk hedging gain or loss resulting from exchange rate fluctuations were real. The results show that there is a high correlation with the price of the coin exchange rate (US Dollar), possibility of covering cross-currency risk using futures contracts provide for gold coins. Also, due to long-term memory between exchange rate fluctuations and price estimation of future coins hedge ratio through BEEK-GARCH model, and using this model include more than 70 percent to compensate losses from currency risk.

During the past several years experience extraordinary explosion of collective investment organisms or of investment companies (who buys the shares of other companies), led to the agencies responsible for controlling and monitoring these investments are a The series is based on Value at Risk management guidelines apply. But the flexibility that many questions regarding the accurate and appropriate estimation model provokes.
The purpose of this article Choose from three parametric method, historical simulation and Monte Carlo Simulation is the best way to predict the possible losses if the investment fund files open Tunisians find. For this purpose, different methods of estimating VaR propose. The descriptive statistical characteristics of 14 cases we analyzed combined investment fund. Then we present the results of experimental studies, so we can take advantage of Monte Carlo simulation method to predict the potential value of the company's chief of investor Tunisian specify.

This paper compares three models of the GARCH family to investigate the volatility dynamics of gold Price returns. Nowadays, GARCH-type models have been extensively used in modeling the volatility process of various asset price returns. Gold plays a critical role as a hedge against adverse market conditions. An accurate understanding about the gold volatility is important for the financial assets pricing, risk management, portfolio selection hedging strategies and value-at-risk policies. In this study, we use Iranian gold returns data from March 25, 2003 to December 25, 2015 and employ the GARCH(1,1), IGARCH(1,1) and FIGARCH(1,d,1) specifications. The research findings show that the FIGARCH is the best model to capture dependence in the conditional variance of the gold returns. Moreover, we examine the long memory behavior in the volatility of gold returns. According to the estimation results, the long memory parameter is positive and statistically significant. Consequently, long memory is an important characteristic of the gold volatility returns and should be taken into consideration in investment decisions. Also, the out-of-sample evaluation criteria (MAE, RMSE and TIC) select the FIGARCH(1,d,1) as the best forecasting model of gold volatility.

An important factor in risk management is optimized conditional value at risk (CVaR) of the portfolio. Choose a model which calculates time depended to variance rather than the model with constant variance lead to improve data modeling. Using an appropriated method for measuring risk in financial asset returns distribution has a great utility. The main purpose of this study is implementing a hybrid procedure to calculate CVaR which, models, volatility and dynamics in clusters, and calculates CVaR value based on fat tail feature. In this case, using Extreme value theory (EVT) leads to calculate CVaR more precisely. In addition to, using some ARCH (autoregressive conditional heteroskedasticity) family models result to dynamic feature in estimating CVaR. Data were used in this study related to TEDPIX during 2001-2015. Total 2781 data were derived from Rahavard Novinand & TseCline softwares as daily. For analysis this TEDPIX data, MATLAB software and EXCELL were used. This result represented, return data distribution has fat tail. The historical simulation (HS) at 95% confidence level isn’t accurate, while the accuracy Generalized Auto-Regressive Conditional Heteroskedasticity-EVT (GARCH-EVT) model at 95% is more suitable. Using (Fractionally integrated generalized autoregressive conditional heteroskedasticity -EVT) FIGARCH-EVT method leads accurate estimates of CVaR in comparison with HS procedure. Calculating CVaR by FIGARCH-EVT-CVaR was more accurate than the GARCH-EVT-CVaR. This model has considered to both GARCH-EVT features and long memory property. The FIGARCH-EVT-CVaR model had acceptable accuracy and its exceptions are independent. In General, models which considered heteroscedastic, had an acceptable accuracy in comparing HS