نشریه مهندسی مالی و مدیریت اوراق بهادار
پیاپی 30 (بهار 1396)

Algorithmic trading system is a trading system that utilizes highly advanced models is used for trade decision-making in the financial markets. The System of "pairs trading" is also typical of these systems. Pairs trading system is one of the oldest systems of algorithmic trading that its performance and profitability have been proven and shown in many of the studies that have been conducted so far in the financial markets. The most important principle in pairs trading is the equivalent long run relations or the mean reversion property. In this study, by calculating and evaluating the Sortino Ratio and return, performance of the pairs trading system has been surveyed through the cointegration approach in the Tehran Stock Exchange. The experimental results on pair stocks of selected in the Tehran Stock Exchange shows that using the pairs trading system as a market neutral trading systems has a significant return than return on ordinary shares in the same period.

In this paper, the effect of credit risk and currency risk on return of prices of bank shares listed on the Tehran Stock Exchange has been investigated. Non-Current measuring credit risk of loans to total loans ratio was used. Currency risk as well as changes in the exchange rate against the euro is defined rials. Data for the period 1392-1394 with a total of 648 data was collected on a daily basis, using the software Eviews using time series data using GARCH-M-ARMA approach is studied According to research findings on Iran's banking system between credit risk and currency risk with returns of stock prices listed on the Tehran Stock Exchange Bank there is a negative relationship and the expression of this research is innovation. As well as credit risk and currency risk with the risk-return between the prices of bank shares listed on the Stock Exchange there is a positive relationship. According to the results, we can conclude that the banking system administrators to increase the efficiency of credit risk and currency risk should the stock price set under management control.

Portfolio selection is one of the problems which is very important for the investors in the exchange market. In order to invest on several kinds of shares instead of a specific one so that they can earn maximum profit through a certain level of risk or have minimum risk with a certain level of profit. What have been introduced concerning financial calculations and the choice of portfolio or selection of an investment up to now, in some ways, determines priority in accordance with degree of risk and rate of return respectively; so that the investor can select his favourite portfolio through financial facilities and other confronting policies. When the investor is faced with different properties, he should make decisions about the number of properties as well as the amount of investment. So in some ways, he faces uncertain decisions in his choices. We are considering fuzzy concepts in the discussion of portfolio selection optimization in order to pursue this uncertainty. Then by using Bonison method, we determine priority and preference among the portfolios so that the investor can decide without confusion and finally through introducing combined metaheuristic algorithm for variable neighbourhood search and genetics, can optimize the resulting model of previous process and comparing it with other solving algorithms.

Due to the fact that traditional univariate approach in portfolio value at risk measurements ignore the time varying correlation between its components, these models underestimate or overestimate value at risk. In addition, complex financial markets make it necessary to use effective approaches, such as multivariate risk measurement. Therefore, in this present study, we tried to evaluate four multivariate value at risk measurement approaches for two portfolios in food industry exchange. The result of Christoffersen, quadratic probability score and root mean squared error tests showed copula-based Monte Carlo approach has more reliable result in comparison with others. Hence, we applied this approach to investigate dependence structure and measure risk, and its result showed the maximum expected loss of dairy portfolio value over a week is 2.01 percent, while for sugar portfolio is 1.09 percent.

Issuer's purpose of issuing securities - debt and capital securities – is financing and it will be given each issue of securities; But issuers have different motivations to choose between these two securities, they choose one of these securities based on their circumstances. Issuers in different markets have incentives and needs for issuing their securities. These incentives and needs should be in other markets. Expert-based method is chosen in order to validation and exploring various incentives. Expert's interview and approving method has been done in two ways: Expert's confirmation at the first phase is done by snowball method; And secondly to rank the motivations and also their indirect confirmation is used of TOPSIS method. A total of 17 extracted motivates from the literature, 3 motivates has not been confirmed. The most important motivational factor for the issuers in the country's capital market is using permanent source of financing

Z-numbers theory was proposed in 2011 by Lotfy Zadeh. This theory describe the uncertainty of information where any z-number is displayed by a pair of fuzzy number. Because of the uncertainty in the financial markets, this theory can be used in the investment portfolio selection. As the first component of z-number is the fuzzy asset return and the second component is reliability of prediction of first component. We can use value at risk criterion for increasing efficiency of investment portfolio selection model. Due to consideration the uncertainty in asset returns and using value at risk, this model is an appropriate model for investment portfolio selection. The advantage of this method compared to the conventional fuzzy method is consideration uncertainty of expert knowledge and allocation reliability to their prediction of fuzzy parameter. Finally, we provide a numerical example from Tehran stock market.

Portfolio or Stock Investment is one of the most important investment methods especially in foreign market. In regulation of Organization for Investment Economic and Technical Assistance of Iran, stock investment is classified in 5 different sections including “Stocks of Corporation”, “Bonds”, “Fellow of Banking Capital”, “Trading Securities”, and “Governmental Treasury Bills”. Gathering the data by means of a questionnaire distributed among the target population including managers and experts of the Organization for Investment Economic and Technical Assistance of Iran, we investigate on stock investment methods in foreign market in hierarchical model based on Topsis algouritm. Due to the answers gathered from the questionnaire, we identified 8 factors effective on portfolio investment to be used in topsis algorithm. These effective factors was entered to a decision making matrix, using in ranking portfolio investment styles based on ideal solutions technique. Results showed that the corporation stocks is the first and the top priority for international portfolio investors.

In this paper, we deal with the ranking of different VaR and ES approaches using daily banking industry index data over the period 2008 to 2016, with an emphasis on Conditional Extreme Value approach. In the first stage, we use Bernoulli coverage and independence of violation tests for VaR models and McNeil & Frey’s backtest for ES models to examine the validity of these models. In the second stage, we import the loss functions of the valid models remained from the first stage into the MCS function and rank statistically them. The loss function used for VaR models is Dowd loss function and the one used for ES models is Olsen loss function. The results show that the in both VaR and ES models, the conditional EV with normal standardized residuals, the conditional EV with student's t standardized residuals and GARCH with student's t residuals models are respectively ranked first to third.

This paper analyzes the role of corporate governance indicators in default risk focusing on data from a 60-member sample of firms listed on Tehran stock exchange during 1389 to 1393. In this paper, default risk was calculated by a measure based on the Black-Scholes-Merton (BSM) option pricing model, where a firm’s default risk is derived from the market prices of its shares. This method overcomes some of the problems associated with the default risk measures used in prior studies. Moreover, corporate governance indicators were divided into four categories: (1) shareholders’ and stakeholders’ rights, (2) board of directors and its committees, (3) auditing, and (4) transparency and public disclosure. Based on each of these categories, a representative index, and by aggregating four representative indexes, a composite corporate governance index was built. Then, the relationship between these indexes and default risk was examined. Results suggest that only governance indicators relating to transparency and public disclosure, at 5% level, are significantly correlated with default risk. However, combining these indicators with other governance indicators (that is, indicators related to shareholders’ and stakeholders’ rights, board of directors and its committees, and auditing), did not have any significant influence on default risk of Tehran stock exchange listed firms.

The main goal of this research is to studying an identifying the main influencing variables on the TEPIX (Tehran Stock Price Index) and modelling them using artificial neural networks and comparing results with technical analysis and Elliot waves. Independent variables used are dollar exchange rate, inflation, GDP, unemployment and liquidity and dependent variable is TEPIX. In this study, artificial neural networks (NLP and GMDH), technical analysis tools (Elliot waves and regression channel) are used that they show between independent variables in GMDH, unemployment is unneeded variable and have low influence, but others have high influence in the model. Further the study shows that technical analysis and artificial neural networks may have same results, but ANN have more power to predict the TEPIX.

In this study, evidence is presented that shows the positive stock return-volatility relationship at the firm level is due to firm's real options. In the real options theory, it can be deduced: A) that the positive stock return-volatility at the firm level for those firms with more real options is much stronger and that the level of the sensitivity of the firm's stock return in response to the changes in the stock return volatility is significantly reduced due to the use of real options.
B) that the positive relationship between stock return and return volatility at the firm level for companies that have investment opportunities are stronger. While, this relationship for asset-based companies is weaker. In other words, the return – volatility relationship is stronger for newly established companies, small companies, companies with modern research and development and finally companies with high growth rates.
C) that, the relationship between return – volatility for companies that have fewer restrictions and greater capabilities to better respond to uncertain demands (greater flexibility) are much stronger. In the real options models, managerial flexibility leads to greater firm's value convexity function.

Diversification and return's relationship is a question that every bank faces with it. It is a routin issue that the bank should answer to it. This study also seeks to evaluate the relationship between diversification and return. The sample of study is 10 banks included Saderat, Mellat, Tejarat, Parsian, Eghtesad Novin, Pasargad, Sina, Hekmat, Day, Sarmaye, Post bank, and Karafrin. The period of study was from 2011 to 2015. Herfindal Herishman index (HHI) was used as the proxy of diversification and two kinds of return included return of assets (ROA) and return on equity (ROE). The multiple regression is used and estimated by estimated generalized least squares (EGLS). The results showed that there was a significant and adverse relationship between diversification of assets and ROA. Also, there wasnt a significant relationship between diversification of loans and ROA. Furthermore, there wasnt a significant relationship between diversification of loans and assets with ROE