FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT)
Year:2016 | Volume:7 | Issue:28|Papers Count:9

Nowadays one of the most critical issues of risk management in banks, financial institutions and credit rating agencies is credit risk. Credit risk refers to the risk of default by the borrower, i.e. the borrower fails to fulfill its obligations to repay debt, or at least does not settle the obligations on time. In this study, we intend to predict the probability of default, in the selected companies at the Tehran stock exchange. It can be divided modes of assessing the default risk in three categories. These are structural models, data based experimental models and expert assessment models that determined by experts without statistical estimations. Our research is based on the structural models.Structural models such as first passage models are developed based on the Merton model. It can be said that the structural literature on credit risk starts with the paper by Merton (1974), who applies the option pricing theory developed by Black and Scholes (1973). Merton model has a number of simplifying assumptions, i.e. occurrence of default only could happen in maturity time.In this study by relaxing the above assumption, we will reach more developed model to calculate the probability of default. After that, we calculate the annual default probability of the selected companies both by the Merton model and the proposed model during the years 1390 and 1391 SH. Ultimately, the performance of both models is compared using Wilcoxon signed-rank test that indicates a significant difference between the two models.

The market capitalization of the main pillars of the economy of any country is mentioned as a showcase and a thermometer of the economy, which represents the country's economy. Design portfolio as one of the most important issues in the financial sector has been considered by capital market participants.This research started by identifying the most important factors affecting capital markets and the use of questionnaires and enjoying views of financial experts to select the critical factors deals. Data obtained from this phase was followed by an assessment questionnaires matched. Experts in this questionnaire to select one of the dimensions of uncertainty and its impact on the index were equal exchange industry. In order to calculate the similarity between uncertainty and industries Kappa test was used. Then analyzes the results of the previous stage using planning assumptions were based. Finally, with regard to the selection of possible scenarios and possible choice of industry-based planning method was assumed. To examine and analyze statistical data and drawing graphs Spss and Excel used.Assumption-based planning of futures studies methods. In this study, a new quantitative approach to portfolio design was introduced. The results show assumption-based planning used for portfolio optimization and also outperformed the market index is.

Investing in the stock market involves analyzing stocks and timing of purchase and sale. For this purpose, different methods and points of view, such as fundamental analysis and technical analysis can be performed. Many studies have investigated the profitability of technical analysis in the capital market and used various trading strategies. The purpose of this paper is to investigate the potential gains from technical analysis through combining oscillators and moving averages in terms of 6 analytical strategies. Investigating the profitability of technical analysis with use analytical strategy not has been done in country and abroad. For this purpose, in terms of 6 buy and sell strategies, the shares of10 stock petrochemical company that accepted in Tehran Stock Exchange is analyzed.The period of investigation has been 2010 to2013. Findings showed that the majority of all strategies that suggested buying signals, has created a return over the risk-free return. In all strategies, while also buying signals being profitable, holding stock for each of these intervals does not make a significant difference in the rate of annual return. In the first strategy, was observed difference in Returns in time periods. Also, with compare the annual return on all timescales strategy, third strategy have been more efficiently than other strategies.

Former local and foreign researches in risk hedging area have investigated optimum delivery month and optimum risk hedging ratio. In this research risk hedging by use of all delivery months by use of weekly data is discussed because of low number of transactions and contract's volumes in Iran Mercantile Exchange. Three scenarios are defined. For this purpose three scenarios is defined. In the first scenario the number of positions on each delivery month is equal with the number of trades on each delivery month in previous week. In the second scenario positions are taken based on number of trades in previous trading day and in the third scenario positions are taken base on average number of trades in the last week. However, optimum hedge ratio should be considered in each delivery month. Static hedging ratio by use of minimum variance method and different econometrics models for in the sample and out of sample tests is calculated. Results show that three scenario has the ability to reduce risk. In the sample tests indicate that the first scenario with use of VAR model has the best efficiency and in the out of sample tests second scenario with Tarch model has the best efficiency.

Recently, real option analysis has gained attention as an effective valuation method for complex real estate projects in developed countries. However, considering its potential, this method has not become as popular as it could have among developers in the country. One major reason may be its complexity and perhaps lack of finance knowledge and real estate data among Iranian developers. Thus, case studies can demonstrate the effectiveness of any theory and introduce real option analysis to Iranian devolopers as a standard valuation method. In this paper, case study analysis is used to examine the application of real options in real estate development projects. Comparison of valuations using traditional discounted cash flow with real option analysis brings out the relevance of real option methods in project decisions.Binomial method that values the project as a compound call option has increased the payoff of the project by 14% in comparison with net present value.

Calculating the option price in Stochastic Volatility models is one of the most important queries in financial mathematics that several investigations published about it in recent decades. But in most of these investigations, Model Parameters are used in calculating options without calibration and referring to the process of doing this. In this Article, besides introducing and studying Double Heston Model, we want to estimate the parameters of this model with the help of Loss Function. For this, we use Microsoft Corporation put options with similar maturities and different strike prices, and with estimating Implied Volatility of put option price of this company in April 2015, we achieved this matter.

In recent years, earnings management in university research has attracted much attention. The aim of this study is to predict earnings management through discretionary accruals based on adjusted Jones model. In this study, two models of artificial neural networks and genetic algorithms - neural network hybrid model as a successful model to predict earnings management based on adjusted Jones model were used in the Tehran Stock Exchange. The sample used in this study is consisted of 570 firm-year between 2008 to 2013. The results showed that neural networks have a high ability to predict earnings management rather than the adjusted Jones linear model. The findings also suggest that, the genetic algorithm through optimizing artificial neural network weights is able to increase power of artificial neural network to predict earnings management.

This study aims to compare explanatory power of Carhart four-factor model and Fama-French five-factor model in prediction of expected stock return in listed firms of Tehran Stock Exchange. To do this, a sample of 142 firms was selected from 2009 to 2013. In this study, hypotheses from multiple regression approach were evaluated using panel data method. Results show that Fama- French five-factor model has more explanatory power than Carhart four-factor model in predicting firms' stock returns. Also, results indicate that adding two factors, profitability and investment, to three-factor model increases the model power in explanation of stock returns of firms.

Portfolio selection is one of the most important area in financial world. Investors always want to make the best decisions which are compatible with conditions of real world. In the real world, data are usually under uncertainty. On the other hand, the most of strategies for portfolio selection are multi-period. Therefore, investors should rebalance their portfolios during investment horizon. In this research we present a multi-period portfolio optimization model which considers transaction costs and deal with uncertainty by application of robust programming. This model is a mean-CVaR multi objective model that is solved by goal programming. Furthermore, most of previous researches have used regression or time series models to forecast future returns of stocks for solving numerical examples, however, in this paper we forecast future returns by using Artificial Neural Networks (ANNs). Finally, solutions of robust model are compared with results of nominal one. These results show that consideration of data uncertainty and other real assumptions lead to more practical solutions.