FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT)
Year:2015 | Volume:5 | Issue:21|Papers Count:9

Markowitz’s mean-variance model of portfolio selection is one the best known models in finance since 1950, which recognized to contribute in salient transformations. Financial theorists have had extremely efforts in making portfolio selection models more applicable which have caused to drive them to the new models. In this paper, portfolio selection in crisp and fuzzy cases is studied respectively, and corresponding model and algorithms in both cases are proposed; an effective way is given to transform and optimal problem with nonlinear objective function or non-linear constraint into a linear problem, which alleviate the computational difficulty greatly. In two models, the risk is taken as the sum of the absolute deviation of the risky assets instead of covariance, the transaction cost is taken as v-shaped function of the difference between the existing and new portfolio. Also, the investor’s subjective impact is reflected in the model of the fuzzy decision-making environment. In order to compare the two models, we have selected 13 top accepted firms in Tehran Securities Exchange among 50 firms as statistical sample in this research, which have been active between1382 to 1391. For achieving model’s portfolios, DEA SOLVER software was used. After determining portfolio and calculating the return and the risk of each of the models, the research hypothesis was tested by using SPSS software. The results show that between two models, the proposed model can generate a favorite portfolio strategy according to the investor’s satisfactory degree.

Financial optimization is one of the most attractive areas in decisionmaking under uncertainty. Portfolio selection problem is a classical financial problem but it relies on three restrictive assumptions. First, the investor is myopic and maximizes a single-period utility. Second, financial market is frictionless. Third, the investor knows the exact parameters that capture asset price dynamics. In this paper, we propose and solve a model to conquer the mentioned assumptions and approach to the real world. Therefore we propose an approach for solving the multistage mean-semivariance-CVaR portfolio optimization problem under transaction cost with genetic algorithm. The model was tested on a dataset drawn from the Tehran Stock Exchange (TSE) and we used the monthly returns of the 24 stocks derived throughout the period January 2009 to July 2013 as the inputs of the model. Results indicate that this algorithm is suitable and efficient for these kinds of problems.

Credit risk plays an undeniable role in the profitability and survival of financial institutions around the globe. This paper intends to propose a hybrid approach based on artificial bee colony (ABC) and neural network (NN) methods for credit risk assessment of customers who want to take loans. Artificial bee colony is regarded as a wrapper technique of feature selection approach, which aims to omit irrelevant, redundant and noisy data. The other two implemented methods in this paper are neural network based principal component analysis (PCA) as a filtering technique of feature selection approach and neural network lonely. In order to demonstrate the effectiveness of suggested approach, we have used a collected dataset associated with customers (good, bad) of credit risk center of Kar Afarin Bank and their eighteen features related to each of these customers in a three-year time period. According to findings of this paper, ABC-NN approach outperformed the other two applied methods of this article in terms of prediction accuracy for our given dataset.

In the complicated world that the risk is inspirable part of investment and for investing in any place the risk should be calculated and should transfer it to investors to decide whether to invest or not. For answering to the need of investors for decision making, different models have emerged with regard to the data for estimating the risk of models.In this research for determining value at risk, 21 of active investment companies in the capital market of Iran have been selected with regard to econometrist and nero. models in order to calculate the explanation capability of models in determining the risk of 21 investment company in Iran’s capital market and introduce the superior model.

Given the adverse consequences of financial distress for companies and institutions, fiscal and monetary economy is strongly related with the use of methods that predict the occurrence and the financial inability of wealth to prevent loss is important. Until now different models for predicting corporate default probability is presented. Major credit risk assessment models based on retrospective data and models that use market data to predict the likelihood of future default to use is essential.The aim of this study is to investigate the applicability of the KMV model to assess the risk of bankruptcy for firms listed in Tehran stock exchange and compare the results to explain the Altman Z-ranking model to determine the accuracy of the model. Data includes all firms listed on the stock market during 1380 and 1384 subject to Article 141 of the Commercial Code have been a total of 50 companies eligible. In addition 50 Non-bankrupt companies were compared between the two groups. The result is applied and quantitative, Show that KMV models predict financial distress and like Altman model to predict the separation between bankruptcy and non-bankrupt companies.

This paper is aimed to test specific risk (SR) pricing in Tehran Stock Exchange (TSE). Also, regarding to contradiction between empirical documents and fundamentals of classic Finance, for investigation of SR pricing reasons, some explanations presented for whyness of mentioned effect is tested using Fama-Macbeth regression (1973) and stochastic discount factor (SDF). Research period is 1378 to 1389 and its sample consists of 270 listed firms in TSE.Obtained results confirm specific risk pricing based on both Fama- Macbeth and SDF methods. Moreover, reason of SR pricing is not attributable to any factors such as size, B/M ratio, momentum, liquidity, third and fourth moments and institutional ownership. However, dependent on SR measure and minimum trading day requirement for thin trading, kurtosis and industry affect specific risk pricing in some cases.

One of the most important challenges among the investors in the world of stock market is to maximize the return with taking minimum number of risks.The investors are always looking to find the best values for the stocks with minimum risk and maximum profit. The best financial model proposed, is the selection of an optimal portfolio, which has been inspired by the classical theory of Markowitz. He believed that the excepted value and variance is based on the return distribution. The model based on mean-variance may not be ideal for investors since the return of their assets may not have a normal distribution. The study showed that this model is not practical and may not be the best choice for the investors in terms of risk that is involved. In contrary, other risk measures such as skewness might be more appropriate. Any investor involved in any investment program, should not only look into the history of the stock but also the future growth of that stock. The objective of current research is to develop a financial model for portfolio selection based on mean-variance and meanvariance skewness by using neuro-fuzzy network to forecast stock price. Finally, these proposed models could be solved by using the state of art genetic evolutionary algorithm. The empirical results will show that the proposed models will provide more profit to investors comparing that to conventional models and stock market index.

How does the optimal risk of assets change as their investment horizons increase? Does this impact investment portfolio decision making, in particular, optimal asset allocation between value and growth strategies over various investment horizons? This paper adopts a new approach to address these questionsby examining portfolio allocation between value and growth stocks over various investment horizons. This new approach is based on wavelet analysis, which decomposes the returns of a particular investment strategy across multiple investment horizons.In this paper, we used month returns of 10 years of Total Index stock exchange of Tehran and portfolio of investment componies accepted in stock exchange. The stocks divided to growth and value stock. Value stocks include BV/MV more than 30 percent and growth stocks include BV/MV less than 30 percent. The results using investment companies portfolios show that as the investment horizon increases, the optimal mean allocation of investors heavily away from growth stocks, particulary for lower and moderate levels of risk aversion. Interestingly for The Total Index stock exchange of Tehran the allocation weights between value and growth do not vary much.

The Foreign Exchange Market is where one currency is exchanged for another. In the 18th century, the Japanese developed an approach to technical analysis that traced and predicted the prices of rice contracts.There are some ways to classification of candlesticks. From one perspective they can be divided into two types of ‘Reversal Patterns’, and ‘Continuation Patterns’.In this paper nine different reversal candlestick patters are analyzed. They include hammer, hanging man, doji gravestone, doji dragonfly, doji long-lagged, bearish and bullish engulfing, piercing pattern, and dark cloud cover.It is analyzed whether various candlestick patterns can predict candles and trends in Foreign Exchange Market. The first group of minor hypotheses include whether or not a candle with an inverted color proceed by these patterns. The second group analyze whether or not the profit of trading after various patterns is significantly profitable.I considered the first, second, and the third candles as different possible points for finishing the trades. These three different points have different results, hence I’ve categorized them in three various scenarios and discussed about them separately.One of the most important sign of power of a specific pattern is when the first next candle be a confirmation of trend changing. In this part only the confirmed cases are analyzed in two different scenarios.At the end on the basis of minor hypotheses, it is concluded that there is not any evidence that we can say “various candlestick patterns predict candles and trends in Foreign Exchange Market effectively”. Although the success rates of correct prediction of the next candle after a confirmed pattern is almost insignificant in all cases, but the profit of trading after this confirmation can be significant. We can conclude that a confirmed pattern could be profitable in all mentioned patterns.