JOURNAL OF FINANCIAL MANAGEMENT STRATEGY
Year:2017 | Volume:5 | Issue:3 (18) |Papers Count:8

The purpose of the present research is to compare portfolio optimization models in a fuzzy credibility environment, aimed for end-of-period wealth maximization and risk minimization. The investor’ s risk was measured using the Value at Risk (VaR), Average Value at Risk (AVaR) and semi Entropy. In order to get closer to the real world investment model, while allowing for transaction costs and investing part of wealth in risk-free assets, in addition to the cardinal constraints, other constraints including the minimum and maximum amount of wealth assigned to each asset, and the minimum and maximum number of stocks present in portfolio were applied. The results of the multi-period models running by MOPSO algorithm indicated for the models Mean-AVaR, Mean-Semi Entropy, and Mean-VaR, respectively, performed better, in terms of Sharp and Treynor measures.

Stock market is one of the options available to invest in liquidity. Investors in this area used a variety of approaches to predict stock prices. But due to the nonlinear relationship between variables affecting stock prices, Artificial Neural Networks are one of the most suitable approaches for this work. These networks, through different search optimization algorithms, try to identify the relationships between these variables. The higher the algorithms used, the higher the efficiency of the algorithms, the more accurate the identification of the relationships between the variables. In this paper, an attempt has been made to combine chaotic maps and colonial competition algorithms with the reform movement angle to the colonial colonies so that we can deal with the possibility of being trapped in local optimum to reduce as much as possible. Therefore, using this approach, it is tried to predict the stock price of Iran Khodro Company. To evaluate the performance of the proposed approach to other conventional approaches of neural network education, three perspectives: the degree of accuracy of prediction, the amount of memory used and the time of execution were used. The results show that the proposed approach has a better performance than other approaches.

Equity Valuation Theory prescribes that free cash flow should not be associated with stock returns because it does not add value. However, free cash flow could become a value-relevant construct in certain contexts. This study considers growth opportunities and transitory earnings as two such contexts and examines the valuation of free cash flows. So, the purpose of this study is the investigation of the effect of growth opportunities and earnings permanence on market valuation of free cash flow. An accounting-based valuation framework is developed where stock returns are regressed on free cash flow interacted with growth and earnings quality proxies, after controlling for book values, dividends, and earnings per share. The data of 64 companies for the period of 2013-2015 in Tehran stock exchange were used. Findings reveal that firms with a positive free cash flow and attractive growth opportunities command a valuation premium from the market. Furthermore, probably 90% free cash flow is found to be positively associated with stock returns when earnings are transitory.

The global financial crisis, which began in September 2008, severely shook investors` confidence, affected corporate earnings and stock market performance, and caused the failure of many firms around the world. According to the research, this crisis has affected Iran`s capital market as well however with delay. In the crisis situation, managers are aware of the effect of financial reporting on users` decisions. This effect raises a question whether crisis conditions cause strong motivation for managers to promote reporting quality in order to gain investors` confidence or managers try to manipulate their earnings and performance to prevent negative market reactions and decreasing rewards. In this regard, and according to assumptions about management motivation for promoting financial reporting quality during the crisis, two main hypotheses were proposed which have been tested for sample of listed companies in Tehran stock exchange during years of 1387 to 1394. Findings show that financial reporting quality of Tehran stock exchange listed companies in global financial crisis was higher than the usual condition and firms with higher needs of finance had higher financial reporting quality. Innovative contribution of present research is the investigating of the effect of unfavorable economic condition on firms reporting strategy and anticipating reporting behavior during specific situations such as financial crisis.

Banks manage their funds by guiding the collected cash from the sectors that have surplus to the ones that are facing a shortage of (cash) liquidity. So, it is highly important to evaluate bank performance because of the impact that these institutions have in the growth and economic development of the countries. This research aims to evaluate and compare the performance of Iranian banks based on the Camels rating system. For this purpose, after studying the related literature, efficiency and soundness and safe assessment factors of the banks on the area of Capital Adequacy, Asset Quality, Management Capability, Earnings, Liquidity, and Sensitivity to Market Risk were detected. Then, using Analytical Hierarchy Process technique, the weights of each factor were determined. The results were used to evaluate the performance of the several Private Banks in years 2012-2017. And then they were ranked by using ARAS technique. The results show that Khavarmiyane, Pasargad, Ghavamin, Karafarin, and Sina banks had better function in comparison to other studied banks in years 2012-2017.

This paper proposes a method based on rough set theory and by using technical indicators to predict the stock price. Rough set theory has several advantages; the most important advantage is that no additional information is needed about the initial data. In the proposed model, a number of technical indicators from the data of Bank Saderat Iran during a year were calculated and used as condition attributes in the decision table and the stock price fluctuation on the next day was selected as decision attribute. It should be noted that by using the correlation matrix analysis, the variables with the highest correlation with decision attribute were selected as conditional attributes. Using rough set theory and different discretization and reduction methods, some rules are extracted based on learning data and methods validity were computed based on control data. Comparing the return of this method and buy and hold method reveals the superiority of proposed model. Also, using data from different years with different price trends as inputs to the model and achieving satisfactory results is a promising reason for using and developing this method in stock price forecasting.

One of the aspects of behavioral finance is risk behavior that is also known as mental risk or perceived risk that in fact is the investors` subjective judgment of the risk that may be estimated more or less than the actual risk. Risk sentiment Index is more influenced by non-economic factors. Several factors are indicators of RSI such as the days of the week, months and seasons, to name a few. In this paper, after examining the effects of risk perception in Tehran Stock Exchange index, using time series regression models and eviews8 software, it became clear that the perceived risk of the investor has the effect of non-random, which demonstrates a perception of risk. However, after seasonal effects influencing the perceived risk was found, that Tehran Stock Exchange has a maximum sense of risk investors on the final day Transactions. This means that during last week, Tehran Stock Exchange’ s investors feel more risks. Likewise, especially in early April RSI is minimum. Then, the return of this month is in maximum.

One of the most important economic project evaluation methods and the comparison between them is current values. This method is one of the most important and the simplest techniques in engineering economy. The calculation of the present value of the traditional methods to compare projects is not enough because it does not consider a major uncertainty factor. In this paper, we present an approach for the selection of projects by using current value. In order to include the uncertainties in the problem, fuzzy logic is used. Then, using analytic hierarchy process and binary integer programming with respect to the other objectives of the project, the most economical project was chosen. This model considers qualitative and quantitative objective and uncertainty to select the best projects in real-world situations.