FINANCIAL KNOWLEDGE OF SECURITY ANALYSIS (FINANCIAL STUDIES)
Year:2020 | Volume:13 | Issue:47|Papers Count:10

Portfolio selection and asset management is one of the most important financial issues that seeks to distribute a specified budget over multiple time periods between available assets in such a way that the return of the portfolio is maximized and, at the same time, its risk does not exceed a certain amount. In this paper, we first propose a nonlinear mathematical programming model for Portfolio selection to maximize Sharpe ratios of stocks. Then, due to the uncertain nature of the input parameters of such a problem, a new robust possibilistic programming model has been developed, which is capable of adjusting the robust degree of output decisions to the uncertainty of the parameters. The proposed model was first tested and evaluated on 42 companies active in the Tehran stock market. In the end, the computational results of the proposed model show the high performance and the utility of the robust possibilistic programming model.

A digital currency is a complex form of electronic money. The process of transferring this system is quite direct, and compared to the traditional method, there is less cost and time for transactions to take place in different parts of the world. Digital currencies use a system called the Block chain, Anyone who understands the benefits of the Chinese block, It's empty in our country. Advantages such as the impossibility of manipulating information, intelligence and decentralizing processes, along with high transparency, are issues that optimistic about the future of this technology. In this research, we select a number of digital currencies with the highest transaction volume and liquidity to create portfolios. and calculated using the Value at Risk (VaR) approach and the return on the portfolio Finally, an optimal investment portfolio is offered.

The aim of the present research was to determine role of decision making styles and risk taking with financial performance of managers. The statistical population of this research was all managers of Hekmat Bank Branches in Tehran year of 2017. The research sample consisted of 70 financial managers who were selected convenience sampling method. Data collected from the Performance of Managers Questionnaire, Decision Making Styles Questionnaire and Scale of Risk taking were used. Pearson correlation and regression analysis were used to analysis the data. The results indicated that there were signification correlation between secure decision making styles and financial performance of managers. Also There are signification positive correlation between secure risk taking and financial performance of managers. The results of multiple regression showed that the decision making styles and risk taking was signification the predictor of financial performance of managers. This finding has important implications for improving the performance of managers.

Islamic banking is developing rapidly around the world and is increasingly recognized as a viable alternative mode of financing especially these last years when capitalism has shown its limits and weaknesses. However, this astronomical growth of banking and Islamic finance was associated with a high level of corruption that plagues many Muslim countries. This ailment of corruption can deter Islamic banking to be a better effective and meaningful pathway for poverty reduction and economic development. The purpose of this paper is to investigate the impact of corruption on the soundness of Islamic banks. The purpose of this study is to assess the effect of corruption on the financial health of Islamic banks. The statistical population of this study is all banks accepted in Tehran Stock Exchange during the period from 2011 to 2016. Sample was selected based on the systematic elimination method and 23 banks were the basis for research hypothesis. The research method's research hypothesis and the fitting of the research model have been done using the EViews econometric software. There is a negative and significant relationship between financial corruption with the health of Islamic banks. Evidence of research suggests that, with the intensification of corruption at the country level, the financial health of banks has decreased and corruption has an adverse effect on the financial health of banks. In other words, financial corruption in the country threatens the financial health of banks.

Today, corporate restructuring strategies are presented as a solution by chief executives officer and in order to exit the company from the crisis. In this regard, it is necessary to examine the impact of implementing these strategies is highlighted. The purpose of this study is to investigate the effect of implementation restructuring strategies in different stages of crisis life cycle companies. For this purpose, we used Z-score Toffler to select crisis companies. 406 years-Company were selected as samples. it was used to determine the stages of the company's life cycle from Dickinson's cash flow model. The Logit regression model was used to test the hypotheses. The results of the paper suggest that Toobin's Q variable only has a significant and negative relationship with the asset restructuring strategy, the total assets variable negatively correlated with the restructuring of management and financial restructuring and with the asset restructuring strategy. Financial leverage variables and institutional shareholders only have a meaningful and negative relationship with the financial restructuring strategy. The cash flow variable only has a positive and meaningful relation with the financial restructuring strategy. There was no relation between the firm's asset return variable and all three restructuring strategies. The growth stage companies use less than the asset-restructuring plan. Birth phases are less likely to be used than restructuring management structures than in other stages. In addition, the decline stage is used as compared to other stages of financial restructuring.

Studying, and analyzing the dependency structure between the markets at the economic boom and bust have been suggested by the researchers and theorists of different areas. Furthermore, there have been various models to explain the correlation between the financial markets. Among them, the Copula model has a high ability to recognize the asymmetric dependence structure. The present research is going to study the dependency structure in the financial markets of four countries; Iran, the United Arab Emirates, Turkey and China at the boom and bust cycling in the period of 2014-2017, applying conditional heterogeneity variance model (GARCH), the Markov switching approach, and the Copula functions. The results illustrate that there is an asymmetric structure in every regime, as at the recession time, the correlation between these markets and Iranian market would be higher than the boom time.

Proper analyzing and forecasting depend on appropriate statistical model identification. This can be done when the variables included in model lead to improve its identification. This results in causality concept. Previously variables’ relations was confined to first moment and linear relation within the framework of a causal relation. If the relation rejected, the variable would be excluded from model. Recently, causality analysis in higher moments and nonlinear way attract researchers. This means that presumed causal variable may contain unique information for some of moments but not all of them. Accordingly, eliminating such a variable from the model results in hiding important information of variable’ s actual behavior which is under study. This research tries to present causality analysis in different orders of moments are presented in MSBVAR model settings. Therefore, free float index of stock exchange, exchange rate, OPEC basket price, gold global price are selected in order to examine causal relation from domestic and international markets to stock market. Results imply that exchange rate, oil and gold variables are causal in distribution for financial variable.

Considering the importance of stocks in financial and accounting literatures, and investors’ reactions to company stocks during different periods, the present study investigates popularity theory in Iran's financial market and its relationship with stock returns and stock return volatilities in Tehran Stock Exchange. A total of 179 companies are analyzed during 2010 to 2015. This is an applied study with post-hoc design. Moreover, it is considered a descriptive-inductive study based on methodology. Linear regression models are applied to examine hypotheses using Eviews8 software. The results show there is a positive and significant relationship between stock popularity and stock returns, stock return volatility and beta coefficients. In other words, stock popularity can increase stock returns, stock volatility, and beta coefficient

Stock price limit range is one of the tools that used to control the market participants sentiments and major changes in stock prices. Apply the above procedure can also reduce the market sentiment and the loss of shareholders, also has consequences such as changing the behavior of stock market actors and magnet effect. In this research, we investigate the magnet effect in ten sample companies that mention in the research for the year ended 1395. The research data are intraday and include 5-minute time series of prices traded in the Tehran stock exchange. Also in this research, the role of institutional investors on the magnet effect has been measured. The results of the research indicate that the magnet effect in some companies have approved but only in one of the companies whose magnet effect has been confirmed the institutional investors transaction caused it, and in other companies there has been a magnet effect caused by the individual investor.

The discovery of intelligent technical sales rules from the complex and making systems for buying and selling is a difficult task. The purpose of this study is to develop an intelligent mixing system for buying and selling to discover the rules of technical sales through the analysis of the Rough series and the genetic algorithm. The datasets used included 30 open, up, down, closing and volume futures contracts of stock indexes in the stock market in the period from 2011 to 2017. For this purpose, it is recommended that when discovering technical rules for future markets and solving optimization problems, discretization and data reduction, analyzing the Ruff series, and ultimately, for making optimal decisions about buying and selling the approach of the genetic algorithm. To test the proposed model and compare it with corresponding approaches, randomizations, correlations and approaches to genetic algorithm interventions were designed. Also, these comprehensive interventions, many issues of the existing buying and selling system, the use of slider windows, the number of sales laws, and the duration of the training course. In order to evaluate the intelligent mixing system, interventions were carried out on historical data of the stock index of Tehran Stock Exchange. Specifically, the analysis of sales performance was performed according to decision sets and volumes of training courses to discover the rules for buying and selling the test period. The results showed that the proposed model had better performance in terms of average returns and adjusted risk scale compared to the benchmark model.