JOURNAL OF FINANCIAL MANAGEMENT STRATEGY
Year:2016 | Volume:4 | Issue:14|Papers Count:7

The aim of this study is to investigate the relationship between dividend policy with stock liquidity and information asymmetry considering the characteristics of the company as controlling variables in Tehran Stock Exchange. For this purpose, all listed companies in Tehran Stock Exchange as the statistical population was considered during the period 2009 to 2014, that after screening by simple random sampling, a total of 45 companies were selected as sample. In this study, for examining the relationship between dividend policy with stock liquidity and information asymmetry, multivariate regression method and panel data model were used. The variables in the model include firm size; firm risk, growth opportunities and profitability as control variables respectively. The results indicate that there is a significant positive relationship between stock liquidity and dividend policy. Furthermore, there is a significant inverse relationship between information asymmetry and dividend policy.

The aim of this study is to investigate the relationship between tax avoidance and firm value with an emphasis on agency cost and disclosure quality of firms listed in Tehran Stock Exchange. The sample of this study consists of 68 firms for the period 2004 to 2015.This study is applied in purpose and correlation- descriptive in method. In this study to test the hypotheses, multivariate linear regression model is used. To test the models, the Chaw test was firstly used and it was indicated that Panel method should be applied. Then, Hausman test was applied for incorporating Panel method's random effects or proof effects. The results indicate that regardless of agency costs and quality of disclosure, there is no significant relationship between tax avoidance and firm value. On the other hand, tax avoidance has no significant impact on agency costs. Also the results of this study indicate that the firm’s disclosure quality has significant effect on the relationship between tax avoidance and firm value.

The present study aimed to identify aspects of the functional model in green banking. The main aspects of the research model were adopted from the literature in the field of monetary and financial services of banks based on the functional aspects in green banking that were confirmed by confirmatory factor analysis. A researcher-made questionnaire was used to collect the data. The study population consisted of 150 branch managers, bank deputy chairmen, university professors, Ph.D. and M.A. students. Thus, the method of Cochrane in the error of 5% and 95% was used to calculate the sample size. 109 questionnaires were distributed among them 100 ones were returned. In order to achieve the goal of research, fuzzy multiple attribute decision making techniques were used to gain weight and priority for each of the factors considered in the research model. The main elements of this model are: retail banking, corporate and investment banking, asset management and green insurance. The results of the prioritization based on the above mentioned method show that retail banking is of very high importance. Therefore, the bank managers are suggested to make the most of the benefits of this crucial factor by determining appropriate strategies.

The main objective of this study was to investigate the effect of executive compensation on the capital structure and financial performance of the companies listed in Tehran Stock Exchange for the period 2005 to 2012 and the information of 58 companies was used. The research hypotheses were tested using panel data regression model and ordinary least squares. Based on the results, factors such as firm size and the ratio of market value to book value changes of cash equity had significant negative effects on the capital structure of the company. There is, however, a significant positive relationship between the rewards of the board and financial performance and parameters such as the size of the company.

This study tests the efficient structure hypothesis and the role of human capital efficiency in predicted relations in this hypothesis as well as the quiet life hypothesis and the role of management optimism in predicted relations in this hypothesis. For this purpose, the data from 136 companies listed in Tehran Stock Exchange during 2010 to 2014, linear regression, and panel data models were used. The results showed that an increase in industry concentration, and in other words, a decrease in industry competition, will improve firm's efficiency index (disapproving the quiet life hypothesis in Iran's Capital Market) and increasing human capital efficiency, in addition to enhancing the direct relationship between industry concentration and firm’s efficiency, improves firm's efficiency (confirming the role of human capital efficiency in the relationship between industry competition and firm's efficiency). Moreover, the firm's efficiency will increase firm's growth (confirming efficient structure hypothesis in Iran’s Capital Market) and management optimism, in addition to increasing firm's growth, would enhance the direct relationship between efficiency and firm's growth (confirming the role of management optimism in efficient structure hypothesis).

In this study, a prediction model based on support vector machines (SVM) improved by introducing a volume weighted penalty function to the model was introduced to increase the accuracy of forecasting short term trends on the stock market to develop the optimal trading strategy. Along with VW-SVM classifier, a hybrid feature selection method was used that consisted of F-score as the filter part and supported Sequential forward selection as the wrapper part, to select the optimal feature subset. In order to verify the capability of the proposed model in successfully predicting short term trends, a trading strategy was developed. The model input included several technical indicators and statistical measures that were calculated for chosen 10 stocks from Tehran Stock Exchange. The results show that the VW-SVM, combined with the hybrid feature selection method, significantly increases the profitability of the proposed strategy compared to rival strategies, in terms of both overall rate of return and the maximum draw down during trading period.

The purpose of this research is to detect manipulation of stock prices in Tehran Stock Exchange that it has been done through Hybrid Genetic Algorithm-artificial neural network (ANN-GA) model and the Simplified Quadratic Discriminant Function (SQDF) Model. In this study, the variables of price, trading volume and free float stock to match the results of the model and the actual data of price manipulation is used. In the Hybrid Model of Genetic Algorithm-Artificial Neural Networks (ANN-GA), at first data of 316 stock companies from 2009/03/21 to 2013/03/20 on a daily basis, including 966 days were put into the GA model, then; weight of each variable were derived from GA. Next, using these weights, Perceptron neural network was designed, implemented and its efficiency was approved. Then, SQDF model was designed and implemented and its efficiency was verified. In the end, using MAPE, RMSE and R2 error measurement, the results of ANN-GA model were compared with those of SQDF model. The results showed that Hybrid model has much better performance and fewer errors than SQDF model in the detection of stock price manipulation and classifying firms into two groups, manipulate and non-manipulate.