JOURNAL OF FINANCIAL MANAGEMENT STRATEGY
Year:2017 | Volume:4 | Issue:15|Papers Count:7

The prediction of future fluctuations in stock index can provide information about the future trend in the capital market. In order to increase the accuracy of the prediction of stock exchange index, this study used a combination of statistical methods and artificial intelligence. In this study, a hybrid stock index prediction model by utilizing principal component analysis (PCA), support vector regression (SVR) and particle swarm optimization (PSO) is proposed. In the proposed model, first, the PCA is used to deal with the nonlinearity property of the stock index data. The proposed model utilizes PCA to extract features from the observed stock index data. The features which can be used to represent underlying/hidden information of the data are then served as the inputs of SVR to build the stock index prediction model. Finally, PSO is applied to optimize the parameters of the SVR prediction model since the parameters of SVR must be carefully selected in establishing an effective and efficient SVR model. The findings show that preprocessing the data can decrease the prediction error of the model significantly.

One of the threats facing the companies is the financial distress and the bankruptcy accordingly. The purpose of this study is to investigate the effect of earning quality as a moderator in companies’ financial distress prediction. The population of study included 41 distressed and 41 non distressed companies listed in Tehran Stock Exchange during 2003 to 2013. The selection criterion to choose distressed companies was the use of five specific criteria for financial distress. Firstly, to verify the separation accuracy of two samples, distress and Non-distress, using the Wilcoxon test, the average of two independent variables were analyzed. In order to investigate the financial distress prediction, three models included Altman, Ohlson and Bharath and Shumway in a binary logistic regression were used. The findings of the study show that the earning quality significantly increases the accuracy of predicting financial distress.  Also Altman model, in comparison to other models, is more accurate in predicting financial distress, so it is considered as a more suitable tool in prediction.

Following the separation of ownership from management and creating the information asymmetry between owners and managers, the importance of the related-party transactions and its full disclosure, fair and transparent of information has been increased. Considering the importance of the nature of the related-party transactions, this study aims to examine the relationship between control-ownership wedge of the controlling shareholder and related-party transactions. To achieve this objective, 75 listed companies at Tehran Stock Exchange were selected during the years from 2009 to 2014. Using the software Eviews 7, hypotheses were tested. The results show a significant negative relationship between the related party transactions and the right gap control and ownership of controlling shareholders. Therefore, this result provides empirical validation of theory of self-interest.

The purpose of this study is to investigate the effect of stock migrations on the spend of portfolios’ return. For this purpose, the return of portfolios that have migration for two consecutive courses was investigated. Due to the few studies in Iran, this issue has no clear and reliable results in Iran. This research can be complementary to studies by Fama and French, Khan Ahmadi and Tehrani. To test the hypotheses, portfolios based on accounting data from 2004 to 2016 were provided. In this research, sampling method is systematic elimination method and Alpha Jensen is used to study the constituted portfolios performance. According to the results of first, second and third hypotheses, there is not a significant relationship between return and stock migration. Meanwhile, there is a significant relationship between the variables of the forth hypothesis. Therefore, the wealth of stock holders increased by investing in companies that had migration from small size to big size in two consecutive courses.

The aim of this study was to investigate the effect of corporate social responsibility disclosure on the investment efficiency. The evidence reveals that corporate social responsibility disclosure decreases investment inefficiency and consequently increases investment efficiency. Content analysis method was used to measure levels of corporate social responsibility disclosure. A sample of 90 firms listed in Tehran Stock Exchange during the years 2010 to 2015 were selected. Multiple regression models based on panel data techniques were used to test the research hypotheses. The independent variable in this study is corporate social responsibility disclosure and Investment efficiency is dependent variable. The findings indicate that social responsibility disclosure will improve investment efficiency in firms. This result is consistent with the expectation that high CSR firms enjoy low information asymmetry and high stakeholder solidarity (stakeholder theory). Taken together, the findings highlight the important role that CSR plays in shaping firms’ investment behavior and efficiency.

Cash flow prediction is an essential component of economic decision making, particularly in investment and credit evaluation. This paper examines the comparative predictive ability of earnings and operating cash flows variables on future operating cash flows, during short-term, medium, and long-term periods. For this purpose, the data collected from financial statements of 203 companies listed in Tehran Stock Exchange, during a ten-year period (2005-2015) were used. In regression model, the changes of current operating cash flows (as proxy for future operating cash flows) under the influence of past one, three and five years of earnings and operating cash flows (as predictors), are evaluated. The results from the regression analysis reveal earnings and operating cash flows are significant in predicting future operating cash flows but their predictive powers are different, so that comparative predictive ability of operating earnings variable about future operating cash flows is more than that of the operating cash flow itself. The results also show that using both operating earnings and operating cash flow with each other for the prediction, has better outcomes. Moreover, the results indicate that the longer the period of prediction is, the higher the prediction power will be.

The conflict of interest between firms' stockholders and managers is a noteworthy concern in corporate finance discussions todays. The purpose of the present research is to investigate the effect of corporate governance criteria on performance of holding companies and subsidiaries in Tehran Stock Exchange. The study can be categorized as applied research regarding objective and descriptive and correlation research concerning research method. The study considers corporate governance criteria and market value added as research variables. Research sample contains holding companies and their subsidiaries in Tehran Stock Exchange which are selected via some criteria, including 27 holding companies and 89 subsidiaries over 2008 to 2015. For testing the research hypotheses, panel data analysis and multiple regression models are employed. The results indicate that board composition and ownership structure of holding companies significantly affect companies' performance. Moreover, holding companies performance has a significant effect on subsidiaries performance.