Advances in Mathematical Finance and Application (AMFA)

The purpose of this study is investigating and determining rate of seizing assets and acquisition other companies by public sector companies. We estimate this rate at various stages of the life cycle of the company. Therefore, according to their size and age, the companies have been divided into small, large, young, and mature groups, and for this purpose, we have collected data from a sample of 45 companies of the public sector from three Iranian provinces. We have tested our analyses from the viewpoints of agency and neoclassical theories and discussed the results of the independent t tests. The results showed that with increase in age and size, public sector companies are more likely to seize the fixed assets of other companies to restructure and achieve improved operations. However, in the case of assuming ownership of other companies done through buying the companies, most public sector companies do this in the middle of their lifetime and in the course of their growth.

In financial markets such as Tehran Stock Exchange, P/E coefficient, which is one of the most well-known instruments for evaluating stock prices in financial markets, is considered necessary for shareholders, investors, analysts and corporate executives. P/E is used as an important indicator in investment decisions. In this research, harmony search metaheuristic algorithm is used to select optimal variables affecting P/E and then, modelling is done through multivariate regression based on panel data. For this purpose, a sample of 87 companies has been selected from listed companies in the Tehran Stock Exchange during a 10-year period (2006-2015). The results indicate the effect of the variables of stock returns, stock price to book value ratio, price to net selling ratio, return on assets, earnings per share, market value to book value, money volume, operating return margin, return on capital, and current assets, as top ten variables, on P/E ratio, which estimates a total of 86% of the P/E ratio changes.

With the aim of portfolio optimization and management, this article utilizes the Clayton-copula along with copula theory measures. Portfolio-Optimization is one of the activities in investment funds. Thus, it is essential to select an appropriate optimization method. In modern financial analyses, there is growing evidence indicating the distribution of proceeds of financial properties is not customary. However, in common risk management methods the main assumption is that the distribution of assets returns is normal. When the distribution of earnings isn’t normal, the linear correlation coefficient isn’t considered to be an appropriate measure to express the dependency structure. The investors are required to make use of methods that concentrate on the aggregated risks, considering the whole positions and the links between risk factors and assets. Therefore, we use copula as an alternative measure to model the dependency structure in this research. In this regard, given the weekly data pertaining to the early 2002 until the late 2013, we use Clayton-copula to generate an optimized portfolio for both copper and gold. Finally, the Sharpe ratio obtained through this method is compared with the one obtained through Markowitz mean-variance analysis to ascertain that Clayton-copula is more efficient in portfolio-optimization.

The aim of this study is to evaluate the impact of cash dividend components on corporates earnings persistence and return on stock. The population of study consists of 109 companies listed in Tehran Stock Exchange from 2011 to 2016. Data was analyzed using regression model. According to results, the cash component of earnings is more persistent than accruals and it can be used to predict future earnings. Therefore, it is suggested that cash dividend component to predict future earnings. In addition, managers should pay attention to the cash component of earnings in their decisions made on the amount of optimal cash fund because this component can positively affect future earnings. Moreover, the cash flow component of earnings cannot be used to predict future return on stock. Therefore, investors are recommended not to rely on the cash component of earnings in their investments, This is because even if corporates have considerable cash funds, their shares will not necessarily be a suitable option for investment and they should take other factors into account.

The study aims to investigate the relationship between the financial incentives of board members and disclosure of corporate risk, emphasizing the levels of corporate performance and risk in Iran. The research sample includes 98 listed firms in Tehran Stock Exchange during 2011-2015 (490 years-firms); the firms have selected by using a systematic removal method. Regarding the aim, the present research is classified as an applied research and concerning its method, it is categorized as a descriptive research. The research hypotheses are examined using the linear regression testing method; Eviews software has employed for data analysis and hypotheses testing. Based on the regression results, financial incentives of board members are effective on the quality and extend of firm’s risk disclosure.

In the world and in our country, Iran, many researches have been done in the field of financing, but due to the importance of this issue, it needs more thought and contemplation. Therefore, this study assessed “the impact of investments on financing of companies listed on the Tehran Stock Exchange”. The statistical sample is consisted of companies listed on the Tehran Stock Exchange during a five-year period (2010-2014). Ultimately, regarding the limitation of the study and using systematic elimination method, data of 155 companies has been collected. This study in terms of the purpose is a practical research. In terms of type of research design because of relying on historical data, is post-event and its inference method is inductive and in correlation type. This study includes four major hypotheses. In this study to assess the hypotheses, the linear regression has been used. To analyse the data and test hypotheses, EVIEWS software is used. After design and test the study hypotheses that have been done by the separation of each hypothesis, we founded that the current and long-term investments have impact on financing methods through the sale of stocks and debt.

The main purpose of this research is the study on effect of Financial and Operating Leverage and Venture Capital on Tobin's Q ratio amongst companies listed in Tehran Stock Exchange. In this research, the Holdings and Investment companies are used as statistical samples and 73 enterprises that are listed in Tehran Stock Exchange within 2001 to 2016 have been studied. The results driven by this research show that Venture Capital, hereafter called "VC", and Operating Leverage, hereafter called "OPL", are positively related to Tobin's Q ratio while Financial Leverage, hereafter called "FL", has negative relation. At the same time, combined variables “FL” and “VC” are negatively correlated with Tobin's Q ratio and combined variable “OPL” and “VC” are neutral on Tobin's Q ratio. The effect estimation of independent variables, namely FL, OPL and VC and control variables, over dependent variable of Tobin's Q ratio is 55%, which is in fact noticeable.

Stock trend forecasting is a one of the main factors in choosing the best investment, hence prediction and comparison of different firms’ stock trend is one method for improving investment process. Stockholders need information for forecasting firm’s stock trend in order to make decision about firms’ stock trading. In this study stock trend, forecasting performs by data mining algorithm. It should mention that this research has two hypotheses. It aimed at being practical and it is correlation methodology. The research performed in deductive reasoning. Hypotheses analyzed based on collected data from 180 firms listed in Tehran stock exchange during 2009-2015. Results indicated that algorithms are able to forecast negative stock return. However, random forest algorithm is more powerful than decision tree algorithm. In addition, stock return from last three years and selling growth are the main variables of negative stock return forecasting.