Organizational risk is often defined as a change in the flow of profit, or as a sys-tematic or non-systematic changes in the stock return flow. The risk taking of management is conceptualized as the actual investment decisions that are indictors due to uncertainty results. The purpose of this study is to investigate the effect of financial characteristics on future corporate risk taking behavior. After designing the indicators for assessing financial characteristics, the transaction data were collected from the Stock Exchange in the five-year period of 2011-2015. A sample of 111 companies was selected by sampling method based on the Cochran formula, which resulted in a total of 555 year-firm observations. In this study, linear regression and correlation were used to investigate the hypothesis, and for analyzing data and hypothesis testing, we used Eviews software. What can be said in the summing-up and conclusion of the general test of research hypotheses is that the disproportionate changes in sales costs, advertising costs, rental costs, liquidity, financial leverage, and disproportionate changes in capital costs have a positive impact on future corporate risk taking behavior. In addition, other results indicate a negative impact of disproportionate changes in sales growth, inventory, liquidity, and asset turnover on future corporate risk-taking behavior. The results obtained in this paper are consistent with the documentation referenced in the research's theoretical framework and financial literature..
The Mediating Effect of Information Asymmetry and Agency Costs on the Relationship Between CSR and Investment Efficiency
The purpose of the present study is to investigate the relationship between corporate social responsibility and investment efficiency with particular emphasis on the mediating role of agency cost and information asymmetry in a sample of 121 firms listed on the Tehran Stock Exchange during the time period from 2012 to 2017. The research hypotheses are tested using multivariate regression analysis based on panel data and Eviews software. The results indicate that corporate social responsibility is negatively correlated with investment inefficiency. In other words, corporate social responsibility leads to reduced investment inefficiency. Also, information asymmetry plays a mediating role in the relationship between corporate social responsibility disclosure and underinvestment, whereas the variable of agency cost mediates the association between corporate social responsibility disclosure and overinvestment.
Recently, understanding the anomalies in financial markets have severely chal-lenged the efficient market hypothesis (EMH). The price momentum is one of the anomalies described as the unexplained short-term return by Fama and French (1996). The present research strives for modeling the price momentum of winner stock in the Iranian capital market. The grounded theory method was used to explain this phenomenon. To this end, in-depth interviews were held with 32 experts operating in the professional and academic fields in 2018. The collected data was encoded in three steps, and the results were presented as a conceptual paradigm. The research findings identified the momentum causal factors in the behavioral level, the background factors in the social, macroeconomics, and mar-ket levels, the intervening factors in the global economics, macroeconomics, mar-ket, and company levels, and the strategies in the social, macroeconomics, market, the investment and finances institutions, and consequences factors in market level. The research findings suggest that the winner stock price momentum phenomenon should not be considered a speculation opportunity. Rather, it is an anomaly that has to be regulated with the proposed strategies according to the experts. The consequences of the adoption of these strategies include the stable and normal income for the market actors, the decrease in the loss inflicted on natural persons due to the market volatility, the management of anomalies, more effective attrac-tion and allocation of liquid capitals, the reduced credit risk of brokerages, and the acceleration of liquidation in the market.
Organizations expose to financial risk that can lead to bankruptcy and loss of business is increased nowadays. This may leads to discontinuity in operations, increased legal fees, administrative costs and other indirect costs. Accordingly, the purpose of this study was to predict the financial crisis of Tehran Stock Exchange using neural network and genetic algorithm. This research is descriptive and practical and in order to collect data Stock Exchange database software has been used. For data analysis, we used artificial neural network in base form and artificial neural network mix with genetic algorithm. In addition for methods comparison, determination coefficient, Mean squared error and Root-mean square error have been used. The result of study shows that the best artificial neural network is a network with a hidden layer and eight neurons in the layer. This network could predict 97.7 percent of healthy and bankrupt companies correctly for test data. Furthermore the best mixed neural network with genetic algorithm is a network with 400 replications and population size 50, one layer and eight neurons which could correctly predict 100% of healthy and bankrupt companies. Finally, comparison of results of two methods shows that the best method for predicting financial crisis is mixed neural network with genetic algorithm.
Stock prices in each industry are one of the major issues in the stock market. Given the increasing number of shareholders in the stock market and their attention to the price of different stocks in transactions, the prediction of the stock price trend has become significant. Many people use the share price movement process when com-paring different stocks while investing, and also want to predict this trend to see if the trend continues to increase or decrease over time. In this research, stock price prediction for 1170 years -company during 2011-2016 (a six-year period) of listed companies in stock exchange has been studied using the machine learning method (Chaid rule-based algorithm and Particle Swarm Optimization Algorithm). The results of the research show that there is a significant relationship between earnings per share, e / p ratio, company size, inventory turnover ratio, and stock returns with stock prices. Also, particle swarm optimization (pso) algorithm has a good ability to predict stock prices.
Investigating the Mathematical Models (TOPSIS, SAW) to Prioritize the Investments in the Accepted Pharmaceutical
Considering the importance of decision- making in investment, this study prioritizes the accepted pharmaceutical companies in Tehran stock exchange, during 2013-2017 using the following criteria: the return on investment (ROI), reminded increment (RI), return on sales (ROS) and the earnings per share (EPS). Price per earnings ratio of each share (P/E), return on equity (ROE), return on assets (ROA). After prioritization mentioned companies, they were ranked using mathematical models: SAW and TOPSIS. The object of the study is to encourage financial decision- makers to use math models (SAW, TOPSIS) instead of previous accounting techniques in order to represent the pharmaceutical companies more perfect than before. The comparison between ranked mentioned companies' according to two math models (SAW, TOPSIS) showed that there is not a significant deference between ranks obtained from SAW and TOPSIS. Furthermore, it is found out that the ranking of the involved companies' was not the same during the study. Some had better process while others not only didn’t have improvement but also gained worse ranking during the study than before.
Application of the two-stage DEA model for evaluating the efficiency and investigating the relationship between managerial ability and firm performance
The aim of this study is to investigate the relationship between managerial ability and firm performance. First, we introduce a new two-stage DEA model with a fuzzy multi-objective programming approach for evaluating the performance of companies listed on the Tehran Stock Exchange. In this regard, the stable operation of companies, into two sub-process, have divided, which includes the profitability (first phase) and the value, creativity (the second phase), that is, the outputs of the first stage are inputs for the second stage, which can be used to identify the status of the company's operations and potential for future growth. Second, In order to measure the ability of managers, we use the model provided by Demerging. Finally, the relationship between managerial ability and firm performance are also investigated by means of the truncated-regression model. The results show that there is a positive relationship between the ability of management and firm performance. It means that managerial ability to be significantly related to the performance of the company. In this sense, the performance of the company improves by increasing managerial ability to better use resources and consequently increase overall efficiency.
The multidimensional exponential Levy equations are used to describe many stochastic phenomena such as market fluctuations. Unfortunately in practice an exact solution does not exist for these equations. This motivates us to propose a numerical solution for n-dimensional exponential Levy equations by block pulse functions. We compute the jump integral of each block pulse function and present a Poisson operational matrix. Then we reduce our equation to a linear lower triangular system by constant, Wiener and Poisson operational matrices. Finally using the forward substitution method, we obtain an approximate answer with the convergence rate of O(h). Moreover, we illustrate the accuracy of the proposed method with a 95% confidence interval by some numerical examples.