درخت حوزه‌های تخصصی

Cash flow is one of the critical resources in the economic unit and the balance between available cash and cash needs is the most important factor in economic health. Since judgments of many stakeholders such as investors and shareholders about the position of the economic unit are based on liquidity situation, so predicting future cash flow is crucial. In this research, the impact of cash and accrual items on cash flow forecasts has been studied. Providing a proper model to predict operating cash flows and review some important characteristics of cash flow forecasting regression models, using a multilayer perceptron and determining the best model by using accrual regression model variables for predicting cash flows. For this purpose, 287 firms listed in Tehran Stock Exchange during 2008 to 2017 were studied; Linear and nonlinear regression, correlation coefficient and artificial neural network statistical methods have been used for data analysis and predictive power of powers was compared by using the sum of squared prediction error and coefficient of determination. Results showed that the accrual regression model can predict future cash flows better than other tested models and among corporate characteristics, the highest correlation belongs to sales volatility and firm size with accrual regression models. On the other hand, results of fitting different neural network models indicate that two structures with 8 and 11 hidden nodes are the best models to predict cash flows.

Market participants use different tools basically technical or fundamental analysis to have a higher return in constructing a well-maintained portfolio. Examining the efficiency of technical strategies in creating a portfolio is the main objective of this study. Technical analysis is based on using historical trading data to launch selling and buying rules that maximize return and still control risks of loss. We use the adjusted trading data of 50 active stocks in the Tehran Stock Exchange as our sample which includes daily trading data from 2008 to 2019. We construct two types of portfolio; strategy-based portfolio versus random one. Then we calculate abnormal returns of each type of portfolio, applying the Monte-Carlo technique. Using Independent-Samples T-Test to compare means of the abnormal returns, our findings show that there is a significant positive abnormal return for both strategies applied in constructing a portfolio (0.057 and 0.062 mean difference for the first and second strategy, respectively), confirming the higher efficiency of applying technical strategies in portfolio management. Therefore, it is suggested to have and apply a strategy or combination of strategies for trading as an active participant, instead of constructing, rebalancing and maintaining one’s portfolio only by chance, since there will be undesirable results in the long-run.