درخت حوزه‌های تخصصی

This work aims to analyze the relationship between stocks in the financial market of the Tehran Stock Exchange embedded in their transfer entropy. In this regard, the behavior of the transfer entropy between indices of 180 corporations of the Tehran Stock Exchange has been studied. Then the footprint of crises of the market has been searched in the trends of the transfer entropy. The result has been compared with the result of the analysis imposed on the stocks included in the Dow Jones industrial index in the stock exchanges of the United States. In order to investigate the financial crisis of the Tehran Stock Exchange, the stock price data of 180 companies in this market that were active in the period from 2008 to 2018 are analyzed. It is observed that the average pairwise transfer entropy of indices in the Dow Jones group declines over the financial crises in the United States. In Iran, despite the United States, the financial crises have not left a footprint in the pairwise transfer entropy over the studied period. Such an observation suggests future studies on the pairwise and possibly collective behaviors of indices in Iran and the United States.

Using a structural VAR with block exogeneity، diagonality and identifying restrictions، this paper analyzes: first، the macroeconomic linkages among the oil price، U.S. output، interest rate، money supply، general price level and exchange rate; and second، the relationships of the macroeconomic variables with the price indices of ten international nonfuel commodity groups. By assuming the block exogeneity of U.S. macroeconomic variables with respect to the international nonfuel commodity prices، the paper discusses how exogenous oil/macroeconomic shocks affect the international commodity prices. It finally explores which oil/macroeconomic shocks are important in explaining the variations in international commodity prices. The results show that the sources of major fluctuations in the international commodities differ greatly by commodity. Soft and hard commodity prices such as those of ‘seafood’، ‘industrial metals’، and ‘gold’ seem to be strongly affected by the financial factor. Moreover، for some commodities، price fluctuations are more affected by the financial factor than by the real factor، supporting the view of “financialization” of commodities. Those commodities include ‘vegetable oils and protein meals’، ‘meat’، ‘seafood’، and ‘industrial metals’. The financial factor is also an important source of fluctuations in the oil prices. Oil price shocks have effects on the volatilities of interest rates، money supply، and general price level instantly، as well as on the exchange rate instead of the general price two years after the shock. Over the whole forecasting horizon، the degree of exchange rate pass-through is low on the general price level but is positive and high on oil and nonfuel international commodity prices.

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.