Providing a Model to Predict Future Cash Flow Using Neural Networks on the Pharmaceutical and Chemical Industries of Tehran Stock Market

Abstract

Prediction of cash flows is of great importance for the inter- and extra-organizational users. The most significant objective of financial reporting is the provision of information for the prediction of such cash flows. Some experts and theorists who work on the theoretical foundations of financial reporting and its objectives believe that cash flows can be predicted from the accounting earning and its components. This research proposes a model for forecasting cash flows in firms listed on the Pharmaceutical and Chemical industries of Tehran Stock Exchange using Multi-Layer Perceptron(MLP) and Radius Based Function(RBF) neural networks and compares estimation accuracy of two models using performance criteria. In order to test hypotheses, it was used the data of 29 companies between 2006 and 2015. Findings of research indicated that the Multi-Layer Perceptron network is significantly more accurate than the Radius Based Function network and the three hypotheses were accepted.