A hybrid forecast marketing timing model based on probabilistic neural network, rough set and C4.5

Abstract

One of the major difficulties in investment strategy is to integrate supply chain with finance for controlling the marketing timing. The present study uses not only the different indexes in fundamental and technical analysis, but also the rough set theory and artificial neural networks inference system to construct three investment market timing classification models. This includes probabilistic neural network classification model, rough set classification model and hybrid classification model combining probabilistic neural network, rough sets and C4.5 decision tree. We use the forecasting accuracy and investment return to evaluate the efficacy of these three classification models. Empirical experimentation shown hybrid classification model help construct a better predictive power trading system in terms of stock market timing analysis.