Forecasting closing price indices using neural networks

Accurate financial prediction is of great practical interest to both individual and institutional investors. This paper proposes an application, which employs artificial neural networks that could be used to assist investors in making financial decisions. The Multi-layer perceptron as well as Radial Basis Function neural network architectures are implemented as classifiers to forecast the closing index price performance. Categorizes that these networks classify are based on a profitable trading strategy that outperforms the long-term "Buy and hold" trading strategy. The Dow Jones Industrial Average, Johannesburg Stock Exchange All Share, Nasdaq 100 and the Nikkei 225 Stock Average indices are considered. The best and worst forecasting classification accuracies obtained were 72% and 64%, respectively. These accuracy levels were attained for the Dow Jones Industrial Average and the Nikkei 225 Stock Average indices, respectively.