An artificial walk down Wall Street : can intraday stock returns be predicted using artificial neural networks?

Abstract

Financial markets are complex evolved dynamic systems. Due to its irregularity, financial time series forecasting is regarded as a rather challenging task. In recent years, artificial neural network applications in finance, for such tasks as pattern recognition, classification, and time series forecasting have dramatically increased. The objective of this paper is to present this powerful framework and attempt to use it to predict the stock return series of four publicly listed companies on the New York Stock Exchange. Our findings coincide with those of Burton Malkiel in his book, A Random Walk down Wall Street; no conclusive evidence is found that our proposed models can predict the stock return series better than a random walk.