Application of Bayesian Techniques for MLPs to Financial Time Series Forecasting

Abstract

Bayesian learning techniques for MLPs are applied to the problem of forecasting the direction of change in daily close values of the Australian All Ordinaries Index. Predictions made over a 13 year out-of-sample period were tested against two null hypotheses—the null hypothesis of a mean accuracy of 0.5 (which is the expected accuracy if prices follow a random walk), and a null hypothesis which takes into account non-stationarity in the prices series. Results show that both null hypotheses can be rejected at the 0.005 level, but much more confidently in the case of the Bayesian approach as compared to an approach using conventional gradient descent based weight optimization.