Selection of Learning Algorithms for Trading Systems Based on Biased Estimators

Abstract

In our previous trading system for the S&P 500 index, promising results were obtained by partitioning the historic data into disjoint subsets used to design two biased local estimators whose partial estimates were combined into a trading recommendation [3, 4]. The objective of this study is to explore whether using cascade-correlation learning instead or in addition to the previously used back-propagation to train either one or both of the local estimators improves the trading system's performance. Several learning algorithm combinations were explored and tested using real financial data. The system yielding the best results used a mixture of learning algorithms (both back-propagation and cascade-correlation) and achieved an annual rate of return of 20.49% without a commission and 14.37% with a 0.05% commission over a five year trading period. This is significantly better than the annual rate of return achieved by both the buy and hold strategy (13.36%) and a system configuration that ..