Fast and Effective Predictability Filters for Stock Price Series using Linear Genetic Programming

A handful of researchers who apply genetic programming (GP) to the analysis of financial markets have devised predictability pretests to determine whether the time series that is being supplied to GP contains patterns that can be predicted, but most studies apply no such pretests. By applying predictability pretests, researchers can have greater confidence that the GP system is solving a problem which is actually there and that it will be less likely to make questionable investment decisions based on non-existent patterns. Previous work in this area has applied regression to randomized versions of time series training data to create a functional model that is applied over a future window of time. This work presents two types of predictability filters with low computational overhead, namely frequency-based and information theoretic, that complement the previous function-based continuous output predictability models. Results indicate that either filter can be beneficial for particular trend types, but the information-based filter involves a greater chance of missing opportunities for profit. In contrast, the frequency-based filter always outperforms, or is competitive with, the filterless implementation.