Genetic Programming of Polynomial Models for Financial Forecasting

This paper addresses the problem of finding trends in financial data series using genetic programming (GP). A GP system STROGANOFF that searches for polynomial autoregressive models is presented. The system is specialized for time series processing with elaborations in two aspects: 1) preprocessing the given series using data transformations and embedding; and, 2) design of a fitness function for efficient search control that favours accurate, parsimonious, and predictive models. STROGANOFF is related to a traditional GP system which manipulates functional expressions. Both GP systems are examined on a Nikkei225 series from the Tokyo Stock Exchange. Using statistical and economical measures we show that STROGANOFF outperforms traditional GP, and it can evolve profitable polynomials.