M. A. Kaboudan, Penn State Lehigh Valley
This is an investigation of forecasting stock returns using genetic programming. We first test the hypothesis that genetic programming is equally successful in predicting series produced by data generating processes of different structural complexity. After rejecting the hypothesis, we measure the complexity, of thirty-two time series representing four different frequencies of eight stock returns. Then using symbolic regression, it is shown that less complex high frequency data are more predictable than more complex low frequency returns. Although no forecasts are generated here, this investi-gation provides new insights potentially useful in predicting stock prices.