Asset-return momentum prediction through pattern recognition

Abstract

Asset-price momentum effects are important to study in finance. A novel nonparametric pattern-recognition algorithm is proposed to identify intraday momentum based on price patterns. Specifically, a classification-regression model for predicting future price-movement patterns is designed by combining Bayesian analysis and a nonparametric density-estimation method (with different similarity measures). The model is applied to asset prices in both original and compressed forms through nonlinear dimension-reduction techniques. Regardless of the data form, this model’s predictive power is statistically and empirically significant. The model most effectively predicts asset prices with high levels of long-range dependence. Empirical analysis demonstrates that the intraday-momentum effect can occur over longer time horizons than the half-hour documented in the literature. The intraday-trading strategy based on the similarity-prediction model achieves an increased expected return and a Sharpe ratio of 11% and 0.7, respectively, compared to alternative benchmark models.