Market Symmetry and Its Application to Pattern-Matching-Based Portfolio Selection

Abstract

Portfolio selection based on pattern matching has shown great potential. The authors show that this approach can be derived from a symmetric market perspective, in which the relationship between market status and optimal portfolio is quantitatively defined in terms of a Pearson correlation. This new perspective motivated a revised pattern-matching algorithm (symmetric CORN-K), which selects the portfolio that simultaneously maximizes the returns of similar periods and minimizes the returns of dissimilar periods. The algorithm was further extended to a general symmetry-based pattern-matching algorithm (functional CORN-K) that uses the symmetry property in a principled way. The authors’ experiments demonstrated that the new algorithms can deliver better returns, larger Sharpe ratios, and lower maximum drawdown, and that the improvements are statistically significant.

TOPICS: Statistical methods, portfolio construction, performance measurement