Comparison of Random Forest Classification and Regression on Zigzag-Labeled Data for Equity Trading Strategies

Anotacija

This paper compares Random Forest classification and regression approaches for equity trading strategy development using Zigzag-based price labeling. Both models are trained to identify buy and sell signals derived from Zigzag pivot points and are evaluated against a buy-and-hold benchmark using three exchange-traded funds: S&P 500, Hang Seng, and MSCI UK. A walk-forward framework is implemented using five-year training windows and two-year out-of-sample test windows. The results indicate that the Random Forest classification strategy delivers the strongest performance, particularly in the Hong Kong market, and is found to statistically significantly outperform the regression strategy. Further analysis of pivot timing shows that both models exhibit higher accuracy in predicting buy signals than sell signals, suggesting an asymmetry in model effectiveness across market turning points. As Zigzag labels are retrospective by construction, thus results should be interpreted as an upper bound on achievable performance rather than a direct estimate of live trading returns.