Sector Rotation Strategies in the TSX 60: A Comprehensive Analysis of Risk-Adjusted Returns, Machine Learning Applications, and Out-of-Sample Validation (2000–2025)

We investigate the profitability of systematic sector rotation strategies in the Canadian equity market using TSX 60 constituents (2000–2025). Testing 72 distinct strategies across three theoretical frameworks—momentum, mean-reversion, and balanced approaches—with varying rebalancing frequencies, we identify that median-performer selection combined with quarterly rebalancing generates statistically significant risk-adjusted returns (Sharpe ratio 0.922 versus 0.624 for equal-weighted buy-and-hold). Our primary contributions include rigorous out-of-sample validation, demonstrating performance persistence from 2020 to 2025, machine learning regime classification with 72.7% accuracy, and a comprehensive transaction cost analysis. Results support intermediate-horizon mean reversion in sector returns and challenge strict efficient market hypothesis interpretations in concentrated markets. Findings inform tactical asset allocation practices and contribute to the momentum-reversal literature by documenting conditions under which rotation strategies generate economically meaningful alpha.