Paper: arXiv 2511.12490

Authors: Mainak Singha

Abstract

We document a high-performing cross-sectional equity factor that achieves out-of-sample Sharpe ratios above 13 through regime-conditional signal activation. The strategy combines value and short-term reversal signals only during stock-specific drift regimes, defined as periods when individual stocks show more than 60 percent positive days in trailing 63-day windows. Under these conditions, the factor delivers annualized returns of 158.6 percent with 12.0 percent volatility and a maximum drawdown of minus 11.9 percent. Using rigorous walk-forward validation across 20 years of S&P 500 data (2004 to 2024), we show performance roughly 13 times stronger than market benchmarks on a risk-adjusted basis, produced entirely out-of-sample with frozen parameters. The factor passes extensive robustness tests, including 1,000 randomization trials with p-values below 0.001, and maintains Sharpe ratios above 7 even under 30 percent parameter perturbations. Exposure to standard risk factors is negligible, with total R-squared values below 3 percent. We provide mechanistic evidence that drift regimes reshape market microstructure by amplifying behavioral biases, altering liquidity patterns, and creating conditions where cross-sectional price discovery becomes systematically exploitable. Conservative capacity estimates indicate deployable capital of 100 to 500 million dollars before noticeable performance degradation.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper uses relatively simple mathematical constructs like linear combinations and binary regime gates, lacking advanced derivations or complex stochastic calculus. However, it demonstrates high empirical rigor through extensive walk-forward backtesting over 20 years, rigorous out-of-sample validation, multiple robustness tests (1,000 randomization trials), sensitivity analyses, and practical implementation details including transaction costs and capacity estimates.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Find High Sharpe OOS Factor"] --> B["Data:<br>S&P 500 2004-2024"]
  B --> C["Methodology:<br>Walk-Forward Validation"]
  C --> D["Compute Signal:<br>Drift Regime > 60% Pos Days"]
  D --> E["Process:<br>Value + Reversal Signals<br>Activated Only in Regime"]
  E --> F["Outcome:<br>13.0 Sharpe Ratio"]
  F --> G["Key Results:<br>158.6% Return | 12% Vol<br>p < 0.001 | Negligible Risk Exposure"]