Paper: arXiv 2602.00080

Authors: Alexander Sheppert

Abstract

Overfitting remains a critical challenge in data-driven financial modeling, where machine learning (ML) systems learn spurious patterns in historical prices and fail out of sample and in deployment. This paper introduces the GT-Score, a composite objective function that integrates performance, statistical significance, consistency, and downside risk to guide optimization toward more robust trading strategies. This approach directly addresses critical pitfalls in quantitative strategy development, specifically data snooping during optimization and the unreliability of statistical inference under non-normal return distributions. Using historical stock data for 50 S&P 500 companies spanning 2010-2024, we conduct an empirical evaluation that includes walk-forward validation with nine sequential time splits and a Monte Carlo study with 15 random seeds across three trading strategies. In walk-forward validation, GT-Score improves the generalization ratio (validation return divided by training return) by 98% relative to baseline objective functions. Paired statistical tests on Monte Carlo out-of-sample returns indicate statistically detectable differences between objective functions (p < 0.01 for comparisons with Sortino and Simple), with small effect sizes. These results suggest that embedding an anti-overfitting structure into the objective can improve the reliability of backtests in quantitative research. Reproducible code and processed result files are provided as supplementary materials.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 6.0/10 (capped from 7.5: transaction costs only partially modelled; see penalty flags)
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper introduces a pragmatic, heuristic objective function (GT-Score) aimed at mitigating backtest overfitting during optimization. While the underlying mathematics relies on basic statistical formulas rather than complex theoretical proofs, the empirical validation is rigorous, utilizing a extensive 15-year dataset across 50 S&P 500 stocks with walk-forward validation and Monte Carlo analysis.

Research Flowchart

  flowchart TD
    A["Research Goal: Reduce Overfitting in<br>Data-Driven Trading Strategies"] --> B["Input Data: S&P 500 Historical Stocks<br>(50 Companies, 2010-2024)"]
    B --> C["Core Methodology: Develop GT-Score<br>(Composite Anti-Overfitting Objective Function)"]
    C --> D["Computational Validation:<br>• Walk-Forward Validation (9 Splits)<br>• Monte Carlo Study (15 Seeds, 3 Strategies)"]
    D --> E["Key Findings & Outcomes:<br>• 98% Improvement in Generalization Ratio<br>• Statistically Detectable OOS Improvements (p < 0.01)"]