Paper: arXiv 2511.08571

Authors: Mainak Singha, Jose Aguilera-Toste, Vinayak Lahiri

Abstract

We test whether simple, interpretable state variables-trend and momentum-can generate durable out-of-sample alpha in one of the world’s most liquid assets, gold. Using a rolling 10-year training and 6-month testing walk-forward from 2015 to 2025 (2,793 trading days), we convert a smoothed trend-momentum regime signal into volatility-targeted, friction-aware positions through fractional, impact-adjusted Kelly sizing and ATR-based exits. Out of sample, the strategy delivers a Sharpe ratio of 2.88 and a maximum drawdown of 0.52 percent, net of 0.7 basis-point linear cost and a square-root impact term (gamma = 0.02). A regression on spot-gold returns yields a 43 percent annualized return (CAGR approximately 43 percent) and a 37 percent alpha (Sharpe = 2.88, IR = 2.09) at a 15 percent volatility target with beta approximately 0.03, confirming benchmark-neutral performance. Bootstrap confidence intervals ([“2.49, 3.27”]) and SPA tests (p = 0.000) confirm statistical significance and robustness to latency, reversal, and cost stress. We conclude that forecast-to-fill engineering-linking transparent signals to executable trades with explicit risk, cost, and impact control-can transform modest predictability into allocator-grade, billion-dollar-scalable alpha.

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 9.2/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper is highly data-driven with a detailed walk-forward backtest, specific cost/impact modeling, and robust statistical validation, but the mathematics, while precise, is relatively accessible (mainly Kelly sizing and basic signal smoothing) rather than conceptually dense or advanced.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Identify durable, interpretable alpha in liquid gold futures using simple state variables"] --> B["Data & Methodology<br>2015-2025 OOS Walk-Forward<br>Rolling 10yr Train / 6mo Test<br>2,793 Trading Days"]
  B --> C["Signal Generation<br>Smoothed Trend-Momentum Regime State"]
  C --> D["Position Sizing & Execution<br>Volatility-Targeted (15%)<br>Fractional Kelly Sizing<br>ATR-based Exits<br>Impact-Adjusted Costs: 0.7bps + sqrt(gamma=0.02)"]
  D --> E["Performance & Statistical Validation<br>Sharpe 2.88, Max DD 0.52%<br>CAGR ~43%, Alpha 37% (Beta ~0.03)<br>Bootstrap CI [2.49, 3.27"], SPA p=0.000]
  E --> F["Key Finding<br>Forecast-to-Fill engineering creates allocator-grade, billion-dollar scalable alpha<br>from modest predictability"]