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"]

Cited References (24)

  1. Volatility‐Managed Portfolios (2017) — link
  2. High Frequency Trading and Market Quality (2017)
  3. The probability of back-test over-fitting (2016) — link
  4. RiskMetrics Technical Document (2015)
  5. High Frequency Trading and Price Discovery (2014) — link
  6. Time Series Momentum (2012) — link
  7. The Kelly Capital Growth Investment Criterion: Theory and Practice (2011) — link
  8. No-dynamic-arbitrage and market impact (2010) — link
and 16 more
  1. A Simple Approximate Long-Memory Model of Realized Volatility (2008) — link
  2. A Test for Superior Predictive Ability (2005) — link
  3. A Reality Check for Data Snooping (2000) — link
  4. Finance: A Selective Survey (2000) — link
  5. Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency (1993) — link
  6. Forecasting, Structural Time Series Models and the Kalman Filter (1990) — link
  7. A new interpretation of information rate (1956) — link
  8. A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix (2014)
  9. … and the Cross-Section of Expected Returns (2013) — link
  10. The Kelly Criterion in Blackjack Sports Betting, and the Stock Market (2008) — link
  11. Optimal execution for portfolio transactions (2006)
  12. Trading and Exchanges: Market Microstructure for Practitioners (2002)
  13. Timeout: the trade has been open for 30 trading days, after which the signal’s predictive power statistically decays
  14. Volatility targeting keeps realized risk constant over time, preventing uncontrolled leverage in quiet markets
  15. Confidence mapping converts that slope into a bounded, interpretable probability that scales exposure naturally
  16. Friction-adjusted sizing ensures that every position has positive expected log-growth after trading costs

0 of 24 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.