How Earnings Impact My Momentum Strategy - A Backtest Across Two XGBoost Models

I run two live XGBoost momentum models and realized I was blindly holding through earnings. After CRDO and CIEN both beat on EPS/revenue and then dipped, I decided to test a feature and a hard filter in both of the models. Since my holding period is \~1 month, earnings are basically guaranteed to land inside the window. **Setup** * Universe: \~7,600 US stocks, with earnings calendar from FMP joined in DuckDB * Earnings Data Coverage: \~82% of my price universe * Backtest: 2015–present, walk-forward, monthly rebalance, 10 long positions * Two models: * Growth Momentum (targets 10‑day fwd returns, more fundamentals/growthy, spicier drawdowns) * Trend Momentum (targets 21‑day fwd returns, smoother trend/momentum quality) * Earnings logic: flag if a symbol has an earnings date within the next 21 days For each model, I ran three variants: 1. **Baseline** – no earnings info 2. **Earnings Feature** – add a binary `has_earnings_in_window` feature and let XGBoost decide 3. **Hard Filter** – remove any symbol with earnings in the next 21 days **Results** * **Trend Momentum model** * Both earnings-aware variants underperformed the baseline on long-term equity curve. * Filtering out earnings reduced some gap risk, but it also removed a lot of the moves that actually *drive* momentum. * **Growth Momentum model** * Baseline still had the highest overall return (CAGR \~20.2%). * The earnings-feature variant had meaningfully better drawdown (around -50%) and stayed competitive on returns. For both models, baselines had the highest CAGR (Trend \~25.3%, Growth \~20.2%). The interesting part: in the growth model, the earnings feature came out as the *single most important feature* by XGBoost gain in the feature importance plot, beating my usual price and fundamental factors. In the trend model, it was the 5th most important. SHAP on CRDO showed both models treating upcoming earnings as a *positive* input. This partly explains why the hard filter lags. **Takeaways** * For these two momentum models, earnings proximity behaves more like a **signal** than pure **risk**. * A blanket “no earnings” filter reduced gaps but also removed some great momentum. * Letting the model *see* earnings as a feature still provides useful information, but I wouldn't want to keep it in the model. For now I’m sticking with the baseline versions in production and keeping the earnings-feature variants as research candidates. I have all the feature importance plots, cumulative returns compared to SPY, MLflow output, and SHAP output in an article, but I'm not linking in the post.