Earnings Season Deep Dive: Quant Signals That Outperformed in Q2 2026

Earnings Season Deep Dive: Quant Signals That Outperformed in Q2 2026

Hook: Earnings seasons still reward preparation. In Q2 2026 certain quant signals—notably earnings-signal momentum and insider-trade-adjusted surprise—consistently outperformed. This deep dive explains the signals and provides trade mechanics.

Dataset and methodology

We tested signals on a universe of 4,200 names across developed markets, adjusting for survivorship bias and using realistic transaction-cost assumptions. Signals were evaluated on 30-, 60-, and 120-day horizons.

Top-performing signals

• Post-earnings drift anomaly refined: Names with small but positive EPS surprise and improving guidance carried returns over 60 days.
• Insider-adjusted surprise: Companies with insider purchases around earnings that also reported positive surprise outperformed peers.
• Options-informed momentum: A surge in pre-release call-buying skewed returns post-release.

How to trade these signals

Implementation matters. Execute via basket structures to limit single-name liquidity risk and use scaled entry ladders. For options-informed momentum, approach with strict size limits and defined hedges.

Operational considerations

Backtest your signals with slippage profiles and staggered fills. Execution engineering resources that handle large-scale testing and data validation—such as Scaling Mongoose—are highly relevant for research groups operationalizing signals.

Cross-market correlations

Some earnings signals were amplified when macro liquidity was high. Cross-asset context matters: when crypto markets were risk-on, certain momentum signals strengthened—see how spot Bitcoin ETFs influence broader price discovery in our recommended reading Explainer: How Spot Bitcoin ETFs Impact Price Discovery.

Case example

A medium-cap industrial name exhibited insider purchases ahead of an earnings beat and delivered a 17% return over 45 days. The trade used a pre-funded basket allocation and a trailing stop defined by volatility-adjusted bands.

Precision in signal definition and ruthless execution discipline separated winners from noise this earnings season.

Further reading and tools

Turn signals into production using robust pipelines and test harnesses. Engineering guides that focus on deployment quality (e.g., analogies from cloud testing) are useful; the cross-discipline approach speeds time-to-production for quant signals.

Author

Rafael Mendes — Quant Researcher. Rafael focuses on event-driven strategies and execution-aware signal design.

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