TradingAgents/docs/iterations/research/2026-04-14-pead-earnings-beat.md

Research: Post-Earnings Announcement Drift (PEAD)

Date: 2026-04-14 Mode: autonomous

Summary

PEAD is one of finance's most-studied anomalies: stocks that beat earnings estimates continue drifting upward for days to weeks after the announcement. QuantPedia backtests (1987–2004) show 15% annualized returns; the effect is strongest in small-to-mid caps with >10% EPS surprise. Our pipeline has an earnings_calendar scanner that predicts upcoming earnings but nothing that captures the drift after a beat — this is the gap.

Sources Reviewed

• QuantPedia — Post-Earnings Announcement Effect: Combined EAR+SUE strategy generates ~12.5% abnormal returns p.a. (1987–2004); optimal hold ~60 trading days; effect strongest in small caps; most returns on long side; -11.2% max drawdown observed.
• Ball & Brown (1968) / Bernard & Thomas (1989): Foundational PEAD literature; B&T (1989) documented ~18% annualized abnormal returns; magnitude has declined since but effect persists — particularly in small caps.
• DayTrading.com PEAD guide: Drift persists through approximately day 9 before plateauing; 5–20 day hold periods are optimal for tactical implementations.
• SSRN / Philadelphia Fed (PEAD.txt, 2021): NLP-enhanced PEAD achieves 8.01% drift over 1-year window; suggests signal is durable when combined with text signals.
• QuantConnect price+earnings momentum: Combined momentum strategy showed mixed results (Sharpe -0.27) when using price momentum alongside earnings growth — not the same as surprise-based PEAD.
• Alpha Architect — 13F data quality warning: 13F-based institutional signals have 45-day lag and data quality issues — screened out as alternative. PEAD is clearly superior for short-horizon plays.
• Finnhub API docs / finnhub-python: earnings_calendar(from_date, to_date) returns epsActual and epsEstimate for all US stocks in the window. Surprise detection requires only a lookback call — no extra data sources needed.

Fit Evaluation

Dimension	Score	Notes
Data availability	✅	finnhub_api.get_earnings_calendar() already integrated; returns epsActual + epsEstimate; lookback call detects recent beats
Complexity	moderate	~3h: query past-14d earnings calendar, filter for beats, compute surprise%, sort by magnitude
Signal uniqueness	low overlap	earnings_calendar scanner = UPCOMING earnings; PEAD scanner = RECENT beats + drift capture; different timing and signal
Evidence quality	backtested	QuantPedia: 15% annualized returns (1987–2004); Bernard & Thomas (1989); 60+ years of academic literature

Recommendation

Implement — All auto-implement thresholds pass.

Key implementation notes:

• Focus on small-to-mid cap stocks where PEAD effect is strongest (B&T 1989)
• Minimum 5% surprise threshold to filter noise
• CRITICAL at >20% surprise, HIGH at 10–20%, MEDIUM at 5–10%
• Hold horizon: 7–14 days (primary drift window per DayTrading.com)
• Declining US large-cap PEAD mitigated by: small-cap bias + significant surprise filter

Known Failure Modes

• US large-cap PEAD has declined since 1989 (more efficient pricing); strategy most effective for small/mid caps and significant surprises (>10%)
• SUE reversal after 3 quarters (price reverts on next earnings); this is beyond our 30d evaluation window so not immediately harmful
• Overlapping earnings: same ticker may appear in earnings_calendar (upcoming) and earnings_beat (recent); ranker should treat these as separate signals

Proposed Scanner Spec

• Scanner name: earnings_beat
• Strategy: pead_drift
• Pipeline: events
• Data source: tradingagents/dataflows/finnhub_api.py → get_earnings_calendar(from_date, to_date, return_structured=True)
• Signal logic:
  • Query past lookback_days (default 14) of earnings calendar
  • Compute surprise_pct = (epsActual - epsEstimate) / abs(epsEstimate) * 100
  • Filter: surprise_pct >= min_surprise_pct (default 5.0%)
  • Filter: epsEstimate != 0 and both fields not None
  • Sort by surprise_pct descending
• Priority rules:
  • CRITICAL if surprise_pct >= 20
  • HIGH if surprise_pct >= 10
  • MEDIUM otherwise
• Context format: "Earnings beat Xd ago: actual $A vs est $B (+Z% surprise) — PEAD drift window open"