Earnings Trading

Earnings Trading

Analyze and trade earnings only from time-stamped expectations, primary filings, and an explicit event-risk plan. Surprise is important, but price also reflects guidance, revisions, positioning, valuation, liquidity, and regime.

Earnings Evidence Matrix

Priced Expectations

Do not invent a numeric "whisper" from price direction. Report observable proxies separately: estimate revisions and dispersion, pre-event abnormal return, short interest, options-implied move/skew, and source-dated analyst commentary. Label any inference about positioning as uncertain.

Post-Earnings Drift

Stocks that surprise on earnings tend to continue drifting in the same direction -- one of the most documented market anomalies.

Post-earnings-announcement drift is an empirical research finding, not a fixed probability or holding period. Define standardized unexpected earnings using point-in-time estimates, form size/liquidity buckets, and test horizons without overlapping-sample leakage. Compare closed-bar entry times and fixed, time, and trailing exits after spread and slippage.

Workflow

1. Get Consensus Estimates

get_fundamentals(ticker="AAPL")

Pull point-in-time EPS/revenue estimates, estimate dispersion, earnings timestamp, and historical surprises. Verify event time and results in the issuer's 8-K/earnings release and 10-Q.

2. Research Expectations and Sentiment

get_financial_news(topic="AAPL earnings <quarter> <current year> expectations analyst", max_results=15)

Look for dated estimate revisions, price-target changes, and positioning proxies. Separate facts from commentary and deduplicate syndicated stories.

3. Analyze Call Transcript (Post-Release)

get_financial_news(topic="AAPL earnings call transcript summary management tone", max_results=10)

Compare the transcript with the prior quarter using reproducible features: quantified outlook changes, topics raised by analysts, answer specificity, and risk-language changes. Quote minimally with timestamps and treat tone as interpretation, not a deterministic label.

4. Score and Decide

Report each dimension separately, price reaction versus any implied move, liquidity, and valid, watch, or no trade. If trading, state entry trigger, gap-aware invalidation, time exit, maximum event loss, and source timestamps.

Evidence and Validation

• Treat the setup as a testable hypothesis, not a prediction. Define thresholds, entry, invalidation, and exit before evaluating outcomes.
• Calibrate on the same instrument, venue, session, and timeframe. Use closed candles and a held-out or walk-forward sample; record every variant tried.
• Include spread, fees, slippage, borrow or funding, partial fills, and latency. Reject the setup when net expectancy is not positive or depends on one narrow parameter.
• Return observed inputs, missing data, cost assumptions, entry, invalidation, exit, and a valid, watch, or no-trade status.
• Research basis: Livnat & Mendenhall documents post-earnings-announcement drift, but its magnitude depends on how surprise is measured and does not justify a fixed continuation probability.

Key Rules

• Do not hold through earnings unless the mandate explicitly permits gap and halt risk.
• Never infer a hidden expectation as fact; show the observable positioning proxies.
• Calibrate entry delay in closed bars for the instrument and session rather than imposing a universal clock time.
• Verify adjusted metrics against GAAP reconciliation and use point-in-time consensus.
• Treat transcript tone and analyst questioning as contextual evidence, not standalone signals.
• Size from the stress gap loss and portfolio risk limit, not a fixed percentage reduction.

Related Skills

• sentiment-analysis -- source-dated expectations and positioning evidence
• economic-calendar-trading -- Macro events near earnings amplify volatility