SNX
TD SYNNEX Corporation
stock NYSE

The post-earnings announcement drift (PEAD) literature going back to Bernard & Thomas (1989) treats "drift" as roughly symmetric — beats drift up, misses drift down. Most retail-facing analytics tools I've seen follow that framing.
When I built a dataset to actually check this, I found the two sides aren't symmetric at all. The miss side dominates.
# The dataset
* 10,279 S&P 500 + S&P 400 earnings events, roughly 2020–2026
* Price data from a paid Finnhub feed, adjusted for splits (Finnhub returns split-adjusted candles by default)
* Returns computed as cumulative abnormal returns (CAR) vs SPY at T+1, T+3, T+5, T+10, T+20 trading days
* Events bucketed by SUE (standardized unexpected earnings) into large\_miss / small\_miss / small\_beat / large\_beat
* Known limitations: survivorship (Finnhub drops delisted names entirely, which biases the sample toward survivors), no options overlay yet, and the SUE bucketing thresholds are my choice, not universal
# The result
Aggregate T+20 CAR by bucket, averaged across the full sample:
large_miss : -2.62%
small_miss : -0.71%
small_beat : +0.31%
large_beat : +0.58%
The magnitude asymmetry is the interesting part: **large\_miss drift is 4.5× larger in absolute terms than large\_beat drift** at T+20.
The win rate (T+20 move ≥ 2% in the surprise direction) tells the same story: large\_miss events resolve in the "expected" direction \~54% of the time; large\_beat events resolve up only \~28% of the time. **Big beats fade far more often than they follow through.**
# Live confirmation
To stop myself from cherry-picking the historical dataset, I've been publishing every scanner-flagged event to a hash-chained public track record since late June. First 4 events completed T+20 last week:
* CCL (large beat) → T+20 **−13.8%** (faded)
* JEF (large miss) → T+20 **−11.4%** (drifted)
* MU (large beat) → T+20 **−12.8%** (faded)
* SNX (large beat) → T+20 **−11.7%** (faded)
3 large beats. 3 double-digit fades. 1 large miss. It drifted down as base rates predicted.
Small sample, obviously — but consistent with the historical asymmetry.
# What I think this means
* The tradeable edge in PEAD is on the miss side, not the beat side. Miss-drift is slower, more consistent, less flashy — which is probably why it persists as an anomaly.
* Large-beat "fat right tails" (AVAV +37% at T+3 recently) are real, but narrow. Most large beats mean-revert.
* Any strategy structured around "buy the beat" needs to grapple with the fact that base rate for large beats at T+20 is only marginally positive with wide dispersion. "Sell the miss" has cleaner risk-adjusted math.
# Interested in critique
Genuinely open to being wrong about the sample composition or the SUE thresholds. If you have thoughts on:
* Better bucketing rules (magnitude thresholds vs percentile-based)?
* How much of the miss-side asymmetry could be explained by short-sale constraints?
* Whether the asymmetry holds across different time periods (2020–22 vs 2023–26)?
...I'd like to hear them.
Full underlying data, methodology, and the ongoing hash-chained live track record is at [driftanalytics.io/track-record](http://driftanalytics.io/track-record) if useful.

The post-earnings announcement drift (PEAD) literature going back to Bernard & Thomas (1989) treats "drift" as roughly symmetric — beats drift up, misses drift down. Most retail-facing analytics tools I've seen follow that framing.
When I built a dataset to actually check this, I found the two sides aren't symmetric at all. The miss side dominates.
# The dataset
* 10,279 S&P 500 + S&P 400 earnings events, roughly 2020–2026
* Price data from a paid Finnhub feed, adjusted for splits (Finnhub returns split-adjusted candles by default)
* Returns computed as cumulative abnormal returns (CAR) vs SPY at T+1, T+3, T+5, T+10, T+20 trading days
* Events bucketed by SUE (standardized unexpected earnings) into large\_miss / small\_miss / small\_beat / large\_beat
* Known limitations: survivorship (Finnhub drops delisted names entirely, which biases the sample toward survivors), no options overlay yet, and the SUE bucketing thresholds are my choice, not universal
# The result
Aggregate T+20 CAR by bucket, averaged across the full sample:
large_miss : -2.62%
small_miss : -0.71%
small_beat : +0.31%
large_beat : +0.58%
The magnitude asymmetry is the interesting part: **large\_miss drift is 4.5× larger in absolute terms than large\_beat drift** at T+20.
The win rate (T+20 move ≥ 2% in the surprise direction) tells the same story: large\_miss events resolve in the "expected" direction \~54% of the time; large\_beat events resolve up only \~28% of the time. **Big beats fade far more often than they follow through.**
# Live confirmation
To stop myself from cherry-picking the historical dataset, I've been publishing every scanner-flagged event to a hash-chained public track record since late June. First 4 events completed T+20 last week:
* CCL (large beat) → T+20 **−13.8%** (faded)
* JEF (large miss) → T+20 **−11.4%** (drifted)
* MU (large beat) → T+20 **−12.8%** (faded)
* SNX (large beat) → T+20 **−11.7%** (faded)
3 large beats. 3 double-digit fades. 1 large miss. It drifted down as base rates predicted.
Small sample, obviously — but consistent with the historical asymmetry.
# What I think this means
* The tradeable edge in PEAD is on the miss side, not the beat side. Miss-drift is slower, more consistent, less flashy — which is probably why it persists as an anomaly.
* Large-beat "fat right tails" (AVAV +37% at T+3 recently) are real, but narrow. Most large beats mean-revert.
* Any strategy structured around "buy the beat" needs to grapple with the fact that base rate for large beats at T+20 is only marginally positive with wide dispersion. "Sell the miss" has cleaner risk-adjusted math.
# Interested in critique
Genuinely open to being wrong about the sample composition or the SUE thresholds. If you have thoughts on:
* Better bucketing rules (magnitude thresholds vs percentile-based)?
* How much of the miss-side asymmetry could be explained by short-sale constraints?
* Whether the asymmetry holds across different time periods (2020–22 vs 2023–26)?
...I'd like to hear them.
Full underlying data, methodology, and the ongoing hash-chained live track record is at [driftanalytics.io/track-record](http://driftanalytics.io/track-record) if useful.