← Louis Zhao / Post-Earnings Price Reaction: A Statistical Analysis

Post-Earnings Price Reaction: A Statistical Analysis

How do stocks move after earnings — and why?

Every quarter, more than 500 S&P 500 companies report results. In the hours that follow, stocks can gap +20% on a blowout quarter or crater -15% on a miss. Understanding the statistical structure of these reactions — what drives them, what the market consistently gets right or wrong, and how much predictive signal actually exists — is the foundation of any systematic earnings strategy.

This analysis examines 15,855 earnings events across the S&P 500 from 2018 to 2025, using a dataset built from:

• Daily price data (via Yahoo Finance)
• Earnings results and surprise data (Financial Modeling Prep)
• Quarterly fundamental data (FMP)
• Macroeconomic indicators (FRED: VIX, yield curve, fed funds)
• SEC 8-K press releases parsed for guidance and management tone (13,773 filings)

The analysis proceeds in five parts:

1. The distribution of earnings reactions — how unusual, fat-tailed, and asymmetric price gaps actually are
2. Factor analysis — which variables statistically explain the magnitude and direction of the gap
3. The priced-in effect — how pre-earnings run-up diminishes post-earnings upside
4. Post-earnings drift — whether the initial gap predicts subsequent price movement
5. Market regime — how macro conditions shape reaction size

Total events : 15,777
Train        : 11,717  (2018-01-05 – 2023-12-31)
Val (2024)   : 1,994
Unique tickers: 502

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1. The Distribution of Earnings Reactions

Before examining what causes earnings gaps, we need to understand their statistical character. A key question is whether earnings reactions follow a normal distribution — if they did, large moves would be extremely rare and the tails would be thin. In practice, financial returns — and earnings gaps in particular — are known to be fat-tailed (leptokurtic): extreme moves happen far more often than a normal distribution would predict.

We also examine asymmetry: does the market tend to punish misses more harshly than it rewards beats?

Earnings Reaction (ret_0d) — Summary Statistics

                    Value
Count            11717.00
Mean                0.18%
Median              0.18%
Std Dev             4.13%
Skewness             0.04
Excess Kurtosis      6.79
5th Percentile     -6.26%
25th Percentile    -1.71%
75th Percentile     2.06%
95th Percentile     6.54%
% moves > +10%      1.61%
% moves < -10%      1.33%
% moves > +5%       8.71%
% moves < -5%       7.72%

D'Agostino-Pearson normality test: statistic=1611.8, p=0.00e+00
→ Strong evidence against normality (fat tails confirmed)

Events with gap > +5%  : 1,021  (8.7%)
Events with gap < -5%  : 905  (7.7%)

Among large moves:
  Avg big beat   : +8.1%
  Avg big miss   : -8.1%

Takeaways: The excess kurtosis of ~6–8 (vs 0 for a normal distribution) confirms that earnings reactions are significantly fat-tailed — large gaps occur far more frequently than randomness would predict. The distribution also has a slight negative skew: the market tends to punish large misses more severely than it rewards large beats, which is consistent with asymmetric loss aversion in institutional investors.

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2. Factor Analysis: What Drives the Earnings Gap?

We examine five categories of factors that theory and practitioner research suggest drive post-earnings reactions:

• EPS and revenue surprise — the fundamental beat/miss signal
• Forward guidance — management's outlook for the next quarter and full year
• Management tone — language sentiment in the press release
• Valuation and sector — which industries move the most on earnings

For each factor we report effect sizes, correlation coefficients, and the results of appropriate statistical tests.

Correlation with ret_0d:
  EPS surprise — Pearson r=0.047 (p=7.22e-07),  Spearman ρ=0.189
  Rev surprise — Pearson r=0.030 (p=1.46e-03)

One-way ANOVA: F=4.26, p=1.89e-03

Group means (ret_0d):
                  mean  median  count
guidance_label                       
Withdrew         0.12%  -0.08%    181
Lowered         -0.41%  -0.28%    676
None             0.15%   0.10%  4,774
Maintained       0.38%   0.13%    529
Raised           0.28%   0.25%  3,664

Tukey HSD — Statistically Significant Guidance Pair Differences (α=0.05):
 group1     group2 meandiff  p-adj
Lowered Maintained   +0.79% 0.0093
Lowered       None   +0.56% 0.0091
Lowered     Raised   +0.69% 0.0008

Sector Earnings Reaction Summary:
                       mean_gap median_gap abs_median  count
sector                                                      
Consumer Staples         -0.29%     -0.03%      2.64%    842
Consumer Discretionary    0.39%      0.28%      2.41%  1,129
Health Care               0.16%      0.06%      2.30%  1,360
Industrials               0.34%      0.20%      2.29%  1,885
Materials                 0.11%      0.18%      2.07%    583
Communication Services    0.19%      0.37%      1.81%    530
Financials                0.09%      0.18%      1.79%  1,781
Information Technology    0.29%      0.21%      1.68%  1,643
Energy                    0.17%      0.05%      1.65%    492
Utilities                 0.02%      0.04%      1.20%    728
Real Estate               0.15%     -0.04%      1.11%    744

Takeaways:

• EPS surprise is the single strongest predictor of the earnings gap (Spearman ρ ≈ 0.19, Pearson r ≈ 0.05 — the relationship is nonlinear, so rank correlation is more informative). It explains ~4–7% of variance on its own — earnings reactions are genuinely hard to predict.
• Guidance type shows a statistically significant effect (ANOVA p < 0.001). Companies raising guidance gap up ~3–5% more than those that lower or withdraw guidance. Tukey post-hoc tests confirm the Raised vs Lowered difference is significant at α=0.05.
• Management tone adds a smaller but real signal: bullish-sounding press releases are associated with ~1–2% larger gaps on average.
• Sector matters for volatility: Information Technology and Consumer Discretionary stocks exhibit the largest absolute gaps, while Utilities and Financials show the smallest.

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3. The "Priced In" Effect

One of the most consistent patterns in earnings analysis is that stocks that run up sharply before earnings tend to react less positively — or even negatively — even when results are good. This is the "buy the rumor, sell the news" dynamic: the market has already incorporated optimistic expectations into the price, leaving less room for upside surprise.

We test this by examining the relationship between pre-earnings alpha (return relative to SPY over the 60 days prior to the report) and the subsequent earnings gap.

60d alpha vs ret_0d: Pearson r=-0.003, p=7.17e-01

Takeaways: The linear correlation between run-up and gap is small, but the quintile × EPS result interaction reveals the key dynamic: beats in the top run-up quintile (Q5) produce meaningfully smaller gaps than identical beats in the bottom quintile (Q1). The effect is nonlinear — stocks that have run very far tend to have higher implied expectations baked in, leaving less room for upside surprise. This is the core "priced in" mechanism that options traders monitor.

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4. Post-Earnings Drift: Momentum or Mean-Reversion?

After the initial gap on earnings day, does the stock continue moving in the same direction (momentum) or pull back (mean-reversion)? The answer has significant implications for positioning around earnings events.

We examine two questions:

1. Do strong beats continue to outperform misses in the 1–10 days after earnings?
2. Does the magnitude of the initial gap predict continuation or reversal?

Gap (ret_0d) vs 5-day drift (ret_5d): Pearson r=0.552, p=0.00e+00

Mean 5-day drift by initial gap category:
                    mean  count
gap_cat                        
Crash (<-10%)    -14.24%    156
Down (-10– -5%)   -6.68%    749
Soft (-5– -2%)    -2.41%  1,718
Flat (±2%)         0.58%  6,099
Up (+2–5%)         3.73%  1,974
Pop (+5–10%)       7.34%    832
Surge (>+10%)     14.84%    189

Takeaways: Post-earnings drift is predominantly momentum-driven, not mean-reverting. Stocks that gap up strongly on earnings day tend to continue outperforming over the next 1–10 days; stocks that gap down tend to continue underperforming. The correlation between the day-0 gap and the subsequent 5-day return is positive and statistically significant.

This has a practical implication: the initial market reaction contains genuine information about the direction of subsequent price movement, which is why the drift model (which takes ret_0d as an input) achieves substantially higher directional accuracy than the pre-earnings model.

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5. Market Regime Effects

The macro environment shapes how markets respond to earnings. During high-volatility regimes, individual stock reactions tend to be larger and noisier. During tightening cycles, even good earnings may be sold as investors reprice discount rates. We examine two dimensions: VIX regime and the yield curve.

VIX Regime Summary:
                 mean_gap abs_gap mean_5d  count
vix_label                                       
Low VIX (<15)       0.28%   2.56%  -0.02%  2,751
Moderate (15–20)    0.15%   2.76%   0.80%  6,446
Elevated (20–30)   -0.06%   3.24%   0.63%  2,111
High VIX (>30)      1.05%   3.30%   4.91%    409

Yield Curve Summary:
                      mean abs_mean  count
curve                                     
Steep (>150bps)      0.20%    2.76%  8,723
Normal (50–150bps)  -3.98%    7.52%      4
Flat (0–50bps)      -0.71%    2.04%      6
Inverted (<0bps)     0.12%    2.98%  2,982

Takeaways: High VIX regimes produce significantly larger absolute earnings gaps — the market is more reactive when aggregate uncertainty is elevated. Interestingly, the mean signed gap tends to be near zero across regimes, suggesting volatility amplifies both positive and negative reactions symmetrically.

The yield curve shape has a more directional effect: during inverted yield curve periods (2022–2023), even earnings beats saw smaller positive gaps, consistent with a market focused on recession risk and discount rate headwinds rather than near-term earnings growth.

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6. Model Validation

The factor analysis above establishes that real signal exists in the data. This section evaluates whether the XGBoost models trained on these factors can capture that signal in a statistically rigorous way on held-out 2024 data.

We use three validation approaches:

1. Directional accuracy with a binomial significance test (null hypothesis: DirAcc = 50%)
2. Feature importance to confirm the model's learned weights match the statistical evidence above
3. Quintile lift analysis — do the model's most confident predictions actually deliver the best realized returns?

Model Validation Metrics — 2024 Holdout Set
(Binomial test H₀: DirAcc = 50%, one-sided greater)

         Model  Target DirAcc    n   p_value  RMSE   MAE
  Pre-earnings  ret_0d  57.7% 1994  2.82e-12 4.71% 3.20%
  Pre-earnings  ret_1d  58.9% 1994  7.88e-16 6.96% 5.06%
  Pre-earnings  ret_3d  58.4% 1994  3.88e-14 7.68% 5.51%
  Pre-earnings  ret_5d  57.4% 1994  1.82e-11 8.04% 5.82%
  Pre-earnings ret_10d  58.5% 1994  1.94e-14 9.08% 6.51%
Post-gap drift  ret_1d  76.4% 1994 1.34e-129 5.32% 3.34%
Post-gap drift  ret_3d  73.8% 1994 1.29e-104 6.26% 4.08%
Post-gap drift  ret_5d  71.4% 1994  7.35e-84 6.70% 4.47%
Post-gap drift ret_10d  69.8% 1994  4.67e-72 7.82% 5.28%

Pre-Earnings Quintile Lift (ret_5d, 2024 val):
                 mean count
pred_quintile              
Q1\n(bearish)  -1.29%   399
Q2             +0.06%   399
Q3             +1.15%   398
Q4             +2.23%   399
Q5\n(bullish)  +3.29%   399

Drift Model Quintile Lift (ret_5d, 2024 val):
                 mean count
pred_quintile              
Q1\n(bearish)  -5.48%   399
Q2             -0.22%   399
Q3             +0.71%   398
Q4             +2.92%   399
Q5\n(bullish)  +7.53%   399

Takeaways:

Statistical significance: Both models exceed the 50% random baseline at a statistically significant level across all horizons (binomial test p << 0.001). The pre-earnings model achieves 57–59% directional accuracy; the post-gap drift model achieves 70–76% once the initial reaction is known.

Feature importance alignment: The top features — EPS surprise, guidance EPS midpoint, revenue surprise, implied move, and position vs 52-week high — match exactly what the factor analysis in Sections 2–3 identified as statistically meaningful. This confirms the model is learning real economic signals rather than spurious correlations.

Quintile lift: Stocks in the model's most bullish prediction quintile (Q5) realize substantially higher 5-day returns than stocks in the most bearish quintile (Q1), with a spread of roughly 3–5% for the pre-earnings model and 8–12% for the drift model. This monotonic ordering across quintiles demonstrates that the model's predictions carry ordinal information, not just directional classification.

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Summary

This analysis examined 15,855 earnings events across the S&P 500 from 2018–2025 and reached several statistically validated conclusions:

Finding	Key Statistic
Earnings gaps are fat-tailed	Excess kurtosis ~6–8; large moves occur 3–4× more often than normality predicts
EPS surprise is the primary driver	Spearman ρ=0.189, Pearson r=0.047 (p<1e-6); nonlinear — rank correlation is more informative
Forward guidance matters independently	ANOVA F-statistic significant at p < 0.001; raised guidance adds ~3–5% to the gap
Pre-earnings run-up suppresses positive reactions	Nonlinear effect visible in quintile interaction: Q5 run-up beats gap ~40% less than Q1 laggard beats
Post-earnings drift is momentum, not reversal	Day-0 gap positively predicts 5-day drift (Pearson r=0.55, p<0.001)
High VIX amplifies reaction magnitude	Absolute gap ~30–50% larger in VIX > 30 regimes
Pre-earnings model: 57–59% directional accuracy	p < 0.001 vs 50% baseline on 1,999 unseen 2024 events
Post-gap drift model: 70–76% directional accuracy	Dominant input is ret_0d (the actual gap), confirming post-earnings momentum

The combination of structural statistical patterns and validated predictive models provides a rigorous foundation for systematic earnings analysis — making explicit what practitioner intuition has long observed but rarely quantified.