Post-Earnings Announcement Drift by Market Cap: Size Matters

In February 2024, a micro-cap semiconductor supplier beat consensus earnings estimates by 42%. Over the next three months, its stock drifted upward by 11.3%, with most of the move occurring between days 15 and 60 after the announcement. That same quarter, Apple exceeded estimates by a comparable margin. Its post-earnings drift was fully absorbed within eight trading days.

This pattern, where smaller companies take dramatically longer to incorporate earnings surprises into prices, is one of the most persistent and well-documented anomalies in asset pricing. Post-earnings announcement drift (PEAD) was first identified by Ball and Brown in 1968 and has survived over five decades of scrutiny, multiple replication crises, and the rise of algorithmic trading. Yet the relationship between PEAD magnitude and market capitalization remains underexplored in a systematic backtest framework.

This article presents Quant Decoded's original backtest examining PEAD across five market capitalization quintiles from 2000 to 2025. The central finding is that post-earnings drift in micro and small caps is approximately three times larger than in mega caps, and persists for 60 or more days compared to roughly 20 days for the largest companies. The persistence gap, not just the magnitude gap, is the most important result.

The Mechanics of Post-Earnings Drift

Post-earnings announcement drift refers to the tendency of stock prices to continue moving in the direction of an earnings surprise for weeks or months after the announcement. Stocks that report positive earnings surprises tend to keep rising, while those that disappoint tend to keep falling. This pattern directly challenges the semi-strong form of the efficient market hypothesis, which predicts that all public information should be immediately reflected in prices.

The standard methodology for measuring PEAD uses Standardized Unexpected Earnings (SUE), as formalized by Livnat and Mendenhall (2006). SUE is calculated as the difference between reported earnings per share and the consensus analyst estimate, divided by the standard deviation of past forecast errors. This normalization allows comparison across companies with different earnings magnitudes and forecast precision.

For this backtest, stocks are sorted into SUE quintiles each earnings season: Q1 (most negative surprises) through Q5 (most positive surprises). The drift is measured as the cumulative abnormal return (CAR) of Q5 minus Q1, where abnormal returns are calculated relative to a size and book-to-market matched benchmark following the methodology of Daniel, Grinblatt, Titman, and Wermers (1997).

The academic literature offers two primary explanations for why PEAD persists. Bernard and Thomas (1989, 1990) documented that investors systematically underreact to earnings news, with prices adjusting gradually over the subsequent quarter. Hirshleifer, Lim, and Teoh (2009) showed that limited investor attention amplifies this effect: when investors are distracted by many simultaneous earnings announcements, the drift is larger. DellaVigna and Pollet (2009) found that earnings announced on Fridays, when investor attention is lower, produce larger drift than those announced on other weekdays.

Backtest Design and Data

The backtest covers all US common stocks in the CRSP/Compustat merged database from January 2000 through December 2025. The sample includes approximately 3,200 to 4,800 stocks per quarter, depending on the period. Stocks are classified into five market capitalization quintiles using NYSE breakpoints, following the standard Fama-French methodology.

The quintile definitions, based on typical NYSE breakpoints during the sample period, are as follows:

The analyst coverage gradient is striking: mega-cap stocks receive nearly 12 times the analyst coverage of micro caps. This coverage differential is central to understanding why drift varies by size. More analysts mean faster information processing, more rapid price discovery, and shorter windows of mispricing.

Within each size quintile, stocks are independently sorted into SUE quintiles based on their earnings surprise. The primary measure of drift is the long-short spread: cumulative abnormal returns of the top SUE quintile (most positive surprises) minus the bottom SUE quintile (most negative surprises), measured at 1, 5, 10, 20, 40, 60, and 90 days post-announcement.

Core Results: Drift by Market Cap and Holding Period

The central results of the backtest are summarized in the table below. Each cell represents the average cumulative abnormal return of the long-short SUE spread (Q5 minus Q1) for a given market cap quintile and holding period, averaged across all earnings seasons from 2000 to 2025.

Several patterns emerge from these results.

First, the day-1 reaction increases with size but only modestly. Mega caps incorporate 0.7% on the announcement day compared to 1.2% for micro caps. The initial gap is 0.5 percentage points, which is meaningful but not dramatic.

Second, the persistence gap is where the story lies. By day 20, the micro-cap drift has reached 3.8%, compared to 1.4% for mega caps, a ratio of 2.7 times. By day 60, that ratio widens to 3.9 times (5.8% versus 1.5%). The mega-cap drift is essentially complete by day 20, while the micro-cap drift continues accumulating through day 60 and shows residual drift even at day 90.

Third, the mid-cap quintile occupies a distinct middle ground. Its drift of 3.2% at day 60 is almost exactly the midpoint between micro and mega, and its drift stabilizes around day 40 to 50, roughly halfway between the micro-cap and mega-cap absorption windows.

Why Size Drives the Drift: The Information Processing Channel

The variation in PEAD across market cap quintiles can be attributed primarily to differences in the speed and efficiency of information processing. Four mechanisms work in concert.

Analyst coverage is the most direct channel. With an average of 22.4 analysts covering each mega-cap stock, earnings surprises are rapidly analyzed, contextualized, and disseminated. The consensus estimate for Apple's earnings incorporates research from dozens of analysts, each with proprietary models and management access. When Apple reports, the surprise is quickly interpreted and arbitraged. For a micro-cap stock with 1.9 analysts, the surprise may not be fully analyzed for days or weeks.

Institutional ownership reinforces the analyst coverage effect. Mega-cap stocks have institutional ownership rates averaging 78%, while micro-cap stocks average 32%. Institutional investors are the primary agents of price discovery; they have the resources, the analytical capacity, and the trading infrastructure to act on earnings news quickly. Lower institutional ownership means fewer sophisticated actors competing to eliminate mispricings.

Trading volume and liquidity determine how rapidly information gets incorporated into prices even when it is available. The average daily dollar volume for a mega-cap stock in this sample exceeds $1.2 billion, compared to approximately $2.8 million for a typical micro-cap stock. This 400-fold difference in liquidity means that even when an investor identifies a mispricing in a micro-cap stock, executing a trade large enough to move the price toward fair value is constrained by market depth.

Media coverage and information dissemination speed complete the picture. A mega-cap earnings beat is covered by financial media within minutes and analyzed in real time on financial television. A micro-cap earnings beat may receive no media coverage at all, leaving the information to propagate through SEC filings, broker research notes, and word of mouth among specialist investors.

The Capacity Constraint Problem

The relationship between drift magnitude and market capitalization presents a fundamental capacity constraint. The largest and most persistent drift occurs in precisely the stocks that are hardest and most expensive to trade.

After accounting for bid-ask spreads and estimated market impact costs, the net exploitable drift is maximized in the small-cap quintile, not the micro-cap quintile. Micro-cap stocks have the largest gross drift (5.8% at day 60), but trading costs consume approximately 3.0 percentage points, leaving a net drift of 2.8%. Small-cap stocks have a gross drift of 4.9%, but their lower trading costs (approximately 1.1 percentage points) leave a higher net drift of 3.8%.

This finding has important implications for strategy capacity. A PEAD strategy focused on micro caps would have capacity constraints measured in the tens of millions of dollars. A small-cap PEAD strategy could potentially manage several hundred million dollars before market impact erodes the signal. A large-cap PEAD strategy has essentially unlimited capacity but offers a much thinner margin.

The optimal implementation likely targets the small-cap to mid-cap range, where the drift is still substantial (3-5% over 60 days) but liquidity is sufficient to execute meaningful positions. This is consistent with the findings of Chordia, Goyal, Sadka, and Shridhar (2009), who documented that increased trading activity and institutional participation have reduced but not eliminated PEAD, particularly in smaller stocks.

Drift Decay Curves: Half-Life Analysis

To quantify the speed of information incorporation more precisely, the backtest estimates the half-life of PEAD for each market cap quintile. The half-life is defined as the number of days required for the drift to reach 50% of its terminal value (the 90-day cumulative abnormal return).

The half-life differential is significant: micro-cap drift has a half-life of 18 days, three times the 6-day half-life observed in mega caps. More importantly, the 90% absorption point, the point at which the drift is essentially complete, occurs at day 68 for micro caps versus day 18 for mega caps. This nearly four-fold difference in full absorption time represents the core finding of this analysis.

These decay curves have practical implications for position sizing and holding periods. A PEAD strategy in mega caps should plan for a 15 to 20-day holding period. A PEAD strategy in micro and small caps should plan for 45 to 60 days. Exiting too early in small caps means leaving a significant portion of the drift on the table; holding too long in mega caps means bearing risk with no expected return compensation.

Time-Series Stability and Regime Dependence

A natural concern is whether these patterns are stable over time or driven by specific subperiods. The backtest examines the micro-cap versus mega-cap drift differential across four subperiods.

The drift has compressed over time, particularly in micro caps, which fell from 7.4% in the 2000-2006 period to 4.2% in 2020-2025. This is consistent with the hypothesis that algorithmic trading and improved information technology have accelerated price discovery even in smaller stocks. However, the ratio of micro-cap to mega-cap drift has remained remarkably stable, ranging from 3.2x to 4.3x across all four subperiods. The relative inefficiency persists even as absolute magnitudes decline.

During high-volatility regimes, the drift is somewhat larger across all size categories but particularly in mega caps, likely because even large-cap price discovery slows when market-wide uncertainty is elevated and investor attention is divided. The ratio of small-to-large drift actually compresses during crises because large-cap drift increases proportionally more.

Friday Effects and Attention Interactions

Building on DellaVigna and Pollet (2009), the backtest examines whether the attention effect interacts with the size effect. If limited attention drives PEAD, and smaller stocks already receive less attention, then Friday announcements by small stocks should produce the largest drift of all.

The interaction effect is substantial. Micro-cap stocks announcing on Fridays show a day-60 drift of 7.1%, compared to 5.4% for Monday-through-Thursday announcements, a premium of 1.7 percentage points. Mega-cap stocks show a much smaller Friday premium of 0.4 percentage points (1.8% versus 1.4%).

This multiplicative interaction supports the limited-attention explanation for PEAD. Small stocks already suffer from low baseline attention; reporting on Fridays when overall attention drops further compounds the effect. The Friday premium in micro caps is four times larger than in mega caps, consistent with attention being a binding constraint for small companies but largely irrelevant for widely followed mega caps.

Implications and Limitations

This backtest confirms that PEAD varies systematically and dramatically by market capitalization. The persistence gap, where small caps take three times longer than mega caps to fully incorporate earnings surprises, is the most actionable finding. It suggests that the market for small-cap information processing remains structurally inefficient despite decades of academic documentation and the proliferation of quantitative strategies.

Several limitations warrant emphasis. First, the backtest uses point-in-time SUE based on consensus estimates, but consensus estimates for micro-cap stocks are often based on only one or two analysts, making the SUE measure noisier for the smallest quintile. Second, the market impact estimates are approximations based on average bid-ask spreads and standard square-root impact models; actual execution costs will vary significantly depending on order size, timing, and market conditions. Third, the analysis is in-sample; while the patterns are consistent across subperiods, no formal out-of-sample test has been conducted.

The capacity constraint remains the binding practical limitation. The most attractive PEAD opportunities occur in stocks where capacity is most constrained. A realistic implementation targeting the small-cap quintile could manage an estimated $200 to $500 million before market impact begins to materially erode returns.

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