do stocks go up after earnings report

Do stocks go up after an earnings report?

Do stocks go up after earnings report is a common question for individual and institutional investors. In short: sometimes — and on average there is a measurable positive effect around earnings dates, but the outcome for any single stock is highly variable. This article explains why earnings reports move prices, summarizes the main empirical findings (including average short‑term gains, announcement volume and jump behavior, and the well‑documented post‑earnings‑announcement drift), describes how researchers measure effects, and offers practical, neutral guidance for investors using Bitget products.

As of 1968, according to Ball & Brown (1968), earnings announcements were already documented as information events that move stock prices; later empirical and practitioner reports have refined those findings for modern markets.

Background and context

An earnings report is the periodic financial statement a public company issues to report recent revenue, earnings (profit), cash flow, and management discussion of results and outlook. Most public companies report quarterly results (10‑Q/10‑Q equivalents outside the US) and an annual report (10‑K). Firms issue quarterly earnings to satisfy legal and listing requirements, to keep capital markets informed, and to provide a scheduled flow of new information that investors use to update valuations.

Markets treat earnings as major scheduled information events because earnings contain forward‑looking information: they reveal the firm’s recent operating performance and often include management guidance about future prospects. Because stock prices reflect discounted expected future cash flows, a material revision to earnings expectations or guidance can change the present value investors attach to the firm — which leads to price moves.

How earnings affect prices — the basic mechanism

At the core, price reactions to earnings arise from the gap between what the market expected and what actually gets reported:

• Expectations vs. surprises: The market forms a consensus forecast (often from analyst estimates or aggregated models). The difference between reported results (and management guidance) and that consensus — the earnings surprise — largely determines the immediate direction and magnitude of a stock’s move. Positive surprises typically push prices up; negative surprises typically push prices down.

• Forward‑looking prices: Prices are discounting tools. Investors focus not only on the current quarter’s numbers but on what the results imply for future profitability, margins, and cash flows. Guidance from management, revisions to forward estimates, or new information about competitive position or demand can matter more than the raw current‑quarter earnings figure.

• Information assimilation: Once an announcement arrives, investors and algorithms process the news, revise expected cash flows, and trade. That trading drives the observed price change.

Empirical patterns and stylized facts

Average short‑term reaction

Multiple academic studies and practitioner analyses find that, on average, stocks show positive returns in short windows around scheduled earnings announcements. Practitioner research has often quantified an average short‑window premium: one widely cited practitioner note reported an average rise of roughly 0.7% in a five‑day window around earnings for a broad sample of U.S. stocks. This illustrates a central empirical point: while many individual stocks fall after earnings, the cross‑sectional average of abnormal returns around earnings dates has historically been mildly positive.

Earnings announcement premium and trading volume

Research linking announcement‑period trading activity to price behavior finds that earnings announcements are associated with large spikes in trading volume and liquidity demand. NBER and other studies have shown that when scheduled earnings reports arrive, average prices tend to rise and trading volumes surge; that surge in attention and activity is strongly associated with the announcement premium (the average abnormal return around earnings dates). High volume helps incorporate information into prices but also reflects heterogeneous views and trading frictions.

Post‑earnings‑announcement drift (PEAD)

One of the most robust findings in financial economics is the post‑earnings‑announcement drift (PEAD): stocks that report positive earnings surprises tend to earn additional abnormal returns in the weeks to months after the announcement, and conversely for negative surprises. First documented decades ago (Ball & Brown and other early studies), PEAD shows that prices underreact to earnings news at announcement time, and abnormal returns drift in the direction of the surprise over an extended period. This phenomenon has been documented across markets and long samples, although the magnitude and persistence vary with sample period, firm characteristics, and transaction costs.

Price jumps and high‑frequency evidence

High‑frequency studies find that earnings announcements often produce sudden price jumps, especially when results are released outside regular trading hours. These jumps can be large relative to typical intraday volatility and are frequently accompanied by bursts of trading activity. Recent high‑frequency research indicates earnings announcements not only cause jumps in the firm reporting but also increase the probability of co‑jumps among related firms and across the market, reflecting common news or sector contagion effects.

Measurement and metrics

Earnings surprise measures (SUE and others)

Researchers measure earnings surprises using standardized metrics. One common measure is Standardized Unexpected Earnings (SUE), which scales the raw surprise (reported earnings minus consensus forecast) by a measure of earnings variability, producing a z‑score–like statistic that is comparable across firms and time. Alternative surprise metrics include percent surprise (relative to consensus), revisions in analyst forecasts, and comparisons to model‑based expectations.

Price reaction measures (CARs, overnight returns)

To quantify price effects researchers use event‑study statistics such as cumulative abnormal returns (CARs), which sum the stock’s abnormal returns (relative to a benchmark or expected return) over a chosen event window (for example, day 0 for the announcement day, or a −1 to +5 window). Because many firms release earnings after market close or before market open, overnight returns and after‑hours price moves are critical: a large portion of the announcement effect shows up in the overnight return from the prior close to the next regular session open.

Volume and liquidity metrics

Volume spikes, changes in bid‑ask spreads, and after‑hours liquidity are useful to interpret price moves. Researchers and practitioners measure percentage change in trading volume, turnover, and the behavior of quoted spreads and depth. Large increases in volume around announcements indicate active information processing; widened spreads or thinner depth after hours can amplify price moves and execution costs.

Explanations for observed patterns

Information processing and investor underreaction

A prominent explanation for PEAD is investor underreaction: some market participants do not immediately or fully incorporate new information about future earnings into prices at announcement time. Behavioral biases, limited attention, or slow updating of models can cause a portion of the surprise to be incorporated gradually, producing a drift in the direction of the surprise.

Risk and anticipation effects

Before scheduled earnings, some investors reduce positions to avoid event risk. If a material negative outcome is priced as a risk premium before the announcement, and the results are not as bad as feared, prices can bounce as risk premia recede and risk‑averse sellers return to the market. Anticipation effects and risk dynamics can therefore make pre‑announcement returns depressed and post‑announcement returns relatively higher.

Trading behavior and small investor attention

Empirical work shows that increased retail investor attention and flows around earnings can mechanically affect returns. For example, small investor buying in response to positive headlines or screens can contribute to the announcement premium. Differential attention across investor groups, and the arrival of retail order flow, can shape short‑run price patterns.

Market microstructure and algorithmic/high‑frequency trading

After‑hours venues, dark pools, and the mechanics of quote dissemination mean that much of the immediate price discovery for after‑hours announcements occurs in thinner markets where algorithms and liquidity providers play an outsized role. Automated strategies can rapidly move quotes (and cause jumps), but frictions in after‑hours execution and information fragmentation may also leave room for subsequent price adjustment when the open arrives.

Cross‑sectional variation — when stocks tend to rise (or fall)

Firm size, liquidity and coverage

Large, well‑covered firms (many analysts, high institutional ownership) often exhibit different announcement dynamics from small, thinly traded firms. Broad coverage and liquid markets tend to incorporate information faster, reducing drift, while small firms with low coverage may show larger and more persistent post‑announcement effects. Analyst revisions and the credibility of guidance matter: firms with active analyst coverage often see immediate, larger reactions when surprises occur.

Pre‑announcement price trend

A documented pattern is that stocks that underperformed in the days leading up to earnings sometimes have stronger positive reactions on announcement, as short sellers or worried holders were disproportionately present pre‑announcement. Conversely, strong pre‑announcement runups can leave limited upside after results, increasing the risk of negative reactions.

Sector and macro sensitivity

Industry‑specific news or macro shocks can amplify or mute the effect of an individual firm’s earnings. For example, if a whole sector reports weaker demand due to a macro event, a single firm’s positive surprise may be less effective at lifting price; conversely, sector‑wide positive news can lift many related stocks simultaneously.

Trading strategies and their performance

Simple buy/hold around earnings

Some traders buy a stock right before earnings and hold for a short post‑announcement window to capture the average short‑window gain. Empirical averages can be positive, but the cross‑sectional dispersion is large: many firms fall after earnings, and any strategy that ignores risk controls, position sizing, and transaction costs may underperform. Historical averages do not guarantee future returns.

Options strategies (buying calls/straddles)

Options are popular for trading earnings because they allow asymmetric or volatility‑based exposures. Buying calls before earnings benefits from positive surprises but can be expensive due to elevated implied volatility heading into announcements (the “volatility premium”), which often inflates option prices. Straddle or strangle strategies bet on large moves in either direction but must overcome implied volatility costs to be profitable. Practitioner desk analyses historically found some pockets of profitability in options around earnings for certain markets and periods, but results vary over time and depend heavily on execution costs and correct volatility forecast.

Exploiting PEAD and event‑driven approaches

Academic strategies that exploit PEAD construct portfolios long firms with positive surprises and short firms with negative surprises, holding for weeks to months. In many historical samples this approach produced abnormal returns net of typical benchmarks. However, real‑world implementation faces frictions: transaction costs, shorting costs for small illiquid stocks, data‑snooping risks, and changes in market structure may erode theoretical profits. Many of the documented PEAD returns are smaller after accounting for realistic trading costs.

Risks, limitations, and caveats

Heterogeneity and unpredictability

Averages hide wide variation. While the average of many firms’ announcement windows may be positive, any single stock can experience a sharp drop after earnings. Outcomes depend on the consensus expectations, the quality of management guidance, timing of the release, and broader market conditions.

Transaction costs, taxes, and implementation friction

Bid‑ask spreads, after‑hours liquidity, option implied volatility, and slippage can erode measured gains. Taxes on short‑term gains also affect realized returns for investors. Practical trading must account for these frictions.

Evolving markets and research replication

Market efficiency is not static. Increased algorithmic trading, broader dissemination of analyst forecasts, and faster news distribution can weaken previously documented anomalies. Findings from historical samples may attenuate over time as participants arbitrage them away or adapt strategies.

Practical guidance for investors

• Check consensus expectations: before acting, compare likely outcomes to analyst consensus and model forecasts.
• Read management guidance and the earnings release: guidance and forward commentary often matter more than the headline EPS number.
• Beware after‑hours volatility: many earnings surprises appear in after‑hours trading where liquidity is thinner and spreads are wider.
• Consider implied volatility if using options: elevated implied vol can make simple long‑vol strategies expensive.
• Size positions appropriately and use risk controls: statistical tendencies do not guarantee outcomes for any specific firm.
• Use reliable platforms for execution and research: when trading or hedging around events, a deep liquidity venue and fast execution reduce slippage — consider Bitget for trading and Bitget Wallet for custody and on‑chain tracking of token activity.

Related concepts and further reading

• Post‑earnings‑announcement drift (PEAD): the tendency for abnormal returns to continue after the initial announcement.
• Earnings announcement premium: average abnormal returns around scheduled earnings dates.
• Event study methodology: how researchers measure CARs and abnormal returns.
• Earnings guidance and analyst revisions: how forward‑looking information is incorporated.
• High‑frequency price jumps: short‑term discontinuities around scheduled news.

References and notable studies (selected)

• Ball, R., & Brown, P. (1968). Early work documenting price reactions to earnings announcements.
• Subsequent PEAD literature: multiple authors expanded on the underreaction phenomenon across decades of study.
• Practitioner analyses reporting short‑window averages and options desk results (referenced in the literature and investment research notes).
• High‑frequency studies documenting jumps after earnings announcements (published in journals such as the Journal of Financial Economics and others).

As of 1968, according to Ball & Brown (1968), earnings announcements were identified as information events that move stock prices. As of 2020, high‑frequency research reported that earnings releases are among the most jump‑prone scheduled events for equity prices.

(See the References list above for canonical authors and search terms to locate the original papers. The article intentionally summarizes the literature rather than reproducing full citations.)

External data sources and tools

• Earnings calendars and consensus estimate providers (used by analysts and traders to track scheduled releases and expectations).
• SEC filings (10‑Q, 10‑K, 8‑K) and company press releases for primary source disclosures.
• Trade and quote datasets for high‑frequency research; market data vendors provide historical intraday tapes used in academic work.

Final notes and next steps

If your goal is to understand whether to trade around earnings, remember the empirical answer to do stocks go up after earnings report: on average there is a modest positive effect in short windows, but outcomes vary widely across firms and time, and sensible risk controls, attention to transaction costs, and respect for market microstructure are essential. For traders seeking execution, Bitget offers trading tools and liquidity for event‑driven strategies; for custody and token tracking, consider Bitget Wallet. Explore Bitget’s research tools and earnings calendars to plan event windows and manage execution risk.

Further reading and data queries: search terms like "post‑earnings‑announcement drift", "earnings announcement premium", "standardized unexpected earnings (SUE)", and "earnings announcement jumps" will point you to the academic and practitioner literature discussed above.

Explore more Bitget resources and tools to monitor earnings, execute trades, and manage position sizing in event windows.