Do stocks usually go up before earnings?

Do stocks usually go up before earnings?

The question "do stocks usually go up before earnings" asks whether publicly traded companies' share prices tend to rise in the days or hours immediately before a quarterly earnings announcement. In this comprehensive guide we summarize empirical evidence, academic research, behavioral and market‑microstructure explanations, trading implications, and practical guidance for investors and traders. You will learn what typical short‑window patterns look like, why they occur, which names are likeliest to move, and how professional studies and retail behavior shape pre‑earnings price action.

Background — what are earnings announcements and why they matter

Earnings announcements are scheduled corporate events in which a company reports its quarterly results: revenues, net income, earnings per share (EPS), and often forward guidance and commentary on trends. Most publicly traded firms in major markets release quarterly reports, follow the release with a conference or earnings call, and may provide management guidance that updates market expectations.

Earnings are a concentrated information event. They resolve uncertainty about recent operating performance, update forecasts, and sometimes include management signals about future trends. Because markets price expectations, any surprise relative to consensus analyst estimates or investor forecasts can cause rapid repricing. That is why traders and investors pay close attention to earnings dates and why price and volatility often spike in short windows around announcements.

Empirical patterns in price behavior around earnings

Academics, sell‑side analysts, and financial commentators have documented systematic patterns in returns and volatility around earnings. Below we summarize the main empirical regularities from event studies and market research.

Average short‑window effects

Several studies and market writeups find that, on average, stocks exhibit abnormal returns in short windows surrounding earnings announcements. Event‑study work aggregated across many firms shows average positive abnormal returns in narrow windows around release dates, though the magnitude and sign depend on the sample period, window length, and how surprises are measured. Research summarized by major broker and academic outlets reports that small positive average moves are common in the immediate pre‑ and post‑announcement windows.

Investors reading the question "do stocks usually go up before earnings" should note that the average positive effect is modest and driven by a subset of firms and episodes rather than a uniform tendency across all names.

Pre‑earnings price moves

Empirical research documents that some stocks experience a run‑up (or, less commonly, a run‑down) in the days leading up to earnings. Patterns include gradual appreciation as market participants reposition, short squeezes when short interest is high, and last‑minute order flow from retail or institutional participants. A notable behavioral finding is that retail investors tend to buy into names with recent positive earnings‑announcement returns, which can amplify pre‑announcement momentum.

The academic paper "Pre‑Earnings Announcement Over‑Extrapolation" (Kelly et al., 2016) provides evidence that retail investors over‑extrapolate prior earnings‑announcement gains and buy before the next earnings release, producing predictable pre‑announcement price patterns in some stocks.

Post‑announcement jumps and persistence

Immediate post‑announcement jumps are common: when reported earnings materially beat or miss consensus, prices can gap up or down in minutes or hours. Recent high‑frequency work (e.g., UC San Diego research) shows that earnings news can cause near‑instantaneous price jumps and can even spill over to related firms and indexes. After‑hours releases matter too; stocks can move sharply in extended trading, and those moves are incorporated into next‑day prices.

Whether an announcement produces a persistent revaluation depends on whether the news changes long‑run fundamentals or only adjusts short‑term expectations. Many post‑earnings moves partially revert, while some reflect lasting updates to growth forecasts.

Cross‑sectional heterogeneity

The simple question "do stocks usually go up before earnings" hides substantial cross‑sectional variation. Effects differ by firm size, sector, prior returns, analyst coverage, and whether expectations are high or low. For example:

• Smaller, less‑covered firms can show more pronounced pre‑earnings moves when retail attention spikes.
• Stocks that underperformed in the days leading up to earnings have sometimes produced larger positive reactions at announcement time (a pattern reported in broker analyses).
• Technology or high‑growth names often see larger implied‑volatility premia in options and larger announcement day moves than stable, dividend‑paying industrials.

In short, average patterns exist, but they are not universal. Context matters.

Explanations and mechanisms

Why do pre‑ and post‑earnings patterns arise? Both rational and behavioral channels, plus market‑microstructure factors, help explain observed regularities.

Expectations and consensus estimates

Analyst consensus and aggregate market expectations are central. The market price before earnings embodies current expectations about EPS, revenue trends, and guidance. If expectations drift upward ahead of release—because of leaked information, industry signals, or positive pre‑announcements—prices can rise. Conversely, if expectations weaken, prices can decline.

The role of surprises is crucial: studies documented by AAII and others show that the magnitude and sign of earnings surprises (actual vs. consensus) are highly predictive of announcement returns.

Risk reduction and reinvestment

Institutional investors often reduce exposure ahead of high‑uncertainty events to limit downside risk. After the event resolves uncertainty, they may re‑establish positions, producing a U‑shaped pattern: partial sell‑off before the event, then buying after a favorable surprise. Broker research (e.g., Goldman/CNBC analyses) suggests this dynamic contributes to average pre‑announcement dips and post‑announcement rebounds in some samples.

Behavioral factors (attention, over‑extrapolation)

Retail attention and cognitive biases play a measurable role. Kelly et al. (2016) show that retail investors tend to over‑extrapolate recent positive earnings‑announcement returns and buy before the next release, inflating pre‑announcement prices. Attention‑driven flows—news, headlines, social media chatter—also correlate with pre‑earnings buying, especially in small‑cap or highly talked‑about stocks.

These behavioral flows can generate predictable patterns that are exploited by higher‑frequency traders or professional investors.

Market microstructure and timing (after‑hours, options, HFT)

Earnings often occur outside regular trading hours. After‑hours releases mean that immediate reactions happen in lower‑liquidity sessions where price moves can be larger relative to the order book. Options markets provide implied volatility measures: implied moves priced into options before earnings reflect anticipated announcement risk. Traders who sell options into inflated implied volatility are exposed to an "IV crush" if realized volatility is lower than expected.

High‑frequency traders and algorithmic desks monitor news feeds and can move markets in milliseconds once a release hits, amplifying very short‑term price changes documented by UC San Diego and others.

Trading strategies and practical implications

Investors and traders use a variety of approaches around earnings. None are guaranteed; each carries tradeoffs between expected return, risk, and costs.

Buying before earnings (long stock)

Rationale: if you believe a firm will report a positive surprise, buying before earnings captures the upside. Empirical studies show average positive returns in some pre‑announcement windows, but this average masks wide dispersion.

Risks and constraints:

• Earnings misses or weak guidance can cause sharp declines.
• Elevated volatility leads to larger bid‑ask spreads and trading costs.
• If many participants buy beforehand, the upside may be partially priced in.

Regulatory and prudential note: this is not investment advice. Individual risk tolerance, position sizing, and timing matter.

Options strategies (calls, straddles, spreads)

Options let traders express views or hedge around earnings. Common choices:

• Long straddle/strangle: buy both call and put to profit from a large move (direction‑agnostic), but expensive because implied volatility is often high before earnings.
• Long calls (or puts): directional exposure with defined downside (premium paid), but heavy time decay if volatility falls.
• Calendar or vertical spreads: reduce premium cost and mitigate implied‑volatility exposure.

Charles Schwab and other brokerage guides note the elevated implied volatility before earnings means options are often overpriced relative to typical realized moves. Traders who buy options before earnings must overcome this implied‑volatility premium to realize net profits.

Filtering and selection (e.g., weak pre‑event performers)

Broker analyses (Goldman/CNBC) suggest one way to tilt probability is to screen names: for example, firms that underperformed in the days before an earnings release have sometimes shown stronger announcement reactions. Similarly, focusing on names with high short interest or low analyst coverage can highlight asymmetric opportunities—but also increases idiosyncratic risk.

Limitations and transaction/risk costs

Practical trading must account for:

• Elevated volatility and slippage
• Wider bid‑ask spreads and option spreads
• Implied‑volatility crush after earnings
• Tax and commission impacts

Historical averages do not translate to guaranteed profit. Risk‑adjusted returns can be negative once costs and drawdown risk are included.

Academic literature and event studies

Researchers use event‑study methodologies to quantify abnormal returns and volatility around scheduled announcements. Key points from the literature:

Event‑study methodologies

Typical event studies define an event window (for example, day −5 to day +5 around announcement) and compare realized returns to a benchmark (market model or factor model) to estimate abnormal returns. Statistical aggregation across many firms produces average abnormal returns and tests significance.

High‑frequency studies take minute‑level or second‑level data to capture instantaneous jumps at release time and to study spillovers across firms and indices.

Notable findings and papers

• Kelly et al. (2016), "Pre‑Earnings Announcement Over‑Extrapolation" — documents retail over‑extrapolation and predictable pre‑announcement price patterns.
• UC San Diego (2025) — shows immediate price jumps at earnings and cross‑firm spillovers, highlighting after‑hours effects and high‑frequency reactions.
• NBER and digest summaries — report that stocks, on average, rise around earnings announcements, though with important caveats.
• Broker and industry analyses (Goldman/CNBC, Charles Schwab, IG Academy) — provide practitioner‑oriented evidence and warnings about options and volatility around earnings.

Together, academic and industry work shows that earnings events are informative, often move prices, and create exploitable patterns for some market participants while posing significant risk for others.

Sectoral and calendar effects

Earnings behavior is not uniform across sectors or across the earnings calendar.

• Sector: tech and growth names frequently show larger announcement‑day moves and higher implied volatility, whereas utilities and consumer staples show smaller moves.
• Firm size and coverage: small‑cap, low‑coverage names are more affected by retail attention and can have larger pre‑announcement swings.
• Timing within season: early in an earnings season (when many firms report) or close to macro events, market responses can be atypical.

Seasonal clustering, macro news, and industry‑wide developments can amplify or dampen typical pre‑earnings patterns.

Practical guidance for investors

The following actionable points summarize safe, neutral guidelines based on evidence and market practice:

• Recognize uncertainty: Even if average pre‑announcement moves are positive, outcomes vary widely.
• Check implied volatility: Options often price a large implied move; buying options without accounting for IV premium is risky.
• Consider position sizing and stop‑loss rules: Limit exposure when entering trades around announcements.
• Use filters grounded in data: Historical patterns suggest names that underperformed before earnings sometimes rebound; test such filters on robust historical samples.
• If you prefer lower event risk, use hedges or avoid initiating large directional positions just before earnings.
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Frequently asked questions (FAQ)

Q: Do stocks usually rise before earnings? A: There is evidence of modest average positive moves in short windows around earnings for many samples, but results are heterogeneous. The phrase "do stocks usually go up before earnings" captures a real tendency in some samples, but it is not universal.

Q: Should I buy before earnings? A: This depends on your risk tolerance, costs, and time horizon. Buying before earnings exposes you to high upside and downside risk. Consider hedging or using options with an understanding of implied volatility.

Q: Do options become cheaper after earnings? A: Often implied volatility drops (an "IV crush") after the event if realized volatility is lower than priced. That reduces the value of long option positions even when direction is correct.

Q: Are there stocks that consistently rise before earnings? A: No guaranteed list exists. Some names show recurring patterns due to retail attention or structural dynamics, but patterns can change with market structure and participant behavior.

Limitations and controversies

Several caveats temper the interpretation of empirical findings:

• Data‑mining risk: When many strategies are tested, some will appear to work by luck.
• Survivorship and selection bias: Studies must account for delisted firms and sample selection.
• Changing market structure: The rise of retail trading platforms, algorithmic trading, and after‑hours liquidity has changed how earnings effects manifest over time.
• Behavioral vs. rational explanations: Some observed patterns can be rational responses to information; others are behavioral biases. Separating mechanisms is nontrivial.

Researchers and practitioners caution against simple extrapolation of past averages into future trading without rigorous testing.

Academic and practitioner references

Sources summarized and used to inform this article include (titles and brief citations):

• Investopedia — "Should You Buy Stock Before an Earnings Call? Here's What You Need to Know" (2025). Source: industry guide on retail buying and risks.
• Goldman Sachs / CNBC analyses (2015–2016) — empirical findings on earnings‑period return patterns and cross‑sectional dynamics.
• UC San Diego Rady School — "Earnings News Cause Immediate Stock Price Jumps, Sometimes Moving Whole Market" (2025). High‑frequency evidence on jumps and spillovers.
• Charles Schwab — guidance on trading options around earnings and risks of volatility and spreads.
• IG Academy — educational overview of how earnings reports influence trading and volatility.
• AAII (2025) — discussion of consensus earnings estimates and the role of surprise.
• NBER digest — summary evidence that stocks rise around earnings announcements.
• Kelly, O’Hara and collaborators (2016), "Pre‑Earnings Announcement Over‑Extrapolation" (SSRN/working paper) — behavioral evidence on retail buying before earnings.

Example: how major names can illustrate earnings dynamics

Large, widely followed companies show how narrative and earnings interact. For instance, high‑profile firms with strong growth narratives can experience outsized pre‑ and post‑earnings moves tied to both fundamentals and story‑driven positioning.

As an example of the interaction between narrative and market reaction, consider widely reported commentary about a major automaker and AI ecosystem leader. As of January 15, 2026, according to MarketWatch, commentators argued that the company’s combination of data collection, compute infrastructure, and distribution created a larger long‑term AI narrative than reflected in short‑term earnings metrics. The same report noted that the company generated nearly $4 billion in free cash flow in a recent quarter and held roughly $41.6 billion in cash and investments (as reported on that date). That reporting date and coverage illustrate how earnings numbers and broader strategic narratives (data moats, infrastructure, and vertical integration) can interact to influence investor expectations and stock movement around announcement dates.

Note: the MarketWatch reporting cited above is an example of market commentary and should be read as background context. It does not constitute investment advice.

Practical checklist before trading around earnings

• Confirm the exact earnings date and time (before market open, during market hours, or after close).
• Review consensus analyst estimates and recent guidance trends.
• Check implied volatility and option spreads if using options.
• Assess liquidity (average daily volume) to estimate slippage risk.
• Evaluate recent pre‑announcement price action—momentum or mean‑reversion tendencies.
• Set position sizing, stop limits, and contingency plans in advance.

Further research directions

If you want to dig deeper, consider: replicating event‑study windows on different market regimes; testing filters (size, coverage, short interest) for robustness; or combining textual sentiment from filings and social media with price reaction analysis. Academic work continues to refine our understanding of how announcement timing, after‑hours releases, and high‑frequency market responses shape outcomes.

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Summary takeaways

• Short answer to "do stocks usually go up before earnings": empirical evidence shows modest average positive moves in some short windows, but effects are heterogeneous and context‑dependent.
• Mechanisms include expectations pricing, institutional positioning, retail attention and behavioral over‑extrapolation, options‑market pricing, and market‑microstructure timing.
• Trading around earnings can be profitable for some strategies, but transaction costs, implied‑volatility premia, and event risk make it challenging.
• Use data, robust filters, and risk management; consider hedging or avoiding large directional bets immediately before earnings if you prefer lower event risk.

Further explore Bitget’s platform features and tools if you trade instruments influenced by earnings cycles, and always conduct independent research before entering event‑driven trades. This article is informational and does not constitute personalized investment advice.

References and further reading

• Investopedia — "Should You Buy Stock Before an Earnings Call? Here's What You Need to Know" (2025).
• Goldman Sachs / CNBC analyses on earnings reactions (2015–2016).
• UC San Diego Rady School — "Earnings News Cause Immediate Stock Price Jumps, Sometimes Moving Whole Market" (2025).
• Charles Schwab — "Tools for Trading Options Around Earnings" (practitioner guidance).
• IG Academy — "The influence of corporate earnings on trading" (educational overview).
• AAII — "Earnings Estimates and Their Impact on Stock Prices" (2025).
• NBER digest — summaries on stock returns around earnings announcements.
• Kelly, O’Hara, et al. (2016) — "Pre‑Earnings Announcement Over‑Extrapolation" (SSRN working paper).
• MarketWatch coverage and commentary on large‑cap corporate narratives (as cited above).

(Reporting note: the MarketWatch piece referenced in this article was reported as of January 15, 2026.)