Every October, financial journalism performs a small act of necromancy. Out comes the “Halloween effect,” the finding that stocks tend to earn higher returns from November through April than from May through October. It gives apparent empirical backing to the old stock-market adage “Sell in May and go away.” The implied strategy is simple: Hold stocks during the historically stronger six months, then park your money in short-term government debt until Halloween rolls around again.
That sounds like trouble for a central idea in finance. The efficient markets hypothesis, developed in Eugene Fama’s foundational research, holds that prices reflect available information. Public knowledge shouldn’t offer investors a reliable route to returns beyond what their risks justify. Yet everyone has a calendar, so why haven’t investors anticipating the winter gains bought earlier and erased the advantage?
The effect’s existence has substantial academic backing. Researchers have documented the seasonal gap across dozens of countries, long stretches of history, and years beyond the original studies. Even cryptocurrency publications give it an annual airing. Last October, DailyCoin explored its application to digital assets, while CCN reported comparisons favoring year-round ownership in most stock indexes it examined, as well as Bitcoin.
But a well-documented pattern isn’t necessarily a profitable trading strategy—or a refutation of market efficiency. The harder questions are what causes the winter advantage, whether it compensates investors for risk, and what happens once people try to trade on it.
The Pattern That Won’t Stay Dead
Sven Bouman and Ben Jacobsen’s 2002 study on the Halloween effect found higher November–April returns in 36 of 37 countries they examined. In 20 of these examples, the gap met their 10% threshold for statistical significance. Under their model, a gap at least that large would arise less than 10% of the time if no seasonal effect existed.
Search enough stock-market history, though, and you’ll find patterns that arose by chance. A stronger test asks whether they repeat in later years. Sandro Andrade, Vidhi Chhaochharia, and Michael Fuerst’s 2013 follow-up examined November 1998 through April 2012, a period the original study didn’t cover. Winter returns exceeded summer returns in all 37 markets, with statistically significant gaps in 13 of them. The average difference approached 10 percentage points.
Cherry Zhang and Jacobsen’s 2021 global study added historical breadth. Across 62,962 observations, it found an average winter advantage of about 4 percentage points. Together, these findings make the effect harder to dismiss as coincidence.
So, the big question is not whether the Halloween effect exists, but how reliably it appears and how useful it is. For example, Júlio Lobão and Ana Costa’s 2023 study of Portuguese markets found an effect early in the 20th century, followed by roughly 60 years without it. It returned after 2000, largely thanks to strong January returns. That’s a long wait for a trader who hopes to retire someday.
Along those lines, other studies question the effect’s practical value as a profitable trading strategy. Hubert Dichtl and Wolfgang Drobetz’s 2015 study found that the winter advantage shrank substantially or disappeared in more recent periods. Thomas Degenhardt and Benjamin Auer’s 2018 review and analysis found continuing seasonality but strategies built around selling stocks in May and buying them back in the fall had limited success.
And while finding the pattern in many countries strengthens the evidence, it should also be noted that those markets often move together. A global crash during the summer could lower May–October returns in dozens of countries at once. That would reflect one shared event, rather than dozens of separate confirmations of the Halloween effect. Bouman and Jacobsen addressed this problem by adjusting for global stock-market movements.
The studies thus ask distinct question: Does the pattern recur? Does it survive publication of the research? Can investors profit after trading costs and the returns available elsewhere during summer?
The publication question echoes Goodhart’s law, developed by economist Charles Goodhart in a 1975 discussion of British monetary policy. A statistical relationship that helps policymakers understand the economy can become unreliable once they try to control the economy through it. Banks and other market participants adjust their behavior, changing the relationship policymakers hoped to exploit.
Anthropologist Marilyn Strathern supplied the familiar formulation in her 1997 discussion of university audits: “When a measure becomes a target, it ceases to be a good measure.”
A seasonal trading rule raises a related problem. Once investors act on a historical pattern, their buying and selling can change the prices that produced it. That doesn’t necessarily doom every published strategy, but it makes historical success an uncertain guide to future profits. After all, the market gets to read the paper, too.
Still Hunting for the Ghost in the Market
Bouman and Jacobsen suggested that summer vacations might help explain the lower May–October returns. Investors might sell shares before going away to avoid worrying about losses while they’re gone. That’s plausible, although six months is a long summer vacation, even for Wall Street.
Of course, the people running public companies also take vacations, and their departures may affect the information investors receive. In his 2013 working paper “Tailspotting,” David Yermack matched corporate jet flights with property records identifying CEOs’ vacation homes. Companies tended to release favorable news before their CEOs departed and delayed subsequent news until around their return. During those absences, companies made fewer announcements, mandatory disclosures were more likely to arrive late, and stock prices fluctuated less.
Summer vacations could therefore change both trading behavior and the flow of corporate news. But smaller price swings don’t necessarily mean lower returns, and executives’ trips don’t establish a market-wide explanation for six months of weaker performance.
Mark Kamstra, Lisa Kramer, and Maurice Levi’s “Winter Blues” proposes a psychological explanation. Seasonal daylight changes may affect investors’ moods and willingness to take risks. Jacobsen and Wessel Marquering’s “Is It the Weather?” points to a problem with that theory. Daylight, weather, and vacations all follow seasonal schedules. Correlations with returns don’t establish which, if any, drives the pattern.
The winter advantage also varies by industry. Jacobsen and Nuttawat Visaltanachoti’s 2009 study found a strong November–April advantage in production sectors, such as manufacturing, but little in sectors tied to consumer spending. That complicates the “mood” explanation. If shorter days change investors’ willingness to take risks, the theory also needs to explain why that change affects some industries more than others. Investors’ gloom apparently has sectoral preferences.
A newer paper suggests that corporate reporting schedules might help explain the pattern. Jan Schroeder’s 2025 study examined filings with the Securities and Exchange Commission (SEC), finding that from 2004 through 2023, companies submitted 17% more filings during November–April than during May–October. Annual reports and shareholder meetings also followed seasonal patterns, suggesting that investors receive more company news at some times of year than others.
Schroeder doesn’t claim that more disclosures cause higher returns. Company news can move prices in either direction. Still, a busy reporting season might expose investors to more unwelcome surprises, leading them to demand lower purchase prices and higher expected returns as compensation. Investors might also struggle to absorb clustered disclosures, causing prices to adjust gradually. Researchers would need to test both explanations. Counting the number of public filings doesn’t establish either one.
Research on Ramadan offers another example of unusual stock returns tied to a holiday. Jedrzej Bialkowski and Mona Yaghoubi’s 2021 study found unusually high returns during the Muslim month of fasting across 11 Muslim countries from 2009 through 2018. But there’s no obvious reason that buying and selling shares would become harder during Ramadan, weakening the explanation that higher returns compensated investors for holding investments they might struggle to sell.
Because Ramadan follows a lunar calendar, it moves through the seasons; it might fall in summer one year and winter in another. If higher returns follow the observance from winter into summer, religious or social practices become more plausible explanations than shorter days. That doesn’t establish a shared cause with the Halloween effect, but it helps separate competing explanations.
The Halloween effect needs similarly discriminating tests. What changes investors’ behavior from November through April, and why would that produce higher returns? Researchers have proposed several plausible answers, but none has emerged as a convincing explanation for the pattern as a whole.
Who’s Afraid of an Efficient Market?
The Halloween effect seems to challenge the idea that stock prices already reflect publicly available information. If investors know stocks tend to earn more from November through April, why haven’t they bought ahead of that rise, pushing prices up earlier and eliminating the seasonal advantage?
But higher expected returns don’t necessarily mean stocks are mispriced. They may compensate investors for taking greater risks or for investing when they’re less willing to risk a loss. Fama’s 1991 review explains why that makes market efficiency hard to test. Researchers must first estimate what return would adequately compensate investors for the risks they face. If that estimate is too low, returns may look unusually generous even when they simply reflect the compensation investors require.
Andrade and his co-authors found similar seasonality in strategies involving smaller companies, stocks priced cheaply relative to their accounting value, and lending to borrowers more likely to default. Those investments involve different risks, but their returns shared a winter advantage. That’s consistent with a seasonal shift in the compensation investors demand, although it doesn’t establish the explanation.
It’s also important to remain cognizant of the arithmetic involved. Suppose stocks gain 12% during winter and 2% during summer. Both seasonal traders and year-round investors earn the winter’s 12% gains. Selling in May exchanges the summer’s 2% for whatever the money would otherwise earn elsewhere. To deliver a higher overall return, the alternative must beat stocks over the summer by enough to cover trading costs.
A seasonal strategy could still be appealing even if it earns less, but substantially reduces potential losses. K. Stephen Haggard and H. Douglas Witte’s 2010 paper found that winter stocks and summer government debt offered better returns relative to risk than year-round stock ownership, even after trading costs. Their findings also survived tests accounting for unusually large market moves and January’s historically strong returns.
Dichtl and Drobetz, and Degenhardt and Auer, reached less encouraging conclusions, with their findings recalling Goodhart’s warning. If investors expect stocks to rise after Halloween, they may buy before then, pushing prices up sooner and leaving less potential profit for those who wait until November. A strategy’s declining profitability could therefore reflect investors learning from published research.
But Goodhart’s law can’t settle the question by itself. As Cedric Chin argues in Commoncog, the maxim describes a problem without necessarily explaining how to address it. Researchers still need evidence that actual trading in actual markets changed the pattern. Different samples, costs, or risks could explain weaker results. And publicity needn’t eliminate returns that compensate investors for risk.
Even when investors correctly spot a mispriced investment, they can lose money while waiting for the market to catch up. Andrei Shleifer and Robert Vishny’s “The Limits of Arbitrage” explains why that creates a particular problem for professionals managing clients’ money. Clients may see the early losses, lose confidence, and withdraw their funds before the investment pays off.
Imagine a fund manager who sells stocks in May, expecting weaker summer returns. Instead, stocks soar through August, leaving the fund behind competitors that stayed invested. Clients may withdraw their money rather than wait to see whether the strategy works over several years.
Those pressures don’t explain why the Halloween effect arises, but they could help explain why it persists. Managers may hesitate to bet on a seasonal pattern if a few bad months could cost them clients or their jobs. The scariest date on their calendar may be the next quarterly client review.
Efficient Markets Aren’t Dead Yet
The Halloween effect is a persistent empirical puzzle, but its existence alone doesn’t undermine the efficient markets hypothesis. Efficient prices can still produce predictable differences in returns if those differences compensate investors for risk. Establishing a seasonal pattern therefore leaves the central question open. Does the winter advantage reflect the risks investors bear, changes in their willingness to bear them, or a pricing mistake?
The research offers several possible explanations without settling the matter. Yermack’s vacation findings suggest one connection between executives’ schedules and the flow of information. Goodhart’s law gives us a reason to ask whether publicizing a trading rule changes its performance. Neither establishes what causes the Halloween effect. If investors trade away a pricing mistake, that would be consistent with markets learning. If a seasonal difference survives, researchers still need to determine whether it compensates for risk or reflects an opportunity too costly or uncertain to exploit.
The more recent findings of a Halloween effect in cryptocurrency offers a potentially useful comparison. Bitcoin doesn’t file earnings reports or have a CEO who leaves for a summer house. A comparable seasonal pattern there would suggest that corporate disclosures and executives’ vacations can’t provide the whole explanation. Shared influences, such as investors’ mood around the holidays or changing risk appetite over the course of the year, would merit closer attention. Similar patterns needn’t share a cause, but comparing markets could help narrow the possibilities.
The task is to explain the returns before treating them as evidence against market efficiency. A recurring October story isn’t an annual obituary for efficient markets.
