Some stocks behave a lot like lottery tickets. They offer a small chance of an eye-popping payoff, and that slim possibility is enough to draw investors in, even when the long-term track record of such stocks is unimpressive. Researchers have documented for years that these “lottery-like” stocks, on average, deliver disappointing returns. Investors appear to overpay for the fantasy of a jackpot, pushing prices above what the underlying businesses justify and setting up later disappointment.
But a new investigation published in the Journal of Behavioral Finance finds that this familiar story has a twist. When lottery-like stocks are combined with a popular trading approach called a momentum strategy, the usual pattern flips. Past winners among high-lottery stocks don’t become much more profitable, but past losers crash far harder, and the overall strategy ends up generating substantially higher returns than the standard momentum approach.
Two well-known patterns that seem to pull in opposite directions
Reihaneh Haghighi Zadeh of Coastal Carolina University set out to untangle how two well-documented stock market patterns interact. The first is the “MAX effect,” identified in earlier research, which uses a stock’s single highest daily return in the previous month as a stand-in for how lottery-like it is. Stocks with a large recent one-day spike tend to underperform in the following month.
The second is the momentum effect, introduced in a widely cited 1993 study by Narasimhan Jegadeesh and Sheridan Titman. Momentum strategies involve buying stocks that have performed well over the past year while selling (or shorting) stocks that have performed poorly. On average, this approach has generated positive returns over many decades.
At first glance, these two patterns appear to pull against each other. If high-MAX stocks are destined to underperform, then filling a momentum portfolio with them should drag returns down. Haghighi Zadeh asked whether that prediction actually holds up, and whether the effects on winners and losers might differ.
Sorting stocks by lottery traits, then by momentum
To examine the question, the analysis drew on a large sample of stocks listed on the New York Stock Exchange, the American Stock Exchange, and Nasdaq from July 1962 through December 2023. Each month, stocks were first divided into five groups based on their maximum daily return during the previous month. Within each of those groups, stocks were then sorted again into five categories based on their cumulative return over the prior 11 months, skipping the most recent month. That produced 25 portfolios that captured combinations of lottery-like behavior and past performance.
The returns for each portfolio were then tracked over the following month, and the process was repeated every month for more than 60 years. The study also adjusted for standard risk factors using two widely used asset pricing models, and it filtered out very cheap stocks priced below one dollar to avoid distortions from thinly traded shares.
What the numbers revealed
The results challenged the straightforward expectation. In value-weighted portfolios, a momentum strategy applied within the group of high-MAX stocks produced an average monthly return of 2.5%, while the same strategy applied within the low-MAX group returned only 0.25%. For comparison, the classic Jegadeesh-Titman momentum strategy applied to all stocks averaged about 1.26% per month.
The key to understanding the finding is what happened on each side of the trade. High-MAX winners, the stocks that both had lottery-like traits and strong prior performance, earned about 1.17% per month, similar to ordinary momentum winners. But high-MAX losers performed terribly, averaging −1.33% per month, compared with essentially zero for ordinary momentum losers. In other words, the momentum spread widened because the losers fell apart, not because the winners soared.
The pattern held up across different sample periods, across small and large firms, across stocks with different book-to-market ratios, and whether portfolios were weighted equally or by company size. It also survived tests that controlled for many alternative explanations, including capital gains overhang (a measure of unrealized gains and losses), investor sentiment, idiosyncratic volatility, growth options, earnings surprises, and whether the extreme daily return happened around an earnings announcement.
A behavioral story about losers
Haghighi Zadeh describes a process that could explain the asymmetry. Investors drawn to “cheap jackpot” stocks tend to concentrate on beaten-down shares that have occasionally produced an extreme positive return. That speculative demand inflates the prices of these struggling companies above what their fundamentals warrant. Because short selling such stocks is expensive and risky, other traders cannot easily correct the overpricing. Over time, reality catches up, and these stocks experience sharp declines that show up in the loser leg of the momentum strategy.
On the winner side, investors can more easily scale back their exposure to overpriced stocks, so the mispricing is less extreme. The result is an asymmetric outcome: lottery demand distorts the loser side far more than the winner side, and that distortion amplifies rather than dampens momentum profits.
What this means for investors
For professional investors running momentum strategies, the analysis suggests that focusing on stocks with lottery-like characteristics can enlarge returns, but the bulk of the gain comes from shorting losers, which is the hardest and most costly part of the trade to execute. Transaction costs, borrowing fees, and short-sale restrictions are not fully accounted for in the raw returns reported, so the practical profits available to real-world investors may be smaller.
There is also a broader takeaway. The familiar claim that lottery-like stocks always underperform turns out to be too sweeping. Their returns depend on what else is happening in the stock. Combined with weak recent performance, lottery traits intensify later declines. Combined with strong recent performance, they do not meaningfully boost returns. Behavioral biases, the study suggests, do not act alone. They interact with other features of a stock in ways that current models do not fully capture.
