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Returns to Buying Winners and Selling Losers (1993)

Academic Research Review Momentum Market Efficiency

Robert Stowe, AAMS® Investment Advisor

This page reviews "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," a 1993 paper by Narasimhan Jegadeesh and Sheridan Titman. The researchers found that buying stocks that went up over the past 3 to 12 months and selling stocks that went down over the same period produces consistent profits over the next 3 to 12 months. This was the paper that put the momentum anomaly on the academic map.

Published in The Journal of Finance, 48(1), 65–91, this study was the first rigorous demonstration that medium-term stock price trends persist in a predictable, profitable way. Before this paper, the prevailing view in academic finance was that past stock returns contain little useful information about future returns. Jegadeesh and Titman showed otherwise, directly challenging the efficient market hypothesis (the idea that stock prices already reflect all available information).

Key Findings

The paper's central result is straightforward: stocks that have performed well recently tend to keep performing well, and stocks that have performed poorly tend to keep performing poorly. The researchers observed this pattern, called momentum, across various historical sub-periods and different ways of measuring it.

The Momentum Effect

The researchers ranked all stocks on the New York Stock Exchange (NYSE) and American Stock Exchange (AMEX) by their returns over a past period (the "formation period") and sorted them into ten equal-sized groups called deciles. They then tracked how each group performed over a subsequent period (the "holding period"). The top decile (recent winners) consistently outperformed the bottom decile (recent losers).

To put this in concrete terms: the formation period is the lookback window used to judge which stocks are winners and losers. If the formation period is 12 months, the researchers looked at how each stock performed over the past year. The holding period is how long the portfolio was held after being formed. If the holding period is 3 months, the researchers bought the winners and sold the losers, then held that portfolio for three months before rebuilding it.

The Best Formation and Holding Period Combination

Jegadeesh and Titman tested 16 different combinations of formation and holding periods, using 3, 6, 9, and 12 months for each. Every combination produced positive average returns. The strongest results came from the 12-month formation period with a 3-month holding period, which generated average returns of about 1.31% per month for the winner-minus-loser portfolio. That is a large number in academic finance, where many documented effects are much smaller.

The 6-month formation period with a 6-month holding period also performed well and has become the most widely used version in later research. Across all 16 combinations, the average monthly return ranged from about 0.9% to 1.3%, demonstrating that the momentum effect was not sensitive to a single specific parameter choice.

Short-Term Reversal vs. Medium-Term Momentum

An important detail in the paper is that the momentum effect does not apply to very short time horizons. At the 1-week and 1-month level, the researchers found reversal: stocks that dropped sharply tended to bounce back, and stocks that surged tended to pull back. This short-term reversal is a separate phenomenon, likely caused by temporary liquidity effects and bid-ask bounce (the mechanical back-and-forth between buying and selling prices).

The distinction matters because it shows that momentum is specifically a medium-term phenomenon. It kicks in at around 3 months and persists through 12 months. Investors who try to apply momentum logic to daily or weekly trading are capturing a different, opposite effect. The paper carefully separated these two patterns, which had been confused in earlier studies.

Practical Implications

Why This Matters for Investors

The existence of momentum profits is a direct challenge to the efficient market hypothesis. If markets were perfectly efficient, all publicly available information would already be reflected in stock prices. Past returns are the most public information imaginable. The fact that past returns predict future returns suggests that prices adjust to information more slowly than the efficient market view assumes.

This finding was significant because it came from two respected academics using rigorous methods on a large, well-known dataset. Earlier claims about stock price patterns had been dismissed as data mining or the result of flawed methods. Jegadeesh and Titman's work was harder to dismiss because of its scale, consistency, and the many different parameter combinations they tested.

Building Momentum into Portfolios

The research suggests that tilting a portfolio toward recent winners can add returns over time. One way researchers apply this is by periodically reviewing holdings and systematically tilting toward stocks that have shown strong recent performance over the past 6 to 12 months, while reducing exposure to stocks that have lagged over the same period.

The trade-off is turnover. A momentum strategy requires regular rebalancing because the set of winners and losers changes constantly. Every time the portfolio is rebuilt, trading costs (commissions, bid-ask spreads, and market impact) eat into returns. The paper did not account for these costs, which means the real-world returns from momentum are smaller than the numbers reported in the study.

Momentum Profits Decay and Reverse

One of the paper's most practically important findings is that momentum profits do not last forever. The researchers tracked their momentum portfolios beyond the initial holding period and found that the extra returns largely dissipated within 12 months. After about two years, momentum portfolios actually gave back some of their gains as prices reversed.

This reversal pattern means that momentum is a medium-term phenomenon requiring active management. A buy-and-hold approach does not capture momentum. The strategy demands regular turnover: selling positions as their momentum fades and replacing them with new winners. For investors, this means momentum is best implemented as a systematic, rules-based process rather than a set-it-and-forget-it approach.

How the Researchers Tested This

Data and Time Period

The study used monthly return data for all stocks listed on the NYSE and AMEX from 1965 through 1989, a 25-year span covering multiple bull and bear markets. This period includes the 1970s bear market, the early 1980s recession, the mid-1980s bull market, and the 1987 crash, providing a wide range of market conditions.

The researchers focused on common stocks and excluded very small companies (those with extremely low market capitalization) to ensure their results were not driven by tiny, illiquid stocks that would be difficult to trade in practice. This was a deliberate choice to make the findings more relevant to real-world investing.

How the Portfolios Were Built

For each test, the researchers ranked all eligible stocks by their returns over the formation period and divided them into ten equally sized groups (deciles). The momentum portfolio bought the top decile (the biggest winners) and sold short the bottom decile (the biggest losers). This long-short approach isolates the momentum effect from the overall market direction.

The portfolios were equally weighted within each decile, meaning each stock received the same dollar allocation regardless of its size. The researchers also tested value-weighted versions (where larger companies receive more weight) and found similar, though slightly smaller, momentum profits. This confirmed that the effect was not limited to small stocks.

Formation and Holding Period Matrix

The researchers tested every combination of 3, 6, 9, and 12-month formation periods with 3, 6, 9, and 12-month holding periods, creating a 4x4 grid of 16 strategies. They also skipped the most recent week before forming portfolios to avoid contamination from bid-ask bounce and short-term reversal effects. This skip-week adjustment has become standard practice in momentum research.

Each strategy was evaluated on its average monthly return, its statistical significance (measured by t-statistics, which indicate how likely the result is to be real rather than random noise), and its consistency across different sub-periods within the 25-year sample. All 16 combinations were statistically significant, with t-statistics well above the conventional threshold of 2.0.

Why Does Momentum Exist?

The paper examines several possible explanations for why momentum profits exist. The answer has important implications: if momentum is compensation for bearing risk, it should persist. If it results from investor mistakes, it might shrink as investors learn about it.

Underreaction Hypothesis

The leading behavioral explanation is that investors are slow to incorporate new information into prices. When a company reports good earnings or receives favorable news, the stock price adjusts upward, but not all the way. Over the following months, the price continues to drift in the same direction as the market gradually absorbs the full implications of the news. This slow adjustment creates the pattern that momentum strategies capture.

The underreaction hypothesis explains both the initial momentum (prices continue in the same direction) and the eventual reversal (prices eventually overshoot fair value and then correct). This two-stage process, with initial underreaction followed by delayed overreaction, fits the empirical pattern documented in the paper.

Risk-Based Explanations

An alternative view is that momentum stocks are genuinely riskier, and the extra returns are compensation for bearing that risk. Under this interpretation, buying winners is profitable because winners tend to be stocks with higher sensitivity to market moves or greater exposure to economic downturns.

Jegadeesh and Titman tested this directly. They adjusted momentum returns for risk using the Capital Asset Pricing Model (CAPM), which measures risk through beta (a stock's sensitivity to overall market movements), and also controlled for company size. The momentum profits persisted even after these adjustments. The extra returns could not be fully explained by the standard risk measures available at the time.

The Reversal Pattern

The long-term reversal the researchers documented (momentum profits fading and partially reversing after 12 or more months) provides additional evidence about what drives the effect. If momentum were pure risk compensation, there would be no reason for it to reverse. Risk premiums are permanent; investors who bear risk get paid for it indefinitely.

The fact that momentum profits reverse over longer horizons is more consistent with the underreaction story. Prices initially move too slowly in response to news, creating a trend. Then they eventually overshoot, creating a subsequent reversal. This pattern of initial underreaction followed by long-term overreaction became a central feature of later behavioral finance models.

Limitations and Caveats

Limitations to Consider

Trading costs not included: The paper reports returns before transaction costs. Momentum strategies require frequent rebalancing, and the associated trading costs (commissions, bid-ask spreads, and market impact) reduce real-world returns. Later research has shown that transaction costs can consume a meaningful portion of momentum profits.
Data limited to U.S. stocks: The study used only NYSE and AMEX stocks from 1965 to 1989. Later research has extended momentum findings to international markets, emerging markets, and other asset classes, broadly confirming the original results.
Momentum crashes: The strategy can suffer sudden, severe losses during sharp market reversals. This risk was not documented in this paper but was later studied in detail by Daniel and Moskowitz (2016), who showed that momentum portfolios occasionally experience devastating drawdowns when markets recover from a crash.
Survivorship bias: The dataset includes only stocks that survived through the study period. Companies that went bankrupt and were delisted may not be fully represented, which could affect the results, particularly on the short (loser) side of the portfolio.
Short selling constraints not accounted for: The momentum portfolio assumes free short selling of loser stocks. In practice, short selling involves borrowing costs, margin requirements, and sometimes outright restrictions. These frictions reduce the profitability of the short side of the strategy.