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«Price Momentum and Trading Volume CHARLES M. C. LEE and BHASKARAN SWAMINATHAN* ABSTRACT This study shows that past trading volume provides an ...»

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THE JOURNAL OF FINANCE • VOL. LV, NO. 5 • OCT. 2000

Price Momentum and Trading Volume

CHARLES M. C. LEE and BHASKARAN SWAMINATHAN*

ABSTRACT

This study shows that past trading volume provides an important link between

“momentum” and “value” strategies. Specifically, we find that firms with high

~low! past turnover ratios exhibit many glamour ~value! characteristics, earn lower

~higher! future returns, and have consistently more negative ~positive! earnings surprises over the next eight quarters. Past trading volume also predicts both the magnitude and persistence of price momentum. Specifically, price momentum effects reverse over the next five years, and high ~low! volume winners ~losers! experience faster reversals. Collectively, our findings show that past volume helps to reconcile intermediate-horizon “underreaction” and long-horizon “overreaction” effects.

FINANCIAL ACADEMICS AND PRACTITIONERS have long recognized that past trading volume may provide valuable information about a security. However, there is little agreement on how volume information should be handled and interpreted. Even less is known about how past trading volume interacts with past returns in the prediction of future stock returns. Stock returns and trading volume are jointly determined by the same market dynamics, and are inextricably linked in theory ~e.g., Blume, Easley, and O’Hara ~1994!!.

Yet prior empirical studies have generally accorded them separate treatment.

In this study, we investigate the usefulness of trading volume in predicting cross-sectional returns for various price momentum portfolios. The study is organized into two parts. In the first part, we document the interaction between past returns and past trading volume in predicting future returns * Both Lee and Swaminathan are from the Johnson Graduate School of Management, Cornell University. We thank Yakov Amihud, Hal Bierman, Larry Brown, Tom Dyckman, David Easley, John Elliott, Eugene Fama, Wayne Ferson, Maureen O’Hara, Jay Ritter, Andrei Shleifer, René Stulz ~the editor!, Avanidhar Subrahmanyam, Yutaka Soejima, two anonymous referees, and workshop participants at Barclays Global Investors, 1998 Berkeley Program in Finance, Carnegie Mellon University, Chicago Quantitative Alliance’s Fall 1998 conference, Cornell University, the University of Florida, George Washington University, the University of Illinois at Urbana-Champaign, the Mitsui Life Finance Conference at the University of Michigan, the 1998 NBER Behavioral Finance Meeting, the Ninth Financial, Economics and Accounting Conference, UNC-Chapel Hill, the 1998 Prudential Securities Conference, the 1999 Q-Group Spring Conference, the Summer of Accounting and Finance Conference at Tel Aviv University, and the 1999 Western Finance Association Annual Meeting for helpful comments. We also thank Bill Gebhardt for his expert research assistance. Data on analyst following and long-term earnings growth forecasts are from I0B0E0S, Inc. Any errors are our own.

2018 The Journal of Finance over intermediate and long horizons.1 In the second part, we evaluate alternative explanations for these empirical regularities. Our findings extend the literature on both price momentum and trading volume. In addition, we establish an important link between intermediate-horizon “momentum” and long-horizon “value” strategies.

We contribute to the literature on price momentum in two ways. First, we show that the price momentum effect documented by Jegadeesh and Titman ~1993! reverses over long horizons. Like Jegadeesh and Titman, we find no significant price reversals through the third year following portfolio formation. However, over Years 3 through 5, we find that initial winner portfolios significantly underperform initial loser portfolios. This finding is important because it refutes the common presumption that price momentum is simply a market underreaction. Instead, the evidence suggests that at least a portion of the initial momentum gain is better characterized as an overreaction.2 Second, we show that past trading volume predicts both the magnitude and the persistence of future price momentum. Specifically, high ~low! volume winners ~losers! experience faster momentum reversals. Conditional on past volume, we can create Jegadeesh and Titman–type momentum portfolios ~winners minus losers! that either exhibit long-horizon return reversals or long-horizon return continuations. This evidence shows that the information contained in past trading volume can be useful in reconciling intermediatehorizon “underreaction” and long-horizon “overreaction” effects.

Our findings also extend the trading volume literature. Prior research ~e.g., Datar, Naik and Radcliffe ~1998!! shows that low ~high! volume firms earn higher ~lower! future returns. We show that this volume effect is long lived ~i.e., it is observable over the next three to five years! and is most pronounced among the extreme winner and loser portfolios. More importantly, our evidence contradicts the common interpretation of trading volume as simply a liquidity proxy. These findings instead show that past trading volume is related to various “value” strategies.

Contrary to the liquidity explanation, we find that high ~low! volume stocks earn higher ~lower! average returns in each of the five years prior to portfolio formation. We show that trading volume is only weakly correlated with traditional liquidity proxies and that the volume effect is robust to various risk adjustments. We find that the volume-based momentum effect holds even in a subsample of the largest 50 percent of New York ~NYSE! and American Stock Exchange ~AMEX! firms. Finally, we show that most of the excess returns to volume-based strategies is attributable to changes in tradWe use average daily turnover as a measure of trading volume. Turnover is defined as the ratio of the number of shares traded to the number of shares outstanding. Any unqualified reference to trading volume henceforth refers to this definition.





Studies that characterize price momentum as an underreaction include Jegadeesh and Titman ~1993!, Chan, Jegadeesh, and Lakonishok ~1996!, Barberis, Shleifer, and Vishny ~1998!, and Hong and Stein ~1999!. Conversely, studies that characterize price momentum to be the result of overreaction include DeLong et al. ~1990! and Daniel, Hirshleifer, and Subrahmanyam ~1998!.

Price Momentum and Trading Volume 2019 ing volume. Firms whose recent volume is higher ~lower! than volume four years ago experience significantly lower ~higher! future returns. The change in volume measures abnormal trading activity and is unlikely to be a liquidity proxy.

On the other hand, we find that low ~high! volume stocks display many characteristics commonly associated with value ~glamour! investing. Specifically, lower ~higher! trading volume is associated with worse ~better! current operating performance, larger ~smaller! declines in past operating performance, higher ~lower! book-to-market ratios, lower ~higher! analyst followings, lower ~higher! long-term earnings growth estimates, higher ~lower! factor loadings on the Fama–French HML factor, and lower ~higher! stock returns over the previous five years.

Further analyses show that the higher ~lower! future returns experienced by low ~high! volume stocks are related to investor misperceptions about future earnings. Analysts provide lower ~higher! long-term earnings growth forecasts for low ~high! volume stocks. However, low ~high! volume firms experience significantly better ~worse! future operating performance. Moreover, we find that short-window earnings announcement returns are significantly more positive ~negative! for low ~high! volume firms over each of the next eight quarters. The same pattern is observed for both past winners and past losers. Evidently the market is “surprised” by the systematically higher ~lower! future earnings of low ~high! volume firms.

The fact that a market statistic widely used in technical analysis can provide information about relative under- or over-valuation is surprising and is difficult to reconcile with existing theoretical work. To help explain these results, we evaluate the predictions of several behavioral models. We conclude that each model has specific features that help explain some aspects of our findings but that no single model accommodates all our findings. We also discuss an interesting illustrative tool, dubbed the momentum life cycle ~MLC! hypothesis, that captures some of the most salient features of our empirical results.

The remainder of the paper is organized as follows. In the next section, we discuss related literature. In Section II, we describe our sample and methodology. In Section III we present our empirical results. In Section IV, we further explore the information content of trading volume and relate these findings to several behavioral models. Finally, in Section V, we conclude with a summary of the evidence and a discussion of the implications.

I. Related Literature In recent years, a number of researchers have presented evidence that cross-sectional stock returns are predictable based on past returns. For example, DeBondt and Thaler ~1985, 1987! document long-term price reversals in which long-term past losers outperform long-term past winners over the subsequent three to five years. Similarly, Jegadeesh ~1990! and Lehmann ~1990! report price reversals at monthly and weekly intervals.

2020 The Journal of Finance But perhaps the most puzzling results are the intermediate-horizon return continuations reported by Jegadeesh and Titman ~1993!. Forming portfolios based on past three- to 12-month returns they show that past winners on average continue to outperform past losers over the next three to 12 months. Although many competing explanations have been suggested for the long-horizon price reversal patterns,3 far fewer explanations have been advanced to explain the intermediate-horizon price momentum effect.

For example, Fama and French ~1996! show that a three-factor model of returns fails to explain intermediate-horizon price momentum. Chan, Jegadeesh, and Lakonishok ~1996! show that intermediate-horizon return continuation can be partially explained by underreaction to earnings news but that price momentum is not subsumed by earnings momentum. Rouwenhorst ~1998! finds a similar pattern of intermediate-horizon price momentum in 12 other countries, suggesting that the effect is not likely due to a data snooping bias.

More recently, Conrad and Kaul ~1998! suggest that the momentum effect may be due to cross-sectional variation in the mean returns of individual securities. Moskowitz and Grinblatt ~1999! claim that a significant component of firm-specific momentum can be explained by industry momentum.

However, the evidence in Grundy and Martin ~1998! suggests momentum effects are not explained by time-varying factor exposures, cross-sectional differences in expected returns, or industry effects.4 None of these studies examine the interaction between past trading volume and past price movements in predicting cross-sectional returns.

At least two theoretical papers suggest that past trading volume may provide valuable information about a security. Campbell, Grossman, and Wang ~1993! present a model in which trading volume proxies for the aggregate demand of liquidity traders. However, their model focuses on short-run liquidity imbalances ~or volume shocks! of a daily or weekly duration and makes no predictions about longer-term returns. Blume et al. ~1994! present a model in which traders can learn valuable information about a security by observing both past price and past volume information. However, their model does not specify the nature of the information that might be derived from past volume. We provide empirical evidence on the nature of this information.

Our study is also tangentially related to Conrad, Hameed, and Niden ~1994!.

Conrad et al. show that, at weekly intervals, the price reversal pattern is observed only for heavily traded stocks; less traded stocks exhibit return

For example, DeBondt and Thaler ~1985, 1987! and Chopra, Lakonishok, and Ritter ~1992!

attribute long-term price reversals to investor overreaction. In contrast, Ball, Kothari, and Shanken ~1995!, Conrad and Kaul ~1993!, and Ball and Kothari ~1989! point to market microstructure biases or time-varying returns as the most likely causes. Similarly, short-horizon price reversals have been attributed to return cross-autocorrelations ~Lo and MacKinlay ~1990!!

and transaction costs ~Lehmann ~1990!!.



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