The High Volume Return Premium
Simon Gervais
Ron Kaniel
Dan Mingelgrin
∗
Finance Department
Wharton School
University of Pennsylvania
Steinberg Hall - Dietrich Hall
Suite 2300
Philadelphia, PA 19104-6367
gervais@wharton.upenn.edu, (215) 898-2370
kaniel73@wharton.upenn.edu, (215) 898-1587
mingel86@wharton.upenn.edu, (215) 898-1209
First Version: 14 January 1998
This Version: 17 December 1998
∗
The authors would like to thank Andrew Abel, Franklin Allen, Gordon Anderson, Michael Brandt, Roger
Edelen, Chris G´eczy, Gary Gorton, Bruce Grundy, Ken Kavajecz, Craig MacKinlay, David Musto, Robert
Verrecchia, S. Viswanathan, and seminar participants at the Wharton School and at the 1998 conference
in “Accounting and Finance in Tel-Aviv” for their comments and suggestions. All remaining errors are the
authors’ responsibility.
Abstract
The idea that extreme trading activity (as measured by trading volume) contains information
about the future evolution of stock prices is investigated. We find that stocks experiencing unusually
high (low) trading volume over a period of one day to a week tend to appreciate (depreciate) over
the course of the following month. This effect is consistent across firm sizes, portfolio formation
strategies, and volume measures. Surprisingly, the effect is even stronger when the unusually high
or low trading activity is not accompanied by extreme returns, and appears to be permanent.
The significantly positive returns of our volume-based strategies are not due to compensation
for excessive risk taking, nor are they due to firm announcement effects. Previous studies have
documented the positive contemporaneous correlation between a stock’s trading volume and its
return, and the autocorrelation in returns. The high volume return premium that we document in
this paper is not an artifact of these results. Finally, we also show that profitable trading strategies
can be implemented to take advantage of the information contained in trading volume.
1 Introduction
The objective of this paper is to investigate the role of trading activity in terms of the information
it contains about future prices. More precisely, we are interested in the power of trading volume in
predicting the direction of future price movements. We find that individual stocks whose trading
activity is unusually large (small) over periods of a day or a week, as measured by trading volume
during those periods, tend to experience large (small) subsequent returns. In other words, a high
volume return premium seems to exist in stock prices. More importantly, we also document the
fact that this premium is even larger for stocks that do not experience abnormal returns at the time
of their abnormal trading volume. So, past trading volume appears to contain information that
is orthogonal to that contained in past returns, which is evidenced by the return autocorrelation
documented by several authors.
1
The high volume return premium is not the product of risk. We show that (i) market risk does
not rise (fall) after a period of unusually large (small) trading activity; (ii) the returns from trading
strategies exploiting this volume effect stochastically dominate returns from diversified strategies;
(iii) informational risk (as measured by the bid-ask spread) goes in a direction opposite to one which
would explain the results. Furthermore, the results are robust to different measures of volume, and
are not driven by firm announcements.
Our analysis complements that of Conrad, Hameed and Niden (1994) (CHN, hereafter), who
document the fact that the contrarian investment strategies of Lehmann (1990) tend to perform
better when conditioning on past trading volume in addition to past returns. First, our paper
shows that conditioning on past trading volume alone (as opposed to both volume and returns)
can generate positive returns. Indeed, the returns that our volume based strategies generate are
of the same magnitude as those documented by CHN, but seem to last for a longer period of time
(four weeks vs one week).
2
So, our paper seems to indicate that trading volume alone does contain
long-lived information about the future evolution of stock prices; trading volume is not just part of
a more complicated joint relationship between current and future returns, as theoretically suggested
by Campbell, Grossman and Wang (1993).
3
Quite surprisingly, when we restrict our strategies to
stocks whose past price movements are not unusually large or small, our results are even stronger.
This strengthens the conclusion that trading volume does not just emphasize the autocorrelation
in returns, but does contain information of its own.
Also related to this paper is the work of Brennan, Chordia and Subrahmanyam (1998), and Lee
and Swaminathan (1998). These two papers document the fact that large trading volume tends to
1
A few papers from this exhaustive literature include DeBondt and Thaler (1985), Fama and French (1988),
Poterba and Summers (1988), Jegadeesh (1990), Lehmann (1990), Lo and MacKinlay (1990), Boudoukh, Richardson
and Whitelaw (1994), and Lo and Wang (1997).
2
In fact, the positive returns generated by their strategies not only die out after the first week, but tend to revert
back to zero over the following three weeks, as opposed to our strategies which generate positive returns for up to
100 trading days (20 weeks).
3
Llorente, Michaely, Saar and Wang (1998) also develop a model in which trading volume of individual stocks
interracts dynamically with their returns.
1
5 10 15 20
0.25%
0.50%
0.75%
1.00%
1.25%
1.50%
1.75%
cumulative
returns
high
volume
normal
volume
low
volume
0
formation
date
trading
day
Figure 1: Evolution of the average cumulative returns of stocks chosen according to the trading
volume that they experienced the day before this graph starts.
be accompanied by lower expected returns. Indeed, since investors demand a premium for holding
illiquid stocks, the stocks with the largest trading volumes (i.e. the most liquid stocks) will not
generate returns that are quite as large on average. The apparent contradiction between their results
and ours comes from the fact that both these papers measure the permanent trading volume of
individual stocks, whereas we only consider trading volume shocks. In other words, a stock that has
a lot of trading activity on average should yield small returns, but a stock that experiences unusually
large trading activity over a particular day or a week is expected to subsequently appreciate.
The essence of our paper’s results is captured in Figure 1. In this figure, we show the evolution
of the average cumulative returns of stocks conditional on the trading volume that they experienced
during the trading day preceding the twenty trading day period shown on the x-axis. We see that the
stocks which experienced an unusually high (low) trading volume
4
outperform (are outperformed
by) the stocks which had normal trading volume. Moreover, this effect appears to grow over time,
especially for the high volume stocks and, although not shown in this figure, does not disappear in
the long run.
As mentioned above, numerous papers have been written about the predictability of stock prices
from past prices. Depending on the horizons over which returns are measured and on the way
portfolios of risky securities are formed, there is vast empirical evidence that stock prices tend to
display either positive or negative autocorrelation.
5
Similarly, a number of papers have documented
the empirical relationship that seems to exist between a stock’s price and its trading volume. A lot
of this research is preoccupied with the contemporaneous relationship between trading volume and
the absolute change in stock price (or its volatility). For example, using different samples, return
intervals, and methods of aggregation, Comiskey, Walkling and Weeks (1985), Wood, McInish
and Ord (1985), Harris (1986, 1987), and Gallant, Rossi and Tauchen (1992) all find a positive
correlation between contemporaneous trading volume and absolute price changes. A related but
4
Later sections of the paper will explain precisely what we mean by “unusual trading volume.”
5
See footnote 1 for a list of these studies.
2
different contemporaneous positive correlation between trading volume and price changes per se
has also been documented by Smirlock and Starks (1985), and Harris (1986, 1987).
Although the intertemporal relationship between trading volume and prices is often neglected
in these studies, a few authors have documented the Granger causality relationship between stock
prices and trading volume through time (Hiemestra and Jones, 1994), as well as the fact that
large absolute and nominal price movements tend to be followed by periods of high trading vol-
ume (Gallant, Rossi and Tauchen, 1992), that large trading volume is associated with negative
autocorrelation in returns (Campbell, Grossman and Wang, 1993), and that volume shocks affect
the high order moments of stock prices (Tauchen, Zhang and Liu, 1996). Our work complements
these studies in that we are primarily concerned with the informational role of trading volume as
it pertains to the direction of future prices.
A theoretical explanation for our results is difficult to find in the current literature, as most
models of trading volume concentrate on explaining the contemporaneous relationship between vol-
ume and prices. Such models include, among others, Copeland (1976), Tauchen and Pitts (1983),
Karpoff (1986), and Wang (1994). Even more disappointing is the fact that in most of this theo-
retical research, the correlation of trading volume with prices is simply a bi-product of the models,
as trading volume does not play any informational role over that of prices.
6
The existing theoret-
ical models that are consistent with and could potentially explain our results are those of Blume,
Easley and O’Hara (1994), Bernardo and Judd (1996), Diamond and Verrecchia (1987), and Mer-
ton (1987). The first two show that, in the presence of uncertainty about the trading aggressiveness
(as measured by the precision of information in these two cases) of some traders, current trading
volume may provide information relevant to the evolution of future prices. Diamond and Verrec-
chia (1987) show that short-sale contraints will create an informational role for trading volume,
as traders will be forced to react asymmetrically to positive and negative signals respectively. Fi-
nally, Merton (1987) argues that more noticeable stocks tend to experience price increases. Since
trading volume arguably makes a stock more prominent or at the very least is correlated with its
prominence, this visibility argument may explain part of the effects that we observe.
Our paper is organized as follows. In the next section, we describe the data used for the
analysis, and present our methodology. In section 3, we explain our trading strategies whose
performance are then evaluated in section 4. We look for risk-based explanations in section 5, and
check the robustness of our results in section 6. The economic profitability of our trading strategies
is assessed in section 7. Concluding remarks and potential explanations for our results are presented
in section 8. Throughout the paper, we use “absolute return” to denote the absolute value of a
return, and “return” to denote the return per se. Although all the figures are within the paper, all
the tables are located at the end of the paper.
6
This fact was pointed out by Blume, Easley and O’Hara (1994), who come up with a model in which volume has
informational content over and above that of prices. In their model, however, volume does not predict price direction,
but only price volatility.
3
