The Dark Side of Trading
Ilia D. Dichev
Emory University
Kelly Huang
Georgia State University
Dexin Zhou
Emory University
September 27, 2010
Abstract: This study investigates the effect of stock trading volume as a long-term,
environmental variable on observed stock volatility. The motivation is that volumes of U.S.
trading have increased more than 30-fold over the last 50 years, truly transforming the
marketplace. We investigate a number of settings, including a mix of natural experiments
(exchange switches, S&P 500 changes, dual-class shares), the aggregate time-series of U.S.
stocks since 1926, and the cross-section of U.S. stocks during the last 20 years. Our main
finding is that, controlling for other factors, there is a reliable and economically substantial
positive relation between volume of trading and stock volatility. The conclusion is that stock
trading produces its own volatility above and beyond that based on fundamentals.
Preliminary. Comments welcome, please send to:
Ilia D. Dichev
1300 Clifton Road
Goizueta Business School, Emory University
Atlanta, GA, 30037
404-727-9353
idichev@emory.edu
We appreciate the helpful comments of Tarun Chordia, Feng Li, and Lasse Pedersen.
1
The Dark Side of Trading
1. Introduction
We investigate the effect of volume of trading on stock volatility. We are specifically
interested in the role of trading volume as a long-term, environmental variable rather than its
more traditional and well-researched role as a short-term transmitter of information. The
motivation is that volume of stock trading has exploded during the last 50 years, increasing from
an annualized value-weighted NYSE/AMEX turnover of less than 10 percent in 1960 to more
than 300 percent in 2008-2009, see evidence in Figure 1. A change of this magnitude can be
fairly characterized as transforming the marketplace, and it is important to carefully document
and assess the parameters of this transformation. Note that dizzying growth in stock market
trading is just one manifestation of a powerful trend of great increases in trading volume across a
number of investment assets, including bonds, commodities, currencies, and many kinds of
derivatives. Thus, the findings of this study have broad utility for the investment world at large.
There is much theory and empirical evidence on the effect of liquidity and volume on the
level of stock prices and returns, see for example the review in Amihud, Mendelson, and
Pedersen (2005). Generally, the findings indicate that higher liquidity and volume are highly
prized and rewarded by investors; they are correlated with lower transaction costs, easier creation
and adjustment of investment positions, and lead to higher prices (e.g., Branch and Freed 1977;
Jones 2002; Datar, Naik, and Radcliffe 1998; Brennan, Chordia, and Subrahmanyam 1998). In
contrast, there has been little attention on the effect of trading on the second moment of returns,
especially for the long-term, environmental aspect of trading. Theoretically, there is a solid
argument that higher investor participation and trading volume lead to better price discovery and
therefore to prices that are closer to fundamental values; thus, more trading reduces estimation
2
noise and reduces the volatility of returns. There are other factors, however, that confound this
prediction. For example, the large presence of what is collectively known as noise traders can
lead prices away from fundamentals, whiplashing them in temporary swings and reversals
(Campbell, Grossman, and Wang 1993). The interplay of these two opposing forces is not
understood well, and we have a poor idea of which of these effects dominates in practice,
especially in view of the dramatic increase in trading during the last half century. It is entirely
possible that different forces dominate at different levels of trading, and thus the properties of the
resulting trading and investor equilibrium critically depend on volume of trading.
The most significant problem in this investigation is that both volume of trading and
stock volatility are endogenously driven by information flow, where news drives both volatility
and volume up (Schwert 1989). We address this problem in two ways. First, we identify a series
of three natural experiments, where the setting controls for information flow and firm and
business characteristics, while there is a significant exogenous variation in volume of trading.
Specifically, we look at stock migration between major U.S. exchanges and at additions and
deletions to the S&P 500 index; both of these settings are characterized by substantial changes in
volume, while there is little change in fundamentals, at least in the short windows surrounding
the effective dates. We also examine dual-class U.S. stocks where typically the two classes have
identical cash flow rights but different control rights and different liquidity. Our main finding is
that in all of these situations, increased volume of trading triggers a clear and substantial increase
in volatility of returns.
Second, we explore the relation between volume of trading and stock volatility in the
aggregate time-series of U.S. stocks since 1926 and in the cross-section during the last 20 years,
while controlling for information flow and other determinants of volatility. The advantage of
3
this setting over the natural experiments is better calibration of the examined effects to the
natural properties of the population of U.S. stocks; the disadvantage is losing some of the
sharpness of the controls in the natural experiments. We find that the correlation between annual
aggregate measures of volume and volatility is on the magnitude of 50 percent in the aggregate
time-series, which is highly statistically significant and economically substantial. Thus, much of
the historical variation in stocks seems to be due to the widely different secular levels of trading.
We also find a positive and convex relation between volume and volatility in the cross-section of
stocks, where the relation is much clearer and stronger for high volumes of trading. In efforts to
more precisely quantify and calibrate the effect of trading on volatility, we estimate that in recent
years trading-induced volatility accounts for about a quarter of total observed stock volatility.
Summarizing, these results suggest that trading creates its own volatility above and
beyond the volatility due to fundamentals. The implication is that the benefits of increased
liquidity and trading are not a one-way street. Given that existing evidence on the benefits of
liquidity is mostly for relatively low levels of trading, the combined impression with the results
in this study is that there is perhaps a point (or range) of optimal levels of trading, and that there
are very real costs of going beyond that. Considering the relentless march of trading volume up
and up during the last several decades, such considerations raise troubling questions about the
future and suggest a possible need to re-evaluate the institutional and regulatory framework of
trading. Further research can help in answering some of these questions.
The remainder of the paper proceeds as follows. Section 2 presents the theory and
existing findings. Section 3 provides the empirical design and the results for the three natural
experiments, while Section 4 contains results for the broad sample of U.S. stocks. Section 5
discusses the results and suggests some research and policy implications. Section 6 concludes.
4
2. Theory and background
Our goal is to investigate the effect of trading on stock volatility, with a particular
emphasis on trading as a long-term, environmental variable. The motivation for this
investigation comes from the fact that volume of stock trading has increased tremendously
during the last 30 to 50 years (e.g., Chordia, Roll, and Subrahmanyam 2010). Figure 1 provides
an illustration of this phenomenon for the full history of volume data on the major U.S.
exchanges, 1926-2009 for NYSE/AMEX and 1983-2009 for Nasdaq; specifically, Figure 1 plots
annualized value-weighted turnover (volume/shares outstanding) over time.1 An examination of
Figure 1 reveals a dizzying growth in trading with NYSE/AMEX turnover of less than 10
percent a year during 1940-1970, a gradual and somewhat uneven rise during 1970-2000, and
hitting a high of more than 300 percent in a pronounced spike of trading in the late 2000’s, a
more than 30-fold increase in a relatively short period of time. The Nasdaq time series, although
much shorter, reveals a similar pattern of 6-fold increase but with a less pronounced spike in the
most recent years. The magnitude of these increases is truly remarkable and has apparently
transformed the marketplace. Simply put, a market in which securities change hands once in 10
years is likely to be qualitatively different from a market in which securities change hands three
times a year, and this difference likely leads to qualitatively different outcomes in fundamental
issues like security valuation, equity risk, and market efficiency. Our study assesses some of
these possibly material changes, concentrating on the effect of volume of trading on stock
volatility.
1
AMEX volume data is available since 1963, here combined with the NYSE data series for parsimony. The value
weighting is accomplished by calculating for each trading day the total dollar-value traded that day (aggregated over
all stocks) and dividing it by aggregate market value outstanding as of that day. This measure is then annualized by
multiplying the mean daily turnover for that year by the convention of 250 trading days.
5
There is a large existing literature which hypothesizes and finds a positive relation
between volume and volatility. Generally speaking, this literature investigates the endogenous
co-movement of volume and returns, where the basic message is that “volume moves prices,” see
Karpoff (1987) for an early review. While this literature is rather broad, its unifying intuition is
that information flow sparks trades and triggers price revisions over relatively short horizons.
There have been significant accomplishments in this line of research, which studies issues like
the effects of private vs. public information, asymmetric information, and information with
different precision on volume and security prices (Roll 1988; Morse 1980; Easley, Kiefer, and
O’Hara 1996, 1997; Kandel and Pearson 1995; Bamber, Barron, and Stober 1999).
Our interest is different, though; more specifically, we are interested in the effect of
trading on security price formation over longer horizons, and essentially holding available
information constant. In other words, existing research views trading volume as the enabler and
transmitter of new information, while we view volume as a trading environment variable. The
large increases in trading in Figure 1 provide the motivation for pursuing such a perspective. It
is possible that newer and faster information sources like the Internet lead to more news and
more trading, and there is some evidence that fundamentally the economy today is more volatile
than in the 1960’s (Wei and Zhang 2006; Irvine and Pontiff 2009). But it also seems plausible
that the more than 30-fold increase in trading since the 1960’s is to a large extent just more
trading as opposed to more trading due to new information. Even more telling in this regard is
actually the comparison of the 1940-1970 period with the 1926-1940 period in Figure 1. Note
that the 1926-1940 period also represents a prolonged episode of heavy trading, and while its
intensity is not as pronounced as in the most recent years, it is remarkable that annualized
turnover both before and after the 1929 crash was over 100 percent a year, ten times as much as
6
during the quiet 1940-1970 period. Differences of such magnitude are difficult to square with
just differences in the amount of available information, and it is highly unlikely that information
sources were better in the 1920s than later.2
In any case, in addition to the indirect and only suggestive evidence in Figure 1, there is
more specific evidence that a great amount of trading is not driven by information, and that the
amount of such trading has increased over time. Perhaps the best known example of noninformation trading is “liquidity trading,” which here means trading driven by needs like
personal consumption or windfalls as opposed to stock fundamentals. More generally, trading
can be thought of as triggered by a number of different reasons, which span a continuum
between trading purely driven by information to trading purely driven by non-information needs.
In fact, much and maybe even most of trading seems to fall in the grey area between pureinformation and non-information trading (Chordia, Huh, and Subrahmanyam 2007). A vivid
illustration of this grey area are various types of algorithmic trading, which apparently account
for more than 70 percent of all trading today (Hendershott, Jones, and Menkveld 2010). A
trading algorithm based on momentum, for example, is based on information from the past
pattern of security prices, essentially from past trading itself. But since momentum trading also
shapes prices, there is a lot of room for feedback loops and other interactions which affect prices
but have nothing to do with actual fundamental information about the traded stocks. More
generally, a lot of trading seems to be based on watching and reacting to the actions of other
2
Of course, by its nature such evidence is indirect and only suggestive, and indeed there is likely to be difference in
the flow of information over time, where even long-horizon periods pack appreciably different amount of
information from each other. For example, the Great Depression of 1929-1934 seems to have been the catalyst in
the decline of early intensity in trading and the transition to the low-trading years of 1940-1970. But the explanatory
role of such macro events seems to be quite limited considering that watershed events like World War II, the Korean
War or the deep recession in 1973-1974 are hard to discern in this graph.
7
traders, and has little to do with true underlying fundamentals. It is the effect of this kind of
trading and this type of effects that we want to capture in our investigation.
It is also worth pointing out that even for trading that is purely based on information,
there is likely a qualitative difference between the kinds of market and valuation equilibria that
obtain when volume of trading differs by a factor of ten or more. The existing literature already
offers evidence consistent with this conjecture, mainly on the effect of volume of trading on
transaction costs and security prices. A number of studies have documented that increased
volume of trading is reliably related to decreased transaction costs (bid-ask spreads, brokerage
fees, execution costs) where these two variables reinforce each other, and innovations in either
one can lead to changes in the other (Branch and Freed 1977; Copeland and Galai 1987) .
Another reliable finding in the liquidity literature is that, everything else equal, higher liquidity
leads to lower cost of capital and higher prices (Amihud and Mendelson 1986; Brennan, Chordia,
and Subrahmanyam 1998; Liu 2006). Although in these studies volume of trading is usually just
one of several liquidity variables, much of this literature can be thought of as examining the
effect of trading on the first moment of prices, holding everything else constant.
More generally, a summary impression from the existing liquidity literature is that higher
liquidity is an almost universally good thing. Since increased volume of trading and decreased
transaction costs reinforce each other in a virtuous circle, it seems like higher liquidity is a real
win-win situation for all parties involved. Investors like higher liquidity because it allows them
to build and adjust investment positions easier, faster, and cheaper, and because it leads to lower
cost of capital and higher asset prices. Market-makers also like liquidity because it generally
makes their job easier and less risky. In addition, liquidity and demand for liquidity generally
8
expand the size and the breadth of the market, both in terms of enhanced investor participation
and in terms of new security offerings.
In contrast to much research on the relation between liquidity and the level of asset
prices, there is little evidence on the relation between long-term patterns in trading and the
second moment of returns, and this is the principal thrust of our investigation. Theoretically,
there is a strong and straightforward argument that increased trading should lead to reduced
volatility of stock returns because of the reduction of estimation risk in pricing company
fundamentals. If trading leads to the incorporation of relevant fundamental information in
security prices, and prices can be thought of as fundamental value plus estimation noise, then the
evolution of prices depends on the innovations in both fundamental value and noise.
Statistically, as the number of traders and trades goes up, the estimation noise is reduced, which
leads to reduced volatility of stock returns. Note that while the specific parameters of estimation
noise elimination depend on the magnitude of this noise (related, for example, to the dispersion
of investor beliefs about future cash flows), statistical properties suggest that the reduction in
estimation noise will be most pronounced at fairly low levels of trading since the standard
deviation of noise is decreasing in the square root of trading. Empirically, there is some
confirmatory evidence that more trading indeed reduces the volatility of stock returns. For
example, Elyasiani, Hauser, and Lauterbach (2000) find that when stocks move from Nasdaq to
NYSE, their volume of trading increases and their volatility decreases. Such evidence, however,
remains tangential and sporadic and is thus difficult to generalize.
In fact, other arguments and evidence suggest exactly the opposite prediction, that more
trading induces higher stock volatility. Predictions along these lines have surfaced in various
forms in the literature but essentially the idea is that trading produces trading noise, and ebbs and
9
tides in trading can lash prices away from fundamentals. For example, Shiller (1981) suggests
that stock prices are “too volatile” given the variability of underlying fundamentals. Extending
his argument further, if it is trading that produces return volatility above and beyond
fundamentals, then a logical next step is to hypothesize that more trading should produce more
volatility. Cutler, Poterba, and Summers (1989) and De Long, Shleifer, Summers, and
Waldmann (1990) argue that positive feedback investment strategies can result in excess
volatility even in the presence of rational speculators. The fascinating finding that stock returns
are on the magnitude of ten times more volatile during trading hours than during non-trading
hours (French and Roll 1986) is also consistent with the view that trading produces its own
volatility. (Note, however, that French and Roll find that trading noise accounts for only about
10 percent of this discrepancy and the rest is due to the more intense production and
incorporation of private information during trading hours.) It is also possible that the relation
between trading and volatility is non-linear and even changes sign depending on level of trading,
e.g., perhaps elimination of estimation noise and reduction of volatility prevail with low levels of
trading but high levels of trading indicate speculative overheating, “irrational exuberance,” and
more volatility.
Finally, some observations from practice also suggest a potential link between trading
and volatility. Stock exchanges often employ circuit-breakers, a policy of shutting down trading
for a pre-specified amount of time after large price drops, either at the aggregate or at the
individual security level. Such policies seem questionable and even counter-productive if one
takes the view that large price drops indicate dramatic revisions of information, and that it is in
precisely such times that trading and the associated pricing process are most needed and should
be allowed to freely flow to their new equilibrium levels. The counterpoint is that such policies
10
are likely not accidental and are really the evolutionary outcome of much historical trial-anderror in similar situations, where the accumulated wisdom indicates that sometimes trading can
go haywire for no particular reason related to fundamentals, and then a mandatory break allows
everyone to cool off. Thus, such policies are consistent with the view that trading can produce
its own volatility, and sometimes this volatility can get so out of hand that the simplest and most
effective way to tame it is to completely shut down trading.3
Summarizing, our goal is to investigate the effect of volume of trading on stock volatility;
specifically, we are interested in the effect of volume as a mostly exogenous, long-term
environmental variable.
3. Natural experiments
We start with a series of natural experiments to investigate the effect of changes in
trading volume on stock volatility, holding fundamental information constant. The advantage of
this approach is that when an appropriate setting is available, there is a natural and efficient
control for a number of explicit and implicit potentially confounding variables. Here, as
discussed earlier, the most important variables to control for are those related to information flow
but an appropriate setting will also naturally control for many other influential variables like firm
size, profitability, nature of business, corporate governance, investor clientele variables, etc.
There are essentially two types of settings where we can look for exogenous variation in trading
while holding other factors constant. The first type of settings relies on temporal liquidity
shocks, where we look at the effect of trading on volatility in narrow windows around a
3
The “flash crash” of May 6, 2010, when the major U.S. indices dropped and rebounded about 6 percent within 20
minutes is possibly an extreme illustration of the trading/volatility theme; while the exact reasons for the flash crash
are still being investigated, the preliminary evidence points to temporary order imbalance and non-information
trading as critical ingredients.
11
significant change-in-liquidity event. Examples include stocks listing and delisting on
exchanges, inclusions and drops from popular indexes like the S&P 500, and adoption of
significant new rules which promote or hinder trading. The assumption in these settings is that
firm fundamentals are largely held constant around the narrow event windows, and that these
significant liquidity events provide a substantial amount of exogenous variation in trading. The
second type of settings rely on comparisons of essentially the same underlying security across
different trading environments, which potentially provide enough exogenous variation in trading
intensity while holding fundamentals constant. Examples include dual-stock firms, ADRs and
the underlying stocks, and dual-listed shares. While none of these natural experiments is perfect,
the triangulation of evidence from several of these disparate settings helps in solidifying the
conclusions and ruling out alternative explanations.
3.1 Stocks switching exchanges
Our first natural experiment uses the setting of stocks switching exchanges. Previous
research finds reliable evidence that exchange switches result in material changes of trading
volume. For example, Elyasiani, Hauser, and Lauterbach (2000) find that Nasdaq stocks that
move to NYSE/AMEX experience an average increase in volume of 30 percent. Thus, the
advantage of this setting is sharply defined events with material changes in liquidity, while the
fundamentals of the firms are held largely the same during our narrow windows of investigation.
A disadvantage of this setting is that the stock switch itself is an information event, and thus
influences both trading and volatility of returns. We deal with this shortcoming in two ways.
First, we examine windows which exclude the announcement and effective dates. Second, we
emphasize relative, within-sample results, which are less subject to the information-event
12
concern. For example, we examine switches from Nasdaq to NYSE, and rank on variation in
trading within this sample.
Based on the Stocknames file on CRSP, we identify 3,611 firms that moved between the
major U.S. stock exchanges (i.e., NYSE, AMEX, and Nasdaq) during 1962-2009 (AMEX data is
available since 1962, with Nasdaq volume data becoming available in 1983).4 We collect daily
trading volume, shares outstanding, and stock returns for these firms from the CRSP daily stock
file. As detailed in Panel A of Table 1, after requiring firms to have nonmissing volume, shares
outstanding, and return data over one-month before and one-month after listing on a new stock
exchange, we are left with 2,860 observations for further analyses. Among these 2,860 switches,
951 moved between NYSE and AMEX, 1,573 firms moved from Nasdaq to NYSE/AMEX, and
336 moved from NYSE/AMEX to Nasdaq. Panel A also reveals that the there is a reasonable
distribution of switches over time and that mean (median) market value is $546 (128) million.
The resulting impression from the statistics in Panel A is that our sample captures the great
majority of stock exchange switches and that these are economically important firms and events.
For stocks traded on NYSE and AMEX, daily share turnover is measured as daily trading
volume divided by the number of shares outstanding on that day. For stocks traded on Nasdaq,
the turnover computation is the same except trading volume is first scaled by two because of the
double-counting of volume in dealer markets like Nasdaq (Anderson and Dyl 2005). Note that
scaling by two is a rather heuristic correction for the different trading environment and volume
statistics on Nasdaq’s dealer market vs. the auction markets on NYSE/AMEX, and the “true”
correction is probably smaller and varies across firms and over time, please see the technical
notes in Appendix A for fuller explanation. For our purposes, the bottom line from these more
4
We use historical exchange code (exchcd) in the Stocknames file to identify exchange switching and 1962 is the
first year where we identify cases of exchange switching.
13
involved considerations is that volume comparisons between Nasdaq and NYSE/AMEX are
prone to error, especially for estimating absolute levels of change in exchange switching. For
this reason, while we present results for all switches, we emphasize the results for the cleaner
subsample of stocks that move between NYSE and AMEX. In addition, for the stocks that move
between Nasdaq and NYSE/AMEX, we emphasize the results for within-sample relative changes
of volume.
For our main results, CH_VOLUME is the percentage change of average daily trading
volume measured over one month before and one month after firms moved from one exchange to
another. It is measured as the difference between the average daily shares turnover over trading
days (-22, -1) and (0, 21), scaled by average daily share turnover over (-22,-1), where day 0 is
the day on which firms were listed on the new stock exchange. Analogously, CH_STDRET is
the percentage change of stock return volatility measured one trading month before and one
month after the switch, scaled by the standard deviation before the switch. Descriptive statistics
about these two variables in Panel A reveal wide empirical variation in the test sample, which
confirms impressions from existing research that exchange switches are a powerful setting to
explore the effect of material changes in trading intensity within a short temporal window. The
descriptive statistics also reveal that these two variables are highly non-normal, with large
differences between means and medians and standard deviations greatly exceeding the
interquartile range of the empirical distribution. Because of these pronounced non-normalities,
most of our subsequent tests rely on robust measures of central tendency (e.g., medians) and nonparametric tests.
We present two types of evidence to characterize the effect of volume of trading on
volatility. First, we present the Spearman correlation between the changes in turnover across the
14
switch and the changes in the standard deviation of returns over the same period, providing a
statistical measure of the strength and significance of this relation (results for Pearson correlation
are similar). Second, within each test group, we sort change of turnover into quintiles and
present the median of change of turnover and the median of change of return volatility for each
quintile. One advantage of this portfolio specification is providing an intuitive and immediate
estimate of the economic importance of the studied relation. Another advantage is the ability to
identify and map out possible non-linear relations between the two variables.
The main empirical results are presented in Panel B of Table 1, by the three types of
available switches.5 An examination of Panel B reveals Spearman correlations on the magnitude
of 0.23 to 0.35, all highly statistically significant (all p-values < 0.001), suggesting that increases
in trading volume increase stock return volatility. This impression is confirmed in the quintile
portfolio specification, where for all three subsamples the ranking on change in volume produces
a near-monotonic ordering on volatility. The magnitude of difference across quintile medians
also looks economically substantial; for the most reliable subsample of switches between NYSE
and AMEX, the differences between extreme quintiles suggest that an increase in turnover of
about 156 percent produces an increase in volatility of 39 percent. If such magnitudes are
anywhere close to a guide for what one can expect in more generalized settings, it is clear that
the previously discussed 30-fold increases in volume likely have a pronounced effect on
observed stock volatility.
The results for switches from Nasdaq to NYSE/AMEX deserve a closer consideration
because they provide a nice illustration of the key themes in this study (and pretty much the same
observations apply for the converse switch, NYSE/AMEX to Nasdaq). Note that on average
such stocks tend to experience an increase in trading volume and a decrease in volatility (see
5
The results for a pooled sample of switches are very similar to those presented in Panel B.
15
middle quintile). This is intriguing because it is the opposite of the pattern observed withinsample across quintiles, where relatively higher changes of trading produce higher changes of
volatility. Thus, the average effect is reminiscent of the elimination of estimation noise
prediction, while the relative effect reflects the trading noise prediction, which implies that both
effects are in play and by extension they likely manifest at different strength in different
environments. We believe that this conclusion is generally correct but for this particular
environment the evidence is more supportive of the trading noise explanation for two reasons.
First, as discussed above, volume data for Nasdaq are problematic and thus tests on switches to
and from Nasdaq are problematic as well.6 Second, there is much evidence that stocks tend to
behave similar to their peer trading group, e.g., ADRs and dual-listed stocks tend to move with
their peer listed stocks rather than with their more naturally twinned counterparts which trade
somewhere else (Froot and Dabora 1999). Since Nasdaq stocks as a group are much more
volatile than NYSE/AMEX stocks, it is possible that Nasdaq stocks moving to NYSE/AMEX
become less volatile for reasons beyond the elimination of estimation noise.
In Panels C and D, we present two additional specifications which serve to expand and
solidify the main results. Since results are similar across the three types of available switches,
for parsimony we limit the additional results to the most reliable subsample of switches between
NYSE and AMEX. In Panel C, we present the results for a robustness specification that employs
the same main tests but uses (-45, -23) and (22, 45) trading windows around the exchange switch
event. The advantage of this specification is that it excludes one trading month before and after
the switch, so the results are less subject to concerns about unusual patterns of trading around the
6
As discussed in the paper and in the Appendix, scaling Nasdaq volume by two is likely an over-correction. The
implication is that the increases in volume in moving from Nasdaq to NYSE/AMEX are likely less pronounced. If
one adjusts for this by reducing the positive changes volume, the average increase in volume disappears and can
even reverse, and therefore the average elimination of noise effect disappears as well.
16
announcement and effective dates of the switch. We find that the tenor of the results remains
nearly the same for this specification, with a similar Spearman correlation and similar range in
volatility changes across extreme quintiles.
In Panel D of Table 1, we present the results for NYSE-AMEX switches with additional
data for the effect on the firm’s bid-ask spread. Existing research shows that different measures
of liquidity move in reliable tandem, e.g., as volume of trading increases, bid-ask spreads tend to
fall (Tinic and West 1972; McInish and Wood 1992). The preceding results, then, raise an
interesting question since volatility increases with volume in the tests above, and since there is
reliable evidence that bid-ask spread increases in stock volatility (Copeland and Galai 1983).
Based on the combination of this evidence, it is possible that increases in volume triggered by
exchange switches decrease bid-ask spread through the direct effect documented in the existing
liquidity literature; it is also possible, though, that increases in volume increase bid-ask spread
through the indirect effect of increased volatility. Since both effects are likely in play, it is
interesting to see whether they cancel each other or possibly one dominates empirically. An
examination of the results in Panel B reveals a strong and monotonic positive relation between
change in volume and change in bid-ask spread, where the difference in spread medians between
extreme quintiles reaches an economically substantial 43 percent. This result is remarkable
considering much existing research that shows a consistently negative relation between volume
and spread. It is also a testament to the economic strength of the volatility result, where a spread
in volatility across quintiles produces a reliable increase in bid-ask spread as well because
market-makers need to protect themselves against the increased uncertainty. In some sense, the
resulting pattern in the bid-ask spread is like a shadow signature of the volatility spread, and is
another confirmation of its existence and importance.
17
3.2 Stocks added or deleted from the S&P 500
The intuition and the characteristics for this setting are similar to those for exchange
switches above. Essentially, the S&P 500 additions and deletions are significant liquidity events
with little change in the underlying firm fundamentals, and so they provide another natural
experiment to investigate the effects of trading intensity on stock volatility (Hedge and
McDermott 2003). The S&P setting, though, has its own unique features, which are important to
consider in test design and the interpretation of the results. The first such feature is that trading
volume effects are strongly concentrated around the announcement and effective dates of index
updates, while these dates span varying time windows over the years (Chen, Noronha, and Singal
2004). During 1976-1989, changes in the index were announced after the close of market on
Wednesdays, and the change became effective on the next day at the market’s opening. With the
growth of indexing and corresponding increasing re-shuffling and order imbalances on the
effective date, Standard & Poors began pre-announcing changes in 1989, and the difference
between announcement and effective day lengthened to typically a week or two but sometimes as
much as a month. The second feature of the S&P 500 setting is that index additions and
deletions are highly asymmetric (Hedge and McDermott 2003; Chen, Noronha, and Singal
2004). Existing research finds reliable evidence that index additions are fairly “clean” goodnews events, with a concentrated burst of trading and positive abnormal returns around the
announcement and effective dates. In addition, the increased price persists over longer horizon,
and there is a moderate increase in trading volume over the long run (on the magnitude of 10
percent). In contrast, index deletions are a much more problematic and heterogeneous collection
of events, often triggered by mergers, spin-offs, bankruptcy, and re-organization and
restructuring, where the resulting firm and its stock are fundamentally changed. As a result, it is
18
much more difficult to derive clean, reasonably-sized samples and offer reliable conclusions for
deletions; in fact, these problems are often so severe that many prior studies of index changes
simply ignore deletions. The documented empirical patterns for deletions are also different from
additions, with negative returns and increased trading at the announcement and effective dates
but with no reliable changes in volume or price over longer horizons.
Our research design for the S&P 500 changes setting is similar to that for exchange
switches. The change in turnover and volatility variables are defined as before, and again we
examine Spearman correlations and quintile rankings for these two variables to assess the
strength of their relation. The trading windows are also the same, where the first change window
spans trading day periods (-22, -1) and (0, 21), i.e., we examine the change in turnover and
volatility over one trading month before and after the effective date of the index change. Given
the considerations above, this window includes the announcement and effective dates over the
whole sample period, and we expect it to reflect the heavy trading accompanying the change
event itself. A disadvantage of this window is that the trading also reflects the information
content of the event itself, and also possible temporary order imbalances. The second time
window we consider is changes over trading days (-45, -23) and (22, 45), i.e., one trading month
on each side of the first trading window. The advantage of this window is that it reflects only
long-term, permanent changes in trading patterns. A disadvantage is that existing research
indicates only small to moderate changes in long-term volume for the S&P 500 setting; recall,
however, that since our tests rely to a large extent on within-sample variations in trading volume
changes, the low average effects are not much of a problem if there is sufficient variation in
changes in volume across firms.
19
Our sample is from Jeff Wurgler’s website, spanning 1976-2000, see Wurgler and
Zhuravskaya (2002) for sample selection criteria and more detailed properties. Brief descriptive
statistics included in Panel A of Table 2 reveal a reasonably large sample of index additions
(453) that are well-spread over the years, and much fewer index deletions (86). Untabulated
summary statistics show that addition firms tend to be large (mean market cap of about $4.7
billion) and deletion firms are smaller (mean market cap of about $1.9 billion), illustrating the
asymmetric nature of the sample and advising caution in the interpretation of the deletion results.
The results for S&P 500 additions are included in Table 2, Panel B. The Spearman
correlations for the two return windows are on the magnitude of 34 and 38 percent respectively,
highly significant, indicating a reliable positive relation between trading volume and stock
volatility. This impression is confirmed in an examination of the quintile results, where the
ranking on change in volume produces a strong and monotonic ranking on changes in volatility.
The difference between extreme quintiles also suggests robust economic significance; taken
literally, these results indicate that an increase in trading volume of 140 to 180 percent increases
volatility by about 40 percent. In untabulated results, we also find that changes in bid-ask
spreads are monotonically increasing in increasing volumes, similar to the results for exchange
switches in Panel D of Table 1. Generally speaking, the pattern and even the magnitude of
results for the S&P 500 additions are remarkably similar to those for exchange switches,
indicating a plausible commonality that unites these disparate settings.
The results for deletions are in Panel C of Table 2. For the (-22, -1) and (0, 21) window,
there is a discernable positive association between changes in volume and volatility; this pattern,
however, is statistically and economically weak, and much weaker than the corresponding
relation for additions. The reasons for this weak association are not entirely clear but the
20
asymmetric role of deletions and the small sample likely play a role. The evidence is much
clearer for the (-45, -23) and (22, 45) event window, where there is again an emphatic positive
relation between volume and volatility, with high statistical and economic significance. Overall,
the evidence from S&P 500 changes is largely in line with the evidence from exchange switches,
and indicates a reliable and substantial positive relation between volume of trading and stock
volatility.
3.3 Dual-class U.S. stocks
Our third natural experiment relies on a comparison of volatility across dual-class U.S.
shares, where the two classes usually have identical cash flow rights but different control rights
(e.g., A-class shares have 10 times the voting power of B-class shares) and often substantially
different volumes of trading. The advantage of this setting is that it provides a near-perfect
natural control for the flow of fundamental information, and thus it is closest to the theoretical
constructs of our investigation. Consistent with this intuition, several previous studies use the
same or similar settings to control for underlying cash flows. For example, Zingales (1994,
1995) use dual-class firms on the Milan stock exchange and in the U.S. (sample of 94 stocks) to
study the pricing of voting rights. Nenova (2003) investigates the characteristics of corporate
control using dual-class firms in 18 countries (including a U.S. sample of 39 firms), while
Gompers, Ishii and Metrick (2010) studies the difference in insiders’ cash flow rights and voting
rights. There are, however, two limitations to the dual-class setting. The first limitation is that
the two classes of shares are close substitutes, and thus arbitrage forces keep their returns and
volatility of returns within fairly tight bounds. The second limitation is that there is usually a
price difference between the two classes, which reflects the value of the control premium. Since
21
the value of the control premium likely varies over time, it creates a separate source of return
differences over time, possibly confounding our investigation. We have some priors, though,
that the second limitation is unlikely to be critical. Lease, McConnell and Mikkelson (1983,
1984) document that superior voting shares generally have a small (5 percent) premium over
inferior voting shares. 7
Our sample of dual-class stocks is obtained by searching CRSP data from 1965 to 2009
for entries with the same PERMCO and company name but distinct PERMNOs. We also require
that both issues are common stocks, are listed on the same major U.S. exchange (NYSE, Nasdaq,
Amex or NYSE Arca), and have an overlap of at least four years of trading. The resulting
sample has 59 firms and 118 issues, comparable to previous research, and with 7,322 firmmonths available for the tests. Brief descriptive statistics in Panel A of Table 3 reveal that these
are sizable firms with mean (median) market cap of $1,789 million ($148 million). Correlations
in monthly returns between the two share classes are high at about 80 percent, which confirms
that the two classes are largely moved by the same underlying fundamental information. Still,
the correlations are sufficiently different from perfect to allow the possible manifestation of
disparate return volatility effects.
Panel A also contains descriptive statistics for the test variables. For each available pairmonth we calculate the turnover for each of the two issues, tag them as “high” and “low” within
each pair, and create the variable DIF_VOLUME defined as the volume difference between high
volume issue and low volume issue, scaled by the volume of low volume issue. Then, we create
the variable DIF_STDRET defined as the return volatility difference between high volume issue
7
In most cases, the articles of incorporation prohibit favorable dividend payout to the superior voting class shares.
However, inferior voting rights shares sometimes receive favorable dividend payout, where the magnitude of
differential payout is generally small.
22
and low volume issue, scaled by the return volatility of low volume issue.8 An inspection of the
empirical distributions of these variables in Panel A reveals that indeed there are large
differences in liquidity between the two share classes, e.g., the median DIF_VOLUME is about
1.446, which means that the median turnover for the high class exceeds the low one by close to
150 percent. Note that the median DIF_STDRET is positive at 0.026, which provides
preliminary evidence that shares with higher turnover have higher volatility of returns as well.
Finally, the descriptive statistics for both variables are again highly non-normal, which again
confirms the need for robust tests and non-parametric statistics.
For the main tests in Panel B, we aim to more fully use the natural variation in the sample
by ranking the firm-month share pairs into quintiles on their DIF_VOLUME variable, and
reporting the spread in DIF_STDRET across quintiles, where the formal test is on the difference
in DIF_STDRET medians between the two extreme quintiles. At first glance, this difference in
the first sorting in Panel B is negligible and not statistically significant, which seems puzzling
given the preliminary evidence in Panel B. A closer inspection of the quintile results plus some
reflection on the causes of the underlying variation in DIF_VOLUME help to resolve this
conundrum. Notice that there is actually a fairly strong positive relation between
DIF_VOLUME and DIF_STDRET across the first four quintiles, which is consistent with the
preceding results and it is only the fifth quintile that breaks this pattern. In addition, notice that
the variation in DIF_VOLUME on which the quintiles are formed is produced by panel data,
which mixes time-series variation in DIF_VOLUME within firms with variation in average
DIF_VOLUME across firms; the point is that looking for a relation between trading and
volatility (holding everything else constant) applies a lot more directly for within-firms variation
than for across-firms variation in trading intensity. To explore this insight, we present the results
8
Similar results obtain if we scale by an average of return volatility of low and high volume issues.
23
for another quintile ranking, where firm-months are sorted on DIF_VOLUME within firms (as
opposed to globally above). The resulting ranking presented at the bottom of Panel B reveals a
strong and monotonic positive relation between DIF_VOLUME and DIF_STDRET, where the
difference in medians between the extreme quintiles is 4.2 percent, highly statistically
significant. For completeness, we also compute Spearman correlations between DIF_VOLUME
and DIF_STDRET for each firm in the sample; the resulting mean and median correlation across
firms are reliably positive, confirming the quintile results.
Summarizing, the results for dual-class shares are largely consistent with the results for
exchange switches and S&P 500 changes, namely more trading is associated with higher
volatility of returns. The identified differences in volatility, however, are much smaller for the
dual-class setting, most likely due to the effect of arbitrage.9
4. Large-sample evidence for U.S. stocks
As previously discussed, the thorniest problem in investigating the relation between
volume of trading and stock volatility is how to control for information flow. This is
problematic because information flow follows a multitude of public and private channels and is
thus difficult to observe and measure. The preceding section provides a series of natural
experiments that aim to control for information flow, and are aimed mainly at establishing the
existence and the direction of the trading/volatility relation. The disadvantage of these settings,
however, is that by definition they are fairly specialized and limited, and thus there is a question
about the generalizability and portability of these findings (especially their magnitudes) to the
9
Using similar variables and test design, we find similar results for a comparable setting, trading and volatility for
ADRs vs. the underlying stocks. The results are not tabulated for two reasons. First, we want to keep the paper to a
reasonable length. Second, the ADR setting has some complications related to using cross-border data that do not
mesh well with the rest of the tests in the paper, which rely on U.S. data.
24
wider world of stock trading. In this section, we address this question by extending the
investigation to the full universe of U.S. stocks. Generally speaking, this extended investigation
is more realistic and is well-calibrated to the naturally-occurring properties of the U.S. stock
market; this benefit, however, comes at the cost of losing some of the sharp controls implicit in
the earlier specifications.
The first type of evidence for the broad stock market looks at the long-run record for a
sample of the 500 largest U.S. stocks over 1926-2009. We use 500 stocks because data
availability is limited to about this number in the early years of the sample, and we want to
preserve some measure of comparability over time. The evidence for this specification is
presented in Figure 2 and Table 4, based on annual observations of value-weighted turnover and
stock return volatility. An inspection of Figure 2 reveals that the evolution of volatility has an
unmistakable synchronicity with the broad ebbs and flows of trading volume. When trading is
lowest in the quiet years between 1940 and 1970, volatility is also lowest, never exceeding 2
percent (daily measure) over this extended period that includes World War II, the Korean War,
and the various upheavals of the Cold War. Volatility is the highest during the two periods with
the most intense trading, peaking at over 4 percent during 1926-1940 and with the second and
third highest peak occurring after the mid-1990s. To be sure, the relation is far from lock-step
and one can identify several instances where the “trading environment” interpretation is
inadequate to describe the empirical behavior of volatility, e.g., volatility spikes during the
recession of 1973-74 with no discernable change in volume of trading. The summary impression
from Figure 2, however, is that even at this broad-brush graphical level volume of trading and
volatility are substantially positively related. This impression is confirmed by the statistical test
25
in Table 4, with a Spearman correlation of 0.52 between these two variables, which is highly
statistically significant and seems economically rather substantial.
The second type of evidence for the broad stock market is based on the cross-sectional
variation in trading intensity during recent years; specifically, we use a sample of all NYSEAMEX stocks over 1988-2007. For this set of tests, we avoid Nasdaq stocks because of the
previously discussed problems in measuring Nasdaq volume and the need to maintain withinsample comparability. We start with a simple specification that examines the univariate relation
between volume and volatility. Stocks are sorted annually into deciles based on their annualized
daily turnover, and we report median turnover and volatility by decile in Panel A of Table 5. An
inspection of Panel A reveals that there is a substantial cross-sectional variation in turnover, with
a low of about 10 percent for the bottom decile and a high of 235 percent for the top decile.
There is also a substantial spread in volatility between the extreme deciles, from about 2 percent
(daily volatility) in the bottom decile to about 3 percent in the top decile, which is both
statistically significant and economically substantial. A closer look at the results also reveals
that this increase is not monotonic, and indeed there is little reliable variation in volatility from
the first decile until about the seventh decile, followed by a quick rise and hitting a high in the
top decile. The combined impression from these observations is that while the relation between
volume and volatility is generally positive, it is also decidedly non-linear, with volatility only
clearly rising in the extremes of high trading.
Of course, the simple analysis in Panel A is inadequate because it does not control for
variation in volatility related to fundamentals. Broadly speaking, stock volatility due to
fundamentals can come from two sources, changes in expectations about future cash flows and
changes in the discount rate. We make no formal attempt to control for discount rate changes
26
because our volatility observations are at the firm-year level, while the empirical variation in
discount rates within a year is likely small (Cochrane 2001); in addition, discount rates are
notoriously difficult to measure (Elton 1999). We control for changes in expectations about
future cash flows by using realized earnings variability over current and future periods as a
proxy; specifically, for any firm i and year t, we use the standard deviation of realized quarterly
earnings over the current and two future years (i.e., years t, t+1, and t+2). Earnings is defined as
earnings before extraordinary items, scaled by the average of beginning and ending total assets,
where earnings and asset data is from Compustat. Given much previous evidence of nonnormality in the underlying variables and non-linearities in the examined relations, we rely on a
portfolio specification to map out the relation between volume and stock volatility, controlling
for fundamentals volatility. Specifically, we first sort the sample on fundamentals variability, by
year, into deciles, and then within these portfolios sort on volume turnover into deciles. The
result is a 10X10 matrix in Panel B of Table 5, with each cell reporting median stock return
volatility for that portfolio; variation down the columns captures the effect of fundamental
variability on stock volatility, and variation across the columns captures the effect of trading
volume on stock volatility, controlling for fundamental variability.
An examination of the results in Panel B reveals that fundamental variability is the
primary driver of observed stock volatility. The bottom line in Panel B captures differences in
the extreme deciles down the columns; while these differences vary, they average 2.5 percent
(daily volatility). This magnitude clearly dominates the corresponding numbers for the effect of
volume of trading, captured in the extreme-right column, which average about 0.8 percent. Of
course, the dominance of fundamental variability is not surprising; in fact, in an efficient market
fundamental variability should be the only variable that affects stock volatility. What is more
27
remarkable, actually, is that the effect of trading intensity remains economically large after
controlling for fundamental variability. If one thinks of total stock volatility as the sum of
volatility due to fundamental variability and volatility due to trading intensity, a literal reading of
the results in Panel B suggests that differences in trading intensity account for about a quarter of
total stock volatility, a rather significant amount. A closer look at the results in Panel B also
reveals the same non-linear pattern in the trading/volatility relation first observed for the
univariate specification in Panel A. Moving across columns, there is little reliable variation in
volatility from column 1 until about column 6 or 7, and then a clear and pronounced increase
over the remaining columns, always hitting a high in column 10.
We extend the analysis of the cross-sectional relation between volatility and volume
using a multivariate regression. The advantage of the regression specification is that it allows for
simultaneous control for a number of variables that have been shown to be determinants of stock
volatility. The disadvantage is the normality and linearity assumptions, which are clearly
violated in this setting, as shown in previous results. We make appropriate adjustments to
variable and regression specification to overcome these limitations; there are residual difficulties
however, in the interpretation of the results, especially for their economic magnitude.
Specifically, for the period 1988-2007, we estimate coefficients in the following
regression:
STDRET i,t = β0 + β1HIGH i,t + β2VOLUME i,t + β3VOLUME*HIGH i,t + β4STDRET i,t-1 +
β5RETi,t + β6STDEARN i,t+2 + β7SIZE i,t-1 + β8AGE i,t-1 + β9LEVERAGE i,t-1 +
β10BTM i,t-1 + εi,t
Where STDRET is the standard deviation of daily stock returns, HIGH is an indicator variable
set to 1 if volume is in the top quartile in year t, VOLUME is the annualized daily volume
turnover, RET is the compounded daily return in year t. STDEARN is the standard deviation of
28
quarterly earnings scaled by average total assets in year t, t+1, and t+2 with a minimum
requirement of eight quarters. SIZE is proxied by the market value of common equity, AGE is
the number of years since the firm first appeared in the CRSP database, LEVERAGE is the ratio
of debt to assets, and BTM is the book to market ratio.
We introduce the HIGH variable to account for the convex relation between volatility and
volume shown in Table 5; thus we expect a positive sign on HIGH*VOLUME. Control
variables are from Wei and Zhang (2006) and Brandt et al. (2010). Briefly, lagged value of
STDRET is included because volatility is known to be positively autocorrelated, and essentially
as a catch-all variable that captures omitted variables and other misspecifications.
Contemporaneous return is included following the intuition that expected return and risk are
positively correlated, and so are their realizations. As above, STDEARN controls for volatility
related to fundamentals, expect positive sign. The rest of the variables are commonly found in
asset pricing tests, and the predicted signs are clear, except for BTM. We replace the original
values of all variables with their percentile ranks to control for non-normalities in their
distributions and to allow for direct comparison of their strength across variables. Thus, the
regression coefficients can be interpreted as the percentage change in volatility for one percent
change in the corresponding variable (controlling for all other variables).
The regression results are presented in Table 6, where regressions (1) through (3) use a
Fama-MacBeth specification to control for cross-sectional dependencies in the residuals. We
start with baseline specifications (1) and (2), which include only VOLUME and then VOLUME
interacted with HIGH. Consistent with the results in Table 5, regression (1) confirms that there
is a positive relation between volatility and volume, while the positive and significant coefficient
on VOLUME*HIGH in regression (2) clarifies that this relation is convex, i.e., it is much
29
stronger for high levels of volume. The main results are in regression (3), which includes all
control variables. An inspection of regression (3) reveals that the relation between volatility and
volume remains statistically significant and economically substantial after the controls, with
sizable coefficients on both VOLUME and VOLUME*HIGH. In fact, the coefficients on
VOLUME are larger than those of any other variable except lagged volatility, indicating that
volume of trading is a prime determinant of stock volatility. A disadvantage of the FamaMacBeth specification in regressions (1) to (3) is that it essentially assumes time-series
stationarity in the volume/volatility relation, and ignores much of the meaningful increase in
volume over time. We address this limitation in regression (4), which uses a panel specification
with standard errors clustered by firm and year as suggested by Petersen (2009). The results of
this panel regression largely remain the same as those for the main specification (3), confirming
the robustness of the findings.
5. Discussion of results
While the results in this paper span a number of specifications and offer many nuances,
they seem to converge on several key themes. We find repeated and economically substantial
evidence that more intensive stock trading is accompanied by increased return volatility. This
relation is weak to non-existent at low to moderate levels of trading but becomes increasingly
strong as volume of trading increases. These findings are robust over a number of research
specifications, and hold after controlling for fundamental information and other relevant firm and
business characteristics. The combined impression from these results is that high volumes of
trading can be destabilizing, injecting a sizable layer of trading-induced volatility over and above
the unavoidable fundamentals-based volatility.
30
In considering the larger meaning of these results, it is useful to remember that existing
research documents a number of benefits from security liquidity and trading (Brennan, Chordia,
and Subrahmanyam 1998; Chordia and Swaminathan 2000; Fang, Noe, and Tice 2009). There is
reliable evidence that traded assets command higher valuations, lower transaction costs, and
wider investor recognition, and that these benefits increase with higher levels of trading. To be
able to reconcile the disparate messages of this study and existing research, note that much of the
previously documented benefits of liquidity come from environments with low trading intensity
e.g., newly listed stocks experience a substantial increase in price and decrease in bid-ask spread
(Kadlec and McConnel 1994). In contrast, the evidence in this study comes almost exclusively
from the largest, most-traded environments and stocks of all time; generally, we examine
prominent companies on the major U.S. stock exchanges, often during the unprecedented surge
in trading activity over the last 20 years.
The totality of evidence suggests that the benefits of trading in financial markets are not a
one-way street. While benefits to investors dominate at low to medium levels of trading, there is
possibly an inflection point or range, beyond which some of the benefits of trading disappear,
new problems start appearing, and some of the remaining benefits become more concentrated
and accrue only to a small circle of traders. For an example of benefits that are likely to stagnate
beyond a certain level of trading, consider the normal trading of typical individual investors or
longer-term institutional investors. Everything else equal, whether their orders are executed in 1
minute or 1 second is unlikely to matter a whole lot for those who are investing for long-term
goals like retirement. Whether transaction costs are on the magnitude of $10 or $1 per trade does
not matter that much either for the returns on a typical round-lot transaction. Whether such
investors adjusts their portfolios once a month or 10 times a month is unlikely to improve
31
performance (and in fact there is evidence that the opposite is true, e.g., Barber and Odean 2000)
and trading once a month is more than enough to fund liquidity needs or invest excess cash.
The results in this study provide an example of the new problems that start appearing
with the intensification of trading. Higher levels of trading seem to generate their own volatility,
with all ensuing consequences, including possible shifts in investor risk preferences and risk
management behavior, and possible destabilization of the market. At this point, these
possibilities are just conjectures, and it will be useful to explore them further in future research.
For example, it will be interesting to examine more closely the origin and dynamics of tradinginduced volatility and compare them to what we know about fundamentals-induced volatility. It
is possible that trading-based volatility is much more endogenous, prone to feedback loops, and
hard to predict and anticipate, and thus more dangerous and damaging than fundamentals-based
volatility. A related theme is further study of the possible destabilization role of trading-induced
volatility. The variable used in this study, standard deviation of returns, is a fairly bland proxy
for destabilization risk, and more targeted work can be done for extreme environments and
events, which are of special interest to investors and regulators.
It is also useful to think more closely about the parties who derive the most benefits from
the current high-trading environment. It seems that while the early gains from trading and
liquidity are widespread, the benefits at very high levels of trading are much more specialized,
accrue to a smaller circle of people, and lean in the direction of re-distribution rather than the
creation of new wealth. While it is helpful to be able to buy and sell sizable investment positions
promptly, the race to trade on slivers of new information a fraction of a second faster than
anybody else is more questionable as a value-enhancing activity at the society level. For the
economy as a whole, the primary function of the stock market is to facilitate the ebb and the flow
32
of capital into and out of the real activities of firms through stock issues and repurchases and
various forms of stock-enabled corporate reorganizations. This primary function can be fulfilled
at fairly low levels of trading, and indeed has been satisfied for quite some time. The high
intensity of trading we observe today is strictly on the secondary market of existing shares, and is
much more about the splitting and re-distribution of private gain based on specialized skills,
resources, and access to information. With the increasing volumes and speed of trading, and the
attendant increase in volatility documented here, the potential for concentration of profits likely
increases as well.
Another question is whether market-makers and regulators need to be more cognizant and
proactive about the fact that high trading leads to high volatility. To a certain extent, such
reactions already exist, e.g., circuit-breakers dampen extreme price moves by halting trading,
which is essentially a forced and extreme reversal of the forces documented in this study. Thus,
the question is not so much whether something needs to be done about it but how to best do it, so
as to avoid such ad hoc and extreme solutions. Some ideas already exist, and some of them have
long history. For example, Ripley (1911) reviews a massive wave of speculation in major U.S.
railroad stocks at the turn of the last century, where annual turnover for several stocks reached
magnitudes of 10 to 20, very high even by our modern standards of hyperactive trading. Ripley
suggests that one way to dampen such speculative excesses is to impose taxes on trading, with
the side benefit of raising government funds. Whether this is the right way to go or not, these
questions remain wide open, and can be a fruitful field for future research.
Another interesting direction for research is to investigate the volume/volatility relation
in investment assets beyond stocks. Corporate and government bonds, closed-end funds,
commodities, currencies – all these instruments provide potential testing ground for the effects
33
documented here. Currency trading, for example, has grown 10-fold during the last 20 years,
and today at $4 trillion/day is arguably much higher than what needs tied to the real economy
would suggest (e.g., total annual global trade is $25 trillion and global money stock is only $12
trillion as of 2009).10 Another intriguing and topical research opportunity is real estate
investments, where for a long time most homes and the associated mortgages were both held as
long-term investments and either not traded or traded chiefly for needs as relocation or changing
family needs and preferences. Perhaps it is not accidental that the great price appreciation in the
early to mid 2000s and the ensuing crash coincided with the re-assessment of real estate as a
tradable and speculative asset, with much “flipping” of homes and re-packaging and continuous
re-trading of mortgages and home-equity loans.
Finally, it is useful to consider the implications of the trading/volatility results for other
investor environments and stock exchanges. The U.S. evidence is important in its own right
since the U.S. stock market at $15 trillion is by far the largest, accounting for about a third of
world market cap of $47 trillion as of the end of 2009.11 But it is also important because the U.S.
experience in volume of trading is ahead of the curve and the rest of the world seems to be
moving in the same direction. Specifically, while volumes of trading have been rising worldwide for the last 20 years, the annualized U.S. turnover of 300 percent as of the end of 2009 is
the highest in the world, and far above the second-highest at 150 percent (China). Most
developed markets (Japan, U.K., Germany) have turnover on the magnitude of 100 percent, and
developing markets (Australia, Brazil, Hong Kong) tend to be even lower around 50 percent. As
illustrated in Figure 1, U.S. markets start registering turnovers around 50 percent in the late
1980s, and around 100 percent in the late 1990’s. The implication is that, if history is any guide,
10
11
Data from the CIA World Factbook.
All data in this paragraph are from the Economist, July 17, 2010 (page 98); data provided by Standard and Poor’s.
34
the U.S. experience is 10 to 20 years ahead of the curve, and thus lessons from this high-volume
trading environment are likely to be portable and useful around the world.
6. Conclusion
This study investigates the effect of trading volume as a long-term, environmental
variable on observed stock volatility. The motivation is that volumes of U.S. trading have
increased more than 30-fold over the last 50 years, truly transforming the marketplace, and it is
important to map out the effects of such a momentous change. First, we employ a series of three
natural experiments to carefully examine the existence and direction of this relation, while
controlling for fundamental information that endogenously drives both volume and volatility.
We use exchange switches, S&P 500 index changes, and dual-class stocks as settings with
substantial variation in trading but good natural controls for underlying fundamentals. Our main
finding is that in all three settings volume of trading is reliably positively correlated with stock
volatility, and this relation seems economically substantial. Second, we examine the aggregate
time-series of U.S. stocks since 1926 and the cross-section of stocks during the last 20 years to
better calibrate the economic parameters of the identified relation. Using annual measures,
volume and volatility are correlated on the magnitude of 50 percent in the aggregate time-series,
suggesting that much of the historical variation in volatility is driven by the prevailing volumes
of trading. Tests in the cross-section confirm the positive volume/volatility relation but also
reveal a pronounced convexity, where the relation is weak to non-existent for low levels of
trading and becomes much clearer and stronger for high levels of trading. Efforts quantifying the
volume effect reveal that trading-induced volatility accounts for about a quarter of total observed
stock volatility today. The combined impression from these results is that stock trading injects
35
an economically substantial layer of volatility above and beyond that based on fundamentals,
especially at high levels of trading.
36
Appendix A
There are a number of difficulties and complications in determining Nasdaq share volume, which
hamper the comparability of not only Nasdaq volume with volume from other exchanges but also
within Nasdaq’s own time-series of data. We refer the interested reader to Anderson and Dyl
(2005) for a full account of these problems, and here provide only a brief summary, which
suffices for our purposes. The most well-known problem with Nasdaq volume arises because
Nasdaq is a dealer market, and thus end-customer to end-customer transactions pass through a
dealer, and are thus double counted in volume; the usual solution to this problem is to divide
Nasdaq volume by two (Atkins and Dyl 1997), and we also employ this adjustment.
Unfortunately, there are several other factors that complicate the interpretation of Nasdaq
volume, and there is no easy way to control for them. First, Nasdaq has much inter-dealer
trading, which varies in intensity across stocks; since these transactions are counted in, reported
volume is further increased, and the increase varies cross-sectionally. Second, electronic
communication networks (ECNs) have accounted for increasing volumes of trade on Nasdaq.
Since ECN’s transactions are counted only once in volume, double-counting is eliminated but
data on ECN participation over time and across stocks is not readily available. Third, in 1997
regulators changed several important rules about the reporting of Nasdaq volume, which
eliminated double-counting for some transactions, see Anderson and Dyl (2005) for full details.
These changes also hamper volume comparability across exchanges, stocks, and time.
37
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40
4.0
3.5
3.0
2.5
2006
2001
1996
1991
1986
1981
1976
1971
1966
1961
1956
1951
1946
1941
1936
1931
2.0
1.5
1.0
0.5
0.0
1926
Annulized Volume
Figure 1
Value-Weighted Stock Trading Volume from 1926 to 2009
Calendar Year
NYSE/AMEX
NASDAQ
This figure shows the annualized value-weighted trading volume turnover for NYSE/AMEX (solid line) from 1926 to 2009 and Nasdaq (patterned line) from
1983 to 2009. Annualized value-weighted volume turnover is the average daily value-weighted market volume turnover for calendar year t multiplied by 250.
Daily value-weighted volume turnover is measured as dollar-value traded (volume * price) on a trading day aggregated over all stocks on the corresponding
exchanges divided by aggregate market value outstanding as of that day. Volume for stocks traded on Nasdaq is volume on CRSP scaled by two.
41
Figure 2
Trading Volume and Return Volatility for the Largest 500 U.S. Stocks from 1926 to 2009
4.0
3.5
3.0
2.5
2.0
1.5
2006
2001
1996
1991
1986
1981
1976
1971
1966
1961
1956
1951
1946
1941
1936
1931
1926
1.0
0.5
0.0
Calendar Year
VOLUM E
STDRET
This figure shows trading volume turnover (solid line) and stock return volatility (patterned line) for the largest 500 stocks on NYSE/AMEX from 1926 to 2009.
VOLUME is annualized value-weighted trading volume turnover as defined in figure 1 with the exception that the calculation is based on the largest 500 U.S.
stocks. STDRET is the value-weighted averages of return volatility at the firm-year level (multiplied by 50 for scaling). Return volatility is the standard
deviation of daily stock returns for firm i in year t and weight is the average of beginning and ending market values for firm i in year t divided by the total market
values of the 500 firms in year t.
42
Table 1
Stocks Switching Exchanges
Panel A: Sample composition and descriptive statistics
Initial sample
Final sample
By year
1962-1970
1971-1980
1981-1990
1991-2000
2001-2009
MVE
CH_VOLUME
CH_STDRET
Between
NYSE and
AMEX
989
951
From
Nasdaq to
NYSE/AMEX
2,217
1,573
From
NYSE/AMEX
to Nasdaq
405
336
Full
Sample
3,611
2,860
163
249
153
204
182
576
842
155
16
120
200
163
249
745
1,166
537
Mean
STD
10%
546
1,877
23
100.4% 1,160.5% -55.0%
4.8%
73.9%
-52.6%
25%
47
-30.1%
-35.9%
50%
128
6.0%
-10.6%
75%
392
63.3%
21.9%
90%
1,066
189.9%
73.3%
This panel reports sample composition and descriptive statistics of stocks that switched between three
major U.S. stock exchanges (i.e. NYSE, AMEX, and Nasdaq). The initial sample consists of all switches
between the three exchanges from 1962-2009 based on Stockname file on CRSP. The final sample consists
of switching stocks with nonmissing daily volume, shares outstanding, and stock price over one calendar
month before and after exchange switch. MVE is the market value of equity ($million) measured on the
effective date of switch, calculated as closing price multiplied by closing shares outstanding.
CH_VOLUME is the percentage change of trading volume turnover, measured as the difference between
average daily volume before and after the switch scaled by average daily volume turnover before the
switch. For NYSE/AMEX stocks, daily volume turnover is daily trading volume divided by daily closing
shares outstanding. For Nasdaq stocks, daily volume turnover is scaled by two. CH_STDRET is the
percentage change of standard deviation of returns, measured as the difference between standard deviations
of daily stock returns before and after the switch scaled by standard deviation of returns before the switch.
Windows (-22, -1) and (0, 21) are used to measure CH_VOLUME and CH_STDRET before and after the
switch, where day 0 is the effective date of switch.
43
Table 1 (continued)
Panel B: Median change of trading volume and return volatility over windows (22, -1) and (0, 21) for the three types of switches
Quintiles
Formed by
CH_
Spearman Corr.
VOLUME
CH_
CH_
(CH_VOLUME,
(Low to High)
VOLUME
STDRET
CH_STDRET)
Between
Q1
-44.1%
-25.4%
NYSE and
Q2
-21.6%
-15.2%
AMEX
Q3
0.8%
-4.5%
(N = 951)
Q4
34.0%
2.9%
Q5
111.8%
13.7%
From
Nasdaq to
NYSE/AMEX
(N = 1,573)
From
NYSE/AMEX
to Nasdaq
(N = 336)
Q5 - Q1 Diff.
155.9%*
39.1%*
Q1
Q2
Q3
Q4
Q5
-47.1%
-11.4%
23.6%
69.2%
258.7%
-34.4%
-20.8%
-10.9%
-4.3%
-7.8%
Q5 - Q1 Diff.
305.8%*
26.6%*
Q1
Q2
Q3
Q4
Q5
-77.8%
-64.9%
-45.4%
-18.7%
55.3%
-6.7%
-2.6%
21.4%
4.4%
44.0%
Q5 - Q1 Diff.
133.1%*
50.7%*
0.352*
0.234*
0.247*
CH_VOLUME and CH_STDRET are defined in Table 1 Panel A. * denotes significance at the 1% level.
The p-value for the difference between top and bottom quintiles is based on wilcoxon z-statistics.
44
Table 1 (continued)
Panel C: Median change of trading volume and return volatility over windows (45, -23) and (22, 45) for the switches between NYSE and AMEX
Quintiles Formed by
CH_VOLUME
(Low to High)
Q1
Q2
Q3
Q4
Q5
CH_VOLUME
-57.1%
-25.9%
-0.6%
41.6%
157.6%
CH_STDRET
-21.2%
-16.1%
-4.2%
6.7%
19.8%
Q5 - Q1 Diff.
214.7% **
41.0% **
Spearman Corr.
(CH_VOLUME,
CH_STDRET)
0.342*
Panel D: Median change of trading volume, return volatility, and bid-ask spread
over windows (-22, -1) and (0, 21) for the switches between NYSE and AMEX
Quintiles Formed by
CH_VOLUME
(Low to High)
CH_VOLUME
CH_STDRET
CH_SPRD
Q1
-44.1%
-25.4%
-24.7%
Q2
-21.7%
-15.2%
-15.4%
Q3
0.8%
-4.5%
-5.8%
Q4
34.0%
2.9%
3.6%
Q5
111.8%
13.7%
18.8%
Q5 - Q1 Diff.
155.9% *
39.1%*
43.5%*
CH_VOLUME and CH_STDRET are defined in Table 1 Panel A. CH_SPRD is the percentage change of
bid-ask spread, measured as the difference between average daily quoted half spread before and after the
switch scaled by average daily spread before the switch. Quoted half spread is the difference between ask
and bid prices scaled by two. In Panel C windows (-45, -23) and (22, 45) are used to measure all the
variables of interest before and after the switch and in Panel D windows (-22, -1) and (0, 21) are used. Day
0 is the effective date of switch. * denotes significance at the 1% level. The p-value for the difference
between top and bottom quintiles is based on wilcoxon z-statistics.
45
Table 2
Stocks Added and Deleted from the S&P 500 Index
Panel A: Sample composition and descriptive statistics
Initial sample
Final sample
By year
1976-1980
1981-1990
1991-2000
MVE
CH_VOLUME
CH_STDRET
Mean
4,248
26.7%
11.8%
Additions
590
453
Deletions
565
86
Full Sample
1,155
539
44
207
202
2
18
66
46
225
268
STD
8,166
70.4%
50.6%
10%
244
-40.3%
-39.8%
25%
607
-19.0%
-21.9%
50%
1,424
9.3%
1.8%
75%
5,311
55.5%
36.1%
90%
8,830
104.5%
73.3%
This panel reports sample composition and descriptive statistics of stocks that were added and deleted from
S&P 500 index. The initial sample is obtained from Jeff Wurgler’s website, spanning from 1976 – 2000.
The final sample excludes stocks with missing CRSP data to calculate CH_VOLUME and CH_STDRET
and additions and deletions as a result of merges, spin-offs, bankruptcy, re-organization, and restructuring.
MVE is the market value of equity ($million) measured on the effective date of change, calculated as
closing price multiplied by closing shares outstanding. CH_VOLUME is the percentage change of trading
volume turnover, measured as the difference between average daily volume before and after the change
scaled by average daily volume turnover before the change. For NYSE/AMEX stocks, daily volume
turnover is daily trading volume divided by daily closing shares outstanding. For Nasdaq stocks, daily
volume turnover is scaled by two. CH_STDRET is the percentage change of standard deviation of returns,
measured as the difference between standard deviations of daily stock returns before and after the change
scaled by standard deviation of returns before the change. Windows (-45, -23) and (22, 45) are used to
measure CH_VOLUME and CH_STDRET before and after the S&P 500 addition or deletion, where day 0
is the effective date of change.
46
Table 2 (continued)
Panel B: Median change of volume and return volatility for S&P 500 additions
Quintiles by
Spearman Corr.
CH_VOLUME
CH_
CH_
(CH_VOLUME
(Low to High)
VOLUME
STDRET
,CH_STDRET)
Q1
-13.4%
-12.1%
Q2
22.0%
-3.4%
Q3
53.2%
2.7%
Window
Q4
88.9%
8.2%
(-22,-1) (0,21)
Q5
164.8%
29.8%
Window
(-45,-23) (22,45)
Q5 - Q1 Diff.
178.2%*
41.9%*
Q1
Q2
Q3
Q4
Q5
-39.0%
-11.4%
8.5%
42.6%
101.0%
-17.7%
-9.1%
0.1%
14.6%
20.1%
Q5 - Q1 Diff.
140.0%*
37.8%*
0.344*
0.380*
Panel C: Median change of volume and return volatility for S&P 500 deletions
Quintiles by
Spearman Corr.
CH_VOLUME
CH_
CH_
(CH_VOLUME
(Low to High)
VOLUME
STDRET
,CH_STDRET)
Q1
-11.8%
0.2%
Q2
57.4%
2.7%
Q3
84.8%
-2.3%
Window
Q4
134.9%
8.6%
(-22,-1) (0,21)
Q5
198.6%
5.7%
Window
(-45,-23) (22,45)
Q5 - Q1 Diff.
210.4%*
5.5%
Q1
Q2
Q3
Q4
Q5
-43.7%
-23.4%
13.2%
47.9%
143.5%
-24.4%
-10.6%
7.1%
7.8%
47.8%
Q5 - Q1 Diff.
187.2%*
72.2%*
0.181***
0.422*
CH_VOLUME and CH_STDRET are defined in Table 2 Panel A. *, **, and *** denotes significance at
the 1%, 5%, and 10% levels respectively. The p-value for the difference between top and bottom quintiles
is based on Wilcoxon z-statistics.
47
Table 3: Evidence from Dual-Class Firms
Panel A: Sample composition and descriptive statistics
Number of firms
59
Number of share classes
118
Firm-month pairs
7,322
MVE
DIF_VOLUME
DIF_STDRET
Mean
1,789
45.465
0.209
10%
11
0.164
-0.373
25%
53
0.483
-0.165
50%
148
1.446
0.026
75%
419
5.225
0.285
90%
2,212
23.619
0.695
Panel B: Difference in median return volatility across quintiles formed on the
scaled difference in trading volume
Global Sort
Quintiles Formed by
DIF_VOLUME
DIF_VOLUME
DIF_STDRET
1
0.164
0.006
2
0.622
0.024
3
1.446
0.054
4
3.805
0.053
5
23.619
-0.018
Q5-Q1 Diff.
23.454*
-0.024
Firm-Level Sort
Quintiles Formed by
DIF_VOLUME
1
2
3
4
5
DIF_VOLUME
0.172
0.592
1.212
2.546
7.436
DIF_STDRET
0.000
0.020
0.024
0.037
0.042
Q5-Q1 Diff.
7.265*
0.042*
The sample consists of dual-class stocks listed on one of the major U.S. exchanges during 1965-2009. For
each share-month, we calculate volume turnover VOLUME as volume/shrout, where volume is the total
shares traded in that month and shrout is the total share outstanding for the given issuance. The stock
return volatility is the standard deviation of daily returns, denoted as STDRET. For each firm-month, we
split the share pairs by turnover into High and Low. DIF_VOLUME is defined as the volume difference
between high volume issue and low volume issue, scaled by the volume of the low volume issue.
DIF_STDRET is defined as the return volatility difference between high volume issue and low volume
issue, scaled by the return volatility of the low volume issue.* denotes significance level at the 1% level,
using Wilcoxon z-statistics. .
48
Table 4
Trading Volume and Return Volatility for the Largest 500 U.S. Stocks
from 1926 - 2009
Spearman Corr. (VOLUME, STDRET) = 0.524*
Year
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
VOL
UME
95.8%
94.0%
124.9%
112.0%
64.9%
50.5%
43.4%
53.9%
20.1%
20.5%
21.8%
19.2%
15.0%
13.1%
9.6%
7.6%
5.9%
9.8%
9.0%
12.6%
13.1%
8.9%
10.2%
8.2%
14.6%
11.1%
8.5%
8.9%
STD
RET
0.014
0.013
0.016
0.029
0.023
0.032
0.043
0.037
0.021
0.018
0.016
0.024
0.024
0.020
0.018
0.014
0.014
0.012
0.010
0.012
0.018
0.013
0.013
0.011
0.013
0.011
0.010
0.010
Year
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
VOL
UME
11.9%
11.8%
9.5%
8.7%
9.7%
9.5%
8.4%
9.6%
10.0%
10.0%
9.2%
10.0%
14.6%
16.0%
18.8%
15.6%
15.0%
17.0%
16.1%
15.9%
14.4%
18.3%
22.5%
20.4%
24.7%
27.9%
38.0%
35.9%
STD
RET
0.011
0.014
0.012
0.013
0.012
0.013
0.014
0.013
0.017
0.011
0.010
0.011
0.015
0.014
0.015
0.015
0.018
0.014
0.013
0.019
0.023
0.018
0.014
0.012
0.014
0.014
0.020
0.018
Year
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
VOL
UME
49.2%
56.3%
58.2%
65.9%
74.5%
84.9%
66.7%
65.1%
55.1%
55.1%
53.8%
61.4%
63.3%
69.3%
71.3%
78.5%
82.2%
92.6%
111.0%
111.8%
128.3%
123.7%
123.2%
136.9%
159.2%
223.3%
356.2%
275.1%
STD
RET
0.020
0.017
0.016
0.014
0.017
0.026
0.016
0.014
0.017
0.016
0.015
0.015
0.015
0.014
0.016
0.019
0.023
0.024
0.031
0.024
0.026
0.016
0.013
0.013
0.013
0.016
0.037
0.028
This table reports trading volume turnover and stock return volatility for the largest 500 stocks on
NYSE/AMEX from 1926 to 2009. VOLUME and STDRET are defined in Figure 2. * denotes significance
at the 1% level.
49
Table 5
Median Return Volatility for Trading Volume Portfolios
Panel A: Return volatility formed on the basis of trading volume
VOLUME deciles (low to high)
D1
D2
D3
D4
D5
D6
D7
D8
9.7%
22.4%
35.0%
47.3%
59.2%
70.7%
84.2%
104.9%
0.020
0.021
0.020
0.019
0.019
0.019
0.020
0.022
D9
136.4%
D10
244.8%
D10 - D1
235.1%*
0.025
0.031
0.011*
Panel B: Return volatility formed on the basis of both earnings volatility and trading volume
STDEARN
deciles
(low
to
high)
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
0.001
0.003
0.004
0.006
0.008
0.011
0.014
0.021
0.036
0.096
D10-D1 0.095*
D1
9.7%
D2
22.2%
D3
34.7%
VOLUME deciles (low to high)
D4
D5
D6
D7
D8
D9
D10
46.5% 58.5% 71.0% 86.0% 107.6% 139.3% 235.0%
0.014
0.015
0.017
0.018
0.019
0.020
0.022
0.025
0.028
0.037
0.015
0.016
0.017
0.019
0.019
0.021
0.022
0.026
0.033
0.043
0.015
0.014
0.017
0.018
0.018
0.020
0.021
0.025
0.031
0.042
0.015
0.015
0.016
0.017
0.018
0.019
0.022
0.024
0.029
0.047
0.015
0.015
0.016
0.017
0.018
0.018
0.020
0.024
0.029
0.040
0.016
0.015
0.017
0.017
0.019
0.019
0.021
0.024
0.028
0.041
0.016
0.016
0.018
0.018
0.020
0.021
0.023
0.023
0.028
0.043
0.018
0.018
0.019
0.020
0.021
0.022
0.024
0.026
0.029
0.040
0.018
0.019
0.021
0.021
0.024
0.025
0.026
0.028
0.031
0.039
0.021
0.023
0.024
0.026
0.027
0.028
0.030
0.033
0.036
0.046
0.023* 0.027* 0.027* 0.032* 0.025* 0.025* 0.026*
0.023*
0.021*
0.024*
D10-D1
225.3%*
0.007*
0.008*
0.007*
0.008*
0.008*
0.008*
0.008*
0.008*
0.008*
0.009*
50
This table reports the median stock return volatility for portfolios formed on the basis of volume turnover. The sample consists of 40,577 firm year observations
from all NYSE/AMEX stocks over 1988-2007 with available CRSP and Compustat data to calculate volume turnover, return volatility, and earnings volatility at
the firm-year level. Volume turnover (VOLUME) is the annualized volume turnover, calculated as average daily volume turnover (volume/shares outstanding)
multiplied by 250 for firm i over year t. Return volatility (STDRET) is the standard deviation of daily stock returns firm i over year t. Earnings volatility
(STDEARN) is the standard deviation of quarterly earnings firm i over year t, t + 1, t + 2. Quarterly earnings are earnings before extraordinary items, scaled by
the average of beginning and ending total assets (Compustat data8/data44). In Panel A all sample firms are sorted into deciles based on volume turnover each
year. In panel B all sample firms are sorted into deciles based on earnings volatility each year and within each earnings volatility decile firms are further sorted
into deciles based on volume turnover. * denotes significance at the 1% level. The p-value is based on Wilcoxon z-statistics.
51
Table 6
The Cross-Sectional Relation between Return Volatility and Trading
Volume, Controlling for Other Factors
STDRET i,t = β0 + β1HIGH i,t + β2VOLUME i,t + β3VOLUME*HIGH i,t +
β4STDRETi,t-1 + β5RETi,t + β6STDEARN i,t+2 + β7SIZE i,t-1 + β8AGE i,t-1
+ β9LEVERAGE i,t-1 + β10BTM i,t-1 + εi,t
Intercept
Predicted
Sign
+
HIGH
VOLUME
VOLUME*HIGH
STDRETt-1
RET
STDEARN
+
(4)
18.112
(45.28)*
(54.50)*
(12.65)*
(6.02)*
-69.782
-20.425
-13.697
(-21.43)*
(-9.06)*
(-2.11)**
0.225
0.011
0.105
0.133
(13.13)*
(0.63)
(10.31)*
(5.93)*
1.004
0.274
0.170
(23.21)*
(10.33)*
(2.12)**
0.663
0.666
(58.46)*
(20.49)*
-0.071
-0.117
(-3.51)*
(-4.88)*
0.103
0.114
(19.17)*
(9.17)*
-0.148
-0.141
(-8.35)*
(-4.84)*
-0.028
-0.032
(-2.87)*
(-2.75)*
0.005
0.014
(0.88)
(2.15)**
0.001
-0.009
(0.10)
(-0.71)
1,916
0.752
38,322
0.706
+
+
AGE
-
(Average) Number of
Observations
(Average) Adjusted R2
(3)
17.515
+
-
BTM
(2)
44.550
-
SIZE
LEVERAGE
(1)
38.377
+
?
1,916
0.055
1,916
0.096
52
This table reports cross-sectional regressions of return volatility on trading volume along with control
variables at the firm level for NYSE/AMEX firms over the period 1988-2007. STDRET is the standard
deviation of returns for stock i in year t. VOLUME is the annualized turnover. High is an indicator
variable coded 1 if volume is in the top quartile of the sample. STDRET t-1 is the standard deviation of
returns for stock i in year t-1. RET is the compounded daily return for stock i in year t. STDEARN is the
standard deviation of quarterly earnings scaled by average total assets(Compustat data8/data44) in year t,
t+1, and t+2 with a minimum requirement of eight quarters. SIZE is proxied by the market value of
common equity (Compustat data25*data199). AGE is the number of years since the firm first appears in
the CRSP database. LEVERAGE is the ratio of debt to assets ((data9+data34)/data6). BTM is the book to
market ratio (data25*data199/data60). Regressions (1) through (3) report estimates from Fama-MacBeth
cross-sectional regressions. The t-values in parentheses are based on Fama-MacBeth standard errors and
the number of observations and R2 are the averages across the twenty annual regressions. Regression (4)
reports estimates from pooled-cross sectional regression to gauge the effect of increasing volume over time
on return volatility. The t-values reported in parentheses are based on standard errors clustered by firm and
year as suggested by Petersen (2009). To control for non-normalities in their distributions and to allow for
direct comparison of their strength across variables, all variables in regressions (1) through (3) are ranked
into percentiles by year and all variables in regression (4) are ranked into percentiles without sorting by
year. * and ** denote significance at the 1% and 5% levels.
53