The authors illustrate the importance of the joint behavior between stock prices
and trading volume by using disagreement models. Unlike traditional
asset-pricing models, these behavioral finance models allow for differences in
the beliefs of investors. The authors present models that are consistent with
the findings of the momentum effect and observations of stock prices and trading
volume from periods before and after speculative bubbles.
In classical asset-pricing models, only risk should be able to predict stock returns;
however, numerous variables, without any clear association with risk, have been
documented to predict stock returns. The authors offer arguments that support
disagreement models as a natural framework for explaining predictable patterns in stock
returns (e.g., momentum, postearnings return drift, and the fundamental reversion of
“glamour” stocks). The disagreement models are a type of heterogeneous-agent
model that explain the specific nature of the patterns of predictability. The
disagreement models can include models with gradual information flow, limited attention,
and heterogeneous priors. The most promising aspect of the disagreement models is that
they include both stock returns and trading volume.
The authors state that the joint behavior of stock prices and trading volume is appealing
to the proponents of traditional asset-pricing models (based on rational expectations)
and to the proponents of behavioral finance. Traditionalists believe that investors who
disagree on the price of a stock will trade. Because of the idiosyncratic nature of the
trades, however, the price will not be affected because the trades of disagreeing
investors will cancel each other out. Thus, factors affecting price are not related to,
or are decoupled from, factors affecting volume. Contrary to this decoupling point of
view, the occurrences of speculative bubbles have been found to be associated with
increased trading volume (e.g., 1928 and 1929 had 13 record-breaking volume trading
days). The authors’ review of the academic literature on speculative bubbles
points to trading volume as an indicator of investor sentiment.
In addition to a review of data on the relationship between prices and volumes during
speculative bubbles, the authors also examine turnover of glamour stocks—that is,
those with a high market-to-book ratio—and low-priced value stocks and find that
over the 20-year period from 1986 to 2005, glamour stocks had consistently higher volume
of trading. The authors interpret these data as a further indicator that higher-priced
stocks are associated with higher volume even outside of speculative bubbles. This
finding is further confirmed by their finding of a robust correlation (0.49) between
changes in price levels and changes in trading volume for the S&P 500 Index between
1900 and 2005. The authors conclude that strong statistical evidence exists for a
price–volume connection, which needs to be theoretically addressed.
The authors suggest that the explanation lies in investor disagreement and identify three
mechanisms that cause this disagreement. The first mechanism is gradual information
flow, which assumes that information is disseminated differently (cheaper and sooner) to
specialists than to generalists. The second mechanism is limited attention, where the
timing and manner of the news release can create disagreement. For example, earnings
releases on Fridays have been documented as stimulating less volume than earnings
releases on other days (i.e., investors fail to remember the importance of the Friday
announcement on Monday). The third mechanism is heterogeneous priors, where investors
all receive the same information at the same time but because of differing expectations,
their interpretations of the information are different. The authors show that increases
in trading turnover spiked around earnings announcements during the 1986–2005
period and volume remained high for the week following the announcement, which is
contrary to the rational expectations model, where publicly released information should
increase agreement among investors rather than reduce it.
The authors use the momentum effect and the recent collapse of the internet bubble to
illustrate the importance of trading volume in an asset-pricing model. The disagreement
model that they present indicates that the momentum effect is larger for companies with
higher trading volume. Empirical findings for the momentum effect from prior studies are
consistent with this prediction. A dynamic disagreement model with a short-selling
constraint is used to illustrate the joint behavior of overpricing and volume (i.e.,
trading increases when the level of disagreement changes). Given the short-selling
constraint, these dynamic models predict that higher volume predicts lower returns,
which is supported by current empirical findings. There is also research, however, that
reports that companies with the largest surges in trading volume around earnings
announcements experience the largest price increases.
The authors conclude that if behavioral finance is to become as enduring as the classical
asset-pricing theory, then behavioral finance will have to become more than just a
collection of empirical facts. The disagreement models are promising because they
include the joint behavior of prices and volume and because they are able provide
explanations for some of the observed anomalous return patterns.