How Wise Are Crowds?
Insights from Retail Orders and Stock Returns
January 2012
Eric K. Kelley and Paul C. Tetlock
*
Abstract
We analyze the role of retail investors in stock pricing using a database uniquely suited
for this purpose. The data allow us to address selection bias concerns and to separately
examine aggressive (market) and passive (limit) orders. Both aggressive and passive net
buying positively predict firms’ monthly stock returns with no evidence of return
reversal. Only aggressive orders correctly predict firm news, including earnings
surprises, suggesting they convey novel cash flow information. Only passive net buying
follows negative returns, consistent with traders providing liquidity and benefitting
from the reversal of transitory price movements. These actions contribute to market
efficiency.
*
University of Arizona and Columbia University. The authors thank the following people for their helpful
comments: Brad Barber, Robert Battalio, Ekkehart Boehmer, Kent Daniel, Stefano DellaVigna, Simon Gervais,
Campbell Harvey, Paul Irvine, Charles Jones, Tim Loughran, Terry Odean, Emiliano Pagnotta, Chris Parsons,
Mitchell Petersen, Tano Santos, Nitish Sinha, Sheridan Titman, Scott Weisbenner, and an anonymous associate
editor and referee, along with participants at the Miami Finance, NYU Five-Star, and WFA conferences, as well as
colleagues at Alberta, AQR Capital, Arizona, Columbia, DePaul, Emory, LBS, LSE, Texas A&M, and USC. The
authors also thank Arizona and Columbia, respectively, for research support, Travis Box for research assistance, and
Dow Jones for access to their news archive. All results and interpretations are the authors’ and are not endorsed by
the news or retail order data providers. Please send correspondence to paul.tetlock@columbia.edu.
What is the role of self-directed retail traders in stock pricing? As managers of their own money,
these investors are not subject to the agency problems, career concerns, or liquidity constraints
that can hurt institutional managers’ performance (Lakonishok et al. (1991), Chevalier and
Ellison (1999), and Coval and Stafford (2007)). Consequently, retail traders have clear incentives
to trade on novel information gleaned from geographic proximity to firms, relationships with
employees, or insights into customer tastes. In addition, using their personal wealth, they may
provide liquidity to institutional investors whose trades can temporarily distort prices as in
Grossman and Miller (1988).
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In stark contrast, novice retail traders with little investment
knowledge and experience may trade on “noise” in the sense of Black (1986) and exert pressure
on prices, pushing them away from fundamental values.
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In this paper, we test these informed trader, liquidity provider, and noise trader theories
by analyzing the relationship between retail traders’ collective actions and market prices. Prior
studies try to address these topics by asking whether net buying by retail investors predicts
firms’ stock returns (Dorn, Huberman, and Sengmueller (DHS, 2008), Hvidkjaer (2008), Kaniel,
Saar, and Titman (KST, 2008), Barber, Odean, and Zhu (BOZ, 2009), and Kaniel, Liu, Saar, and
Titman (KLST, 2011)). Yet the impact of retail traders on stock pricing remains unsettled
because existing studies arrive at conflicting conclusions. Severe data limitations may explain
the lack of consensus. Prior studies rely on measures of retail trading that are based on either a
single broker, orders selectively routed to a single exchange, or an indirect proxy. This could
lead to biased inferences about the population of retail investors.
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This paper introduces a database that is uniquely well-suited for evaluating the competing
theories. The data include over $2.6 trillion in executed trades, which is roughly one-third of all
self-directed retail trading in the United States, coming from dozens of retail brokerages from
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2003 to 2007. In contrast to previous studies, the data not only directly identify retail orders, but
they also allow us to address concerns about biases in the sample of the retail population.
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Moreover, the data allow us to separately examine aggressive (market) and passive (limit) orders
to trade, as well as the subset of passive orders resulting in trades. Traders’ choices of order
types may provide insights into their underlying motives and influence the extent to which their
trades move prices. Our analysis focuses on net share imbalance for each order type—measured
as the difference between buys and sells divided by the sum of buys and sells—and its
relationship with future stock returns.
We combine our data on retail orders with comprehensive newswire data from Dow
Jones (DJ) to test the informed trader hypothesis. We infer that traders have novel information
about firms’ cash flows if retail imbalances correctly predict the linguistic tone of firm-specific
news. The tone of news is a proxy for daily changes in firms’ fundamental values that includes
information that is revealed between firms’ regular quarterly reporting dates. We also use a
proxy for fundamentals based on firms’ quarterly earnings surprises, which are ten times less
frequent than news.
Our three main findings offer new insights into the role of retail investors in stock
pricing. First, daily buy-sell imbalances from both retail market orders and retail limit orders
positively predict the cross-section of stock returns at monthly horizons. This result actually
becomes slightly stronger in stocks in which our data include a greater fraction of the population
of retail traders, implying that biases in the sample of retail traders cannot explain these findings.
Even at horizons up to one year, point estimates of return predictability are typically positive and
never significantly negative, which is inconsistent with the noise trader hypothesis.
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Furthermore, we find only weak evidence that return predictability is greater in stocks with more
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persistent order imbalances, which may be subject to more price pressure, casting further doubt
on the noise trader hypothesis.
Second, only market order imbalances correctly predict news about firm cash flows, as
measured by either the linguistic tone of DJ news stories or earnings surprises. These results hold
at daily, weekly, monthly, and yearly horizons. The findings are consistent with retail market
orders aggregating novel information about firms’ cash flows. Although the findings do not
preclude the possibility that some retail traders using limit orders have information about firms’
cash flows, there is no evidence that the aggregate of limit order traders acts on such information.
Third, limit order imbalances follow negative daily and intraday returns (i.e., they are
contrarian), but market order imbalances do not. Furthermore, return predictability from limit
orders is particularly strong in stocks that tend to experience large return reversals, where the
compensation for providing liquidity may be higher according to models such as Grossman and
Miller (1988). Return predictability from market orders is actually weaker in these stocks. These
facts are consistent with only limit orders responding to liquidity shocks.
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Even though our tests offer no direct evidence linking limit order imbalances to
information about firms’ cash flows, some limit order traders may be informed about future
demand for the stock. Indeed, we find that submitted limit orders have a positive end-of-day
price impact, which is a broad measure of informed trading used in the microstructure literature
(see, e.g., Kaniel and Liu (2006) for similar evidence on the price impact of limit orders). One
interpretation is that certain limit order traders recognize possibly long-lasting transitory price
and order flow shocks as they are corrected in the absence of innovations to cash flows.
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Collectively, these findings contribute to the ongoing debate on whether retail trading
conveys information about future stock prices. Our paper joins a budding literature including
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KST, KSLT, and Griffin, Shu, and Topaloglu (2010) to paint these traders in a positive light. On
the surface, this view is inconsistent with the large literature that has categorized retail investors
as unsophisticated, behaviorally biased, and otherwise uniformed. However, such conclusions
are largely drawn from retail investors’ poor portfolio performance after transaction costs, which
is severely harmed by offsetting buy and sell trades. Offsetting trades effectively incur the bid-
ask spread, even though they have little or no impact on stock prices—e.g., their price impacts
exactly offset in the Kyle (1985) model. Because our primary focus is the role of retail investors
in stock pricing, we focus on net retail buying imbalances and whether they predict future stock
returns. It is quite plausible that the subset of offsetting trades could fully explain the portfolio
underperformance of retail investors, despite our robust finding of positive return predictability
coming from net retail imbalances.
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If so, this would also reconcile our findings with studies that
report outperformance by other groups of investors, such as institutions.
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These points notwithstanding, we offer two additional channels for reconciling these
recent findings on retail investors with prior work. First, the trading skill of retail clientele may
vary across brokers; and prior findings could be based on particularly unskilled segments of the
retail trading population. Second, through learning or attrition, the aggregate skill of retail
traders may have changed over time. In Section 5, we provide evidence that both of these
channels are plausible explanations.
Our paper is also one of the first to show that retail traders’ choices of order type provide
insights into their underlying motives. We find that retail market orders convey fundamental
information and benefit as it is fully incorporated in prices, and retail limit orders primarily
benefit from the gradual reversal of price pressure. Both actions contribute to market efficiency
4