Local investors, price discovery, and market efficiency
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Sophie Shive
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Mendoza College of Business, University of Notre Dame, Notre Dame, IN 46556, United States
article info
Article history:
Received 16 November 2010
Received in revised form
10 May 2011
Accepted 14 July 2011
Available online 9 December 2011
Keywords:
Local bias
Market efficiency
Adverse selection
JEL classification:
G14
abstract
This study examines the effect of locally informed investors on market efficiency and
stock prices using large power outages, which are exogenous events that constrain
trading. Turnover in stocks headquartered in an outage area with 0.5% of U.S. electrical
customers drops by 3–7% on the first full day of the outage, and bid–ask spreads narrow
by 2.5%. Firm-specific price volatility is 2.3% lower on blackout dates. This effect is larger
for smaller, lesser-known stocks and in higher income areas. Consistent with a valuation
discount and higher expected returns for stocks with more informed traders, firms with
a one-standard-deviation higher local trading propensity have market-to-book values
that are 5% lower, Tobin’s Q that is 6% lower, annualized four-factor alphas that are 1.2%
higher, and average spreads that are 6.5% higher. Together, the evidence suggests that
informed investors contribute disproportionately to both liquidity and price discovery,
and that these contributions are reflected in valuations and expected returns.
& 2011 Elsevier B.V. All rights reserved.
1. Introduction
There is evidence that investors of all stripes prefer to
hold and trade local stocks.
1
It is plausible that some
investors have privileged information about firms that are
local to them, and studies of several classes of investors’
holdings show that they realize superior profits on local
stocks.
2
Little is known, however, about how local
investors’ firm-specific information advantage affects
market quality in these stocks, and whether this advan-
tage in turn affects prices and returns.
This study uses large local power outages to address
these questions. The first advantage of power outages is
that they are a sudden, unexpected, and significant trading
friction for local investors. For example, on Thursday,
December 15, 2005, beginning at 4:00 AM, an ice storm
caused power outages to 683,000 electrical customers in
parts of Piedmont North Carolina and South Carolina.
Schools were closed and power was not fully restored until
six days later.
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The blackout area was home to the head-
quarters of 57 firms with Compustat, Center for Research in
Security Prices (CRSP), and Trade and Quote (TAQ) data.
Although U.S. aggregate market trading volume was higher
on December 15 than the daily average of the previous
month, volume was lower for 41 out of the 57 firms
headquartered in the outage area. Closing spreads dropped
for 38 of the firms on the blackout date, and idiosyncratic
price volatility dropped for 37 of the firms.
Contents lists available at SciVerse ScienceDirect
journal homepage: www.elsevier.com/locate/jfec
Journal of Financial Economics
0304-405X/$ - see front matter & 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.jfineco.2011.12.003
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Thanks to Robert Battalio, Shane Corwin, Margaret Forster, Ken
French, Paul Gao, Alok Kumar, Tim Loughran, Rick Mendenhall, Chris
Parsons, Richard Rendleman, Mark Seasholes, Phil Stocken, Wei Wang,
Hayong Yun, and especially Paul Schultz, Bill Schwert (the Editor) and an
anonymous referee. Thanks to seminar participants at the University of
Notre Dame and Dartmouth, the 2010 First Inaugural University of
Miami Behavioral Finance Conference, the 2010 Financial of Research
Association meeting, and Queen’s University Second Annual Behavioral
Finance Conference. Hang Li provided excellent assistance with TAQ.
Errors are mine.
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Tel.: þ1 574 631 8301; fax: þ1 574 631 5544.
E-mail address: sshive1@nd.edu
URL: http://www.nd.edu/~sshive1/
1
Some examples are Huberman (2001), Ivkovic
´
and Weisbenner
(2005), Seasholes and Zhu (2010), Bodnaruk (2009), and Becker,
Cronqvist, and Fahlenbrach (2011).
2
Coval and Moskowitz (2001), Hau (2001), Malloy (2005), and Baik,
Kang, and Kim (2010), for example.
3
Sources: Energy Information Administration (EIA) and ‘‘Retailers
welcome big crowds,’’ Winston-Salem Journal, N.C., 17 December 2005.
Journal of Financial Economics 104 (2012) 145–161