Geographic Diffusion of Information and Stock Returns
Jawad M. Addoum*
Alok Kumar
Kelvin Law
University of Miami
University of Miami
Tilburg University
October 21, 2013
Abstract
This study shows that value-relevant information about publicly traded U.S. firms is geographically
distributed and the market is slow in aggregating this information. Specifically, we demonstrate that
the earnings and cash flows of firms can be predicted using the past performance of other firms in
economically relevant geographical regions. However, sell-side equity analysts and institutional
investors do not fully incorporate geographically dispersed information in their earnings forecasts
and trades, respectively. Consequently, firms exhibit stronger post-earnings-announcement drift and
stronger momentum in returns when geographic information is more dispersed and difficult to
aggregate. A Long-Short trading strategy that exploits the slow diffusion of geographic information
earns an annual, abnormal risk-adjusted return of about 9%.
Keywords: Geographical connections, earnings predictability, return predictability, information
diffusion, mispricing, sell-side analysts.
Please address all correspondence to Jawad M. Addoum, Department of Finance, School of Business Administration,
514 Jenkins Building, University of Miami, Coral Gables, FL 33124; Phone: 305-284-8286; Email:
jawad.addoum@miami.edu. Alok Kumar can be reached at 305-284-1882 or akumar@miami.edu. Kelvin Law can be
reached at (+31) 13-466-2219 or k.f.law@tilburguniversity.edu. We thank Major Coleman, George Korniotis and Chris
Malloy and seminar participants at University of Miami, Tilburg University, and Syracuse University for helpful
comments and valuable suggestions. We are responsible for all remaining errors and omissions.
*
Geographic Diffusion of Information and Stock Returns
Abstract
This study shows that value-relevant information about publicly traded U.S. firms is geographically
distributed and the market is slow in aggregating this information. Specifically, we demonstrate that
the earnings and cash flows of firms can be predicted using the past performance of other firms in
economically relevant geographical regions. However, sell-side equity analysts and institutional
investors do not fully incorporate geographically dispersed information in their earnings forecasts
and trades, respectively. Consequently, firms exhibit stronger post-earnings-announcement drift and
stronger momentum in returns when geographic information is more dispersed and difficult to
aggregate. A Long-Short trading strategy that exploits the slow diffusion of geographic information
earns an annual, abnormal risk-adjusted return of about 9%.
Keywords: Geographical connections, earnings predictability, return predictability, information
diffusion, mispricing, sell-side analysts.
1.
Introduction
A growing literature in finance demonstrates that value-relevant firm-specific information is
distributed geographically (García and Norli (2012), Giroud (2013), Bernile, Kumar, and Sulaeman
(2013)). In particular, this literature finds that the typical U.S. firm has geographical presence in six
U.S. states and its business activities often occur in locations away from its corporate headquarters.
Each of these economically-relevant non-headquarters locations may contain bits and pieces of
information about the firm that may not be easily accessible and may be difficult to aggregate. Even
firm insiders may have some difficulty accessing and aggregating this geographically dispersed
information. In a recent study, Giroud (2013) shows that firm managers are able to improve their
monitoring and information gathering activities when the plant locations become more accessible
following the introduction of airline routes.
If firm-specific value-relevant information is dispersed geographically, it is natural to ask
how quickly this information gets aggregated into stock prices. Given the prior evidence in the
literature, it is unlikely that market participants are able to aggregate the geographically dispersed
information immediately. The frictions generated by physical distance between corporate
headquarters and centers of economic activities relevant to the firm are likely to slow down the
information aggregation process. This delay in the information aggregation process could
subsequently generate predictable patterns in stock returns.
To better understand our core idea, consider the following example of Walmart. While
headquartered in Arkansas, Walmart has over 4,000 retail locations across the U.S. and it also has
158 distribution centers serving as distribution hubs for daily operations. Thus, Walmart’s day-to-day
economic activities and operations are often located in states other than its corporate headquarters.
Walmart’s customers, suppliers, research and development facilities, and other economic activities
1
are geographically dispersed. Since firms located in the same geographical regions are likely to
experience common local economic shocks, Walmart’s future performance would be correlated with
the performance of firms located near its corporate headquarters (HQ) and economically connected
(EC) states. If value-relevant information from non-HQ economically relevant geographical
locations is aggregated and incorporated into stock prices with delay, Walmart’s performance and
stock returns may be predictable.
In this paper, we generalize this idea and test the potential asset-pricing implications of slow
diffusion and delayed aggregation of geographically distributed firm-specific information. First, we
demonstrate that the geographical connections of firms contain information that can be used to
predict their future fundamentals. In particular, we find that the one-quarter-ahead earnings and cash
flows of a firm are predictable based on the performance of other firms located around corporate
headquarters and in regions that are economically relevant for the firm. To measure the economic
interest of a firm in a given U.S. state, we follow García and Norli (2012) and conduct textual
analysis of firms’ annual 10-K filings. Specifically, we capture the relative importance of a state to a
firm by measuring the citation share of each U.S. state, which is defined as the number of times a U.S.
state is mentioned in the 10-K filings divided by the total number of times all U.S. states are
mentioned in those filings. A state with firm-level citation share of one (zero) indicates that the firm
has all (none) of its economic activities located in that state.
Using these firm-state-year citation share estimates of economically connected states, we
measure the geographical dispersion in a firm’s economic interests and estimate a series of FamaMacBeth (1973) type predictive regressions. Specifically, we construct EC Earnings (EC Cash Flows)
measures, defined as the citation-share weighted average earnings (cash flows) of firms located in
economically relevant states, excluding the headquarters state. We also measure the earnings and
cash flows of other firms located around firm headquarters.
2
Consistent with our conjecture, we find that the performance of other firms located in both
HQ and EC states contain value-relevant information for the future earnings and cash flow of a
given firm, even after we account for the lagged performance and fundamentals. Further, the
evidence of predictability using information in EC states is stronger than predictive power of HQbased measures. We also examine the predictive power of geographical information over longer
horizons, and find that our geography-based measures have predictive ability for up to eight quarters.
Our evidence of cash flow predictability is incremental over the predictive ability of firmspecific lagged earnings measures (e.g., Fama and French (2000), Vuolteenaho (2002)). We also
demonstrate that our results are not driven by state-level business cycles or firm characteristics, as all
regressions include controls for state-level economic variables and firm fundamentals.
In economic terms, a one standard deviation increase in the earnings (cash flows) of other
firms in the HQ state in the current quarter predicts a 0.075% (0.197%) increase in the firm’s
earnings (cash flows) in the next quarter.1 Relative to the average quarterly earnings (cash flows) of
0.9% (1.7%), these magnitudes are economically meaningful. Further, a one standard deviation
increase in the earnings (cash flow) of firms headquartered in EC states in the current quarter
predicts a 0.156% (0.468%) increase in their earnings (cash flows) next quarter. Again, compared to
the average quarterly earnings (cash flows), these magnitudes are economically meaningful and
stronger than the effects of HQ-based measures.
In the next set of tests, we examine how quickly geographically-dispersed information is
aggregated by market participants and subsequently reflected in stock prices. We first examine
whether sell-side analysts help incorporate firms’ geographically dispersed information through their
earnings forecasts. We find that equity analysts do not fully incorporate firms’ geographically
The economic magnitude is calculated as the coefficient estimate multiplied by its standard deviation: 0.050 × 0.015 =
0.075% or 0.048 × 0.041 = 0.197%. 1
3
dispersed earnings-relevant information into their forecasts. Instead of using dispersed geographical
information, they form their forecasts primarily based on lagged firm earnings. As a result, their
earnings forecasts do not fully account for the earnings information of other firms headquartered in
economically connected states. Specifically, the lagged EC-based earnings measure significantly
predicts analysts’ future forecast errors. A one standard deviation increase in lagged EC-Earnings
corresponds to a 5.251% increase in analysts’ consensus forecast error in the next quarter. Further,
we find that the explanatory power of EC-based earnings is higher than the lagged earnings or HQbased earnings measures.
Since equity analysts are an important part of the price formation process and geographically
dispersed information is not fully incorporated into their earnings forecasts, it is likely that
geographic information diffuses slowly and generates predictable patterns in stock returns. We
examine the effects of slower diffusion of information in two economic settings: post-earnings
announcement drift (PEAD) and momentum in stock returns. Our choice is motivated by the
evidence in previous studies, which suggest that slow diffusion of information may be a key
mechanism that generates predictable patterns in stock returns in these two settings.
We find that both post-earning-announcement drift and momentum returns are more
pronounced among firms that have more geographically dispersed information that is difficult to
aggregate. In both instances, we find that the slow diffusion of geographically dispersed information
generates predictable patterns in stock returns. In economic terms, our estimates indicate that firms
with above-median geographic dispersion have monthly momentum returns that are about 0.35%
higher (t-statistic = 2.78) than firms with below-median dispersion. Similarly, we find that a one
standard deviation increase in geographic dispersion is associated with 0.20 to 0.35% (t-statistics are
between 2.37 to 2.72) higher post-earnings-announcement returns. This evidence is consistent with
4
our broad conjecture and shows that market participants do not fully incorporate geographically
dispersed information.
To quantify the economic magnitudes of these predictable return patterns, we construct outof-sample geography-based trading strategies. We use look-ahead bias-free forecasts of firm-level
earnings-per-share (EPS) and sort stocks based on their Expected Earnings Surprise, which is defined
as the forecasted EPS minus the analyst consensus. A Long-Short trading strategy that takes a Long
(Short) position in firms with high (low) expected earnings surprise generates a monthly alpha of 75
basis points or an annual premium of about 9%. The performance of our trading strategy is robust
during the sample period, and cannot be explained by standard asset pricing factors such as the
market, size, value, momentum, short- and long-term reversal, and liquidity factors.
We also analyze the sensitivity of the trading performance estimates when there is a delay
between portfolio formation date and the start of the portfolio performance measurement period.
We find that the performance of our trading strategy deteriorates as the gap between the portfolio
formation date and the start of the portfolio performance measurement period increases. Consistent
with the gradual information diffusion model of Hong, Lim, and Stein (2000), we find that it takes
approximately three months for geographically dispersed information to be eventually incorporated
into stock prices.
Collectively, these results make several contributions to a growing literature in finance that
exploits the geographical links among publicly-traded firms in the U.S. (García and Norli (2012),
Giroud (2013), Bernile, Kumar, and Sulaeman (2013)). Our first main contribution is to show that
earnings of geographically related firms have the ability to predict the future performance of a given
firm. To our knowledge, the evidence of earnings and cash flow predictability using geographically
dispersed information is new to this literature.
5
Second, we present novel evidence of predictable patterns in stock returns. Specifically,
stock market participants incorporate geographically dispersed information about firms with a delay
and this slow information diffusion generates predictable patterns in stock returns. Even relatively
more sophisticated market participants such as equity analysts and institutional investors do not
successfully incorporate firms’ geographical information in their forecasts and trades, respectively.
As a result, earnings surprises of firms are predictable. In addition, firms exhibit stronger postearnings-announcement drift and stronger momentum in returns when geographic information is
more dispersed and more difficult to aggregate.
Our study also relates to prior studies, which demonstrate that local investors and equity
analysts exhibit local bias and may possess an informational advantage.2 Our findings suggest that a
significant part of local information still remains unexploited. Last, our paper is related to recent
studies that examine economic links among firms that emerge through customer-supplier networks
(e.g., Cohen and Frazzini (2008), Menzly and Ozbas (2010)). Our evidence suggests that common
exposures to geographical regions generate additional economic links among firms.
The rest of the paper is organized as follows. Section 2 provides a summary of various data
sources used in the empirical analysis. Section 3 presents evidence of earnings and cash flow
predictability using geographic connections. Sections 4 and 5 examine whether market participants
are able to quickly aggregate this geographically dispersed information into stock prices. We
conclude in Section 6 with a brief discussion.
2.
Data and Methods
In this section, we provide details about our data sources and present summary statistics for
the main variables used in the empirical analysis.
For example, see Coval and Moskowitz (1999, 2001), Huberman (2001), Hau (2001), Grinblatt and Keloharju (2001),
Malloy (2005), Ivković, Sialm, and Weisbenner (2008), Teo (2009), van Nieuwerburgh and Veldkamp (2009).
2
6
2.1
Measures of Economic Connections
We identify the headquarters of all U.S. publicly listed firms by retrieving their annual Form
10-K filings from Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system. Our sample
period is from 1994 to 2012.
To comply with listing disclosure requirements, all U.S.-based firms are required to file Form
10-K with the U.S. Securities and Exchange Commission (SEC) annually. Each 10-K has a
prescribed reporting structure and disclosure items, containing a comprehensive overview of a firm’s
business operations, summary of financial conditions, and audited financial statements. Apart from
containing the operational details relating to a firm’s business activities (e.g., organizational structure,
executive compensation, industry competition, auditor’s reports, and regulatory issues etc.), it also
lists the detailed information regarding physical locations of a firm’s assets (e.g., factories,
warehouses, distribution centers, and sales offices, etc.), segment reporting, and management
discussions and analysis of financial condition or results of operations.3
We capture the economic exposure of a firm to each U.S. state by conducting a textual
analysis of its annual 10-K filings (García and Norli (2012), Bernile, Kumar, and Sulaeman (2013)).
For each firm-year, we parse the 10-K filings and count the number of times references are made to
each of the 50 U.S. states and Washington D.C. Specifically, we focus on the occurrence of
geographic references in the following four specific sections: (a) “Item 1: Business”, (b) “Item 2:
Properties”, (c) “Item 6: Consolidated Financial Data”, and (d) “Item 7: Management’s Discussion
and Analysis”. As these four sections summarizes the locality of a firm’s main business operations –
including a firm’s plant and equipment, major physical assets, store locations, office locations, and
3
See http://www.sec.gov/answers/form10k.htm for additional details about the information reported in Form 10-K.
7
acquisition activities – the resulting citation count broadly captures the economic ties between a firm
and its geographical distribution of economic interests.
While our textual analysis based measure of economic connections has measurement noise,
it has several advantages. First, instead of relying on disclosure in firm’s filings (e.g., Exhibit 21 or
geographical segment reporting), our method greatly expands to a broader sample. Moreover, as
some of the voluntary disclosures may be subject to management’s discretion or interpretation (e.g.,
use of geographical regions in segment reporting, defining materiality in Exhibit 21), content analysis
is less prone to this potential measurement bias. Specifically, firm managers have a great discretion in
deciding a reportable geographical segment (under accounting standards SFAS 131 or IFRS 8), they
have incentive to pool geographical segments to avoid disclosing commercially valuable information
to competitors that is unavailable elsewhere (Harris (1998)).
After obtaining the total count of each state’s mentions in 10-K filings, we compute a citation
share (CS) for each state in a firm’s 10-K filing, defined as the number of times a U.S. state is cited
divided by the total number of citations of all U.S. states in a firm’s 10-K filing in year t. The
maximum (minimum) of citation share is zero (one), where a higher (lower) citation share implies
that a given firm’s economic activities are more (less) concentrated in a given state.4
2.2
Other Datasets
We use the Central Index Key (CIK) to merge firms’ 10-K filings data with firm
fundamentals from Compustat. CIK is assigned by the SEC to uniquely identify registered firms for
meeting disclosure requirements. After merging, we retrieve firms’ headquarter location from the
CRSP-Compustat Merged (CCM) file. The fundamentals of firms with at least $10 million of average
total assets, two years of data, and $1 closing price are obtained from Compustat. All firm-year
4
See Bernile, Kumar, and Sulaeman (2013) for additional details about the citation share measure. 8
observations with SIC codes in the ranges of 4900-4949 (utility firms) and 6000-6999 (financial
institutions) are excluded. Corresponding price and return data for trading strategies and factor
model tests are then obtained from the Center for Research on Security Prices (CRSP). For analyst
forecasts, we obtain split-adjusted earnings per share (EPS) forecasts from Thomson-Reuters’ IBES
unadjusted detail file. To identity analysts’ geographical location, we use the names and brokerage
house information in the Broker Translation File to hand-match records in Nelsons’ Directory of
Investment Research (Malloy (2005)).
Monthly data on the risk-free rate, market excess return (MKT), size factor (SMB), value
factor (HML), and momentum factor (UMD), short-term reversal (STR), and long-term reversal
(LTR) factors are obtained from Ken French’s website. The quarterly measures of the economic
activity indices of U.S. states are obtained following the Korniotis and Kumar (2013) method.
3.
Earnings and Cash Flow Predictability
We begin our empirical analysis by estimating a series of predictive regressions. Specifically,
we investigate whether the earnings of a firm can be predicted using the earnings information of
other firms headquartered in the same state where the firm is headquartered or has an economic
presence. Our conjecture is that the earnings information of other firms located in the same
geographical area would contain information about a firm’s future earnings. Thus, if a firm’s
operations are geographically dispersed, then the lagged performance of firms with economic
presence in those economically connected states may contain valuable information about the future
performance of the firm.
We test this conjecture using data on both earnings and cash flows.
Specifically, we
investigate whether the earnings and cash flows of other firms headquartered in economically
connected (EC) states have any incremental power to predict a given firm’s future earnings and cash
9
flows. We also compare the predictive power of the EC based measures and earnings measures
obtained using firms around corporate headquarters.
3.1
Predictability Using Information Around Corporate Headquarters
Our first test examines whether a firm’s earnings and cash flows can be predicted by the
financial performance of other firms headquartered in the same state. Specifically, we estimate the
following predictive Fama-MacBeth (1973) style regression:
,
,
,
,
,
(1)
At the end of each quarter, we regress firm j’s Earnings in quarter t+1 on an intercept, lagged Earnings
in quarter t, lagged HQ Earnings in quarter t, and a vector of control variables (X), which include
firm-specific characteristics and measures of the HQ state’s economic environment in quarter t.
Earnings is defined as operating income after depreciation divided by average total assets (Richardson,
Sloan, Soliman, and Tuna (2005)). The lagged Earnings is included in the specification as prior studies
show that firm’s earnings is persistent (Fama and French (2000), Dichev and Tang (2009), Frankel
and Litov (2009)). HQ Earnings is the value-weighted Earnings of firms headquartered in the same
state. β2 and β3 jointly measure the one-quarter-ahead predictability of firm earnings, where the main
coefficient of interest is β2.
Our main conjecture is that HQ Earnings in quarter t would contain incremental explanatory
power beyond the predictive ability of firm-specific lagged Earnings in quarter t. A positive
coefficient estimate would indicate that the earnings information of other firms headquartered in the
same state can be used to predict a given firm’s earnings in quarter t+1. As the error terms are likely
to be auto-correlated in the Fama-MacBeth (1973) regressions, all standard errors are adjusted using
10
the Newey-West (1987) method with 4-year lag (i.e., 16 quarters). To minimize the noise in our
measures, we require at least two other firms when we compute the HQ-based earnings measures.
X is a vector of control variables, which includes the following firm attributes: 1) Firm Size, 2)
Leverage, 3) Loss, 4) Market-to-Book, 5) Dividend Yield, 6) No-Dividend Indicator, 7) Dividend-Price, and 8)
Economic Activities Index. Firm Size is the natural logarithm of total assets. Leverage is the sum of shortterm and long-term debts, divided by total assets. Loss (No-Dividend Indicator) is a dummy that takes a
value of one when operating income (dividend) is negative (zero), and zero otherwise. Market-to-Book
is the sum of market equity, short-term debt, and long-term debt, divided by total assets. Dividend
Yield is the dividends divided by shareholders’ equity. Together, these control variables should
account for differences in firm size, growth opportunities, operations, and profitability, which could
affect firm earnings. Additional details about all variables are available in Appendix A.
Beyond these firm attributes, we include two variables that capture the differences in statelevel business cycles across headquarter states. The first control variable is the Dividend-Price, defined
as the value-weighted average of the log of one plus the dividend-price (D/P) ratio of firms
headquartered in the same state. Dividend D is the sum of the past four quarterly dividends, whereas
P is the end-of-month stock price. The monthly stock prices are obtained from CRSP, and the
quarterly stock-level dividends are obtained from Compustat.
We also include the Economic Activities Index in the specification, which is a summary index of
state-level macroeconomic conditions in a firm’s headquarters state. We consider three state-level
economic indicators that are likely to capture business cycle variation at the local state-level: 1)
income growth, 2) unemployment rate, and 3) housing collateral ratio. All these state-level
macroeconomic data are available at the quarterly frequency. Following Korniotis and Kumar (2013),
the Economic Activities Index is defined as the sum of standardized values of state-level income growth
11
and state-level housing collateral ratio, minus the standardized value of the relative state-level
unemployment ratio, divided by three.
In this definition, the state-level income growth is the log difference between state income in
a given quarter and state income in the same quarter in the previous year. This measure is motivated
by prior studies that interpret this measure as a proxy for the return to human capital (e.g.,
Jagannathan and Wang (1996), Campbell (1996)). A high-level of income growth reflects positive
macroeconomic conditions in a state.
State-level unemployment rate is the ratio of the current unemployment rate to the moving
average of the unemployment rates from the previous 16 quarters. It is a recession indicator for the
state economy. The moving average serves as a proxy for the projected or expected level of
unemployment and a positive (negative) deviation from this projected or expected level signals a
good (bad) signal for the local economy. The unemployment rates are obtained from the Bureau of
Labor Statistics (BLS).
State-level housing collateral ratio is the log ratio of state-level housing equity to state labor
income following Lustig and van Nieuwerburgh (2005, 2009). It is computed using the Lustig and
van Nieuwerburgh (2005) method, where the data are obtained from Stijn van Nieuwerburgh’s
website. This measure captures the tightness of borrowing constraints and the degree of risk sharing
at the local state level. A high housing collateral ratio predicts high consumption growth at the statelevel, as the consumption of individuals is better insulated against adverse labor income shocks.
For our second set of baseline tests, we use a similar specification as equation (1) to examine
the predictability of one-quarter-ahead cash flows. Specifically, we replace the dependent variable
Earnings with Cash Flow, and estimate the following specification:
,
,
12
,
,
,
(2)
In this equation, Cash Flow is the cash flows from operating activities divided by average total
assets. Similar to the Earnings specification, the main coefficient of interest is β2. A positive
coefficient would indicate that the cash flows of firms situated in the same state predicts a given
firm’s future cash flows.
Our choice of firm fundamentals—Earnings and Cash Flow—are motivated by prior studies
that widely study these metrics as proxies of financial performance of firms (e.g., Sloan (1996), Fama
and French (2000)). The primary difference between these two measures is that the former captures
the accrual component of a firm’s earnings, which typically includes future or deferred cash flow,
depreciation, and allowances. In contrast, the latter measures the actual cash flow component of
earnings, and depends on the actual timing of the earnings realization.
We present the summary statistics of all variables in Panel A of Table I. The mean Earnings
(Cash Flow) is 0.009 (0.017), which indicates that the average firm earnings (cash flows) is 0.9% (1.7%)
of total assets each quarter. This finding is consistent with prior studies that find that cash flows
from operations are on average higher than earnings (e.g., Dichev and Tang (2009)). We also find
that firm-specific earnings (Earnings) are more volatile than aggregate state-level earnings (HQ
Earnings), as the standard deviation of Earnings is about 3.5 times the standard deviation of HQ
Earnings. The same pattern is observed with the Cash Flow measure, as the aggregate state-level cash
flow (HQ Cash Flow) is less volatile than firm-specific cash flows (Cash Flow).
Panel B of Table I reports the correlations among these measures. We find that the
aggregate state-level earnings/cash flows (i.e., HQ Earnings/HQ Cash Flow) is strongly and positively
correlated with firms’ earnings/cash flows (i.e., Earnings/Cash Flow) at the 1% level.
Table II reports the estimates from the earnings and cash flow regression specifications.
Specifically, Columns 1 and 4 report the univariate regression results, Columns 2 and 5 report the
results regressing firm’s Earnings/Cash Flow on lagged Earnings/Cash Flow and lagged HQ
13
Earnings/Cash Flow, while Column 3 and 6 reports the results with the full specification that includes
all control variables. First, consistent with prior evidence, we find that firms’ earnings and cash flows
are persistent, as the estimated coefficients on lagged firm-specific Earnings and Cash Flow are
positive and statistically significant. The average R2 is 0.680, confirming the findings in prior
literature that the firm-specific earnings process is fairly persistent. The economic magnitudes of
lagged Earnings and Cash Flow are also similar to the findings in prior studies.6
Second, we find that controlling for firms’ lagged performance, the earnings and cash information of
other firms headquartered in the same state predicts the firm’s future financial performance.
Specifically, the estimated coefficients on HQ Earnings and HQ Cash Flow are positive and statistically
significant, ranging from 0.048 (t-statistic = 2.87) to 0.124 (t-statistic = 3.58). Examining the
economic magnitudes of these estimates, we find that a one standard deviation change in HQ
Earnings (HQ Cash Flow) leads to a 0.075% (0.197%) change in Earnings (Cash Flow) in quarter t+1.
Relative to average quarterly earnings (cash flow) of 0.9% (1.7%), these magnitudes are economically
meaningful.
Overall, the estimates from earnings and cash flow regression specifications indicate that the
information contained in the financial performance of other firms headquartered in the same state
predicts a given firm’s earnings and cash flows.
3.2
Predictability using Information From Economically Relevant Regions
Next, we examine whether the fundamental information in a firm’s EC states has
incremental ability to predict its future performance, incremental over the ability of firm
fundamentals of other firms located in the same state. To test this conjecture, we re-estimate our
6
For instance, see Richard, Sloan, Soliman, and Tuna (2005), Dichev and Tang (2009), and Frankel and Litov (2009).
14
baseline regressions with three additional independent variables using the following regression
specification:
,
,
,
,
,
,
(3)
Similar to HQ Earnings, EC Earnings is the citation-share weighted Earnings of firms located in
economically connected states, excluding the firms in the HQ state. As before, all EC-based
measures require a minimum of two firms.
The following example illustrates the intuition behind our identification strategy: consider a
technology firm that is headquartered in Texas but has 2/3 of its operations in California and 1/3 of
its operations in Florida. EC Earnings is the sum of a) 2/3 of the average earnings of firms that are
headquartered in California or have economic presence in that state, and b) 1/3 of the average
earnings of all firms with corporate headquarters or economic presence in Florida. If these
geographical locations contain value-relevant information about the technology firm, our measure
should predict the firm’s future performance, as captured by the coefficient of interest β3. A positive
coefficient would indicate that the earnings information of firms located in a firm’s economically
connected states is able to predict the firm’s earnings in the next quarter.
Beyond the standard firm-level control variables, two additional control variables—EC
Dividend-Price and EC Economic Activities Index—are included to capture the macroeconomic
conditions in EC states. These control variables are important, as EC Earnings could potentially
capture the overall economic environment of firms’ economically-relevant states. Thus, including
these additional variables helps mitigate this concern. Specifically, EC Dividend-Price is constructed
following HQ Dividend-Price, defined as the citation-share weighted dividend-price index (DividendPrice) of all firms located in economically connected states, excluding the HQ state. Similarly, EC
Economic Activities Index is the citation-share weighted Economic Activities Index of all firms in
15
economically connected states, with HQ state excluded. We estimate the following predictive cash
flow regression specification:
,
,
,
,
,
,
(4)
Here, EC Cash Flow is the citation-share weighted Cash Flow of firms located in economically
connected states, excluding the HQ state. A positive coefficient of β3 would indicate that the cash
flow information of firms in a firm’s economically connected states can predict its future cash flows.
The summary statistics in Panel A of Table I show that EC Earnings/EC Cash Flow exhibit
high and significant positive correlations with firm-specific Earnings/Cash Flow. EC-measures also
have the lowest volatilities, as on average they have less than a quarter of the standard deviation of
Earnings/Cash Flow. This evidence is not surprising. Since firms typically have economic presence in
multiple states, a diversified geographical presence lowers the volatility of these measures.
The estimation results for EC based earnings and cash flow regressions are reported in Table
III. Similar to the previous results, Columns 1 and 4 report the results regressing firm’s
Earnings/Cash Flow on 1) lagged Earnings/Cash Flow, 2) lagged HQ Earnings/Cash Flow, lagged EC
Earnings/Cash Flow, and control variables, whereas Columns 3 and 6 report the results for the full
specification that includes all control variables.
We find that EC Earnings contain information about the future earnings of a firm. This
effect is incremental over the ability of HQ Earnings to predict a firm’s future earnings. Specifically,
the estimated coefficients on EC Earnings are all positive and statistically significant, ranging from
0.275 (t-statistic = 12.95) to 0.441 (t-statistic = 24.13). In economic terms, a one standard deviation
increase in EC Earnings corresponds to a 0.156% increase in firms’ Earnings in the next quarter,
which is economically meaningful. This economic magnitude is greater than the predictive power of
HQ Earnings in economic terms. In this expanded regression specification, we also find that the
16
estimated coefficients on HQ Earnings remain positive and statistically significant across all
specifications. However, when EC Earnings is included in the specification, the magnitudes of HQ
Earnings weaken in comparison to the corresponding estimates reported in Table II, and ranges from
0.039 (t-statistic = 6.42) to 0.080 (t-statistic = 6.50).
A similar pattern is present in the Cash Flow regression results. We find that EC Cash Flow
exhibits a strong ability to predict firms’ cash flows in the next quarter. Specifically, the estimated
coefficients on EC Cash Flow are positive and statistically significant across Columns 4-6, ranging
from 0.152 (t-statistic = 2.35) to 0.382 (t-statistic = 4.47). Similar to our earlier evidence, EC Cash
Flow has stronger predictive ability than HQ Cash Flow, where a one standard deviation increase in
EC Cash Flow predicts a 0.468% increase in firms’ Cash Flow in the next quarter.
Since all regressions specifications include the EC Economic Activities Index, these results are
unlikely to reflect the effects of the overall macroeconomic environment in economically-relevant
states. Moreover, since all EC measures exclude firms’ HQ states, our estimates only reflect the
average performance of firms in economically-relevant states. Collectively, these predictive
regression results show that the fundamental information of firms located in economically
connected states contain valuable incremental information about the future performance of firms.
3.3
Longer-Horizon Earnings and Cash Flow Predictability
In this section, we examine the predictive power of EC based measures over longer horizons.
The results from longer-horizon predictability are reported in Table IV. Specifically, Columns 1, 2, 3,
and 4 present the estimates using 2-, 3-, 4-, and 8-quarter-ahead earnings measures. All estimated
coefficients on EC Earnings remain positive and statistically significant across all specifications,
ranging from 0.294 (t-statistic = 5.49) to 0.391 (t-statistic = 14.80). Based on the estimated
coefficients on EC Earnings, the predictability gradually weakens as the prediction horizon increases
17
from t+1 (one quarter ahead) to t+8 (two years ahead). Specifications focusing on firms’ cash flows
from operations exhibit a similar trend, with all coefficients on EC Cash Flow remaining positive and
statistically significant. Moreover, the average R2s for Earnings (Cash Flow) gradually decrease from
0.593 in t+2 to 0.469 in t+8 (0.397 in t+2 to 0.355 in t+8).
Overall, the evidence from long-horizon predictability regressions shows that our
geography-based earnings and cash flow measures have predictive power even over longer horizons.
3.4
Earnings Predictability: Robustness Tests
In the last set of earnings predictability tests, we examine whether the baseline results are
robust to an alternative definition of economically connected states. In the first test, instead of using
all cited states, we alternatively compute our earnings and cash flow measures only using the top
three states with the highest citation-share (and HQ state excluded). We re-estimate our baseline
regression specifications using this alternative definition to examine the robustness of our earlier
results using citation shares unconditionally.
The estimation results are summarized in Table V. First, the EC information continues to
exhibit predictive power even under this alternative definition. Specifically, the coefficients on EC
Earnings remain positive and statistically significant, ranging from 0.209 (Column 3) to 0.320
(Column 1), and statistically significant at the 1% level. The same pattern is observed in the Cash
Flow regression estimates. The estimated coefficients on EC Cash Flow are positive and statistically
significant at the 1% level, ranging from 0.140 (in Column 6) to 0.296 (in Column 4).
Further, we find that EC information continues to exhibit stronger predictive power relative
to that of HQ information across all specifications. For instance, given that the mean and standard
deviation of EC Earnings (HQ Earnings) is 0.010 and 0.005 (0.003 and 0.015), respectively, a one
18
standard deviation change in EC Earnings (HQ Earnings) leads to 0.120% (0.062%) increase in firms’
Earnings in the next quarter, which is economically significant.
In the next test, we examine whether our results are sensitive to firms incorporated in
Delaware. A potential concern with our baseline results may be that our results are simply driven by
firms incorporated in Delaware. A significant portion of firms are incorporated in Delaware and
firms may cite Delaware often in their 10-K reports even if they do not have a meaningful economic
presence in the state. To address this potential concern, we exclude all sample firms that are
incorporated in Delaware and re-estimate our baseline regressions as in Table III.
Although, the total number of observations drops significantly, our main results remain very
similar. Specifically, the coefficient estimates of EC Earnings range from 0.233 (t-statistic = 5.60) to
0.281 (t-statistic = 6.23), whereas the estimated coefficient estimates of EC Cash Flow range from
0.221 (t-statistic = 3.96) to 0.285 (t-statistic = 7.78). This evidence indicates that our baseline results
reported in Table III are unlikely to be driven by Delaware firms.
For additional robustness, we also examine whether our results are similar when we use data
with annual frequency. Although the number of cross-sections drops substantially from 64 quarters
to 16 years, the strong predictive power of EC Earnings and EC Cash Flow remains intact. This
evidence suggests that our earnings and cash flow predictability regressions are robust.
4.
Aggregation of Geographically Dispersed Information
Our results so far show that information about earnings and cash flows of firms are
geographically dispersed. Specifically, the aggregated fundamental information of firms located in
economically relevant states of a firm predicts its future firm-specific earnings and cash flows. A
logical question is how this geographically-dispersed information is aggregated by market
participants and subsequently reflected in stock prices.
19
There is mounting evidence that financial intermediaries such as equity analysts play an
important role in processing and disseminating firm-specific information. In this section, we
examine whether those financial intermediaries speed up the information aggregation process
through timely updates of their earnings forecasts. We also investigate whether institutional
investors around HQ and EC locations speed up the information aggregation process. This analysis
is motivated by the prior evidence in the literature, which demonstrates that local investors at both
HQ and EC locations have a local informational advantage. In the last part of the section, we focus
on the asset pricing implications of potential inefficiencies in the information aggregation process.
4.1
Geographic Dispersion of Information and Analyst Behavior
In this section, we examine whether equity analysts incorporate geographically dispersed
information about a firm in their earnings forecasts. Before presenting the main results, we reestimate our baseline earnings predictability regressions using the subsample of firms that have
analyst coverage. This test is motivated by prior studies, which demonstrate that stocks without
analyst coverage are small stocks with relatively low liquidity (Hong, Lim, and Stein (2000)). We
want to ensure that our evidence of earnings predictability is not restricted to firms with no analyst
coverage.
The estimates in Column 1 of Table VI show that the predictability patterns are strong even
among stocks with analyst coverage. Specifically, the estimated coefficient on EC Earnings is 0.304 (tstatistic = 9.36), which is larger than the estimate of 0.275 (t-statistic = 12.95) reported previously in
Column 3 of Table III where we consider the full sample of firms. Since prior evidence shows that
analyst coverage is skewed toward stocks with large market capitalization (Bhushan (1989), Hong,
Lim, and Stein (2000)), this evidence suggests that our predictability results are not concentrated
among small stocks with low market capitalization or poor liquidity.
20
Next, we examine whether equity analysts incorporate firms’ geographical information into
their forecasts. Given the prior evidence on the information aggregation role of equity analysts, we
expect analysts to incorporate geographically dispersed earnings information into their forecasts. We
test this conjecture using two additional dependent variables: Analyst Consensus Forecast and Forecast
Error. Analyst Mean (Median) Consensus Forecast is the mean (median) consensus based on all
outstanding forecasts issued within the 90 days period prior to the earnings announcement. If
analysts issue multiple forecasts, only their last forecasts are used to ensure that analysts with
frequent forecasts are not over-weighted when computing the consensus forecast. Forecast Error is
defined as the absolute value of the actual earnings minus analyst consensus scaled by lagged stock
price.
We also include Number of Analysts as an additional control variable, which is defined as the
natural logarithm of the number of analysts following a stock in a given quarter. This additional
control variable is included in the specifications because prior studies show that the number of
analysts is strongly correlated with a firm’s information environment (e.g., Hong, Lim, and Stein
(2000)).
To reduce the noise generated by extremely small denominators when computing these two
measures, we follow Livnat and Mendenhall (2006) and include only firms with at least $1 per share
at the end of each quarter, non-missing market (book) value of equity, and at least $5 million market
capitalization at the end of the prior quarter. These filters screen out stocks with low liquidity or low
market capitalization from the sample.
In our tests, we use the consensus forecast and forecast error measures to examine whether
analysts use publicly available information from firms’ economically relevant locations. If analysts
incorporate firms’ geographically dispersed information into their forecasts, this geographic
information would predict the levels of their consensus forecasts but not the errors in their forecasts.
21
In particular, if equity analysts are more effective in incorporating firms’ fundamentals from
economically connected states, their consensus forecasts should be more informative. As their
aggregated forecasts deviate less from the actual earnings, the forecast error would be lower. In
contrast, if analysts do not fully adjust their earnings forecasts to reflect firms’ economic activities in
economically relevant states, their consensus should not be related to the earnings information
aggregated from firms’ economically relevant states, while consensus-based forecast error would be
positively related to the earnings information in those economically relevant states.
The results from Analyst Consensus Forecast regressions are reported in Columns 2 and 3 of
Table VI. We find that analysts do not fully incorporate firms’ geographically dispersed information
into their forecasts, as the estimated coefficients on EC Earnings are marginal in statistical
significance (estimate = 0.038; t-statistic = 1.97). This result is unlikely to be driven by extreme
values, as the results using mean and median consensus forecasts are nearly identical. However,
analysts form their forecasts using firms’ historical earnings, as the estimated coefficients on Earnings
are significantly positive. This evidence is consistent with findings from prior research (e.g., Fama
and French (2000)). Those coefficient estimates have the highest statistical significance across all
independent variables, followed by the estimated coefficients on HQ Earnings, which are also
significantly positive (estimate = 0.015; t-statistic = 6.21).
These results suggest that, when forming their earnings forecasts, analysts incorporate more
information from a given firm’s historical earnings and the average performance of firms located in
that firm’s headquarters state. While this evidence confirms prior findings that analysts use past
earnings information when they form their earnings forecasts (e.g., Mendenhall (1991), Abarbanell
and Bernard (1992)), it also indicates that analysts have difficulty aggregating value-relevant
geographically dispersed information.
22
The results from Forecast Error regressions portray a similar picture and further illustrate that
analysts do not fully incorporate geographically distributed information into their earnings forecasts.
Specifically, the estimated coefficients on EC Earnings are positive and statistically significant
(estimates vary between 0.156 and 0.157; t-statistics vary between 3.50 and 3.56). Comparing the
magnitudes of the coefficient estimates, we find that estimates of EC Earnings are larger than those
for HQ Earnings and Earnings. A one standard deviation change in EC Earnings leads to a 5.251%
change in Forecast Error (vs. 3.276% for HQ Earnings and 0.977% for Earnings). Moreover, the
estimated coefficients on lagged Earnings are statistically insignificant, which provides further
evidence suggesting that analysts’ forecasts tend to fixate on firms’ lagged fundamentals.
In additional tests, we also examine whether local analysts who are located close to corporate
headquarters or economically relevant states are more likely to incorporate information about local
firms more efficiently. The evidence reported in Appendix B shows that these local analysts are
unable to incorporate geographically dispersed information either. We find this evidence both before
and after the implementation of Regulation Fair Disclosure (Reg FD).
Overall, these regression estimates show that, while value-relevant information about a firm
is geographically dispersed, equity analysts do not incorporate this information effectively into their
earnings forecasts. They are unable to facilitate the aggregation of this dispersed information.
4.2
Geographic Dispersion of Information and Market Reaction to Earnings
If equity analysts are unable to effectively account for geographical information in their
earnings forecasts, a natural question is how this information is incorporated into asset prices. While
earnings surprises are usually incorporated into stock prices quickly, an extensive literature
documents and analyzes the drift in prices that occurs in the weeks and months following earnings
announcements (Mendenhall (1991), Abarbanell and Bernard (1992)). If market participants have
23
limited information processing capacities (Merton (1987), Hong and Stein (1999), Hirshleifer and
Teoh (2003)), the earnings drift may be stronger for geographically dispersed firms as the
geographical information contained in earnings surprises would be more difficult to process.
We test this conjecture by constructing a new measure of the difficulty in processing
geographically dispersed information: the dispersion of EC citation shares (EC Dispersion). This
measure is defined as the negative value of the Herfindahl index of a firm’s EC citation shares, and
we denote it as EC Dispersion. We examine the effect of EC Dispersion on post-earningsannouncement returns by estimating Fama-MacBeth (1973) type regressions of cumulative abnormal
returns (CARs) on EC Dispersion, controlling for the signed and scaled Earnings Surprise, defined as
actual earnings minus analyst consensus scaled by lagged stock price.
We also include High Dispersion and High Surprise interaction term, where High Dispersion is an
indicator variable that is set to one if a firm has higher than median EC Dispersion in a given quarter,
and 0 otherwise. High Surprise is a signed indicator variable that is set to 1 (-1) if Earnings Surprise is in
the top (bottom) tercile of firms in a given quarter, and zero otherwise. Beyond these variables, we
include our standard set of control variables in all specifications.
The regression estimates are reported in Table VII. They confirm the importance of EC
Dispersion in the post-earnings-announcement price formation process. The estimates in specification
1 show that the CAR over the three days spanning from the day before to the day after an earnings
announcement (CAR[-1,+1]) is highly dependent on the magnitude of the Earnings Surprise, which is
consistent with prior studies (Ball and Brown (1968), Bernard and Thomas (1989), Livnat and
Mendenhall (2006)). Additionally, the coefficient estimate of EC Dispersion is economically and
statistically insignificant.
The coefficient estimates of EC Dispersion become statistically important when we consider
post-earnings-announcement returns in specifications 2 through 5 (estimates range from 0.012 to
24
0.021, t-statistics vary between 2.37 and 2.72). Moreover, both the economic and statistical
magnitude of the EC Dispersion coefficient is monotonically increasing as we consider longer postearnings-announcement windows. This evidence confirms our hypothesis that price continuation is
stronger among stocks with relatively difficult-to-process geographical information. Finally, the
coefficient estimates on the interaction between the High Surprise and High Dispersion indicators
indicate that price formation is incrementally slower among stocks with large earnings surprises
when geographic information is relatively dispersed.
4.3
Geographic Dispersion of Information and Momentum Profits
The evidence in the previous section indicates that dispersion in firms’ geographical
information is associated with delayed incorporation of value-relevant information in stock prices,
leading to post-earnings price continuation. Further, we show that the magnitude of this
continuation is increasing in the size of the post-announcement window. This evidence suggests a
potential interaction between geographic information dispersion and another well-known price
continuation pattern in asset prices: price momentum (Jegadeesh and Titman (1993)).
We test this possibility by estimating Fama-MacBeth (1973) style cross-sectional pricing
regressions in which we interact the High Dispersion indicator variable with lagged 12-month stock
returns (Lagged 12m Return). When we also include the lagged 12-month stock returns, the interaction
measures the differential momentum effect between firms with high- and low-geographic
information dispersion. If high geographic information dispersion leads to slow information
diffusion and price continuation, then the coefficient on the interaction term should be positive and
significant.
Table VIII reports the coefficient estimates from monthly cross-sectional pricing regressions.
We follow the approach in Ang et al. (2009), and control for contemporaneous Fama-French 325
factor loadings estimated using daily data. Further, we also control for firm size and book-to-market
ratio observable at time t to account for Daniel and Titman’s (1997) observation that factor loadings
may not account for all variation in expected returns. In specification 1, we report the coefficient
estimates from pricing regressions using monthly data from July 1963 to December 2011. The
estimates for this period confirm the familiar patterns documented in past asset pricing studies:
positive premiums for stocks with higher market betas, smaller size, and higher book-to-market
ratios. Further, we find evidence of price momentum, with a positive and significant coefficient
estimate on lagged 12-month stock returns (t-statistic = 3.44).
In specification 2, we restrict our sample to the January 1994 to December 2011 for which
we can calculate the EC Dispersion measure. In specification 3, we further restrict the estimation
sample to those stocks for which have the EC citation share data, and hence for which we can
calculate the EC Dispersion measure. The results from both subsamples are fairly consistent. Notably,
we find that momentum in stock returns become weaker during the 1994 to 2011 period. The
coefficient on lagged 12-month stock returns is statistically indistinguishable from zero.
In specification 4, we introduce the interaction between lagged 12-month stock returns and
EC Dispersion into the cross-sectional pricing regressions. While the lagged 12-month return
continues to load weakly, the coefficient on the interaction term is positive and significant (t-statistic
= 2.78). This finding is consistent with the gradual information diffusion model of Hong, Lim, and
Stein (2000) and suggests that dispersion in geographic information is associated with slower
diffusion of information into stock prices.
5.
Geographic Dispersion of Information and Predictable Returns
If stock prices exhibit under-reaction because value-relevant information is geographically
dispersed, stock returns would exhibit predictable patterns. In this section, we develop a set of
26
trading strategies that exploit this slower diffusion of geographically dispersed information. These
trading strategies also allow us to better assess the economic significance of our evidence from
earnings and cash flow predictive regressions.
5.1
Trading Strategy Performance: Baseline Estimates
We define the trading strategy by sorting stocks on their Expected Earnings Surprise, defined as
forecasted EPS minus analyst consensus, scaled by end-of-month stock price. Specifically, at the end
of each month, we compute forecasted EPS for all stocks with a scheduled quarterly earnings
announcement during the next month. We obtain the forecasted EPS using fitted values from a
Fama-MacBeth (1973) predictive regression of quarterly EPS on lagged EPS, lagged EC Earnings,
lagged HQ Earnings, and a host of firm characteristics and state-level variables (see specification (3)
of Table III).
To ensure that we do not have any look-ahead bias in our portfolio formation procedure, we
use financial statement data available at the time of portfolio formation. We do this by restricting the
sample used to estimate the Fama-MacBeth (1973) predictive regressions to one quarter before
portfolio formation. We then compute the expected earnings surprise for each firm by subtracting
the mean analyst consensus forecast at the time of portfolio formation from the fitted EPS value,
and normalize this difference by the end-of-month share price.
We sort all stocks with a scheduled quarterly earnings announcement during the next month
on the expected earnings surprise. We form six portfolios. The Short portfolio is a value-weighted
portfolio of the quintile of stocks predicted to have the largest negative earnings surprises in the
next month. The Long portfolio is a value-weighted portfolio of the quintile of stocks predicted to
have the largest positive earnings surprises in the next month. The Long portfolio contains firms
with lowest degree of information diffusion and the Short portfolio contains firms whose
27
geographically dispersed information has largely been incorporated into prices. Further, the Long–
Short portfolio captures the difference in the returns of the Long and Short portfolios. Last, portfolios
2 to 4 are value-weighted portfolios of the remaining quintiles of stocks sorted on expected earnings
surprise in the next month. All portfolios are rebalanced each month.
Similar to the restrictions on the regression sample, the trading strategy sample includes
firms with at least $1 per share at the end of each quarter, non-missing market (book) value of equity,
and at least $5 million market capitalization at the end of the prior quarter. 7 The imposition of
minimum price and market capitalization filters are especially important, as this mitigates the
potential concern that our trading strategy performance estimates only reflect the illiquidity among
small stocks (see Menzly and Ozbas (2010)).
Table IX reports the mean monthly returns (in %) of these six portfolios during the sample
period. In Panel A, we report the portfolio returns in % in Column 1. We find that the performance
of our geography-based trading strategy is robust and economically significant. The differential
between the Long and the Short portfolios is positive and statistically significant. Specifically, the
average monthly return to the Long (Short) portfolio is 1.058% (0.478%) per month. The average
monthly return of 0.581% (t-statistic = 2.86) for the Long-Short portfolio translates into an annual
performance differential of approximately 7.2%.
The predictive regression captures the expected earnings surprise reasonably well, as the
returns across the portfolios (from Long to Short) exhibit a monotonic pattern. Moreover, the Sharpe
ratios reported in Column 3 show that most of the profitability is generated by the long-side of the
trading strategy. This evidence indicates that the profitability of the strategy does not exclusively
depend on investors’ ability to take short positions on hard-to-short stocks.
7
In unreported tests, we find that the performance of the trading strategy is robust to restricting the sample to stocks
with a nominal share price of at least $5, to constructing equal-weighted portfolios, and to subsampling the first and
second halves of the sample period. 28
We further examine the risk-adjusted performance of our trading strategy using various
factor models. We estimate factor exposures for the Long, Short, and Long-Short portfolio returns. The
factor models contain some combination of the following factors: the market excess return (MKT),
the size factor (SMB), the value factor (HML), the momentum factor (UMD), two reversal factors
(short-term reversal STR and long-term reversal LTR), and the liquidity factor (LIQ). We report the
factor exposures and risk-adjusted performance estimates (i.e., alphas) in Table X.
The performance of geography-based portfolios remains statistically and economically
significant even when we include a large number of factors to adjust for factor exposures.
Specifically, the monthly 1-, 4-, 6-, and 7-factor alphas are 0.635% (t-statistic: 3.10), 0.789% (tstatistic: 4.12), 0.746% (t-statistic: 4.24), and 0.749 (t-statistic: 4.34), respectively. These Long-Short
alpha estimates translate into risk-adjusted performance of over 7.8% per annum, which is
economically significant.
5.2
Trading Strategy Performance using Delayed Portfolio Formation
To quantify the length of time it takes for market participants such as analysts and
institutional investors to gather and incorporate geographical information into stock prices, we
examine the performance of the Long-Short portfolio as we delay portfolio formation. Figure I
shows the effect of delaying portfolio formation. The Long-Short trading strategy return (solid line)
is plotted against the delay in portfolio formation (in months). We also plot two standard bars
(dashed lines). From the plot, we find that the Long-Short portfolio returns become statistically
insignificant once the delay in portfolio formation reaches three months and beyond. Thus, market
participants are able to incorporate firms’ geographical information only gradually, as the zero-cost
arbitrage strategy generates substantial economic profits over several months.
29
5.3
Arbitrage Costs and Trading Strategy Performance
Last, we analyze the importance of security market frictions in the delayed incorporation of
value-relevant information exhibited in our trading strategy returns. Motivated by studies showing
that limits to arbitrage can lead to extended periods of mispricing (Shleifer and Vishny (1997),
Wurgler and Zhuravskaya (2002), Nagel (2005)), we focus our examination on the role of arbitrage
costs.
In our first test of the role of arbitrage costs, we condition on firms’ institutional ownership
(IO), a proxy for short-sale constraints (Nagel (2005)). We classify low- (high-) IO firms as those
with below- (above-) median IO in a given month. Consistent with arbitrage costs impairing the
incorporation of geographic information into stock prices, we find that the trading strategy is most
profitable among the subsample of low-IO firms. These findings are reported in Panel A of Table XI.
In the next test, we condition on firms’ idiosyncratic volatility (IVOL) measured with respect
to the Fama-French 3-factor model estimated using daily returns each month. We classify low(high-) IVOL firms as those with below- (above-) median IVOL in a given month. We report the
trading strategy returns for the low- and high-IVOL subsamples in Panel B of Table XI. The results
in Panel B again suggest that limits-to-arbitrage stocks are at least partly responsible for the delayed
incorporation of geographic information in stock prices.
Overall, the trading strategy estimates provide strong out-of-sample evidence of the
predictive power of firms’ geographical information on earnings surprises, even when we account
for differences in risk across portfolios using various factor models
6.
Summary and Conclusion
Value-relevant information about a typical U.S. firm is geographically dispersed as the
economic interests of the firm are not concentrated only around corporate headquarters. In this
30
paper, we show that this geographical dispersion in information generates predictable patterns in
stock returns because market participants are unable to aggregate the geographically dispersed
information very quickly. Even relatively sophisticated equity analysts are unable to fully incorporate
geographically dispersed information into their earnings forecasts.
In the first part of the paper, we demonstrate that the earnings and cash flows of firms can
be predicted using the past performance of other firms in economically relevant geographical regions.
Then, we test the potential asset-pricing implications of slow diffusion and delayed aggregation of
geographically distributed firm-specific information. Specifically, our asset pricing tests focus on two
economic settings where slow diffusion of information is known to generate predictable patterns in
stock returns: post-earnings announcement drift (PEAD) and momentum in stock returns. We find
that both post-earning-announcement drift and momentum returns are more pronounced among
firms that have more geographically dispersed information, which are difficult to aggregate.
Last, to quantify the economic magnitudes of these predictable return patterns, we construct
out-of-sample geography-based trading strategies. A Long-Short trading strategy where we long
(short) firms with high (low) expected earnings surprise generates a monthly alpha of 75 basis points
or an annual premium of 9%. The performance of our trading strategy is robust during the sample
period, and cannot be explained by standard asset pricing factors such as the market, size, value,
momentum, short- and long-term reversal, and liquidity factors.
Collectively, these results demonstrate that slow diffusion of geographically dispersed
information generates predictable patterns in stock returns. This evidence is consistent with our
broad conjecture and shows that market participants do not fully incorporate geographically
dispersed information.
In future work, it would be interesting to examine how the information gathering activities
of other market participants such as institutional investors affect the speed of information
31
aggregation. Future research can also examine whether corporate insiders use the predictable
patterns in earnings and returns to make better corporate decisions. Last, while our study focuses on
geographical dispersion within the U.S., geographical diffusion of information across countries could
generate predictable earnings and return patterns among firms with international presence. We hope
to examine these interesting questions in our future research.
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36
Figure I
The Effect of Delayed Portfolio Formation
This figure shows the effect of delayed portfolio formation on the average monthly returns (solid
line) of the predicted earnings surprise based Long−Short portfolio. Also shown are the ±2 standard
error bars (dashed lines). The estimation period is from January 1995 to March 2010.
125
100
Monthly Return (bps)
75
50
25
0
0
1
2
3
4
5
6
7
-25
-50
Delay in Formation Period (months)
37
8
9
10
Table I
Summary Statistics
Panel A summarizes the summary statistics of the variables in this study. Earnings is defined as the
operating income after depreciation divided by average total assets. Cash Flow is defined as the cash
flow from operating activities divided by average total assets. HQ Earnings (HQ Cash Flow) is the
value-weighted Earnings (Cash Flow) of firms headquartered in the same state. EC Earnings (Cash Flow)
is the citation-share weighted Earnings (Cash Flow) of firms located in economically connected states
with headquarter state excluded. HQ (EC) Dividend-Price is the value-weighted log of one plus the
dividend-price ratio of firms headquartered in the same state (located in economically connected
states with headquarter state excluded). HQ (EC) Economic Activities Index is an index of the macroeconomic conditions in the headquartered (economically connected) state. Firm Size is the natural
logarithm of total assets. Leverage is the sum of short-term and long-term debts, divided by total
assets. Loss (No-Dividend Indicator) is a dummy that takes a value of one when operating income
(dividend) is negative (zero), and zero otherwise. Market-to-Book is the sum of market equity, shortterm debt, and long-term debt, divided by total assets. Dividend Yield is the dividends divided by
shareholders’ equity. Fundamentals of firms with at least $10 million of average total assets, two
years of data, $1 closing price are obtained from Compustat. Utilities and financial institutions are
excluded from the sample. The sample period is from 1994 to 2012. Panel B reports the Pearson
and Spearman rank correlations below and above the diagonal, respectively. *** represents
significance at the 1% level.
Panel A: Summary Statistics
Std.
P10
P25
P50
0.052 -0.048 -0.002
0.018
0.015 -0.016 -0.008
0.005
0.005
0.002
0.006
0.010
0.130 -0.095 -0.015
0.030
0.041 -0.051 -0.035 -0.012
0.030 -0.038 -0.029 -0.006
1.853
3.243
4.164
5.387
0.199
0.000
0.018
0.175
0.440
0.000
0.000
0.000
1.728
0.646
0.875
1.293
0.003
0.000
0.000
0.000
0.281
1.000
1.000
1.000
0.007
0.007
0.010
0.014
0.735 -0.362 -0.072
0.290
0.006
0.011
0.013
0.016
0.384 -0.227 -0.007
0.190
Variables
Mean
Earnings
0.009
HQ Earnings
0.003
EC Earnings
0.010
Cash flow
0.017
HQ Cash Flow
-0.007
EC Cash Flow
-0.003
Firm Size
5.557
Leverage
0.210
Loss
0.262
Market-to-Book
1.855
Dividend Yield
0.001
No-div. Indicator
0.914
HQ Dividend-price 0.015
HQ Eco. Act. index 0.448
EC Dividend-Price
0.017
EC Eco. Act. index 0.216
38
P75
0.035
0.014
0.013
0.079
0.020
0.017
6.773
0.338
1.000
2.118
0.000
1.000
0.019
0.907
0.020
0.416
P90
0.054
0.022
0.016
0.135
0.046
0.039
8.038
0.487
1.000
3.650
0.000
1.000
0.025
1.449
0.025
0.696
# Obs.
194,255
194,255
194,255
194,255
194,255
194,255
194,255
194,255
194,255
194,255
194,255
194,255
194,255
194,255
194,255
194,255
Table I
Summary Statistics – Continued
Variables
CAR [-1,+1]
CAR [+1,+39]
CAR [+2,+40]
CAR [+1,+59]
CAR [+2,+60]
Earnings Surprise
EC Dispersion
Monthly Excess ret%
Lagged 12m Return
Earnings
HQ Earn.
EC Earn.
Mean
0.001
-0.013
-0.012
-0.023
-0.022
-0.001
0.854
0.859
0.182
Panel A: Summary Statistics – Continued
Std.
P10
P25
P50
0.097 -0.098 -0.043 -0.002
0.209 -0.226 -0.120 -0.024
0.198 -0.212 -0.113 -0.022
0.254 -0.280 -0.153 -0.037
0.246 -0.269 -0.146 -0.035
0.012 -0.004
0.000
0.001
0.168
0.633
0.803
0.915
18.621 -16.579 -7.288 -0.236
1.126 -0.432 -0.192
0.044
Panel B: Correlations
Earnings HQ Earn. EC Earn.
1.000
0.207*** 0.171*** Cash flow
0.222*** 1.000
0.352*** HQ CF
0.190*** 0.368***
1.000
EC CF
39
P75
0.043
0.075
0.070
0.081
0.076
0.002
0.969
7.176
0.323
Cash flow
1.000
0.215***
0.176***
P90
0.102
0.197
0.184
0.226
0.214
0.003
0.989
17.480
0.769
HQ CF
0.208***
1.000
0.628***
# Obs.
112,226
111,433
111,333
97,516
96,486
112,226
194,255
969,319
969,319
EC CF
0.197***
0.681***
1.000
Table II
Earnings Predictability Using Information From Corporate Headquarters
This table reports coefficient estimates from Fama-MacBeth (1973) predictive regressions. The
dependent variables are Earnings (Columns 1 to 3) and Cash Flow (Columns 4 to 6). Earnings is
defined as the operating income after depreciation divided by average total assets. Cash Flow is
defined as the cash flow from operating activities divided by average total assets. HQ Earnings (HQ
Cash Flow) is the value-weighted Earnings (Cash Flow) of firms headquartered in the same state. HQ
Dividend-Price is the value-weighted log of one plus the dividend-price ratio of firms headquartered in
the same state. HQ Economic Activities Index is the index of the economic activities of firms
headquartered in the same state. Firm Size is the natural logarithm of total assets. Leverage is the sum
of short-term and long-term debts, divided by total assets. Loss (No-Dividend Indicator) is a dummy
that takes a value of one when operating income (dividend) is negative (zero), and zero otherwise.
Market-to-Book is the sum of market equity, short-term debt, and long-term debt, divided by total
assets. Dividend Yield is the dividends divided by shareholders’ equity. Additional details on all
variables are available in Appendix A. Standard errors are adjusted using the Newey-West (1987)
method with 4-year lag. The t-statistics are reported in parentheses below the coefficient estimates.
Dependent Variables:
Independent Variables
Earnings (t)
(1)
0.834
(71.80)
HQ Earnings (t)
Earnings (t+1)
(2)
0.829
(72.15)
0.098
(8.29)
(3)
0.798
(51.30)
0.051
(7.85)
Cash Flows (t)
(4)
0.782
(40.92)
0.778
(43.57)
0.124
(3.58)
0.005
(5.95)
64
194,255
0.533
0.004
(9.65)
64
194,255
0.534
HQ Cash Flows (t)
Firm Size (t)
Leverage (t)
Loss (t)
Market-to-Book (t)
Dividend Yield (t)
No-Dividend Indicator (t
HQ Dividend-Price (t)
HQ Economic
Activities Index (t)
Constant
Number of Quarters
Number of Observations
Average R2
0.001
(1.76)
64
194,255
0.680
0.000
(1.82)
64
194,255
0.681
0.162
(11.75)
0.257
(3.24)
-0.002
(-2.29)
-0.009
(-1.36)
-0.089
(-1.69)
0.002
(6.10)
6.171
(5.73)
-3.569
(-2.90)
-0.011
(-10.88)
64
194,255
0.687
40
Cash Flow (t+1)
(5)
(6)
0.694
(42.80)
0.048
(2.87)
0.322
(7.85)
-0.317
(-1.57)
-0.045
(-41.67)
-0.198
(-5.97)
-0.383
(-3.49)
0.003
(3.58)
-3.860
(-0.94)
0.965
(0.47)
0.002
(0.54)
64
194,255
0.563
Table III
Predictability using Information From Economically Connected States
This table reports coefficient estimates from Fama-MacBeth (1973) predictive regressions. Earnings is
defined as the operating income after depreciation divided by average total assets. Cash Flow is defined as
the cash flow from operating activities divided by average total assets. HQ Earnings (HQ Cash Flow) is the
value-weighted Earnings (Cash Flow) of firms headquartered in the same state. EC Earnings (Cash Flow) is
the citation-share weighted Earnings (Cash Flow) of firms located in economically connected states with
headquarter state excluded. HQ (EC) Dividend-Price is the value-weighted log of one plus the dividendprice ratio of firms headquartered in the same state (located in economically connected states with
headquarter state excluded). HQ (EC) Economic Activities Index is the index of the economic activities of
firms headquartered in the same state (located in economically connected states with headquarter state
excluded). Additional details on all variables are summarized in the Appendix. Standard errors are
adjusted using Newey-West (1987) method with 4-year lag. The t-statistics are reported in parentheses.
Dependent Variables:
Independent Variables
Earnings (t)
EC Earnings (t)
HQ Earnings (t)
(1)
0.828
(74.78)
0.441
(24.13)
Cash Flows (t)
Earnings (t+1)
(2)
0.825
(73.80)
0.388
(22.91)
0.080
(6.50)
(3)
0.796
(52.14)
0.275
(12.95)
0.039
(6.42)
-0.003
(-7.47)
64
194,255
0.682
0.159
(11.53)
0.201
(2.65)
-0.002
(-2.12)
-0.003
(-0.50)
-0.091
(-1.77)
0.002
(6.45)
-8.445
(-3.60)
3.167
(1.98)
6.288
(6.20)
-3.638
(-2.97)
-0.012
(-16.64)
64
194,255
0.688
EC Cash Flows (t)
HQ Cash Flows (t)
Firm Size (t)
Leverage (t)
Loss (t)
Market-to-Book (t)
Dividend Yield (t)
No-Dividend Indicator (t)
EC Dividend-Price (t)
EC Economic
Activities Index (t)
HQ Dividend-Price (t)
HQ Economic
Activities Index (t)
Constant
Number of Quarters
Number of Observations
Average R2
-0.003
(-8.82)
64
194,255
0.681
41
(4)
Cash Flow (t+1)
(5)
0.778
(43.52)
0.382
(4.47)
0.775
(45.26)
0.334
(4.59)
0.100
(3.77)
0.004
(1.29)
64
194,255
0.534
0.004
(1.14)
64
194,255
0.535
(6)
0.692
(43.85)
0.152
(2.35)
0.036
(2.98)
0.317
(7.48)
-0.386
(-1.80)
-0.045
(-39.63)
-0.189
(-5.22)
-0.384
(-3.60)
0.003
(3.60)
-21.018
(-3.02)
9.162
(0.85)
-3.348
(-0.92)
1.267
(0.67)
0.003
(0.76)
64
194,255
0.564
Table IV
Long-Run Earnings Predictability
This table reports coefficient estimates from Fama-MacBeth (1973) predictive regressions. The
dependent variables are Earnings (Columns 1 to 4) and Cash Flow (Columns 5 to 8). Earnings is
defined as the operating income after depreciation divided by average total assets. Cash Flow is
defined as the cash flow from operating activities divided by average total assets. HQ Earnings (HQ
Cash Flow) is the value-weighted Earnings (Cash Flow) of firms headquartered in the same state. EC
Earnings (Cash Flow) is the citation-share weighted Earnings (Cash Flow) of firms located in
economically connected states with headquarter state excluded. Additional details on all variables are
available in Appendix A. Standard errors are adjusted using the Newey-West (1987) method with 4year lag. The t-statistics are reported in parentheses below the coefficient estimates.
Dependent Variables:
Independent Variables
Earnings (t)
EC Earnings (t)
HQ Earnings (t)
t+2
(1)
0.733
(43.92)
0.391
(14.80)
0.054
(6.06)
Earnings
t+3
(2)
0.707
(36.50)
0.389
(10.63)
0.056
(6.16)
t+4
(3)
0.731
(34.68)
0.293
(5.75)
0.041
(3.65)
t+8
(4)
0.619
(21.61)
0.316
(6.26)
0.046
(2.55)
Cash Flows (t)
EC Cash Flows (t)
HQ Cash Flows (t)
Firm Size (t)
Leverage (t)
Loss (t)
Market-to-Book (t)
Dividend Yield (t)
No-Dividend Indicator (t)
EC Dividend-Price (t)
EC Economic
Activities Index (t)
HQ Dividend-Price (t)
HQ Economic
Activities Index (t)
Constant
Number of Quarters
Number of Observations
Average R2
0.201
(11.41)
0.299
(2.46)
-0.001
(-1.36)
-0.019
(-1.37)
-0.165
(-2.71)
0.003
(5.99)
-13.190
(-4.08)
0.315
(0.11)
7.985
(4.97)
-6.225
(-3.87)
-0.015
(-16.64)
63
187,158
0.593
0.210
(10.53)
0.320
(2.47)
-0.002
(-1.91)
-0.048
(-2.29)
-0.151
(-2.41)
0.003
(6.73)
-12.707
(-3.54)
1.320
(0.40)
7.940
(3.56)
-9.322
(-4.29)
-0.015
(-13.84)
62
180,424
0.562
0.174
(10.24)
0.452
(4.01)
-0.003
(-4.06)
-0.071
(-2.69)
-0.053
(-0.70)
0.003
(4.75)
-9.645
(-2.44)
0.942
(0.25)
7.835
(2.50)
-9.212
(-4.51)
-0.012
(-11.06)
61
175,644
0.596
42
0.210
(13.55)
0.663
(4.81)
-0.002
(-3.31)
-0.114
(-2.30)
0.047
(0.43)
0.003
(4.51)
-7.506
(-2.78)
1.867
(0.34)
11.060
(2.16)
-12.086
(-5.43)
-0.014
(-8.34)
57
158,722
0.469
t+2
(5)
0.512
(29.11)
0.243
(2.38)
0.073
(2.94)
0.480
(8.20)
-0.436
(-1.22)
-0.061
(-28.97)
-0.270
(-4.54)
-0.540
(-4.70)
0.005
(3.97)
-37.356
(-3.92)
12.082
(0.81)
-9.314
(-1.50)
5.873
(2.02)
0.002
(0.34)
63
187,158
0.397
Cash Flow
t+3
t+4
(6)
(7)
0.452
(26.98)
0.288
(3.07)
0.081
(2.85)
0.520
(7.88)
-0.116
(-0.33)
-0.055
(-23.62)
-0.271
(-4.22)
-0.555
(-4.81)
0.006
(4.61)
-44.454
(-4.39)
13.658
(0.89)
-4.679
(-0.94)
3.025
(0.80)
-0.003
(-0.58)
62
180,424
0.355
0.559
(19.49)
0.229
(4.33)
0.058
(4.13)
0.381
(6.10)
0.391
(1.51)
-0.042
(-13.29)
-0.183
(-2.82)
-0.291
(-2.17)
0.005
(3.30)
-34.685
(-3.33)
3.220
(0.25)
-6.863
(-1.49)
-5.745
(-1.49)
-0.001
(-0.26)
61
175,644
0.429
t+8
(8)
0.490
(14.68)
0.260
(6.37)
0.059
(6.04)
0.414
(7.58)
0.663
(2.22)
-0.037
(-9.36)
-0.090
(-0.92)
0.283
(1.23)
0.006
(3.19)
-33.386
(-3.23)
5.394
(0.38)
-1.904
(-0.18)
-11.810
(-2.95)
-0.004
(-0.70)
57
158,722
0.355
Table V
Robustness of Predictability Regression Estimates
This table reports coefficient estimates from Fama-MacBeth (1973) predictive regressions. The
dependent variables are Earnings (Columns 1 to 3) and Cash Flow (Columns 4 to 6). All regression
specifications and variable definitions are identical to those in Table III, with the following
exceptions. EC Earnings (EC Cash Flow) is the citation-share weighted Earnings (Cash Flow) of firms
with economic connections in the top three economically connected states (HQ state excluded) with
the highest citation-shares. The regression specifications are identical to the corresponding columns
in Table III. We suppress extraneous coefficient estimates in the interest of brevity. Standard errors
are adjusted using Newey-West (1987) method with 4-year lag. The t-statistics are reported in
parentheses below the coefficient estimates.
Dependent Variables:
Indep. Variables
Earnings (t)
EC Earnings (t)
(1)
0.830
(74.03)
0.320
(21.60)
HQ Earnings (t)
Earnings (t+1)
(2)
(3)
0.826
0.797
(73.33)
(51.97)
0.276
0.209
(20.12)
(12.85)
0.086
0.041
(7.02)
(6.69)
Cash Flows (t)
(4)
0.779
(42.56)
0.296
(4.73)
EC Cash Flows (t)
HQ Cash Flows (t)
Controls
Number of Quarters
Num. of Observations
Average R2
No
64
194,255
0.681
No
64
194,255
0.682
Yes
64
194,255
0.688
43
No
64
194,255
0.534
Cash Flow (t+1)
(5)
(6)
0.776
(44.53)
0.257
(4.90)
0.106
(3.70)
No
64
194,255
0.535
0.693
(43.66)
0.140
(2.50)
0.037
(2.99)
Yes
64
194,255
0.564
Table VI
Information Dispersion and Analyst Behavior
This table reports coefficient estimates from Fama-MacBeth (1973) predictive regressions. The
dependent variables are Earnings (Column 1), Analyst Consensus (Columns 2-3), and Forecast Error
(Columns 4-5). Earnings is defined as operating income after depreciation divided by average total
assets. Consensus Forecast is the mean (median) consensus based on all outstanding forecasts issued
within the 90 days prior to the earnings announcement. All analyst forecasts are split-adjusted, onequarter ahead earnings per share forecasts obtained from the Thomson-Reuters’ IBES unadjusted
detail file. Forecast Error is defined as the absolute of the actual earnings minus analyst consensus
scaled by lagged stock price. Number of Analysts is the natural log of the number of analysts covering
a firm in a given quarter. Additional details about all variables are available in Appendix A. Standard
errors are adjusted using the Newey-West (1987) method with 4-year lag. The t-statistics are reported
in parentheses below the coefficient estimates.
Independent Variables
Earnings (t)
EC Earnings (t)
HQ Earnings (t)
Firm Size (t)
Leverage (t)
Loss (t)
Market-to-Book (t)
Dividend Yield (t)
No-Dividend Ind. (t)
EC Dividend-Price (t)
EC Economic
Activities Index (t)
HQ Dividend-Price (t)
HQ Economic
Activities Index (t)
Num. of Analysts (t)
Constant
Num. of Quarters
Num. of Obs.
Average R2
Earnings (t+1),
Firms with
Analyst
Coverage
(1)
0.788
(47.31)
0.304
(9.36)
0.048
(5.20)
0.001
(12.45)
0.001
(0.95)
-0.003
(-3.39)
0.001
(5.82)
-0.174
(-3.65)
0.002
(4.82)
-0.073
(-3.20)
0.000
(1.68)
0.052
(5.42)
-0.000
(-1.95)
-0.011
(-8.61)
64
118,917
0.691
Dependent Variables:
Analyst
Analyst
Consensus
Consensus
Forecast (t+1), Forecast (t+1),
Mean
Median
(2)
(3)
0.019
0.019
(15.90)
(16.10)
0.038
0.038
(1.97)
(1.97)
0.015
0.015
(6.21)
(6.28)
0.001
0.001
(6.53)
(6.50)
-0.003
-0.003
(-9.92)
(-9.92)
-0.002
-0.002
(-7.64)
(-7.62)
0.000
0.000
(1.28)
(1.28)
-0.178
-0.178
(-6.12)
(-6.12)
-0.001
-0.001
(-2.52)
(-2.51)
0.009
0.010
(2.00)
(2.02)
0.000
0.000
(1.02)
(1.03)
0.016
0.016
(3.40)
(3.37)
0.000
0.000
(1.13)
(1.08)
-0.000
-0.000
(-3.91)
(-3.74)
-0.002
-0.002
(-3.22)
(-3.22)
64
64
100,039
100,039
0.322
0.322
44
Forecast Error
(t+1), Mean
Consensus
(4)
-0.003
(-0.41)
0.156
(3.50)
0.034
(3.01)
-0.000
(-2.01)
0.031
(14.42)
0.004
(8.12)
-0.003
(-12.48)
0.776
(10.92)
-0.001
(-4.71)
0.008
(0.48)
-0.000
(-0.62)
0.039
(2.33)
0.001
(3.64)
-0.002
(-9.56)
0.016
(7.65)
64
99,982
0.261
Forecast Error
(t+1), Median
Consensus
(5)
-0.003
(-0.40)
0.157
(3.56)
0.034
(3.01)
-0.000
(-2.04)
0.031
(14.29)
0.004
(8.22)
-0.003
(-12.55)
0.779
(11.05)
-0.001
(-4.84)
0.010
(0.66)
-0.000
(-0.58)
0.038
(2.34)
0.001
(3.61)
-0.002
(-9.88)
0.016
(7.76)
64
99,982
0.262
Table VII
Information Dispersion and Post-Earnings-Announcement Drift
This table reports coefficient estimates from Fama-MacBeth (1973) predictive regressions. The
dependent variables are Cumulative Abnormal Returns (CAR) over varying windows relative to
quarterly earnings announcement dates. The main explanatory variable is EC Dispersion, measured as
one minus the concentration (Herfindahl index) of a firm’s EC citation shares each year. Earnings
Surprise is defined as actual earnings minus analyst consensus scaled by lagged stock price. High
Dispersion is an indicator variable that is set to one if a firm has higher than median EC Dispersion in a
given quarter, and zero otherwise. High Surprise is a signed indicator variable that is set to 1 (-1) if
Earnings Surprise is in the top (bottom) tercile of firms in a given quarter, and zero otherwise.
Additional details on all variables are summarized in the Appendix. Standard errors are adjusted
using Newey-West (1987) method with 4-year lag. The t-statistics are reported in parentheses below
the coefficient estimates.
Independent Variables
Earnings surprise (t)
EC Dispersion (t)
High Surprise
× High Dispersion (t)
Firm Size (t)
Leverage (t)
Loss (t)
Market-to-Book (t)
Dividend Yield (t)
No-Dividend Indicator (t)
EC Dividend-Price (t)
EC Econ. Activities Index (t)
Number of Analysts (t)
Constant
Number of Quarters
Number of Observations
Average R2
Dependent Variables:
CAR[-1,+1] CAR[+1,+39] CAR[+2,+40] CAR[+1,+59] CAR[+2,+60]
(1)
(2)
(3)
(4)
(5)
0.445
0.422
0.091
0.166
-0.094
(3.60)
(4.74)
(0.87)
(1.17)
(-0.48)
0.001
0.012
0.013
0.017
0.021
(0.75)
(2.37)
(2.87)
(2.68)
(2.72)
0.007
0.007
0.004
0.007
0.004
(3.69)
(2.74)
(2.24)
(3.03)
(2.25)
-0.001
0.002
0.001
0.004
0.003
(-1.58)
(1.20)
(0.80)
(1.75)
(1.47)
-0.006
-0.026
-0.020
-0.045
-0.040
(-2.25)
(-2.80)
(-1.96)
(-3.82)
(-3.23)
-0.013
-0.014
-0.009
-0.019
-0.013
(-11.39)
(-2.78)
(-1.65)
(-2.89)
(-2.03)
-0.002
-0.002
-0.001
-0.001
-0.000
(-4.72)
(-0.88)
(-0.48)
(-0.42)
(-0.14)
0.610
0.592
0.342
1.198
1.061
(2.18)
(1.93)
(1.23)
(1.48)
(1.44)
0.002
0.000
-0.002
0.001
-0.001
(1.01)
(0.12)
(-0.88)
(0.15)
(-0.15)
-0.066
0.187
0.154
0.064
-0.015
(-0.40)
(1.83)
(1.25)
(0.21)
(-0.05)
-0.000
-0.000
0.001
0.001
0.002
(-0.30)
(-0.21)
(0.40)
(0.16)
(0.47)
0.002
0.002
0.002
0.003
0.004
(2.09)
(1.55)
(1.37)
(0.98)
(1.28)
0.010
-0.025
-0.026
-0.051
-0.054
(1.50)
(-1.24)
(-1.37)
(-2.09)
(-2.49)
64
112,226
0.024
64
111,433
0.043
45
64
111,333
0.044
64
97,516
0.052
64
96,486
0.053
Table VIII
Information Dispersion and Momentum Returns
This table reports coefficient estimates from Fama-MacBeth (1973) regressions. We regress monthly
stock returns on the stock’s lagged previous-12 month return, the stock’s Fama-French 3-factor
loadings calculated over the current month, value-weighted log market capitalization at the
beginning of the previous month, and book-to-market ratio of available six months prior. We also
interact High Dispersion with the lagged previous-12 month return in some specifications. High
Dispersion is an indicator variable that is set to one if a firm has higher than median Dispersion in a
given quarter, and zero otherwise. EC Dispersion is measured as one minus the concentration
(Herfindahl index) of a firm’s EC citation shares each year. We report the time-series average of
cross-sectional adjusted R-squared. The t-statistics computed using Newey-West (1987) adjusted
standard errors are reported in parentheses below the estimates.
Independent Variables
Lagged 12m Return
× High Dispersion
Lagged 12m Return
β(MKT)
β(SMB)
β(HML)
Size
Book-to-Market
Constant
Average Adjusted-R2
Number of Months
Average Monthly Num. of Firms
Dependent Variable: Monthly Return
Baseline
Interact with EC Dispersion
1963 - 2011
1994 - 2011
1994 - 2011
1994 - 2011
(1)
(2)
(3)
(4)
0.355
(2.78)
0.459
-0.097
-0.110
-0.213
(3.44)
(-0.40)
(-0.48)
(-1.13)
39.136
7.577
11.657
11.732
(5.04)
(0.55)
(0.83)
(0.84)
10.350
14.311
14.517
14.424
(3.09)
(2.14)
(2.37)
(2.36)
-3.038
2.909
5.480
5.540
(-0.79)
(0.34)
(0.64)
(0.64)
-0.152
-0.131
-0.163
-0.168
(-3.21)
(-1.83)
(-2.27)
(-2.32)
0.318
0.261
0.251
0.251
(5.70)
(2.88)
(2.80)
(2.80)
1.727
1.942
2.309
2.376
(2.38)
(1.70)
(2.01)
(2.04)
0.056
582
3,724
0.045
216
4,860
46
0.046
216
4,488
0.048
216
4,488
Table IX
Earnings Surprise Forecast Portfolios: Performance Estimates
This table reports the average monthly returns of trading strategies defined using the earnings
surprise prediction model. We report the performance of six portfolios: i) the “Short” portfolio,
which is a value-weighted portfolio of the quintile of stocks with a scheduled earnings
announcement in the next month predicted to have the largest negative earnings surprise, ii) the
“Long” portfolio, which is a value-weighted portfolio of the quintile of stocks with a scheduled
earnings announcement in the next month predicted to have the largest positive earnings surprise, iii)
the “Long – Short” portfolio, which captures the difference in the returns of the Long and Short
portfolios, and iv) - vi) portfolios 2 - 4, value-weighted portfolios of the remaining stock quintiles.
The t-statistics computed using Newey-West (1987) adjusted standard errors are reported in
parentheses below the estimates. Monthly portfolio market shares are calculated as the average
proportion of the total CRSP market capitalization contained in each portfolio over the sample
period. The portfolio formation period is from January 1995 to April 2010.
Portfolios
Long – Short (5 – 1)
Long (5)
4
3
2
Short (1)
Number of Months
Portfolio
Returns
(1)
0.581
(2.86)
1.058
(2.73)
0.737
(1.98)
0.539
(1.20)
0.579
(1.28)
0.478
(1.17)
184
Performance Characteristics
Standard
Deviation
Sharpe Ratio
(2)
(3)
2.893
0.201
4.821
0.161
16.332
4.734
0.097
11.897
5.538
0.047
8.327
5.555
0.054
7.544
5.157
0.038
8.709
184
184
184
47
Monthly Portfolio
Market Share (%)
(4)
25.041
Table X
Earnings Surprise Forecast Portfolios: Factor Model Estimates
This table reports the factor model risk-adjusted performance estimates of trading strategies defined
using the earnings surprise prediction model. We report the performance of three portfolios: i) the
“Long” portfolio, which is a value-weighted portfolio of the quintile of stocks with a scheduled
earnings announcement in the next month predicted to have the largest positive earnings surprise, ii)
the “Short” portfolio, which is a value-weighted portfolio of the quintile of stocks with a scheduled
earnings announcement in the next month predicted to have the largest negative earnings surprise,
and iii) the “Long-Short” portfolio, which captures the difference in the returns of the Long and Short
portfolios. The factor models contain some combination of the following factors: the market excess
return (MKT), the size factor (SMB), the value factor (HML), the momentum factor (UMD), two
reversal factors (short-term reversal STR and long-term reversal LTR), and the liquidity factor (LIQ).
The t-statistics computed using Newey-West (1987) adjusted standard errors are reported in
parentheses below the estimates. The estimation period is from January 1995 to March 2010.
Factors
Alpha
MKT
SMB
HML
UMD
STR
LTR
LIQ
Dependent Variables: Return of Earnings Surprise Forecast Portfolios
Long Short
LS
Long Short
LS
Long Short
LS
Long
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
0.611 -0.024 0.635 0.757 -0.032 0.789 0.734 -0.012 0.746 0.728
(3.63) (-0.21) (3.10) (5.17) (-0.26) (4.12) (5.25) (-0.11) (4.24) (5.29)
0.909 1.020 -0.111 0.883 1.011 -0.128 0.871 1.023 -0.152 0.878
(20.16) (35.17) (-1.87) (21.26) (27.73) (-2.24) (17.40) (28.04) (-2.28) (17.56)
-0.167 0.046 -0.213 -0.191 0.050 -0.241 -0.190
(-4.06) (1.06) (-3.90) (-3.80) (0.91) (-3.04) (-3.73)
-0.229 0.033 -0.262 -0.253 0.038 -0.290 -0.249
(-3.14) (0.67) (-2.72) (-2.92) (0.70) (-2.54) (-2.76)
-0.037 -0.020 -0.017 -0.034 -0.026 -0.008 -0.034
(-0.89) (-0.83) (-0.36) (-0.94) (-1.03) (-0.21) (-0.95)
0.066 -0.060 0.126 0.065
(1.02) (-1.21) (1.26) (1.01)
0.062 -0.010 0.072 0.058
(0.77) (-0.13) (0.59) (0.69)
-0.017
(-0.76)
Adjusted-R2
0.807
Num. months 184
0.893
184
0.034
184
0.836
184
0.894
184
0.149
184
48
0.840
184
0.896
184
0.181
184
0.840
184
Short
(11)
-0.021
(-0.18)
1.033
(29.21)
0.052
(0.93)
0.044
(0.84)
-0.026
(-1.10)
-0.061
(-1.24)
-0.017
(-0.23)
-0.027
(-1.51)
LS
(12)
0.749
(4.34)
-0.156
(-2.31)
-0.241
(-3.03)
-0.293
(-2.52)
-0.008
(-0.21)
0.126
(1.26)
0.075
(0.60)
0.011
(0.37)
0.898
184
0.182
184
Table XI
Earnings Surprise Forecast Portfolios: Impact of Arbitrage Costs
This table reports the average monthly returns of trading strategies defined using the earnings
surprise prediction model. In Columns 1-2, we condition on firms’ institutional ownership (IO).
Firms with low (high) IO are defined as those falling below (above) the median IO across all sample
stocks each month. IO is calculated as the proportion of all outstanding stocks held by institutions
using quarterly portfolio holdings from 13F filings data in the Thomson-Reuters 13f Institutional
Holdings database. In Columns 3-4, we condition on firms’ idiosyncratic volatility (IVOL). Firms
with low (high) IVOL are defined as those falling below (above) the median IVOL across all sample
stocks each month. IVOL is calculated with respect to the Fama-French 3-factor model estimated
using daily data each month. The t-statistics computed using Newey-West (1987) adjusted standard
errors are reported in parentheses below the estimates. The portfolio formation period is from
January 1995 to April 2010.
Portfolios
Long – Short (5 – 1)
Long (5)
4
3
2
Short (1)
Number of months
Institutional Ownership
Low
High
(1)
(2)
0.735
0.486
(2.45)
(2.33)
1.158
1.068
(2.59)
(2.86)
0.739
0.765
(2.08)
(1.68)
0.444
0.593
(0.88)
(1.29)
0.477
0.644
(0.97)
(1.36)
0.422
0.582
(0.99)
(1.27)
184
184
49
Idiosyncratic Volatility
Low
High
(3)
(4)
0.439
0.512
(1.30)
(2.51)
0.995
1.089
(1.54)
(3.13)
0.594
0.856
(0.87)
(2.52)
0.580
0.598
(0.87)
(1.50)
0.654
0.624
(1.00)
(1.59)
0.556
0.577
(0.88)
(1.52)
184
184
Appendices
A. Variable Definitions
Variable
Earnings
Cash Flows
Analyst Consensus
Earnings Surprise
Firm Size
Leverage
Loss
Market-to-Book
Dividend Yield
No-Dividend Indicator
Dividend-Price
Economic Activities
Index
Number of Analysts
Description/Construction Details
Operating income after depreciation (OIADPQ) divided by average total assets
(ATQ)
Cash flow from operating activities (OANCFY) divided by average total assets
(ATQ).
The consensus based on all outstanding forecasts issued within the 90 days
prior to the earnings announcement. All analyst forecasts are split-adjusted,
one-quarter ahead earnings per share forecasts from Thomson-Reuters’ IBES
Unadjusted Detail.
The absolute of the actual earnings minus analyst consensus scaled by lagged
stock price. All analyst forecasts are split-adjusted, one-quarter ahead earnings
per share forecasts from Thomson-Reuters’ IBES Unadjusted Detail.
Natural logarithm of total assets (ATQ).
The sum of short-term debt (DLCQ) and long-term debt (DLTTQ), divided
by total assets (ATQ).
A dummy that takes a value of one when operating income after depreciation
(OIADPQ) is negative, and zero otherwise.
The sum of market equity (PRCCQ×CSHOQ), short-term debt (DLCQ), and
long-term debt (DLTTQ), divided by total assets (ATQ).
Dividends (DVPQ) divided by shareholders’ equity. The shareholders’ equity
is, depending on availability and in the following order, the shareholders’
equity (SEQQ), or commons/ordinary equity (CEQQ). If both items are
missing, the shareholders’ equity is total assets (ATQ) minus total liabilities
(LTQ) and minority interests (MIBQ).
A dummy that takes a value of one when dividend (DVPQ) is zero, and zero
otherwise.
Value-weighted log of one plus the dividend-price (D/P) ratio of firms in the
same state. Dividend D is the sum of the past four quarterly dividends,
whereas P is the end-of-month stock price. The monthly stock prices are
obtained from CRSP, and the quarterly stock-level dividends are obtained
from Compustat.
The index of a state’s economic activities. It is the sum of standardized values
of state-level income growth and state-level housing collateral ratio, minus the
standardized value of relative state-level unemployment ratio, divided by three.
State-level income growth is the log difference between state income in a given
quarter and state income in the same quarter in the last year. State-level
housing collateral ratio is the log ratio of state-level housing equity to state
labor income. It is computed using the Lustig and van Nieuwerburgh (2005)
method. State-level unemployment rate is the ratio of the current
unemployment rate to the moving average of the unemployment rates from
the previous 16 quarters. The unemployment rates are obtained from the
Bureau of Labor Statistics.
The natural log of the number of analysts covering a firm in a given quarter.
50
B.
Geographic Dispersion of Information and Local Analysts
Given our results in Section 4.1, a logical question is whether local analysts—analysts who
are closely located to firms’ HQ or EC states—are better in incorporating firms’ geographically
dispersed information. Prior studies show evidence that local financial intermediaries often have
informational advantages than distant ones (Coval and Moskowitz (1999, 2001), Petersen and Rajan
(2002), Malloy (2005), Bae, Stulz, and Tan (2008), Agarwal and Hauswald (2010), etc.). Thus, our
primary conjecture is that analysts who are located in firms’ primary economically connected or
headquarter states are able to use local information more effectively, and facilitate the incorporation
of local information more readily into their consensus forecasts.
To examine the above conjecture, we examine whether the Forecast Error is affected by the
proportion of analysts located in headquarter and economically connected states. Specifically, if local
analysts are more effective to incorporate local information, forecast error should be lower under a
high proportion of local analysts, who facilitate the incorporation of local information into their
forecasts. Thus, we construct Proportion of EC Analysts, defined as the number of covering analysts
located in the top three economically connected states with HQ excluded, divided by the total
number of covering analysts in a given quarter. Similarly, Proportion of HQ Analysts variable is defined
as the number of analysts located in headquarters state divided by the total number of covering
analysts in a given quarter. We augment these two additional variables into our baseline in Column 4
of Table VI.
Our primary conjecture is that an increase in the proportion of local analysts would lead to a
decrease in forecast error. Specifically, we examine how much forecast error is affected by the level
of local information available, which is proxied by the proportion of analysts in economically
connected and headquarter states.
51
The estimated results on Proportion of EC (HQ) Analysts are summarized in Table A.1. We
find that having more analysts in firms’ top economically connected states does not seem to facilitate
the incorporation of EC-based information into earnings forecasts. Specifically, the estimated
coefficient on Proportion of EC Analysts in Column 1 is positive but not statistically significant
(estimate = 0.210; t-statistic = 1.62). While having more analysts in firms’ headquarter states
facilitate the incorporation of firm information into stock prices (as the estimated coefficient on
Proportion of HQ Analysts is negative), the estimated coefficient on Proportion of HQ Analysts in Column
2 is not significant at conventional levels. Including these measures together in Column 3 does not
significantly alter the results. Overall, these results indicate that analysts do not incorporate
geographically dispersed information into their forecasts, which is consistent with the findings in
Table VI.
We also further condition the level of information available at analysts’ locations. Specifically,
we construct the following indicator variables to capture the level of local information available to
local analysts. High (Low) Dispersion Firms is an indicator that takes a value of one when the
Herfindahl dispersion of firms’ citation-share is above median (below or at median) in a given
quarter. A firm with high geographical dispersion indicates that its operations tend to scatter across
multiple states. Our conjecture is that a firm with highly dispersed economic activities should have
low information available to local analysts.
Last, we include the Regulation FD or Regulation Fair Disclosure variable in the specification.
Prior studies suggest that the level of local information tends to gradually dissipate after the passage
of Reg FD, whose main purpose was to eliminate selective disclosure between firm managers and
market participants (Bernile, Kumar, and Sulaeman (2011)). HQ Boom (Bust) is an indicator that takes
a value of one when the HQ Economic Activities Index is above the median (below or at median) in a
given quarter. EC Boom (Bust) is similarly defined based on EC Economic Activities Index. Our
52
conjecture is that these distinct phases of business cycles provide clear signal on the state-level
business environment, where more local information is available. We interact each of these variables
with the Proportion of EC (HQ) Analysts in the extended specifications.
We report the estimates interacting with the proportion of analysts with High (Low) Dispersion
Firms in Columns 4-6 of Table A.1. We find that the clustering of analysts in firms’ economically
connected states does not help facilitate the incorporation of dispersed geographical information, as
the estimated coefficient on Proportion of EC Analysts × High Dispersion Firms is positive and
statistically significant (estimate = 0.156; t-statistic = 1.77 in Column 4). Further, analysts located in
firms’ economically connected states also do not appear to adjust their earnings forecast with respect
to geographical information of firms that have relatively concentrated business activities. In contrast,
the signs of those estimates on all Proportion of EC Analysts-based interactive variables are consistently
positive. This evidence provides further confirmation to our previous findings that local analysts do
not adjust their earnings forecasts with respect to firms’ geographical information.
We further partition the results before and after the Regulation Fair Disclosure (Reg FD).
We report the results in Columns 7-9 of Table A.1. First, it is harder for analysts in economically
connected states to incorporate geographical information post-Reg FD (from 2001 onward).
Specifically, the estimated coefficients are positive and statistically significant, ranging from 0.222 (tstatistic = 1.77 in Column 7) to 0.223 to (t-statistic = 1.73 in Column 9). The magnitudes on
Proportion of EC Analysts × Post-Regulation FD are also much larger than other interactive measures.
Second, a high clustering of analysts in firms’ headquarter states appears to help facilitate to
incorporate firms’ HQ information post Reg FD when selective disclosure between firm
management and analysts on firms’ material events is prohibited. Specifically, the estimated
coefficients Proportion of HQ Analysts × Post-Regulation FD are negative and statistically significant,
ranging from -0.051 (t-statistic = -2.01 in Column 8) to 0.042 (t-statistic= -1.73 in Column 9).
53
Moreover, analysts located in firms’ economically connected states do not incorporate the
local information at HQ states. Specifically, EC-based analysts do not take HQ-state level business
cycle into consideration, as all interactions between Proportion of EC Analysts and HQ Boom/HQ Bust
are all positive. Specifically, the estimates of Proportion of EC Analysts × HQ Boom are positive and
statistically significant, ranging from 0.129 (t-statistic = 1.89 in Column 10) to 0.130 (t-statistic =
1.96 in Column 12). In other words, analysts in economically connected states do not react to the
salient business conditions in firms’ headquarter states. This evidence is consistent with the evidence
in local bias literature which suggests that investors tend to exhibit a stronger preference or
informational advantage for local investments.
Overall, among the 14 estimated coefficients for Proportion of EC Analysts and its interactive
terms, 12 of them are positive. This evidence indicates that, contrary to our conjecture, analyst
located in firms’ economically connected states do not incorporate local EC-information into their
earnings forecasts. Consequently, they have higher forecast errors. In contrast, analysts located in
firms’ headquarters states facilitate the incorporation of local HQ information more effectively than
EC-based analysts, as 9 of 14 estimated coefficients for Proportion of HQ Analysts and its interactive
terms are negative. This evidence indicates that HQ-based analysts are better in incorporating local
information than EC-based analysts.
Collectively, these findings indicate that equity analysts, especially those located in firms’
economically connected states, do not appear to aggregate firms’ local information into their
earnings forecasts.
54
Table A.1
Analyst Consensus Earnings Forecasts: Conditional Tests
This table reports coefficient estimates from Fama-MacBeth (1973) predictive regressions. The
dependent variable is the Earnings Surprise, defined as the absolute of the actual earnings minus mean
analyst consensus scaled by stock price. Proportion of EC (HQ) Analysts is defined as the number of
covering analysts located in the top three economically connected states with HQ excluded (located
in headquarter states), divided by the total number of covering analysts in a given quarter. High (Low)
Dispersion Firms is an indicator that takes a value of one when the Herfindahl dispersion of citationshare is above median (below or at median) in a given quarter. Regulation FD refers to Regulation
Fair Disclosure. HQ Boom (Bust) is an indicator that takes a value of one when the HQ Economic
Activities Index is above the median (below or at median) in a given quarter. EC Boom (Bust) is
similarly defined based on EC Economic Activities Index. Specifications follow Column 4 of Table VI.
Additional details on all variables are summarized in the Appendix. Standard errors are adjusted
using Newey-West (1987) method with 4-year lag. The t-statistics are reported in parentheses below
the coefficient estimates.
55
Table A.1
Analyst Consensus Earnings Forecasts: Conditional Tests – Continued
Independent Variables
Earnings (t)
EC Earnings (t)
HQ Earnings (t)
Proportion of EC Analysts (t)
(1)
-0.003
(-0.42)
0.158
(3.53)
0.033
(2.98)
0.210
(1.62)
(2)
-0.003
(-0.41)
0.155
(3.45)
0.034
(3.07)
(3)
-0.003
(-0.42)
0.157
(3.48)
0.034
(3.06)
0.212
(1.61)
× High Dispersion Firms (t)
(4)
-0.003
(-0.41)
0.157
(3.53)
0.033
(2.92)
Dependent Variables: Forecast Error (t+1)
(5)
(6)
(7)
(8)
-0.003
-0.003
-0.003
-0.003
(-0.40)
(-0.39)
(-0.42)
(-0.41)
0.152
0.154
0.158
0.155
(3.47)
(3.48)
(3.53)
(3.45)
0.033
0.033
0.033
0.034
(3.06)
(2.99)
(2.98)
(3.07)
0.156
(1.77)
0.231
(1.44)
× Low Dispersion Firms (t)
(9)
-0.003
(-0.42)
0.157
(3.48)
0.034
(3.06)
(10)
-0.003
(-0.42)
0.158
(3.53)
0.032
(3.02)
-0.012
(-1.01)
0.222
(1.77)
× Post-Regulation FD (t)
-0.011
(-0.86)
0.223
(1.73)
× HQ Boom (t)
0.129
(1.89)
0.215
(1.31)
× HQ Bust (t)
Proportion of HQ Analysts (t)
-0.039
(-1.30)
-0.066
(-1.30)
0.018
(0.44)
× Low Dispersion Firms (t)
-0.052
(-1.03)
0.021
(0.45)
0.012
(1.29)
-0.051
(-2.01)
× Pre-Regulation FD (t)
× Post-Regulation FD (t)
0.011
(1.15)
-0.042
(-1.73)
× HQ Boom (t)
× HQ Bust (t)
Included
99,954
0.262
Included
99,954
0.263
Included
99,954
0.263
Included
99,954
0.262
56
0.130
(1.96)
0.227
(1.29)
-0.030
(-1.05)
× High Dispersion Firms (t)
Included
99,954
0.262
(12)
-0.003
(-0.43)
0.157
(3.49)
0.034
(3.17)
0.155
(1.80)
0.233
(1.38)
× Pre-Regulation FD (t)
Controls
Number of Observations
Average R2
(11)
-0.003
(-0.41)
0.156
(3.46)
0.035
(3.13)
Included
99,954
0.264
Included
99,954
0.262
Included
99,954
0.262
Included
99,954
0.263
Included
99,954
0.263
-0.014
(-0.65)
-0.058
(-1.14)
Included
99,954
0.262
0.001
(0.07)
-0.056
(-0.99)
Included
99,954
0.264
57