The Effect of Information Quality on Liquidity Risk

The Effect of Information Quality on
Liquidity Risk
Jeffrey Ng
The Wharton School
University of Pennsylvania
1303 Steinberg Hall-Dietrich Hall
Philadelphia, PA 19104
teeyong@wharton.upenn.edu
Current Draft: February 19, 2008
The relation between information quality and cost of capital is of significant academic
interest and many explanations (e.g., estimation risk, market risk, liquidity) have been
posited for the relation. In this paper, I investigate whether information quality could
affect cost of capital through liquidity risk. Liquidity risk is measured as the covariation
between the returns of a stock and unexpected changes in market liquidity. The empirical
evidence indicates that: i) higher information quality is associated with lower liquidity
risk; and ii) a firm’s cost of capital is lower due to the effect of higher information quality
in lowering liquidity risk. In additional analyses, I find some evidence that the effect of
higher public information quality in lowering liquidity risk is greater for firms with less
private information. Assuming that private information substitutes for public information,
this evidence supports the argument that information effects drive the negative
association between information quality and liquidity risk. I also present some evidence
of an asymmetry in the effect of information quality on liquidity risk. Higher information
quality is associated with lower liquidity risk when there are significant declines in
market conditions, but not when there are significant improvements.
__________________
I thank my dissertation chair, John Core, and members of my dissertation committee, Robert Holthausen,
Richard Lambert, Craig MacKinlay, and Robert Verrecchia for their much-appreciated guidance. I also
thank Michael Boldin, Brian Bushee, Gavin Cassar, Nick Gonedes, Tjomme Rusticus, Cathy Schrand,
Lloyd Tanlu, Rodrigo Verdi, Regina Wittenberg, and workshop participants at the 2007 London Business
School Trans-Atlantic Doctoral Conference, Carnegie Mellon University, Dartmouth, Ohio State
University, and University of Pennsylvania for their helpful comments. I appreciate the financial support
from the Wharton School. I am also grateful for the financial support from the Deloitte & Touche
Foundation.

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1.
Introduction
Lambert, Leuz, and Verrecchia (2007) highlight that in the traditional Capital
Asset Pricing Model (CAPM) higher information quality could lower a firm’s cost of
capital through non-diversifiable market risk (i.e., covariation between a firm’s cash flow
and the market cash flow). The CAPM is based on a model of perfect competition in
which the firm’s share price is a function of investors’ expectations about the firm’s cash
flow, but is independent of the order flow for the shares. In contrast, in a model of
imperfect competition, a firm’s share price is also a function of order flow (Verrecchia,
2001). In a model of imperfect competition, order flow captures the element of adverse
selection in the trades of a firm’s shares and there could be non-diversifiable risk
incremental to market risk that captures the effect of order flow on share
prices. Following Pastor and Stambaugh (2003), I refer to this incremental risk as
“liquidity risk.” Being an incremental component of a firm’s cost of capital, higher
liquidity risk increases the discount in the pricing of a firm’s expected cash flow in much
the same fashion as higher market risk increases the discount. In this paper, I investigate
whether liquidity risk could provide an additional explanation for the relation between
information quality and cost of capital.
Pastor and Stambaugh (2003) define liquidity risk as the covariation (“liquidity
beta”) between the returns of a stock (due to the effects of order flow) and the market
liquidity factor.1 The market liquidity factor captures the unexpected changes in market
liquidity, with market liquidity measured as the aggregate (i.e., market-level) price
1 Liquidity and liquidity risk are distinct properties of a stock. In this paper, liquidity generally refers to the
ease and cost of trading the stock without moving its price and this property is idiosyncratic to the stock. In
contrast, liquidity risk, being a type of systematic risk, is the covariation between the returns of a stock and
market liquidity changes. Similarly, market risk is the covariation between the returns of a stock and the
market returns.
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fluctuations induced by order flows in the equity market. Lower market liquidity reflects
greater aggregate price fluctuations induced by order flows. Stocks with higher liquidity
risk have returns that covary more positively with changes in market liquidity because of
the greater impact of their order flows on their prices. Ex-ante, investors expect higher
returns from stocks with higher liquidity risk because the returns of these stocks when
market liquidity declines are expected to be relatively more negative than other stocks.2
Consistent with this argument, Pastor and Stambaugh provide evidence that stocks with
higher liquidity risk have higher expected returns, i.e. higher cost of capital.
I hypothesize that higher information quality could lower liquidity risk. In this
paper, I define information quality as an attribute of publicly available information that
could lower i) investors’ information uncertainty over the value of a stock and/or ii)
adverse selection among investors when stock trades occur.3 Higher information quality
reduces uncertainty and adverse selection, and thereby could reduce liquidity risk by
attenuating the sensitivity of a firm’s share price to the non-diversifiable component of
risk due to order flows. For example, the more market makers know about firm value
from public information of higher quality, the less they need to depend on order flows to
make inferences about firm value and price-protect against the possibility of adverse
selection. This means that in times of a decline (improvement) in market liquidity when
there is significant selling (buying) pressure on equities in general, the negative (positive)
price impact of sell (buy) order flows could be less for a stock with higher information
2 Given that liquidity risk is a covariation property, stocks with high liquidity risk are also expected to have
higher returns when there are increases in market liquidity. In standard asset pricing theory, risk-averse
investors expect higher returns ex-ante for the downside risk of lower returns in bad market conditions,
even when there is the upside potential of higher returns in good market conditions.
3 Higher information quality can be interpreted as more information or higher quality information (Leuz
and Verrecchia, 2000). In this paper, I use both types of information quality attributes to investigate the
relation between information quality and liquidity risk.
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quality. Consequently, in a model of imperfect competition higher information quality
could reduce a firm’s cost of capital through liquidity risk, along with the reduction in
cost of capital through market risk. More details on the above hypothesis, including some
arguments against the hypothesis, are provided in section 2.
The empirical results indicate that information quality is negatively associated
with liquidity risk. I measure information quality as the relevance and reliability of
reported earnings, frequency and precision of management earnings forecasts, and
coverage and consensus of analyst earnings forecasts. I find significant evidence that
more precise management forecasts, more frequent management forecasts, greater analyst
coverage, and more consensus among analysts are associated with lower liquidity risk.
Consistent with Lambert et al.’s (2007) theoretical prediction that higher information
quality lowers market risk, I also find that information quality is negatively associated
with market risk.
The economic significance of the effect of higher information quality in lowering
cost of capital through lower liquidity risk appears to be reasonable and larger than that
through lower market risk. For example, a firm with management forecast frequency that
is one standard deviation above the mean has an annual cost of capital that is lower by
about 0.85% due to lower liquidity risk and 0.24% due to lower market risk. This result
suggests the importance of information quality in affecting trade-related outcomes such
as liquidity risk. As for market risk, investors’ assessment of the covariation between the
cash flow of a firm and the market might be driven more by the economic fundamentals
of the firm than by the quality of the information used in the assessment. I also find
evidence that suggests that the attributes of management forecasts and analyst forecasts
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are more economically significant than those of reported earnings in lowering cost of
capital through liquidity risk and market risk. This may be due to the fact that
management forecasts and analyst forecasts are timelier, forward-looking, and less
constrained by general accounting standards.
Using cross-sectional analyses, I find the effect of higher public information
quality in lowering liquidity risk is stronger for firms with less private information.
Assuming that private information substitutes for public information, this evidence
supports the argument that information effects drive the relation between information
quality and liquidity risk. Finally, I present some evidence of an asymmetry in the effect
of information quality on liquidity risk. Higher information quality is associated with
lower liquidity risk when there are significant declines in market conditions in terms of
market liquidity changes and market returns, but not when there are significant
improvements. This suggests that information quality may be more important in lowering
liquidity risk when market conditions deteriorate, perhaps because of the importance of
information in influencing trade-related outcomes in these market conditions.
My paper contributes towards the broader objective of improving our
understanding of the mechanisms underlying the relation between information quality
and cost of capital, a relation that has been of significant academic interest (e.g., Botosan,
1997; Francis et al., 2004, 2005; Core, Guay, and Verdi, 2007). More specifically, I
investigate and provide empirical evidence on the relation between information quality
and liquidity risk, a risk that has been identified recently in the asset pricing literature to
be significantly associated with cost of capital. By providing evidence that higher
information quality could lower cost of capital through lower liquidity risk (and lower
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market risk), my paper extends Lambert et al. (2007) who demonstrate theoretically that
higher information quality could lower cost of capital through lower market risk. I
acknowledge, however, that my hypothesis on the effect of information quality on
liquidity risk is exploratory, and thus requires more rigorous study before it could be
interpreted as offering a comprehensive theory of how information quality relates to asset
pricing under imperfect competition. Finally, I also investigate related issues such as the
differences in the effect of different information quality attributes, as well as compare and
contrast the effect of information quality on cost of capital through liquidity risk and
market risk.
The rest of this paper is organized as follows. Section 2 provides a review of the
related literature and develops the hypothesis on the effect of information quality on
liquidity risk. In Section 3, I describe the main variables used in my empirical tests.
Sections 4 and 5 present my empirical designs and the results of my empirical analyses.
Section 6 concludes.
2.
The effect of information quality on liquidity risk
2.1
Brief overview of liquidity risk
The recent asset pricing literature highlights that liquidity risk is a significant
systematic risk that is priced by investors (Pastor and Stambaugh, 2003; Acharya and
Pedersen, 2005). Pastor and Stambaugh define liquidity risk as the covariation between a
stock’s return and the market liquidity factor (LIQ) that represents unexpected changes in
market liquidity. A higher covariation indicates higher liquidity risk. Note also that lower
market liquidity reflects greater aggregate price fluctuations induced by order flows in the
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equity market. Pastor and Stambaugh provide evidence that liquidity risk has a significant
incremental risk premium using the following four-factor asset pricing model
L
M
S
H
r ? r
= ? + ? LIQ + ? MKT + ? SMB + ? HML + ?
(1)
t
rf ,t
t
t
t
t
t
where rt – rrf,t is the monthly return in excess of the risk-free rate for a stock at time t, LIQ
is the market liquidity factor at time t, and MKT, SMB, and HML are the Fama and
French (1993) factors at time t.
The focus of this paper is on the effect of information quality on liquidity risk, ?L.
In this paper, I also provide some analyses of the effect of information quality on market
risk, ?M, to compare and contrast the effect of information quality on cost of capital
through the two types of systematic risk. In this paper, I do not examine the potential
effects of information quality through the risk related to the SMB and HML factors (i.e.,
?S and ?H). While the literature suggests that size and book-to-market capture
covariations in returns beyond the covariation explained by market returns, the exact
nature of the covariations remains unclear (Davis, Fama, and French, 2000). This makes
it difficult to develop hypotheses on the relation between i) information quality and ?S
and ii) information quality and ?H.4 In addition, I do not examine the potential effect of
information quality on cost of capital that arises through liquidity, as opposed to liquidity
risk (Leuz and Verrecchia, 2000; Verrecchia and Weber, 2006). Nor do I examine the
cost of capital effect that may occur if information quality itself is a priced risk factor
(Francis et al., 2005).5
4 For example, assume that information quality is negatively associated with ?S. The direct interpretation of
the result is that stocks with higher information quality are smaller and consequently have returns that are
similar to smaller firms. However, it is difficult to make further inferences about how information quality is
associated with any specific type of covariation (i.e., systematic risk) captured by size.
5 I do not include an information risk factor into Eq. (1) for three reasons. First, from a theoretical
perspective, it is not clear that information quality per se is a risk factor (Lambert et al., 2007). Second,
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2.2
The effect of information quality
Lambert et al. (2007) demonstrate theoretically that firms with higher information
quality have lower market risk. The intuition for this result is fairly straightforward.
Market risk is defined by the covariation between the expected cash flow of a firm and
the market. When information quality is better, expectations about a firm’s cash flow are
more precise, and therefore the covariation is smaller. This means that market risk, ?M, is
expected to be negatively related to information quality. As discussed earlier in the
introduction, in a model of imperfect competition, the exposure of the returns of a stock
to changes in market liquidity could give rise to liquidity risk, ?L. Early studies on
liquidity risk document the existence of liquidity risk as a systematic risk (Chordia, Roll,
and Subrahmanyam, 2000; Hasbrouck and Seppi, 2001; Huberman and Halka, 2001) and
examine the asset pricing consequences of liquidity risk (Pastor and Stambaugh, 2003;
Acharya and Pedersen, 2005; Sadka, 2005). To my knowledge, there is no prior literature
on whether information quality could be a determinant of liquidity risk.
I hypothesize that higher information quality could lower liquidity risk. That is, I
argue the returns of a stock with higher information quality will be less sensitive to
changes in market liquidity. Higher information quality reduces uncertainty and adverse
selection, and thereby could reduce liquidity risk by attenuating the sensitivity of a firm’s
share price to the non-diversifiable component of risk due to order flows. For example, in
times of a decline in market liquidity, there is generally selling pressure on equities.6
there is no clear consensus based on empirical evidence that information quality per se has the properties of
a risk factor (e.g., Francis et al., 2005; Core et al., 2007). Finally, for comparability, I want to obtain the
betas using an empirical asset pricing model that follows Pastor and Stambaugh (2003).
6 Pastor and Stambaugh (2003) provide some evidence of phenomenon, which they term as “flight to
quality”. For parsimony, I illustrate the hypothesis using an economic state in which there is a decline in
market liquidity. The illustration can be easily adapted to an economic state in which there is an increase in
market liquidity.
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Stocks with lower information quality could experience more negative returns if buyers
offer lower prices to sellers of these stocks because of the higher information uncertainty
and/or greater probability of adverse selection associated with poor information quality.
For example, market makers, in response to the sell orders, may offer lower bid prices or
reduce depth (i.e., quantities that they are willing to buy at each bid price) due to
concerns of buying stocks with more uncertain outcomes or of buying “lemons” due to
the greater adverse selection.7 Note that a reduction in depth increases the downward
price impact of large sell trades. These concerns may be exacerbated by the fact that there
is usually significant market volatility/uncertainty when market liquidity is low. For
example, Pastor and Stambaugh (2003) provide evidence of a significant negative
correlation (correlation = -0.57) between market liquidity and market volatility.
Furthermore, the relative returns of stocks with lower information quality could
be even more negative if: i) investors tend to be more risk averse in times of low market
liquidity and commonality in trading decisions creates pressure on stock prices (Pastor
and Stambaugh, 2003); and ii) investors selling some stocks in their portfolios generally
prefer to mitigate risk by selling stocks with more information uncertainty. The second
assumption relies on the notion that investors perceive stocks with lower information
quality (and consequently higher information uncertainty) as being riskier (e.g., Klein and
Bawa, 1976; Barry and Brown, 1985; Zhang, 2006).
Hence, my hypothesis, stated in the alternative form, is:
Liquidity risk is negatively associated with information quality.
7 Note that other investors may also not be willing to step in to act as trade counterparties for similar
reasons.
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It is important to note, however, that there are also some arguments for liquidity
risk to be positively associated with information quality. For example, in times of
declines in market liquidity, if investors prefer to sell liquid stocks to save on transaction
costs and higher information quality is associated with more liquid stocks, then the
selling pressure will be on stocks with higher information quality (Pastor and Stambaugh,
2003). This, in turn, may lead to stocks with higher information quality having higher
liquidity risk. Other reasons for preferring to sell stocks with higher information quality
include less uncertainty about getting a fair price and greater availability of buyers who
are willing to act as trade counterparties due to the higher information quality. Hence,
there appears to be some “tension” in the above hypothesis. In fact, given the positive
risk premium for liquidity risk, a positive association between information quality and
liquidity risk would be intriguing in that it suggests that higher information quality could
result in higher cost of capital (note that cost of capital equals risk multiplied by risk
premium).
3.
Measurement of systematic risk and information quality
3.1
Measurement of systematic risk (liquidity beta and market beta)
I measure the systematic risk of each stock at the end of each year, using data
from 1967 to 2005 that is obtained from the CRSP database. The computation of a
stock’s liquidity beta, ?L, and market beta, ?M, as expressed in Eq. (1), requires the
following steps (details provided in Appendix A): i) estimate for a stock its monthly
liquidity, ?, using the daily stock data within each month, ii) create a monthly time-series
of monthly market liquidity by averaging the ? of all stocks in each month, iii) estimate
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