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Does High-Quality Financial Reporting Mitigate the Negative Impact of Global Financial Crises on Firm Performance? Evidence from the United Kingdom

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Does High-Quality Financial Reporting Mitigate the Negative Impact of Global Financial Crises on Firm Performance? Evidence from the United Kingdom

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Prior literature has claimed that accounting plays a negative role in a financial crisis. The current study sought to determine whether this effect is dependent on the quality of financial reporting. Specifically, this study examined the impact of the quality of financial reporting (as measured via earnings quality) on liquidity (measured by the bid-ask spread) in the equity market during the 2008–2009 global financial crisis in the United Kingdom. We found, as expected, that market liquidity was much lower during the crisis than prior to the crisis; however, firms with high-quality financial reporting suffered fewer negative effects as a result of the financial crisis. The results were robust after controlling for other influences, such as return volatility, loss making, market value of equity, and other potential endogeneity problems. In addition, adopting alternative models for earnings quality did not alter our inferences. Our results support the notion that high-quality accounting information can reduce information asymmetry and hence enhance investor confidence during a financial crisis. The results suggest that a stable financial reporting system is an important part of that overall economic fabric. Our findings will help build a framework on which an overall financial crisis risk-management strategy can be developed to avoid future crises.
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Does High-Quality Financial Reporting Mitigate the Negative Impact
of Global Financial Crises on Firm Performance?
Evidence from the United Kingdom
Zhiwei Lin, Shenzhen Stock Exchange, China
Yihong Jiang, Shanghai University of Finance and Economics
*Qingliang Tang, University of Western Sydney
Xiangjian He, University of Technology, Sydney
Forthcoming, 2014, Australasian Accounting Business and Finance Journal
*Corresponding author: Dr. Qingliang Tang, School of Business, University of Western Sydney,
Locked Bag 1797, Penrith South DC, NSW 2751, Australia
Tel: +61 2 9685 9465, E-mail: q.tang@uws.edu.au
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Does High-Quality Financial Reporting Mitigate the Negative Impact
of Global Financial Crises on Firm Performance?
Evidence from the United Kingdom
Abstract: Prior literature has claimed that accounting plays a negative role in a financial crisis.
The current study sought to determine whether this effect is dependent on the quality of
financial reporting. Specifically, this study examined the impact of the quality of financial
reporting (as measured via earnings quality) on liquidity (measured by the bid-ask spread) in the
equity market during the 20082009 global financial crisis in the United Kingdom. We found, as
expected, that market liquidity was much lower during the crisis than prior to the crisis; however,
firms with high-quality financial reporting suffered fewer negative effects as a result of the
financial crisis. The results were robust after controlling for other influences, such as return
volatility, loss making, market value of equity, and other potential endogeneity problems. In
addition, adopting alternative models for earnings quality did not alter our inferences. Our
results support the notion that high-quality accounting information can reduce information
asymmetry and hence enhance investor confidence during a financial crisis. The results suggest
that a stable financial reporting system is an important part of that overall economic fabric. Our
findings will help build a framework on which an overall financial crisis risk-management strategy
can be developed to avoid future crises.
Key words: Financial crisis, Liquidity, Financial reporting quality, Earnings management
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I. Introduction and Research Motivation
The global financial crisis that began in 2008 significantly shook investor confidence worldwide
and raised serious concerns about the stability of the financial system. The crisis originated in the
collapse of the subprime mortgage market in the United States and subsequently evolved into a
much more dangerous phenomenon, causing many corporate casualties in the United States and
Europe. The literature concerning the causes of the crisis has attempted to clarify the role played
by accounting; more specifically, there has been heated debate regarding whether fair-value
measurement substantially accelerated the market meltdown. Critics have alleged that fair values
are less transparent and increase information asymmetrythat after fair-value accounting was
performed to recognize many unrealized losses for financial assets and liabilities in an attempt to
avoid a vicious cycle of falling prices, fair-value accounting in fact triggered this cycle, or at least
amplified and exacerbated its severity (see Liao et al., 2010). However, Madras Gartenberg and
Serafeim (2009) found inconsistencies in this idea, showing that fair valuation depressed equity
values during the financial crisis. Laux and Leuz (2010) found little evidence that downward
spirals or asset fire sales in certain markets were the result of fair-value accounting. Barth and
Landsman (2010) concluded that fair-value accounting played little or no role in the 2008
recession.
The debate highlights the importance of accounting in the stability of economic systems and in
maintaining investor confidence, both of which were notably absent during the crisis. The nature
of the debate motivated us to consider more generally the role of financial reporting in the crisis.
Fair valuation is only one part of financial reporting. Moreover, fair valuation is not likely to be a
serious issue for non-financial institutions (Madras Gartenberg and Serafeim, 2009). Therefore, in
the current paper, we decided to focus our attention on the quality of financial reporting as a
whole to determine its effect on the quality of information. We argue that high-quality financial
reporting will provide timely, relevant, and transparent information that could help minimize
uncertainty. Conversely, we argue that low-quality financial reporting is associated with
ambiguous, misleading, or unreliable information, which is likely to increase information
asymmetry and market illiquidity. Because market participants are likely to face great uncertainty
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and risk, they may pay closer attention to the credibility of information in making a decision
during a financial crisis. Hence, we would expect to see a positive relationship between the
quality of financial reporting and liquidity of the equity market. In our context, this would mean
that firms with higher-quality financial reporting would have been less adversely affected by the
2008 meltdown, all other factors being equal.
Our research design relied on prior studies (Barth et al., 2008; Jones et al., 2008; H. Chen et al.,
2010), and we used earnings quality as a valid proxy for financial reporting quality (Leuz et al.,
2003; Biddle et al., 2009; F. Chen et al., 2010). Our proxy for information asymmetry was the
bid-ask spread, which is also frequently used in this line of study (Mohd, 2005; Leuz and
Verrecchia, 2000; Bhat and Jayaraman, 2009). We chose the United Kingdom as our research
setting, because, after the United States, the United Kingdom was hit most severely by the global
economic recession.
The findings were generally consistent with our predictions. We found that the bid-ask spread
widened during the financial crisis, suggesting that liquidity of the share market decreased.
However, we also found evidence that liquidity increased with financial reporting quality during
the period of financial distress. That is, firms with higher-quality financial reporting suffered a
relatively less significant negative effect of the crisis on their liquidity. Financial reporting played a
mitigating role, not only for large firms but for small and medium-sized firms as well. Finally, we
particularly concerned ourselves with whether our prediction would hold for financial firms,
because they were the most seriously impacted. Our results offer unambiguous evidence that a
transparent accounting system can also help financial institutions to be more stable during an
economic downturn. Our findings regarding the relationship between financial reporting quality
and bid-ask spread were robust when we controlled for endogeneity and other confounding
factors.
This paper makes several contributions to the literature. First, our evidence corroborates the
findings from a glowing body of research on the relationship between accounting and auditing
and the global financial crisis (e.g., Liao et al 2010, 2013; Aldamen et al, 2012). Second, there is
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limited evidence regarding the overall financial reporting system on the crisis. Our research fills
this gap by exploring and explicitly showing the links between information asymmetry and the
quality of financial reporting. Third, the vast majority of extant studies in this area focus on U.S.
firms, whereas there is a lack of analysis of the European experience on the association between
accounting and the financial crisis. Because the institutional and economic factors are likely to be
different between the two regions, the associative pattern is likely to differ as well. Therefore, our
study should expand our knowledge on this issue. Fourth, Lang and Maffett (2011) found that
firms in a global sample with greater transparency experienced less liquidity volatility and fewer
extreme illiquidity events during the financial crisis. However, there is a concern that, since their
study was conducted in an international setting, the observed differences in liquidity at the firm
level may not have been caused by differences in accounting quality; instead, they might have
been a result of institutional differences on the national level. To address this issue, we chose a
national setting for our study, which has the following advantages: (1) firms in our sample used
the same accounting standards; (2) this approach reduced the difficulty in controlling for many
national institutional differences that potentially affect financial reporting characteristics, such as
legal protection for investors, disclosure requirements, and ownership concentration; and (3) it is
well documented that different countries experienced very different impacts of the financial crisis
on their stock market, which could not be easily resolved in an international setting. Thus, we
believe our results will complement evidence reported by earlier studies.
In sum, our robust empirical results suggest that a sound financial reporting system mitigates
investor concerns about information uncertainty and increases investor confidence, improving
market liquidity, and thus will play a positive role in a financial crisis. The results are consistent
with the notion that a stable financial reporting system is an important part of our overall
economic fabric. In this sense, our project provides new data for the international accounting
literature, and important policy implications should follow to improve regulatory arrangements
with respect to financial reporting quality, such as reducing ambiguous accounting methods and
enhancing auditor independence, to mention two examples. Our findings will help build a
framework for capital market regulators and other government agencies, on the basis of which a
financial crisis risk management strategywhether national or internationalincluding
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accounting and corporate governance, can be developed with the hope that future liquidity crises
of a similar nature could be avoided.
The remainder of the article proceeds as follows. Section II provides a literature review and
develops our hypothesis. Section III discusses the research methodology. Section IV presents the
results. Section V concludes the article.
II. Literature Review and Hypothesis Development
Information asymmetry, investor decision-making, and market liquidity
The association between financial information and information asymmetry is a key issue in
understanding the role of accounting in the financial crisis. Previous studies have shown that
information asymmetry arises as a result of separation of ownership and control, and managers
of a company may have private inside information that is not available to outside investors.
However, managers are not allowed to trade their company’s shares on inside information, so
their knowledge should not directly affect liquidity. On the other hand, outside investors are not
equally informed. Some investors, such as institutional shareholders who have a close
relationship with managers, may have access to or share some private information with the
managers, meaning that they might have a comparative advantage in processing the accuracy of
accounting estimates. These investors are informed investors. In contrast, uninformed investors
have difficulty in evaluating the quality of and risks associated with the reported assets and
liabilities of a given firm. This information asymmetry leads to an adverse selection problem, in
which informed investors exploit their informational advantage at the expense of uninformed
investors (Glosten and Milgrom, 1985). Consequently, market participants facing an adverse
selection problem will seek price protection to increase the bid-ask spread as a means of
protecting themselves against expected losses from trading with more informed investors. This
argument suggests that information asymmetry increases the bid-ask spread, thereby reducing
market liquidity.
Determinants of financial reporting quality
There is a general consensus that the purpose of financial reporting, particularly earnings
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information, is to narrow information asymmetry and market uncertainty for external users and
investors. However, the quality of financial reporting is not constant. Extant literature shows that
the quality of financial reporting depends on the following country-level factors: the underlying
legal system (La Porta et al., 1998); whether the economy is market-oriented or bank-oriented
(Durnev and Kim, 2005); the accounting standards adopted (Barth et al., 2008; Tang et al., 2010);
and the level of law enforcement (Hope, 2003). At the firm level, quality of financial reporting is
associated with characteristics of the firm, such as size, auditor type, overseas listings, recent
increases in capital or debt, and complexity (Morris and Gray, 2007).
It is also widely accepted that management has incentives to manage earnings, which would
result in higher earnings opacity and increase information risk. Such incentives include improving
market performance, boosting share price, increasing analyst following, and others (Barth et al.,
1999; Schrand and Walther, 2000). If the market price is expected to react to unexpected
earnings (Ball and Brown, 1968) and rational managers believe that investors are unable to detect
opportunistic behaviour (Bernard and Thomas, 1990, 1989; Abarbanell and Bernard, 1992; Ball
and Bartov, 1996; Sloan, 1996), then managers will take advantage of the inherent subjectivity in
accounting assumptions and standards to achieve personal benefit by engaging in earnings
management (Ahmed et al., 1999; Holthausen and Verrecchia, 1990; Healy and Palepu, 1993).
Apart from capital market considerations, there may be direct economic consequences of
earnings measures, for example, regulatory and political costs, debt covenants, and CEO
compensation (Aboody and Kasznik, 2000; Aboody et al., 2004; Watts and Zimmerman, 1990).
Dechow et al (2010) concluded that management discretion, distortions of disclosure, estimation
errors, and manipulation of the size of reported gains or losses all reduce the quality of financial
reporting.
In addition, previous literature has emphasized that earnings management often focuses on the
discretionary (rather than the usual) component of accruals. Discretionary accruals are subject to
arbitrary interpretation of flexible accounting standards by self-interested managers and
consequently are believed to be obscure and biased about the underlying income and financial
position of a firm (e.g., Chen, H. et al., 2010). Since discretionary inputs are less precise and
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involve more risk, information asymmetry between informed and uninformed investors would be
more severe with earnings assessed on the basis of discretionary accruals.
Market perception of accounting information quality
Prior literature has shown that high-quality financial disclosure and earnings figures reduce
information asymmetry and increase investor confidence (Francis et al., 2004, 2005; Lambert et
al., 2007) and has documented a positive association between the quality of accounting
information and capital market performance (Kim and Verrecchia, 1994; Lang and Maffett, 2011).
For example, Welker (1995) found that analysts’ ratings of firms’ disclosures are significantly and
negatively associated with the bid-ask spread. Leuz and Verrecchia (2000) showed that firms
committing themselves to the International Accounting Standards or the U.S. Generally Accepted
Accounting Principles (a proxy for increased levels of disclosure) experienced a lower bid-ask
spread than firms that used the German Generally Accepted Accounting Principles. Brown and
Hillegeist (2007) also documented an inverse relationship between the spread-based measure of
information asymmetry and disclosure quality, which reduces the likelihood that informed
traders will discover and trade on private information. Other studies that examined the cost of
capital found that disclosure not only reduced the estimation risks of future cash flows, but it
also helped constrain agency problems (Lambert et al., 2007). Consistent with this argument,
Francis et al. (2004, 2005) found that the cost of capital is negatively correlated with seven
earnings attributes, especially accrual quality.
In sum, higher-quality financial reporting should allow investors to make more informed business
decisions and should restrict opportunities for insiders to expropriate the wealth of outside
investors and creditors. The empirical evidence suggests that information asymmetry is an
inverse function of financial reporting quality. These studies also imply that there is a link
between information quality and liquidity. Systematic risk is a covariation/sensitivity effect. A
firm with higher systematic risk will perform relatively worse (better) during bad (good)
macroeconomic conditions (Campbell et al., 1997). Market liquidity reflects the ability to trade
large quantities of shares quickly, at a low cost, and without moving the price (Pastor and
Stambaugh, 2003). A decline in liquidity is typically associated with an economic status in which
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there is investor outflow from the equity markets amidst high market volatility and risk aversion
(e.g., Chordia et al., 2000; Brunnermeier and Pedersen, 2009). In addition, because of investors’
aversion to risk, the demand for shares of firms with higher-quality information is subject to less
fluctuation conditional on market liquidity. Decreased liquidity will affect the investor behaviour
of different firms differently. Investors in companies associated with a high degree of uncertainty
and adverse selection problems because of poor information quality are more likely to leave the
market. In addition, market makers are less likely to provide liquidity because of concerns about
adverse selection, resulting in a further reduction in investor demand for these shares. Thus,
these firms perform worse when liquidity decreases. Conversely, when liquidity increases, there
is an inflow of investors and market makers, which increases the demand and liquidity of the
shares of the firms associated with greater uncertainty and adverse selection. Thus, the returns
of firms with lower information quality (i.e., higher information risk) are more sensitive to
changes in market liquidity. That is, information quality contributes to liquidity risk (Ng, 2011).
Moreover, from the signalling theory perspective (Spence, 1973, 2002), it can be argued that
higher-quality accounting information provides a more accurate indication of underlying
performance, and firms with higher operating performance are expected to have more
incentives to provide earnings information of higher quality to show the true status of the firm
and thereby avoid adverse selection. Consequently, this should narrow information asymmetry
and reduce information risk and illiquidity, particularly during a period of financial distress. In
contrast, poor performing firms do not want to make their financial results transparent, making
it more difficult for investors to understand the true situation. In addition, previous studies
suggest that business transaction costs include the cost to search, collect, and interpret relevant
information (Williamson, 1981). Thus, a higher-quality earnings figure can help provide market
participants with reliable information, so that the buyer and seller of shares of the firm can
relatively easily reach an agreement about the true value of the firm, speeding the transaction.
As a result, market liquidity can be enhanced.
Financial reporting quality and market liquidity during the crisis
We assert that high-quality financial reporting is even more important for investors during times
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of financial distress. Financial crises often occur suddenly, seriously disrupting capital markets
(Mishkin, 1992) and resulting in substantial share price volatility and asset value meltdowns. In
the context of a financial crisis, market uncertainty is greater than in a normal business
environment and investor confidence is often significantly reduced, which consequently leads to
increased demands for reliable information. On the other hand, managements incentives for
wealth expropriation become stronger, because tough times make some insiders act more
unethically, perhaps as a survival tactic (Johnson et al., 2000). As a result, earnings management
would be more likely to be used to hide expropriation or poor performance caused by depressed
economic conditions. Outside investors, therefore, become more sensitive than previously to
firms’ integrity regarding financial disclosures (Rajan and Zingales, 1998), and they are inevitably
more aggressive in seeking price protection. Accordingly, the current global financial crisis
provides a unique setting to empirically test the link between financial reporting quality and
information asymmetry as measured by lower market illiquidity, or bid-ask spread. Based on the
above arguments, we propose the following testable hypothesis:
Hypothesis: Higher-quality financial reporting mitigates the negative effect of a
financial crisis on market liquidity, as measured by the bid-ask spread.
III. Research Design
a. Empirical Model
According to the literature (e.g., Leuz et al., 2000; Christensen et al., 2011), we used the following
model to determine the impact of a crisis on bid-ask spread (see Table 1):
0 1 2 3 4 1
5 1 6 1
log( ) *
it it it it it it
it it it
Spread Crisis AQ Crisis AQ Turnover
Size Volatility IndFE
 
 

 
 
We used the log value of the bid-ask spread, measured as the yearly median and mean quoted
spread, as the dependent variable that increases (decreases) illiquidity (liquidity).
1
Crisis is an
indicator variable that equals 1 for observations during the crisis period (20082009) and 0 for
the non-crisis period (20052007). AQ is a financial reporting quality proxy (see next section for
details of calculation of AQ). The interaction term, Crisis*AQ, captures the effect of financial
1
Our model implies that annual earnings information is associated with investors’ daily trading activities (Ng,
2011; Dechow et al., 1996; Affleck-Graves et al., 2002).
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reporting on market liquidity. If high-quality reporting mitigates the impact of the crisis, the
coefficient is expected to be negative.
We used one market microstructure measure, the bid-ask spread, to measure information
asymmetry. The bid-ask spread is a well-developed and often employed proxy in the accounting
and finance literature (Krinsky and Lee, 1996; Leuz and Verrecchia, 2000; Roger, 2008; Bhat and
Jayaraman, 2009; Ball et al., 2012). Muller et al. (2011, p. 1144) suggested that the bid-ask spread
has good theoretical underpinnings and that the component attributable to information
asymmetry can be isolated. When information asymmetry among equity investors is high,
informed investors can exploit the information advantage at the expense of uninformed investors.
Uninformed investors realize that they are faced with an adverse selection problem and therefore
seek to increase the bid-ask spread to protect themselves against expected losses from trading
with more informed investors (Venkatesh and Chiang, 1986; Chae, 2005). In particular, the bid-ask
spread is a better measure of information asymmetry among market participants that is
especially useful as a dependent variable in a setting characterized by rapidly changing levels of
market uncertainty, i.e., during financial crises (Liao et al., 2010).
We considered the following control variables with reference to prior literature. We included
turnover to control for market makers’ inventory holding costs and risk, and a negative
coefficient is predicted (Muller et al., 2011). Turnover is the log value of yearly share median
turnover (daily US$ trading volume divided by the market value at the end of each trading day).
Size is the log value of equity, calculated as the stock price times the number of shares
outstanding (in US$ million) at the end of the year. We controlled for firm size because large
firms are likely to be scrutinised more closely and thus could be more transparent, such that
bigger firms are expected to have lower market risk than smaller firms (Ng, 2011). Volatility is the
log value of return volatility, which is the standard deviation of daily stock returns for the year.
b. Financial Reporting Quality Proxies
AQ serves as a proxy for financial reporting quality, which equals the absolute value of
discretionary accruals multiplied by 1. Discretionary accruals equal total accruals minus
estimated normal accruals, and we used the following five widely adopted models to estimate
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discretionary accruals.
2
Note that AQ is actually a measure of earnings quality that decreases
earnings management and thus increases financial reporting quality.
Decow et al. (2010) defined earnings quality as follows: Higher-quality earnings provide more
information about the features of a firm’s financial performance that are relevant to a specific
decision made by a specific decision maker. They organized the earnings quality proxies into three
broad categoriesproperties of earnings, investor responsiveness to earnings, and external
indicators of earnings misstatementsand emphasized that they reached no conclusion about
the single best measure of earnings quality. Researchers typically use the earnings response
coefficient as a proxy for investor responsiveness; this measure is not suitable for our study, as
the bid-ask spread also gauges investor responsiveness. External indicators, such as earnings
restatements, are available for a limited number of firms. Therefore, it appears that the
properties of earnings are the most appropriate proxy for earnings quality in our context.
Properties of earnings include earnings persistence and accruals, earnings smoothness,
asymmetric timeliness and timely loss recognition, and target beating. These proxies are used for
earnings management, which is assumed to erode earnings quality. Accruals and abnormal
accruals have the following features. In its favour, measurement of accruals attempts to isolate
the managed or error component of accruals, and the use of these models has become the
accepted methodology in accounting to capture discretion. However, tests of the
determinants/consequences of earnings management are joint tests of the theory and the
abnormal accrual metric as a proxy for earnings management. Correlated omitted variables
associated with fundamentals, especially performance, are of concern, given the dependence of
normal accruals on fundamentals and the endogeneity of the hypothesized
determinants/consequences with the fundamentals (Decow et al., 2010). Despite these
limitations, it appears that they influence other measures and are more suitable for our project.
For instance, it is still not clear whether earnings smoothing would increase or decrease earnings
quality. Because we have already considered the effect of loss as firm, timely loss recognition is
unlikely to increase our test power.
2
Bartov et al. (2000) found that cross-sectional models were better than time-series models in detecting
earnings managements.
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We next discuss the specific models used in our study to measure earnings quality.
(1) Jones Model
Jones (1991) argued that normal accruals are determined by two fundamentals: change of
revenue and fixed assets investment. Accruals not explained by fundamentals are discretionary
and will add bias into earnings, which lowers the quality of earnings as a measure to reflect
performance of the firm. Thus, the basic model (Jones, 1991; DeFond and Jiambalvo, 1994) is:
1 -1 2 3
(1 )
it it it it it
TA Assets REV PPE
 
 
TA is total accruals scaled by lagged total assets for firm i in year t, in which total accruals are
calculated as the difference between income before extraordinary items and operating cash flows.
Assets are the year-end assets for company i in year t-1. ΔREV is the change in sales from year t-1
to year t. PPE is gross property, plant, and equipment. TA, ΔREV, and PPE are scaled by Assets.
We estimated coefficients of the model from cross-sectional industry regressions by two-digit SIC
groups for the year. We required a minimum of 10 observations for each two-digit SIC group for
the year. The discretionary accrual is the predicted residual of the model. We then multiplied 1
by the absolute value of the residual and referred to it as AQ_JM. The higher the AQ_JM, the
higher the earnings quality/financial reporting quality.
(2) Modified Jones Model
The Jones model assumes that all credit sales are non-discretionary; however, Dechow et al.
(1995) argued that change of accounting receivables is discretionary and should be deducted
from change of sales to estimate normal accruals. Therefore, the modified Jones model (Dechow
et al., 1995; DeFond and Subramanyam, 1998) is:
1 -1 2 3
(1 ) ( )
it it it it it it
TA Assets REV AR PPE
 
 
ΔAR is the change in accounts receivable from year t-1 to year t, deflated by total assets of year
t-1. The definitions of other variables in the model are the same as for the Jones model. Similar to
Model 1, discretionary accruals (AQ_MJM) are equal to the absolute value of the predicted
residuals of the model, multiplied by 1. The higher the AQ_MJM, the higher the quality of
financial reporting.
(3) Adapted Jones Model
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Dechow et al. (2003) contended that the modified Jones model assumes that all credit revenues
are discretionary and thus induces a positive correlation between discretionary accruals and
current sales growth. They proposed the following adapted Jones model, which includes only the
unexpected portion of the change in accounts receivables:
1 -1 2 3
(1 ) ((1 ) )
it it it it it it
TA Assets k REV AR PPE
 
 
The coefficient k is estimated in each two-digit SIC group for the year by the following model,
which captures the expected change in accounts receivable for a given change in sales:
it it it
AR k REV

 
The definitions of the variables in the two models are the same as in the modified Jones model.
Then, similarly, the discretionary accrual (AQ_AJM) is the absolute value of the predicted residual
of the model multiplied by 1.
(4) Modified Jones Model with Book-to-Market Ratio and Cash Flow from Operations
Larcker and Richardson (2004) argued that the discretionary accruals estimated using the
modified Jones model are correlated with growth in operating performance and contain
measurement errors. Hence, they added the book-to-market ratio and cash flow from operations
to the modified Jones model:
where BM is the book-to-market value of the common equity and CFO is the cash flow from
operations in year t scaled by total assets in t-1. The definitions of other variables are the same as
modified Jones model, and again, the discretionary accrual (AQ_MBCFO) is the absolute value of
the predicted residual of the model multiplied by 1.
(5) Modified Jones Model with Last-Year ROA
To control for measurement errors in discretionary accruals caused by performance, Kothari et al.
(2005) added ROA to the modified Jones model (see following for definition of ROA). The
performance-matched Jones model (PJM) has since become a standard model to estimate normal
accruals:
1 -1 2 3 4 1
(1 ) ( )
it it it it it it it
TA Assets REV AR PPE ROA
 
 
ROA is income before extraordinary items for firm i in year t-1 over total assets in year t-2, and
the discretionary accrual (AQ_PJM) is the absolute value of the predicted residual of the model
multiplied by 1.
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[Table 1 inserted here]
c. Sample Selection, Descriptive Statistics, and Correlation Coefficients
Sample Selection
Bid-ask spread data, price-related data, and financial data were obtained from the Datastream
database. Our sample period started in 2005 and ended in 2009. The European Union and its
member states formally adopted International Financial Reporting Standards IFRS for the
preparation of financial statements on 1 January 2005. To prevent the shift in accounting
standards from affecting determinations of earnings quality, we started sampling at 2005. We
ended sampling in 2009 because most serious events of the current crisis took place in 2008 and
2009.
We started the selection process for the sample firms from all listed firms. In calculating
discretionary accruals, we then excluded firms that did not have the required data and industries
that had fewer than 10 firms. In line with previous analyses (Hail, 2011; Christensen et al., 2011),
we excluded firms with a market value of equity of less than 1 million US dollars. The final sample
was distributed across 30 industries and included 4271 firm-year observations. The numbers of
observations for 2005 to 2009 are 624, 745, 877, 987, and 1038, respectively.
Descriptive Statistics and Correlation Coefficients
Table 2 provides descriptive statistics of our sample firms. Panel A provides statistics for the
whole sample, while Panel B divides it into two subsamples, a pre-crisis sample and a crisis
sample. The log value of the yearly median spread was significantly larger in the crisis period
(3.194) than in the pre-crisis period (3.571), which means that liquidity decreased in 2008 and
2009 (see also Figure 1). With respect to financial reporting quality, we did not find any
significant changes in the five accounting quality proxies between the crisis period and the
pre-crisis period; this indicates that the accounting quality during our sample period was stable.
We also found, as expected, that share turnover and share prices were lower and return volatility
was higher during the crisis period than in the pre-crisis period. In addition, 46.7% of the sample
firms reported negative net income in the crisis period, which was significantly higher than during
16
the pre-crisis period (34.2%), suggesting that the sample firms suffered from financial and
operating difficulties in the crisis. We also calculated Pearson correlation coefficients, and the
results (not tabulated) showed that illiquidity was positively and significantly correlated with
Crisis but negatively correlated with AQ (financial reporting quality), as expected. However, we
did not find any correlation between financial reporting quality proxies and the crisis.
[Table 2 inserted here]
d. The Role of Financial Reporting Quality in the Financial Crisis
Table 3 reports OLS coefficient estimates and (in parentheses) t-statistics based on robust
standard errors that are heteroscedasticity-consistent and clustered by firm. We first used the
discretionary accruals estimated by applying the performance-matched Jones model (Kothari et
al., 2005) to proxy for financial reporting quality. Column (1) of Table 3 shows that the coefficient
on Crisis is positively significant (t = 4.80 and P < .01), which suggests that the overall effect of the
crisis was a drop in market liquidity, as expected. This means that, during the financial crisis, firms
suffered from bad market conditions. However, we conjecture that the degree of market impact
was conditional on the characteristics of each firm, particularly the quality of financial reporting,
which is a key element associated with investor confidence. Thus, our primary interest is the
interaction between the variables of crisis and financial reporting quality proxy (Crisis*AQ_PJM).
If higher financial reporting quality helps firms, the coefficient of the interaction term will be
negative. The coefficient was 0.605 and therefore significant (t = 3.61, P < .01), supporting our
hypothesis that higher-quality financial reporting reduced the negative effect of the financial
crisis on share liquidity. Note that the coefficient of AQ was insignificant, suggesting that bid-ask
is not sensitive to accounting quality during ordinary business periods. However, in the context of
economic meltdown and a crisis of investor confidence, high-quality financial information
became important as a consequence of the unusually high degree of uncertainty and risk, which
increased the sensitivity of market price to financial disclosure. The negative coefficient of
Crisis*AQ_PJM is consistent with this interpretation. Our inferences held when we controlled for
the known influence from a set of control variables, such as share turnover, firm value, return
volatility, as well as industry fixed effects on market liquidity. Table 4 shows the results of our use
of the Jones model, the modified Jones model, the adapted Jones model, and the Jones model
17
with book-to-market ratio and cash flows from operations to estimate discretionary accruals
multiplied by 1; the results were virtually the same as Table 3.
[Table 3 inserted here]
[Table 4 inserted here]
Note that while we reached a similar conclusion as did Lang and Maffett (2011), who addressed
this issue in an international setting, we focused exclusively on financial reporting quality, while
Lang and Maffett additionally considered auditor quality. In addition, our research design
enabled us to avoid many international/national institutional effects, such as degree of legal
protection for outside investors, stringency of disclosure requirements, media penetration,
ownership concentration, and adoption of different accounting standards (H. Chen et al., 2010).
These factors are likely to vary across nations and are hard to control. Because of these
differences in research setting, methodology, and sample selection, the results are not directly
comparable, although our evidence is generally consistent with theirs.
e. Robust Tests
We conducted a number of robustness tests. First, we increased the threshold from 10
observations to 20 observations in each two-digit SIC-year grouping to calculate financial
reporting quality indicators and rerun our tests; the results (not reported) were qualitatively the
same. Second, we included an intercept in the discretionary accruals model as an additional
control for heteroscedasticity (Kothari et al., 2005); the results (not tabulated) were virtually
unchanged. Third, we reduced the sample size by excluding firms with less than US$1 million in
total assets (instead of market value of equity), and again, the results remained qualitatively the
same. In addition, some previous studies did not include the variable of leverage (e.g., Muller et
al., 2011), which might be a factor associated with market risk (Ng, 2011). Therefore, we reran
our model with leverage as an additional control, and the result (not reported) was qualitatively
the same.
Fourth, our sample included both financial and non-financial institutions. Because financial firms
were hit more severely by the crisis, it is possible that the positive relationship between reporting
18
quality and liquidity may not apply to financial firms. This appears to be a valid concern and is
worthy of further investigation, because these firms lost huge amounts of money for their
investors; it can be argued, therefore, that it is very unlikely that their financial reporting would
have maintained investor confidence. Therefore, we ran the tests separately on financial and
non-financial firms using the same model specifications. We found virtually the same results (not
tabulated) from the two subsamples, suggesting that, although financial sectors are inherently
more sensitive to market volatility, high-quality financial reporting remained successful in
mitigating the negative impact of the crisis, even for financial firms.
Fifth, there are some endogeneity concerns about our research design. That is, liquidity and
financial reporting quality may be determined by some other omitted variables. For example,
financially distressed firms (e.g., loss-making firms) may have more uncertainty about future
return, which is inevitably correlated with liquidity. Thus, we added two control variables into the
regression: whether a firm had negative earnings in one year or not (LOSS) and share price at the
end of the year (PRICE). PRICE was included to control for market makers’ order processing costs,
which are proportionately lower for higher-priced stocks. A negative coefficient for PRICE was
expected (Muller et al., 2011). Table 5 reports the results of regressions with these additional
control variables, and our interpretations did not alter.
3
[Table 5 inserted here]
Finally, we have emphasized here that financial reporting quality may increase or decrease
liquidity. However, it can be argued that firms that suffer from a decrease in liquidity may have a
greater incentive to manipulate earnings. In this case, it is the illiquidity that affects the quality of
financial reporting, rather than the other way around. Thus, our results may be driven by this
factor. To address this reverse causality, we averaged the discretionary accruals of years t-2, t-1,
and t and multiplied this number by 1 to serve as a proxy for financial reporting quality. Table 6
reports the results of regressions using the average past earnings quality proxy, and, once more,
our inferences remained intact.
3
We also added the interaction of Loss and Financial Reporting Quality proxies, and our results were
qualitatively the same.
19
[Table 6 inserted here]
IV. Conclusion
This paper examined the impact of financial reporting quality on the liquidity of the equity
market during the recent financial crisis (and by extension, any financial crisis). Many studies
have attempted to explore the possible reasons for the illiquidity of the financial market
(Copeland and Galai, 1983; Glosten and Milgrom, 1985; Stoll, 1989; Callahan et al., 1997; Laux et
al., 2010), and some have claimed that accounting plays a negative role but provided inadequate
evidence for this assertion (Barth et al., 2010; Laux et al., 2010). Moreover, previous research
typically focused on particular items (such as financial items measured by fair value) and financial
institutions. However, such a narrowly focused approach might not provide a complete picture
of the role played by accounting in financial meltdowns.
In contrast, we adopted a broader perspective to guide our research design. Our sample included
not only financial firms but also non-financial firms. We examined the quality of the financial
reporting system as a whole (rather than just fair-value accounting). Based on previous findings
regarding information asymmetry and investor confidence (Liao et al., 2010), we conjectured
that, during the recent financial crisis, uncertainty and information risk were much greater than
before the crisis, and if financial reporting was known to be reliable, it would have helped
mitigate information uncertainty and helped restore investor confidence. This would mean that
accounting may play a more observable role in liquidity, and we would find evidence that firms
that provided high-quality financial information were less adversely affected by the crisis. Our
findings supported this prediction, and the effect was manifested in financial firms with even
lower investor confidence. Our results were robust after controlling for possible confounding
factors and have the potential to help resolve the controversy regarding the role of accounting in
the crisis.
20
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Figure 1 Quarterly Liquidity in the United Kingdom, 20052010.
25
Table 1 Variable Definitions
Variable
Definition
Liquidity
Log(spread)
Log value of the yearly median quoted spread (defined as the difference between the bid price and ask price divided by
the mid-point and measured at the end of each trading day).
Log(spread1)
Log value of the yearly mean quoted spread.
AQ (financial reporting quality)
AQ_JM
The product of 1 and absolute value of discretionary accruals. Discretionary accruals are estimated using the
cross-sectional Jones model, the cross-sectional modified Jones model, the cross-sectional adapted Jones model, the
cross-sectional modified Jones model with book-to-market ratio and cash flow from operations, and the cross-sectional
modified Jones model with the last-year ROA, respectively. We require a minimum of 10 observations for each two-digit
SIC group for the year.
AQ_MJM
The product of 1 and absolute value of discretionary accruals. Discretionary accruals are estimated using
cross-sectional modified Jones model.
AQ_AJM
The product of 1 and absolute value of discretionary accruals. Discretionary accruals are estimated using
cross-sectional adapted Jones model.
AQ_MBCFO
The product of 1 and absolute value of discretionary accruals. Discretionary accruals are estimated using the
cross-sectional modified Jones model with book-to-market ratio and cash flow from operations.
AQ_PJM
The product of 1 and absolute value of discretionary accruals. Discretionary accruals are estimated using the
cross-sectional modified Jones model with the last-year ROA.
Crisis
Indicator variable equals 1 for observations in the crisis period (20082009) and 0 for observations in the non-crisis
period (20052007).
Turnover
Log(share turnover). Share turnover is defined as daily US$ trading volume divided by the market value at the end of
each trading day.
Size
Log(market value). Market value is defined as stock price times the number of shares outstanding (in US$ million).
Volatility
Log(return volatility). Return volatility is defined as the standard deviation of daily stock returns in a given year.
Loss
Indicator variable; equals one if net income is negative in a given year and zero otherwise.
PRICE
The ending trading price in a given year.
Industry FE
Industry fixed effects. There are 30 SIC2 industries in our sample. We generated 29 industry dummy variables.
26
Table 2 Descriptive Statistics
Panel A Descriptive Statistics for All Firms During 20052009
Variable
N
Mean
Median
SD
Min
P5
P95
Max
Log(Spread)
4271
3.393
3.147
1.577
7.044
6.426
1.153
0.405
Log(Spread1)
4271
3.242
3.036
1.51
6.733
6.17
1.06
0.047
Crisis
4271
0.474
0
0.499
0
0
1
1
AQ_JM
4271
0.098
0.059
0.118
0.622
0.349
0.005
0.001
AQ_MJM
4271
0.099
0.059
0.118
0.625
0.354
0.005
0.001
AQ_AJM
4271
0.098
0.059
0.119
0.639
0.358
0.005
0.001
AQ_MBCFO
4271
0.091
0.055
0.109
0.599
0.323
0.005
0.001
AQ_PJM
4271
0.096
0.059
0.113
0.604
0.342
0.005
0.001
Turnover
4271
7.084
7.272
1.26
9.746
8.993
4.919
4.483
Size
4271
4.719
4.428
2.152
0.536
1.557
8.646
10.571
Volatility
4271
3.679
3.709
0.521
4.819
4.51
2.779
2.407
Loss
4271
0.401
0
0.49
0
0
1
1
PRICE
4271
4.013
1.5
6.749
0.01
0.04
16.69
42.89
Panel B Descriptive Statistics for Pre-Crisis Period and Crisis Period
Crisis=0 (20052007)
Crisis=1 (20082009)
Variable
Mean
Median
Mean
Median
Log(Spread)
3.571
3.351
3.194***
2.823***
Log(Spread1)
3.432
3.233
3.032***
2.711***
AQ_JM
0.1
0.06
0.096
0.058
AQ_MJM
0.1
0.06
0.097
0.058
AQ_AJM
0.1
0.06
0.096
0.058
AQ_MBCFO
0.092
0.057
0.09
0.054
AQ_PJM
0.097
0.059
0.094
0.059
Turnover
6.945
7.069
7.237***
7.482***
Size
4.707
4.397
4.733
4.469
Volatility
3.814
3.891
3.531***
3.545***
Loss
0.342
0
0.467***
0***
PRICE
5.047
2.325
2.867***
0.91***
Observation
2246
2246
2025
2025
Log(spread) is the log value of the yearly median quoted spread (defined as the difference between the bid and ask price divided by the
midpoint and measured at the end of each trading day), and log(spread1) is the log value of the yearly mean quoted spread. Crisis is a
dummy variable, which equals 1 if the observation is from 2008 and 2009 and is 0 otherwise. AQ_JM, AQ_MJM, AQ_AJM, AQ_MBCFO,
and AQ_PJM are five proxies of earnings quality (defined as the product of 1 and the absolute value of discretionary accruals;
discretionary accruals were estimated using the cross-sectional Jones model, the cross-sectional modified Jones model, the
cross-sectional adapted Jones model, the cross-sectional modified Jones model with book-to-market ratio and cash flow from operations,
and the cross-sectional modified Jones model with the last-year ROA). Turnover is the log value of last year ’s median share turnover,
which is defined as daily US$ trading volume divided by the market value at the end of each trading day. Size is the log value of last
year’s median market value of equity, which is defined as stock price times the number of shares outstanding (in US$ million). Volatility
is the log value of last year’s median share volatility, which is defined as the standard deviation of daily stock returns in a given year. Loss
is a dummy variable, which equals one if net income is negative in a given year and 0 otherwise. PRICE is the ending trading price in a
given year. The t-test was used to test mean differences between the non-crisis sample and the crisis sample, and the Wilcoxon
rank-sum test was used to test median differences. *, **, *** denote significance at the 10%, 5%, and 1% levels, respectively
(two-tailed).
27
Table 3 The Role of Earnings Quality on Liquidity during the Financial Crisis
(1)
(2)
Liquidity
Log(spread)
Log(spread1)
Constant
1.797***
1.461***
(12.06)
(9.71)
AQ_PJM
0.148
0.086
(1.14)
(0.64)
Crisis
0.122***
0.140***
(4.80)
(5.25)
Crisis*AQ_PJM
0.605***
0.680***
(3.61)
(3.99)
Turnover
0.330***
0.298***
(23.42)
(21.49)
Size
0.518***
0.493***
(60.28)
(58.38)
Volatility
0.395***
0.411***
(14.67)
(15.09)
Industry FE
Y
Y
N
4271
4271
R2
0.867
0.853
Adj. R2
0.866
0.852
Log(spread) is log value of the yearly median quoted spread (defined as the difference between the bid and ask price divided by the
mid-point and measured at the end of each trading day), and Log(spread1) is log value of the yearly mean quoted spread. Crisis is a
dummy variable, which equals 1 if the observation is from 2008 and 2009 and is 0 otherwise. AQ_PJM is a proxy of earnings quality
(defined as the product of 1 and the absolute value of discretionary accruals; discretionary accruals were estimated using the
cross-sectional modified Jones model with the last-year ROA). Turnover is the log value of last year’s median share turnover, which is
defined as daily US$ trading volume divided by the market value at the end of each trading day. Size is the log value of last years median
market value of equity, which is defined as stock price times the number of shares outstanding (in US$ million). Volatility is the log value
of last year’s median share volatility, which is defined as the standard deviation of daily stock returns in a given year. The table reports
OLS coefficient estimates and (in parentheses) t-statistics based on robust standard errors that are heteroscedasticity-consistent and
clustered by firm. *, **, *** denote significance at the 10%, 5%, and 1% levels respectively (two-tailed).
28
Table 4 The Role of Earnings Quality on Liquidity during the Financial Crisis (Different EQ Proxies)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Liquidity
Log(sprea
d)
Log(spread
1)
Log(sprea
d)
Log(spread
1)
Log(sprea
d)
Log(spread
1)
Log(sprea
d)
Log(spread
1)
Constant
1.826***
1.490***
1.821***
1.489***
1.822***
1.488***
1.861***
1.530***
(12.20)
(9.86)
(12.20)
(9.87)
(12.17)
(9.83)
(12.36)
(10.08)
Crisis
0.126***
0.145***
0.125***
0.145***
0.126***
0.145***
0.136***
0.156***
(4.92)
(5.38)
(4.88)
(5.37)
(4.91)
(5.36)
(5.44)
(5.88)
AQ_JM
0.052
0.009
(0.41)
(0.06)
Crisis*AQ_JM
0.564***
0.621***
(3.42)
(3.64)
AQ_MJM
0.069
0.004
(0.56)
(0.03)
Crisis*AQ_MJM
0.569***
0.621***
(3.48)
(3.68)
AQ_AJM
0.065
0.001
(0.52)
(0.01)
Crisis*AQ_AJM
0.559***
0.618***
(3.42)
(3.66)
AQ_MBCFO
0.073
0.149
(0.53)
(1.01)
Crisis*AQ_MBCF
O
0.505***
0.569***
(2.92)
(3.16)
Turnover
0.331***
0.298***
0.331***
0.298***
0.331***
0.298***
0.331***
0.299***
(23.48)
(21.54)
(23.48)
(21.55)
(23.47)
(21.54)
(23.52)
(21.59)
Size
0.517***
0.491***
0.517***
0.491***
0.517***
0.491***
0.516***
0.491***
(60.05)
(58.02)
(60.06)
(58.06)
(59.99)
(57.98)
(60.17)
(58.08)
Volatility
0.393***
0.409***
0.393***
0.409***
0.393***
0.409***
0.389***
0.404***
(14.56)
(14.98)
(14.59)
(14.99)
(14.58)
(14.99)
(14.37)
(14.80)
Industry FE
F
F
F
F
F
F
F
F
N
4271
4271
4271
4271
4271
4271
4271
4271
R2
0.867
0.853
0.867
0.853
0.867
0.853
0.867
0.853
Adj. R2
0.866
0.852
0.866
0.852
0.866
0.852
0.866
0.852
Log(spread) is the log value of the yearly median quoted spread (defined as the difference between the bid and ask price divided by the
mid-point and measured at the end of each trading day), and log(spread1) is the log value of the yearly mean quoted spread. Crisis is a
dummy variable, which equals 1 if the observation is from 2008 and 2009 and is 0 otherwise. AQ_JM, AQ_MJM, AQ_AJM, and
AQ_MBCFO are four proxies of earnings quality (defined as the product of 1 and the absolute value of discretionary accruals;
discretionary accruals were estimated using the cross-sectional Jones model, the cross-sectional modified Jones model, the
cross-sectional adapted Jones model, and the cross-sectional modified Jones model with book-to-market ratio and cash flow from
operations). Turnover is the log value of last year’s median share turnover, which is defined as daily US$ trading volume divided by the
market value at the end of each trading day. Size is the log value of last year ’s median market value of equity, which is defined as stock
price times the number of shares outstanding (in US$ million). Volatility is the log value of last year’s median share volatility, which is
defined as the standard deviation of daily stock returns in a given year. The table reports OLS coefficient estimates and (in parentheses)
t-statistics based on robust standard errors that are heteroscedasticity-consistent and clustered by firm. *, **, *** denote significance at
the 10%, 5%, and 1% levels, respectively.
29
Table 5 The Role of Earnings Quality on Liquidity during Financial Crisis (Controlled for
Performance)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
Liquidity
Log(spr
ead)
Log(spre
ad1)
Log(spr
ead)
Log(spre
ad1)
Log(spr
ead)
Log(spre
ad1)
Log(spr
ead)
Log(spre
ad1)
Log(Spr
ead)
Log(Spre
ad1)
Constant
2.421*
**
2.121**
*
2.417*
**
2.120**
*
2.418*
**
2.118**
*
2.441*
**
2.143**
*
2.402*
**
2.102***
(15.76
)
(13.78)
(15.76
)
(13.79)
(15.73
)
(13.76)
(15.83
)
(13.90)
(15.69)
(13.72)
Crisis
0.090***
0.108***
0.091***
0.109***
0.091***
0.108***
0.099***
0.116***
0.089***
0.104***
(3.56)
(4.04)
(3.58)
(4.08)
(3.57)
(4.03)
(3.94)
(4.40)
(3.48)
(3.92)
AQ_ _JM
0.181
0.128
(1.49)
(0.99)
Crisis*AQ_J
M
0.562*
**
0.618**
*
(3.55)
(3.80)
AQ_MJM
0.190
0.124
(1.58)
(0.98)
Crisis*AQ_
MJM
0.549*
**
0.599**
*
(3.48)
(3.70)
AQ_AJM
0.191
0.134
(1.58)
(1.06)
Crisis*AQ_A
JM
0.551*
**
0.609**
*
(3.49)
(3.76)
AQ_MBCFO
0.104
0.039
(0.79)
(0.28)
Crisis*AQ_
MBCFO
0.525*
**
0.589**
*
(3.16)
(3.45)
AQ_PJM
0.264**
0.209
(2.11)
(1.62)
Crisis*AQ_PJ
M
0.589*
**
0.663**
*
(3.62)
(4.01)
Turnover
0.334*
**
0.302**
*
0.334*
**
0.302**
*
0.334*
**
0.302**
*
0.334*
**
-0.302***
-0.334**
*
-0.301***
(23.22
)
(21.50)
(23.21
)
(21.49)
(23.21
)
(21.49)
(23.23
)
(21.51)
(23.19)
(21.47)
Size
0.482*
**
0.455**
*
0.483*
**
0.455**
*
0.482*
**
0.455**
*
0.482*
**
0.455**
*
0.483*
**
0.456***
(51.93
)
(50.49)
(51.95
)
(50.51)
(51.90
)
(50.47)
(51.95
)
(50.46)
(52.08)
(50.69)
Volatility
0.306***
0.317***
0.307***
0.317***
0.307***
0.317***
0.304***
0.315***
0.308***
0.319***
(11.34)
(11.72)
(11.36)
(11.73)
(11.36)
(11.73)
(11.24)
(11.62)
(11.41)
(11.80)
Loss
0.305***
0.324***
0.305***
0.324***
0.306***
0.324***
0.304***
0.322***
0.307***
0.326***
(13.17)
(13.58)
(13.16)
(13.57)
(13.18)
(13.59)
(13.07)
(13.46)
(13.23)
(13.65)
PRICE
0.010*
**
0.010**
*
0.010*
**
0.010**
*
0.010*
**
0.010**
*
0.010*
**
0.010**
*
0.010*
**
0.010***
(3.15)
(3.55)
(3.14)
(3.54)
(3.14)
(3.55)
(3.12)
(3.52)
(3.13)
(3.53)
Industry FE
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
N
4271
4271
4271
4271
4271
4271
4271
4271
4271
4271
R2
0.875
0.863
0.875
0.863
0.875
0.863
0.875
0.863
0.875
0.863
Adj. R2
0.874
0.862
0.874
0.862
0.874
0.862
0.874
0.862
0.874
0.862
Log(spread) is the log value of the yearly median quoted spread (defined as the difference between the bid and ask price divided by
the mid-point and measured at the end of each trading day), and log(spread1) is the log value of the yearly mean quoted spread.
Crisis is a dummy variable, which equals 1 if the observation is from 2008 and 2009 and is 0 otherwise. AQ_JM, AQ_MJM, AQ_AJM,
AQ_MBCFO, and AQ_PJM are five proxies of earnings quality (defined as the product of 1 and the absolute value of discretionary
accruals; discretionary accruals were estimated using the cross-sectional Jones model, the cross-sectional modified Jones model, the
cross-sectional adapted Jones model, the cross-sectional modified Jones model with book-to-market ratio and cash flow from
operations, and the cross-sectional modified Jones model with the last-year ROA). Turnover is the log value of last year’s median share
turnover, which is defined as daily US$ trading volume divided by the market value at the end of each trading day. Size is the log value
of last year’s median market value of equity, which is defined as stock price times the number of shares outstanding (in US$ million).
Volatility is the log value of last year’s median share volatility, which is defined as the standard deviation of daily stock returns in a
given year. Loss is a dummy variable, which equals 1 if net income is negative in a given year and 0 otherwise. PRICE is the ending
30
trading price in a given year. The table reports OLS coefficient estimates and (in parentheses) t-statistics based on robust standard
errors that are heteroscedasticity-consistent and clustered by firm. *, **, *** denote significance at the 10%, 5%, and 1% levels,
respectively.
31
Table 6 The Role of Earnings Quality in Liquidity during the Financial Crisis (Average
Discretionary Accruals as EQ)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
Liquidity
Log(spr
ead)
Log(spre
ad1)
Log(spr
ead)
Log(spre
ad1)
Log(spr
ead)
Log(spre
ad1)
Log(spr
ead)
Log(spre
ad1)
Log(spr
ead)
Log(spre
ad1)
Constant
2.085*
**
1.729**
*
2.082*
**
1.729**
*
2.085*
**
1.728**
*
2.109*
**
1.758**
*
2.096*
**
1.743**
*
(11.27)
(9.40)
(11.21)
(9.37)
(11.24)
(9.37)
(11.28)
(9.45)
(11.39)
(9.55)
Crisis
0.074**
0.073**
0.069*
0.068*
0.073**
0.072*
0.072**
0.071*
0.088**
0.085**
(2.10)
(1.98)
(1.94)
(1.83)
(2.07)
(1.94)
(1.97)
(1.84)
(2.46)
(2.28)
AQ_JM
0.472*
0.515*
(1.80)
(1.92)
Crisis*AQ_J
M
0.902*
**
0.995**
*
(3.09)
(3.30)
AQ_MJM
0.442*
0.494*
(1.67)
(1.82)
Crisis*AQ_M
JM
0.943*
**
1.036**
*
(3.19)
(3.39)
AQ_AJM
0.460*
0.497*
(1.76)
(1.86)
Crisis*AQ_A
JM
0.902*
**
1.001**
*
(3.09)
(3.32)
AQ_MBCFO
0.552*
0.611**
(1.90)
(2.03)
Crisis*AQ_M
BCFO
0.989*
**
1.099**
*
(2.95)
(3.16)
AQ_PJM
0.581*
*
0.630**
(2.06)
(2.20)
Crisis*AQ_PJ
M
0.779*
*
0.893**
*
(2.55)
(2.86)
Turnover
0.338*
**
0.307**
*
0.338*
**
0.307**
*
0.338*
**
0.307**
*
0.338*
**
0.307**
*
0.338*
**
0.307**
*
(20.39)
(18.96)
(20.40)
(18.99)
(20.39)
(18.98)
(20.47)
(19.05)
(20.40)
(18.98)
Size
0.505*
**
0.480**
*
0.505*
**
0.480**
*
0.505*
**
0.480**
*
0.505*
**
0.480**
*
0.505*
**
0.480**
*
(51.22)
(49.81)
(50.99)
(49.64)
(51.02)
(49.67)
(51.48)
(50.05)
(51.30)
(49.86)
Volatility
0.377***
0.399***
0.378***
0.400***
0.377***
0.400***
0.374***
0.396***
0.376***
0.398***
(11.71)
(12.24)
(11.73)
(12.25)
(11.72)
(12.25)
(11.51)
(12.06)
(11.72)
(12.27)
Industry FE
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
N
3398
3398
3398
3398
3398
3398
3398
3398
3398
3398
R2
0.877
0.865
0.877
0.865
0.877
0.865
0.877
0.865
0.877
0.865
Adj. R2
0.876
0.864
0.876
0.864
0.876
0.864
0.876
0.864
0.876
0.864
Log(spread) is the log value of the yearly median quoted spread (defined as the difference between the bid and ask price divided by
the mid-point and measured at the end of each trading day), and log(spread1) is the log value of the yearly mean quoted spread.
Crisis is a dummy variable, which equals 1 if the observation is from 2008 and 2009 and is 0 otherwise. AQ_JM, AQ_MJM, AQ_AJM,
AQ_MBCFO, and AQ_PJM are five proxies of earnings quality (defined as the average value of year t-2, t-1, and t’s AQ. AQ is the
product of 1 and the absolute value of discretionary accruals; discretionary accruals were estimated using the cross-sectional Jones
model, the cross-sectional modified Jones model, the cross-sectional adapted Jones model, the cross-sectional modified Jones model
with book-to-market ratio and cash flow from operations, and the cross-sectional modified Jones model with the last-year ROA).
Turnover is the log value of last year’s median share turnover, which is defined as daily US$ trading volume divided by the market
value at the end of each trading day. Size is the log value of last year’s median market value of equity, which is defined as stock price
times the number of shares outstanding (in US$ million). Volatility is the log value of last year’s median share volatility, which is
defined as the standard deviation of daily stock returns in a given year. The table reports OLS coefficient estimates and (in parentheses)
t-statistics based on robust standard errors that are heteroscedasticity-consistent and clustered by firm. *, **, *** denote significance
at the 10%, 5%, and 1% levels, respectively.
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... In general, investors need such information in order to evaluate their investment decisions taken before and to take decisions regarding their investment in securities. According to agency theory, financial information helps in reducing information asymmetry between managers, creditors, investors and other stakeholders (Lin et al., 2014;Martínez-Ferrero, 2014). It is important that the information presented by firms on their financial position and operations to be of a higher quality, as according to the signaling theory, high quality accounting information may be a signal of firms' good performance and managers are more encouraged to offer high quality accounting information in order to avoid adverse selection (Lin et al., 2014). ...
... According to agency theory, financial information helps in reducing information asymmetry between managers, creditors, investors and other stakeholders (Lin et al., 2014;Martínez-Ferrero, 2014). It is important that the information presented by firms on their financial position and operations to be of a higher quality, as according to the signaling theory, high quality accounting information may be a signal of firms' good performance and managers are more encouraged to offer high quality accounting information in order to avoid adverse selection (Lin et al., 2014). Jonas & Blanchet (2000, p. 357) defined quality of financial reporting as "full and transparent financial information that is not designed to obfuscate or mislead users". ...
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