News Sentiment, Accruals Quality, and Liquidity Volatility

Rochester H. Cahan Deutsche Bank Securities Inc., 60 Wall Street, New York, NY 10005, USA

Steven F. Cahan University of Auckland Business School, Private Bag 92019, Auckland 1142, New Zealand

Nhut H. Nguyen University of Auckland Business School, Private Bag 92019, Auckland 1142, New Zealand

June 2012

We thank seminar participants at the University of Auckland and the 2012 Quantitative Accounting Research Symposium for their useful comments. We thank Thompson Reuters for providing data from News Analytics. News Sentiment, Accruals Quality, and Liquidity Volatility

Investors prefer predictable liquidity so they are not forced to transact at inopportune times. In this study, we examine how the sentiment conveyed in news items affects the variability of liquidity. We expect that news sentiment will affect liquidity volatility because it reduces information uncertainty. We also examine whether accruals quality affects the relation between news sentiment and liquidity volatility. We expect that accruals quality will moderate this relation since firms with high accruals quality have less information uncertainty to start with. Using a measure of news sentiment from Thompson Reuters’ News Analytics database, we find that both positive and negative news sentiment reduce liquidity volatility. Further, we find a positive and significant coefficient for the interaction between news sentiment and accruals quality, consistent with news sentiment having a smaller impact when information asymmetry is low. Our results suggest that investors’ interpretations of the news are conditioned on a firm’s accounting quality.

I. Introduction

The business press may be omnipresent, but researchers have only started to examine its role as an information intermediary. Bushee, Core, Guay, and Hamm (2010) find that greater press coverage is associated with lower information asymmetry, measured by bid-ask spreads and market depth. Fang and Peress (2009) find higher returns for firms that receive no media coverage, suggesting a no-coverage premium. Other studies quantify the tone or sentiment of the news items. Kothari, Li, and Short (2009) use content analysis and find that favorable

(unfavorable) reports by the business press reduce (increase) cost of capital and return volatility.

Tetlock, Saar-Tsechansky, and Macskassy (2008) find that the fraction of negative words in firm-specific news stories is related to lower future earnings and that this relation is stronger for stories about the firm’s fundamentals. The purpose of this study is to extend this research by examining whether investors react to a news item’s sentiment and whether the quality of a firm’s accruals affects this relation. Regarding the latter, if accruals quality and news sentiment both reduce information uncertainty, news sentiment might have less impact on firms that have high accruals quality to start with.

We examine the interactive effect of news sentiment and accruals quality on liquidity volatility. We choose to examine liquidity volatility for two reasons. First, illiquidity became a major focal point during the financial crisis (e.g., Allen and Carletti 2008, Brunnermeier 2009).

As Lang and Maffett (2011) point out, the timing of illiquidity matters. For example, investors who want to sell securities can face a ‘liquidity black hole’ if they cannot find other investors who will trade with them (e.g., Moorthy 2003). Consequently, investors prefer firms with more predictable liquidity. Acharya and Pedersen (2005) find firms with a lower covariance between firm liquidity and market returns or market liquidity have lower costs of capital, reflecting

1 investors’ ability to exit positions more easily when market illiquidity increases. Thus, understanding factors that affect variations in liquidity over time is important.

Second, two recent papers link accounting quality to the systematic risk associated with liquidity. Using US data, Ng (2011) finds that measures of information quality, which includes accruals quality, is inversely related to liquidity risk and that this relation is stronger when there are shocks to market liquidity. Lang and Maffett (2011) use international data and different measures of liquidity risk and accounting quality (including a measure of earnings management), and obtain similar results – higher transparency is associated with less liquidity risk. As a result, we predict that accruals quality could moderate the relation between news sentiment and liquidity volatility. Specifically, news sentiment might have less effect on firms with high accruals quality because these firms already have less information uncertainty.

News sentiment measures the tone of the qualitative content of a news item. Our measure of news sentiment is taken from Thomson Reuter’s News Analytics which uses an automated news analytic tool that examines linguistic patterns to rate the content of news items by sentiment in real-time. This database has three distinct advantages. First, compared to prior research, it covers significantly more firms and includes more news items.1 Second, News

Analytics data is more current than used in prior research which is critical given that the news industry has been transformed in recent years by the internet and information technology.2

Third, since the News Analytics data is fed directly to trading desks and can be input immediately into algorithmic trading programs, it provides a more complete and direct mapping

1 For example, Kothari et al.’s (2009) sample includes 889 firms from four industries. They download 326,357 items, but not all of these are news items. Our sample includes 2,683 firms and 2.594 million news items or, on average, 1,265 news items per firm. 2 Our sample period is from January 2003-December 2011. In contrast, the sample periods for Bushee et al. (2010), Fang and Peress (2009), Kothari et al. (2009), and Tetlock et al. (2008) end in 2004, 2002, 2001, and 2004, respectively.

2 of the universe of news items to the set of news that is being received and acted on by investors.

In contrast, prior studies rate news items ex post, often years after the initial release of a news item. As a result, it is not possible to determine if their ratings correspond with investors’ interpretations of those news items at the time they were first released. We are not the first to use the News Analytics data (e.g., see Dzielinski 2011, Groβ-Kluβmann and Hautsch 2011), but to our knowledge, no prior studies use this data in conjunction with measures of accruals quality or liquidity volatility.

We compute a monthly measure of liquidity volatility based on a principle components analysis of three measures – standard deviation of Amihud’s (2002) price impact measure, skewness of Amihud’s price impact measure, and idiosyncratic liquidity volatility as measured by Akbas, Armstrong, and Petkova (2011). Lang and Maffett (2011) use the first two measures as measures of liquidity risk. In Akbas et al. (2011), they regress Amihud’s measure on a measure of aggregate market illiquidity and the excess market return, and use the standard deviation of the daily residuals as a measure of firm-specific liquidity volatility.

While we are interested in variations in liquidity, we are not interested in liquidity risk per se. One reason is that liquidity risk is not singularly defined in the literature. For example,

Acharya and Pedersen (2005) identify three different types of systematic risk related to liquidity, i.e, the covariations between firm returns and market liquidity, firm liquidity and market liquidity, and firm liquidity and market returns. Ng (2011) focuses on the first of these while

Lang and Maffett (2011) use the second two (along with the standard deviation and skewness of the Amihud measure). Further, Pastor and Stambaugh (2003) capture liquidity risk by estimating a liquidity beta using Fama and French’s (1993) three-factor model with a market liquidity factor

3 included. Sadka (2006) uses a price-impact measure based on Glosten and Harris (1988) as his proxy for liquidity risk.

More important, we focus on the firm-specific component of liquidity volatility because we expect firm-specific news sentiment to have a direct effect on this component.3 That is, comovements with the market depend on the impact of market-wide events on the firm, but we are interested in firm-specific news, not market-wide news. Additionally, Akbas et al. (2011) argue that firm-specific liquidity volatility can affect investors’ behavior and returns. In particular, they argue that stocks with high firm-specific liquidity volatility are more likely to be illiquid at times that liquidity-motivated investors may be forced to sell them. For example,

Edelen (1999) finds that the poor performance and negative returns of open-end mutual funds may reflect the cost of liquidity-motivated trading. However, the same reasoning could be applied to non-professional traders, e.g., households facing unexpected consumption needs and firms facing a drying up of short-term financing. The financial crisis may have created the need for more liquidity-motivated trading.

We expect news sentiment to affect liquidity volatility because news sentiment can reduce information asymmetry. The link is through fundamental values. News sentiment provides investors with a different layer of information about future cash flows which could reduce their uncertainty about fundamental values. Since liquidity volatility is affected by information uncertainty (e.g., Akbas et al. 2011), this suggests that news sentiment could reduce liquidity volatility. This reasoning implies a unidirectional effect for both positive and negative news sentiment (both reduce liquidity volatility) and is consistent with theoretical studies on

3 Correlations reported in Lang and Maffett (2011) confirm that liquidity volatility and covariability are distinct concepts. For example, the correlations between liquidity volatility and three different measures of comovement from Acharya and Pedersen (2005) range from 0.03 to 0.09. Lang and Maffett (2011) use both liquidity volatility and covariability as measures of liquidity risk.

4 disclosure (e.g., Diamond and Verrecchia 1991, Lambert, Leuz, and Verrecchia 2007) and estimation risk (e.g., Klein and Bawa 1976, Barry and Brown 1984). However, Kothari et al.

(2009) find that favorable (unfavorable) news items have positive (negative) effects on cost of capital, standard deviation of returns, and standard deviation of analysts’ forecast errors. This suggests that negative news sentiment could increase uncertainty about fundamental values – a view supported by Ng, Verrecchia, and Weber (2009) – leading to greater liquidity volatility.

Whether negative news sentiment has a negative or positive effect on liquidity volatility is an empirical issue.

Our data cover the period 2003-2011 and include 150,681 firm-month observations.

For every news item linked to a sample firm, we collect the sentiment score from News

Analytics. Since the predicted sign of negative news sentiment is ambiguous, we separate the positive and negative news items. For each set, we compute a mean sentiment score using all news items released about the firm in a month. For accruals quality, consistent with Ng (2011), we use the Dechow and Dichev’s (2002) measure as modified by McNichols (2002). We examine the relation between our liquidity volatility factor (LVF), news sentiment, and accruals quality after controlling for the level of news coverage (i.e., number of stories).

Our results indicate that positive and negative news sentiment is negatively related to

LVF which indicates that news sentiment provides useful information for estimating fundamental values, making it less likely that the firm will be vulnerable to large swings in illiquidity. The similar signs for positive and negative news sentiment indicate that both good and bad news can reduce information uncertainty. Further, we find that the interaction between accruals quality and news sentiment (whether positive or negative) is consistently positively related to LVF. We interpret this as evidence that accruals quality moderates the relation between news sentiment

5 and liquidity volatility, and this effect is incremental to the level of news coverage. In other words, news sentiment has a smaller effect on liquidity volatility when a firm has high quality accounting – and hence less information uncertainty – to start with. Additional analyses suggest that the role of the business press and accruals quality are greater in months not following an earnings announcement, for non-presswire (i.e., not firm initiated) news, in less concentrated industries, and during the recent financial crisis, consistent with information asymmetry, the credibility of the news signal, and the relevance of accounting information having an impact.

This study contributes to the literature in several ways. First, we link the small, but growing, literature on the business press to accruals quality. Given that both the business press and accounting are mechanisms for reducing information asymmetry, it seems logical to investigate how they interact. Second, we focus on liquidity volatility which has received renewed interest in the wake of the financial crisis. We are aware of only two other papers, Lang and Maffett (2011) and Ng (2011), that explore the effect of accounting quality on liquidity volatility or liquidity risk, but these studies do not consider news. Third, to our knowledge, we are the first accounting study to utilize news sentiment scores from News Analytics. Prior studies in accounting and finance that analyze the content of news items (e.g., Tetlock et al.

2008, Kothari et al. 2009) develop their own programs for reading and classifying the items, making it difficult to replicate their work. Wider use of News Analytics could promote greater consistency between studies and lead to further growth in this line of research. Further, Berger

(2011) calls for additional research using novel measures of disclosure.

The remainder of the paper is organized as follows. Section II discusses the background and develops hypotheses. Section III describes the research method and data. Section IV provides results. Section V is the conclusion.

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II. Background and Hypotheses

Researchers in accounting and finance have only recently started to analyze the role of the business press in financial markets. Bushee et al. (2010) focus on press coverage and examines whether greater coverage is associated with new information incremental to other sources, i.e., financial analysts, institutional investors, and the firm itself. Using a sample of medium-sized growth firms listed on NASDAQ, they find that greater press coverage around quarterly earnings announcements is related to larger reductions in the bid-ask spread and increases in depth after controlling for other information sources. Fang and Peress (2009) examine the association between press coverage and stock returns. They find that firms neglected by the press have higher future returns which they view as a no-coverage premium.

Based on Merton (1987), investors in obscure stocks need to be compensated for being imperfectly diversified.

Engelberg and Parsons (2011) consider the causal link between press coverage and investors. They identify 19 local markets and a local news source for each of those markets.

They find that daily trading volume in a firm by local retail investors increases by 8-50% if the local newspaper covered the firm’s earnings announcement. Moreover, they show that this link is severed on days when the delivery of the local paper was likely to have been delayed, e.g., on blizzard days in Minneapolis, which helps rule out that an omitted variable problem where both press coverage and investor trading are driven by some factor not included in the model.

A few studies go beyond the mere presence of press coverage and try to examine the content of press reports. Tetlock et al. (2008) use the text analysis program General Inquirer that scores the negativity news stories based on the frequency of negative words in the story. They focus on S&P 500 firms and find that greater negativity in firm-specific press reports predicts lower future earnings and that this effect is incremental to analysts’ forecasts and historical

7 accounting information. Using similar software, Kothari et al. (2009) identify 889 firms from four industries and analyze all the press reports as well as corporate reports and analyst disclosures over a 15-year period. They expect, and find, a directional relation where favorable

(unfavorable) reports are associated with lower (higher) cost of capital, stock return volatility, and analyst forecast dispersion. Further, they find that the effects of media reports are most significant, a finding they interpret as evidence of the greater credibility attached to the business press relative to management or analysts.

While the authors of the two papers above collect and analyze the news stories themselves, an alternative approach is to use outputs of a commercial automated news engine like News Analytics. News Analytics delivers the information about news stories directly to traders’ screens in real-time. Groβ-Kluβmann and Hautsch (2011) use news sentiment scores from News Analytics to examine the impact of intradaily news for 39 firms listed on the LSE.

They delete news released on the earnings announcement day so they can assess unscheduled news. They document that news sentiment scores generated by News Analytics are related to future price trends. Sinha (2011) investigates a larger sample of US stocks from 2003-2008 and finds that news sentiment predicts future performance. An implication of the findings of these two studies is that the news sentiment scores from News Analytics contain information.

Brunnermeier and Pedersen (2009) model a link between an asset’s liquidity to the ability of traders to receive funding. Traders provide liquidity when there are trade imbalances, but they need capital to do so. They can rely on collateralized borrowings to cover part of the cost of the security, but when funding tightens, traders become more risk adverse and avoid higher margin securities (also see Vayanos, 2004). For these securities, market liquidity decreases and volatility increases. When liquidity volatility is high, investors may be forced to sell their

8 securities at inopportune times. Brunnermeier and Pedersen’s (2009) model predicts that as liquidity dries up, there will be a “flight to quality”, consistent with empirical findings of

Acharya and Pedersen (2005). Moreover, Brunnermeier and Pedersen (2009) show that margins are higher and illiquidity is greater for firms with more uncertainty about fundamental value, suggesting a link between liquidity volatility and fundamental value uncertainty.

News is likely to affect liquidity volatility because news provides investors with more information about fundamental values. For example, news can provide insights about the firm’s business environment, its operations, and it future prospects (e.g., Tetlock et al. 2008). This can reduce information asymmetry and lower estimation risk. Since investors have to estimate the parameters associated with underlying cash flows, e.g., the variance/covariance matrix of expected cash flows (e.g., Klein and Bawa 1976, Barry and Brown 1984), news can provide information that allows them to better understand these parameters, leading to more accurate estimates.

Two studies provide evidence consistent with the tone or sentiment in news items containing information. Tetlock et al. (2008) find that negative words in news articles can help predict future earnings and that the earnings predictability is greatest for articles that focus on fundamentals. Kothari et al. (2009) argue favorable (unfavorable) news is associated with less

(greater) cash flow risk, and they find a higher cost of capital for firms facing negative news.

While these studies suggest that news sentiment may affect estimates of fundamental value, the direction of the relation is unclear. On one hand, positive and negative news sentiment may provide a lower variance signal that allows investors to estimate fundamental value more precisely (e.g., reduces estimation uncertainty). This view is supported by theoretical work on disclosure (e.g., Diamond and Verrecchia 1991, Lambert et al. 2007) and estimation risk (e.g.,

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Klein and Bawa 1976, Barry and Brown 1984). On the other hand, consistent with Kothari et al.

(2009) and Ng et al. (2009), positive (negative) news sentiment may reflect less (greater) uncertainty about underlying cash flows which, in turn, leads to more (less) precise estimates of fundamental values. Hence, we examine the following non-directional hypothesis:

H1 News sentiment is associated with liquidity volatility.

Lang and Maffett (2011) provide a link between accounting and liquidity volatility, as well as accounting and liquidity covariability, at a cross-country level. They focus on accounting transparency which they define broadly to include discretionary earnings management, audit quality, choice of accounting standards, analyst coverage, and analyst forecast accuracy.

Consistent with accounting transparency resolving uncertainty about fundamental values, they find that accounting transparency is negatively related to liquidity volatility, including measures based on the standard deviation and skewness of the Amihud (2002) price impact measure. Ng’s

(2011) research question is similar to Lang and Maffett (2011), but his study is US based and uses Dechow and Dichev’s (2002) measure of accruals quality measure and Pastor and

Stambaugh’s (2003) measure of liquidity risk. Nonetheless, he finds that better accruals quality reduces liquidity risk, which he interprets as evidence of higher quality information reducing uncertainty about firm value. Also, Welker (1995) and Healy, Hutton, and Palepu (1999) find better disclosure quality is associated with higher liquidity levels.

We explore the effect of accruals quality on the relation between news sentiment and liquidity volatility. Bhattacharya, Desai, and Venkataraman (2012) provide evidence that links accruals quality to information asymmetry. They use Dechow and Dichev’s (2002) accruals quality measure and use the percentage price impact, developed by Huang and Stoll (1996), as a measure of information asymmetry. They find higher accruals quality is associated with lower

10 information asymmetry. If firms with high accruals quality have less information asymmetry to start with, news sentiment may play less of a role in resolving information asymmetry in these firms.

We are unaware of any studies that examine the interaction between news sentiment and accruals quality. The closest may be Frankel and Li (2004) who include measures of the value relevance of earnings and book value (a proxy for financial statement quality) and number of news items in regressions explaining insider trading purchases (a proxy for information asymmetry). However, they do not consider the content or sentiment of the news; contrary to expectations, they do not find that news decreases insider trading purchases; and they do not consider whether financial statement quality and news interact. Thus, we examine a second hypothesis:

H2 The effect of news sentiment on liquidity volatility is moderated by accruals quality.

III. Data and Method

Liquidity Volatility

Our sample focuses on NYSE- and AMEX-listed stocks. We exclude NASDAQ-listed stocks due to NASDAQ’s different microstructure (e.g., Reinganum 1990, Atkins and Dyl 1997).

We also exclude all assets whose CRSP share codes are not 10 or 11. We use three measures of liquidity volatility and use principle components analysis to reduce these to a single factor.

Our first two measures of liquidity volatility are based on Amihud’s (2002) price impact of order flow measure. Investors care about the price impact because an investor who wants to sell her shares immediately may be forced to sell at an unfavorable price if the shares are illiquid and if her sale has a large impact on the price. We calculate the Amihud (2002) measure, Liqi,d, as:

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Liqi,d = | Ri,d |/ DVoli,d (1) where Ri,d is the return on the stock of firm i on day d and DVoli,d is the dollar trading volume for stock of firm i on day d in millions, and Liqi,d is increasing in illiquidity. Liqi,d reflects the price effect of trading $1 million of stock i on day d. For a daily observation of Liqi,d to be included in our sample, the stock’s price at the end of the previous day has to be at least $2 but no more than

$1,000. Also, we require a minimum of 15 days on which Liqi,d is observed in a month in order to include that firm-month in the sample.

Following Lang and Maffett (2011), we compute the standard deviation (LiqSDi,t) and skewness (LiqSkewi,t) of Liqi,d for each firm-month. The greater the variability in liquidity, the greater the risk that the investor will face illiquidity when transacting in shares of firm i.

Similarly, higher values of LiqSkewi,t suggests a higher frequency of days where illiquidity is high.

Our third measure of liquidity risk is based on Akbas et al. (2011) measure of idiosyncratic liquidity volatility. We estimate the following model:

Liqi,d = αi + β1,i LiqM,d + β2,i (RM,d – rf,d) + εi,d (2) where Liqi,d is the Amihud measure for firm i on day d, LiqM,d is the aggregate market illiquidity on day t, RM,d is the CRSP total market return on day d, and rf,d is the risk-free rate on day d. The error term, εi,d, is the firm-specific residual liquidity after accounting for the commonality of market liquidity and market returns (Chordia, Roll, and Subrahmanyam 2000, Hameed, Wang, and Viswanathan 2010). Our measure of idiosyncratic volatility of liquidity is the standard deviation of εi,d in a month scaled by the average level of illiquidity in that month, Liqi,t.

Based on these three liquidity risk proxies, we create an aggregate liquidity risk measure using Korajczyk and Sadka’s (2008) principle component analysis approach. We calculate the

12 cross-sectional average of each liquidity risk measure on a monthly basis. We then compute the time-series mean and standard deviation of this series. The next step involves standardizing each of the time-series observations by scaling the difference between the observation and the time- series mean by the time-series standard deviation. The process is repeated for all three liquidity volatility measures. We then obtain the first principle component across the three measures to obtain the aggregate liquidity volatility proxy.4 We use this aggregate measure in our analyses throughout the paper.5

News Sentiment

Our measure of news sentiment is based on data from Thomson Reuters News Analytics.

News Analytics analyze news items in real-time to determine the sentiment of the item. Using a text processing engine developed by Lexalytics, each news item is scored based on a three-step process (see Sinha, 2011, for more detail). In the pre-processing phase, each word in a sentence is broken down into parts of speech, in a process known as “shallow parsing”. Based on lexical identification, the second phase identifies the words or phrases in the sentence that are more important for conveying sentiment. In the third phase, these sentiment-relevant words and phrases are analyzed to determine if the sentiment is positive, negative, or neutral using a three- layer back-propagation neural network. The output of the process is three probability scores that reflect that the news item is positive, negative, and neutral, respectively. If the item refers to more than one firm, each firm receives its own sentiment score.

News Analytics also provides a relevance score for each item. Relevance is scored from

0 (low) to 1 (high). For example, a news item that mentions several firms may not be as relevant for all the firms if one firm is the main focus. News Analytics also provides a count of the

4 The first principle component explains 60% of the total variance, the second and third principle components explain 30% and 9% of the total variance, respectively. 5 The results for individual liquidity risk measure are qualitatively similar and available upon request.

13 number of previous news items that an item is linked with using a “linguistic fingerprint”. This can be used to determine the novelty of the item. For example, an item that is linked to two other items that appeared in the previous 24 hours is less novel than an item that is not linked to any items in the previous 24 hours.

Consistent with Sinha (2011), we use the relevance score and link count to filter news items for importance. We exclude alerts (headlines only) and news items that have a relevance score of less than 0.35 and a link count greater than 2.6 Then, for each firm-month, we compute

PosSenti,t, which is the aggregate positive news sentiment score for all news items for firm i in month t scaled by the total number of news items for firm i in month t, and NegSenti,t, which is the aggregate negative news sentiment score for all news items for firm i in month t scaled by the total number of news items for firm i in month t. We treat the positive and negative sentiment scores separately since the implication of negative sentiment is unclear. We have News

Analytics data for each month from January 2003 to December 2011. .

We also compute NStori,t which is the total number of news items for firm i in month t.

In all our multivariate tests, we control for NStori,t as Bushee et al. (2010) and Fang and Peress

(2009) find evidence that the level of news coverage is important. Thus, PosSenti,t and NegSenti,t represent the effects of positive and negative news sentiment above and beyond the level of news coverage.

In our tests, we examine the relation between news sentiment from month t-1 and liquidity volatility in month t. First, while not conclusive, chronological ordering is more suggestive of a causal relation. Second, using the lagged news sentiment, reduces concerns that both news sentiment and liquidity volatility are being driven by contemporaneous factors or that

6 The results are qualitatively similar if we include these news items in our sample or if we further restrict the sample to news items with relevance score of one and link count of zero.

14 the relation runs from liquidity volatility to news sentiment (e.g., the media reporting pessimistically about a firm’s increase in liquidity volatility). Third, while our hypotheses are based on an information argument, news sentiment has also been linked to investor psychology.

For example, Tetlock (2007) provides evidence that pessimism in the “Abreast of the Market” column in the Wall Street Journal affects market prices and trading volume, but that the effects are short-lived, consistent with negative investor sentiment. Baker and Stein (2004) develop a model explaining time-series variation in liquidity that depends on investor sentiment as well as short-sales constraints. They assume there is a class of investors who are irrationally overconfident who put too much weight in their own private signals which can give rise to positive or negative sentiment shocks. These investors are only active traders when their sentiment is positive, resulting in an overvalued market. On the flip-side, they will stay out of the market when there is negative sentiment because of the short-sale constraints. As Tetlock

(2007) points out, it is possible to distinguish between the information and behavioral explanations by examining longer horizons (more than five days) since the information argument predicts enduring effects while the investor sentiment argument does not. In our setting, using lagged news sentiment makes the likelihood of a behavioral explanation remote.

Accruals quality

Similar to Francis, LaFond, Olsson, and Schipper (2005), Ng (2011), and Bhattacharya et al. (2012), we measure accruals quality using Dechow and Dichev’s (2002) model as modified by McNichols (2002). Rather than focus on specific episodes of earnings management, we are interested in the firm’s reputation for high quality earnings which is better captured using a multi-period measure of accruals quality. We estimate the following model on a cross-sectional basis for each of 48 Fama-French industry groups with at least 20 observations in year t:

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TCAi,t = φ0,j + φ1,jCFOi,t-1 + φ2,jCFOi,t + φ3,jCFOi,t+1 + φ4,jΔRevi,t + φ5,jPPEi,t + νi,t (1) where TCAi,t is total current accruals equal to the change in current assets (ΔCAi,t) less the change in current liabilities (ΔCLi,t) less the change in cash (ΔCashi,t) plus the change in short-term debt

(ΔSTDEBTi,t); CFOi,t is the cash flow from operations in year t equal to net income before extraordinary items (NIBEi,t) less total accruals [ΔCAi,t – ΔCLi,t – ΔCashi,t – ΔSTDEBTi,t – depreciation and amortization expense (DEPNi,t)]; ΔRevi,t is the change in revenues from year t-1 to year t; and PPEi,t is the firm’s gross value of plant, property, and equipment in year t. As

McNichols (2002) shows, including ΔRevi,t and PPEi,t significantly increases the explanatory power of the basic Dechow and Dichev (2002) model.

We save the firm- and year-specific residuals, and for each firm i, we compute accruals quality, AQi,t, as the standard deviation of firm i’s residuals over the years t-4 to t. A small standard deviation indicates less uncertainty about accruals, i.e., higher quality accruals. To ease interpretation, we multiply the standard deviation by -1 so our measure AQi,t is increasing in accruals quality. Similar to news sentiment, we use the lagged accruals quality when examining liquidity volatility in year t. However, following Bhattacharya et al. (2012), we define the lagged measure as AQi,t-1 for months 4-12 of the fiscal year and as AQi,t-2 for months 1-3 of the fiscal year. This ensures our accruals quality measure is based on financial statements that were publicly available at the time.

Control variables

Following previous literature, we include a number of trading and firm characteristics as control variables. They are the monthly average of liquidity, Liqi,t; return variability, STDRETi,t, measured as the standard deviation of daily return in a month; turnover, TURNi,t, calculated as the total traded volume in a month divided by the number of outstanding shares at the beginning of the year; market capitalization, SIZEi,t, measured as the stock price at the end of the previous

16 month multiplied by the number of shares outstanding; past six-month returns, MOMi,t, measured as the cumulative monthly returns from month t-7 through t-1; leverage, LEVi,t, being ratio of the total long-term debt to total assets in the previous fiscal year; and return on assets, ROAi,t, computed as income before extraordinary items over the average assets.

IV. Results

Main Results

Table 1 provides descriptive statistics for variables used in the study. Our initial sample consists of 150,681 firm-month observations related to 2,683 firms. The mean for NStor is 15.71 stories per firm month, but the median is only 5 stories, indicating that news coverage is highly skewed. Similarly, Fang and Peress (2009) report a few firms receive a huge amount of media coverage, while a majority of firms receive no or scant coverage. In our sample, 2,050 firms

(76.4 percent of the sample) receive at least some coverage during the sample period; 633 firms

(23.6 percent) receive no coverage at all during the sample period.

The mean (median) for PosSent is 0.300 (0.304), which indicates that the likelihood that a news item contains positive sentiment is 30 percent. For NegSent, the mean and median are

0.195 and 0.172, respectively, so the probability of negative sentiment is about 35-43 percent less than the probability of positive sentiment. Kothari et al. (2009) also report that positive news dominates negative news and note that this could reflect macroeconomic conditions, e.g., an expanding national economy. In their sample, positive news is 2.8 times more likely than negative news while, in our sample, positive news is only around 1.5 times more likely. The difference most likely reflects the sample period – their period is from 1996 to 2001 which includes the apex of the dotcom boom, while our period from 2003-2011 includes the recent financial crisis. LVF has a mean (median) of -0.216 (-0.396). As the output of a principle

17 components analysis, LVF provides a relative, rather than absolute, measure of liquidity volatility. The mean (median) for AQ is -0.042 (-0.032) is almost identical to Francis et al.

(2005).

Insert Table 1

Table 2 provides the correlations for LVF and the independent variables. We find significant correlations between LVF and news sentiment. The correlation between LVF and

PosSent (NegSent) is -0.141 (-0.135). Both correlations are consistent with news sentiment – whether positive or negative – reducing liquidity volatility. This provides some initial support for H1 and suggests that news sentiment can reduce information uncertainty. The correlation between PosSent and NegSent is 0.110 which suggests that, in a month, a firm can receive both positive and negative news.

Insert Table 2

We also find that LVF is negatively and significantly correlated with AQ (r = -0.121, p <

0.001). Thus, higher accruals quality is associated with lower liquidity volatility, a finding that is consistent with Lang and Maffett (2011) and Ng (2011). Further, we find a significant negative correlation between LVF and NStor of -0.133, indicating that liquidity volatility is decreasing in the level of news coverage. While broadly consistent with Bushee et al. (2010), when we control for firm size in our multivariate test, we find that the relation between LVF and

NStor becomes positive, a finding we discuss later.

Not surprisingly, as suggested by Sadka (2011), the correlation between LVF and the

(level of the) Amihud liquidity measure is high (r = 0.423, p < 0.001). Size is also highly correlated with LVF (r = -0.390, p < 0.001), NStor (r = 0.459, p < 0.001), and AQ (r = 0.342, p <

18

0.001). Large firms have lower liquidity volatility, receive more news coverage, and have higher quality earnings. Finally, Turn and StdRet are highly correlated (r = 0.408, p < 0.001).

Table 3 provides the regression results for H1. We regress liquidity volatility on news sentiment and the control variables which includes the number of news stories, NStor. We control for news stories since Bushee et al. (2010) find that news coverage affects the bid-ask spread and market depth around the earnings announcement. If a firm receives no coverage in a month, NStor is equal to zero. Thus, our tests examine whether news sentiment has an effect on liquidity volatility that is incremental to news coverage. In all of the regressions in Table 3 (as well as subsequent tables), standard errors are adjusted for two-way clustering (e.g., Gow,

Ormazabal, and Taylor 2010).

Insert Table 3

Table 3, model 1 shows that PosSent is negatively and significantly related to LVF. The negative coefficient of -0.245 indicates that for an interquartile range increment in PosSent, LVF decreases by 19.0 percent (based on the median) which is economically meaningful. In addition, all of the control variables are significant. Contrary to the correlation in Table 2, we now find a positive and significant coefficient for NStor. Further investigation suggests the switch in sign for NStor is due to its positive correlation with firm size (r = 0.500 in Table 2) – when Size is omitted from the regression, NStor is negative and significant. The positive sign for NStor in

Table 3 suggests that after controlling for the size effect in media coverage (e.g., Fang and Peress

2009), a greater number of stories reflects more news shocks that could increase uncertainty about fundamentals.

Consistent with Pastor and Stambaugh (2003), we find Size is negatively related to LVF and highly significant, indicating large firms have lower liquidity volatility. As Sadka (2011)

19 suggests, Ami has a strong positive relation with liquidity volatility. Similar to Lang and Maffett

(2011), we find a negative and significant relation between LVF and StdRet. Mom is positively related to liquidity volatility, a finding that is broadly consistent with Sadka (2006). Turn is negatively related to LVF, consistent with turnover being a measure of liquidity. Lev and ROA are also significantly related to LVF, possibly through their effect on fundamentals.

Table 3, model 2 provide a similar analysis, but we substitute NegSent for PosSent.

NegSent has a significant negative coefficient, indicating that greater negative news sentiment is associated with lower liquidity volatility. This is consistent with the correlations in Table 2.

Moreover, the interquartile range increment and the coefficient of -0.277 suggests an economic effect that is slightly smaller than positive sentiment, i.e., a 16.5 percent decrease in LVF for negative sentiment compared to a 19.0 percent in LVF for positive sentiment (as reported above).

Table 3, model 3 reports the results for a regression containing both PosSent and NegSent. In this model, both sentiment variables are negatively and significantly related to liquidity volatility. Analysis of the interquartile range increment again shows similar economic effects for positive and negative sentiment (19.9 percent and 17.6 percent, respectively). Combined, the results in Table 3 provide support for H1, i.e., news sentiment has a significant effect on liquidity volatility after controlling for news coverage and other control variables.

Table 4 examines the interaction between news sentiment and accruals quality. Because of data requirements related to AQ, our sample for these tests decreases to 103,284 firm-month observations. As discussed above, we expect that AQ will moderate the relation between LVF and news sentiment because firms with good accruals quality should have less information asymmetry to start with. We rank AQ into deciles and use the corresponding AQ rank in our regressions. Similar to Table 3, we estimate three models.

20

Insert Table 4

Table 4, model 1 contains the results for the model based on positive news sentiment only. Consistent with Table 3, we continue to find a negative and significant direct effect for

PosSent on LVF. However, we also find that the coefficient of PosSent*AQ is significant. Its positive sign means that increasing AQ reduces the negative effect of PosSent on LVF. We compute the interquartile range increment for positive news sentiment for the highest and lowest

AQ deciles. For the highest decile (decile 10), the interquartile range increment for PosSent *AQ is a 13.2 percent increase in LVF while the interquartile range increment for PosSent is 22.2 percent decrease in LVF. In other words, for the highest accruals quality firms, the net change in

LVF across the interquartile range increment is a 9 percent decrease (-22.2 percent plus 13.2 percent). For the lowest decile (decile 1), the interquartile range increment is a 1.3 percent increase in LVF, meaning the net decrease in LVF is 20.9 percent for these firms (-22.2 percent plus 1.3 percent). Thus, the effect of positive news sentiment on LVF is 56.9 percent smaller if the firm has high accruals quality [(-20.9 – (-9))/-20.9].

Our results are similar when NegSent is used in place of PosSent. In this case, the net interquartile range increment due to NegSent is -7.8 percent for firms in decile 10 compared to

-20.1 percent for firms in decile 1, a 61.2 percent difference. Thus, we find support for H2 both statistically and economically. Finally, when PosSent, PosSent*AQ, NegSent, and NegSent*AQ are all included in the same model (i.e., model 3), PosSent and NegSent remain negative and significant, and the two interactions have signs opposite to the main effects, although

PosSent*AQ is only significant at the 0.102 level. The differences in LVF across the interquartile range increment for high and low AQ firms are smaller, but are still substantial in an

21 economic sense. The effect of positive (negative) news sentiment on LVF is now 44.0 (45.7) percent smaller for firms in AQ decile 10 compared to AQ decile 1.

In all three models in Table 4, AQ is insignificant. In this setting, the implications of AQ are limited since it represents the effect of accruals quality on liquidity volatility when news sentiment is equal to zero. PosSent and NegSent will be zero mainly for firms that have no news coverage in a month. No coverage firms tend to be smaller, as Fang and Peress (2009) suggest, and these firms also have lower accruals quality.7 Aside from Mom which is not significant in models 2 and 3, all the remaining control variables are significant and have signs consistent with

Table 3.8

Additional Analyses

Tetlock et al. (2008, Figure 1) reports a dramatic spike in news stories around the earnings announcement date. Bushee et al. (2010) find that news coverage during the earnings announcement window significantly reduces information asymmetry. As a result, we expect that accruals quality will be less important once earnings are announced because the uncertainty about current earnings is resolved. Further, as Li, Ramesh, and Shen (2011) argue, news released around the earnings announcement is more likely to play a confirmatory role. While this does not suggest that information asymmetry will be eliminated, it does suggest that the role of accruals quality may vary over the reporting cycle.

Consequently, in Table 5, we partition our sample into months following an earnings announcement (post-EA) and other months. We estimate three models for each subset, i.e.,

7 In our sample, the mean for Size (AQ) is 14.621 (-0.039) for firm-months with press coverage and 12.891 (-0.05) for firm-months with no press coverage. Both differences are significant at the 0.01 level. 8 Bushee et al. (2010) also control for analyst following, a measure of the firm’s information environment. We re-estimate our analyses in Tables 3 and 4 with the log of one plus the number of analysts covering the firm as an additional control variable and the results are qualitatively the same. In addition, analyst following is significantly and negatively related to LVF, indicating that a larger analyst following is associated with lower information asymmetry and lower liquidity volatility.

22 positive sentiment separately, negative sentiment separately, and positive and negative sentiment together. Models 1-3 reflect the results using LVF from the post-EA months. Models 4-6 reflect the results using LVF from the other months. Since news sentiment is lagged in all our tests, this means news sentiment in models 1-3 is related to the earnings announcement month.

Insert Table 5

In the post-EA months, we find that positive and negative news sentiment, both individually and when included in the same model, continue to have a significant and negative effect on liquidity volatility. However, we find no evidence that either interaction, PosSent*AQ or NegSent*AQ, is significant. Thus, the sentiment contained in the news continues to provide incremental information that reduces uncertainty about fundamentals, regardless of the quality of the firm’s accounting. However, consistent with the earnings announcement resolving uncertainty about current earnings, accruals quality no longer moderates the effect between news sentiment and liquidity volatility.

In contrast, in other months which reflect news released outside the earnings announcement month, we find that PosSent*AQ and NegSent*AQ continue to be significant with positive signs in models 4 and 5, similar to Table 4. When we include both interactions in the same model (model 6), NegSent*AQ remains significantly positive, while PosSent*AQ loses significance. Still, the results are broadly consistent with accruals quality having a moderating effect in periods when earnings uncertainty is high but not when it is low.

Next, we consider the source of the news. Specifically, the news can be initiated by the firm, e.g., by issuing a press release, or it can be initiated by the business press. Kothari et al.

(2009) find that investors view information generated by the business press as being more credible than information that originates from management or analysts. They argue that the

23 business press has more incentives to act independently. On the other hand, Bushee et al. (2010) find evidence that dissemination of firm-initiated news has a larger impact on information asymmetry than press-initiated news, but they do not consider the content of the news items and they focus on the earnings announcement window.

We identify firm-initiated news as items that are carried on presswires. Presswires receive and distribute press releases from client firms. News Analytics includes a code for the source of each news item. Based on Bushee et al. (2010), Solomon and Soltes (2011), Wikipedia

(http://en.wikipedia.org/wiki/News_agency), and web searchers, we identify the following as presswires: Asian Corporate Newswire, Business Wire, Cision, Filing Services Canada

Newswire, Globe Newswire, Hugin, Marketwire, Prime Newswire, and PR Newswire. Next, we create two subsamples: a ‘firm-initiated’ news subsample that contains all firm-month observations with at least one news item that was carried on a presswire, and a ‘press-initiated’ news subsample that contains firm-month observations with no news items carried by a presswire.

Table 6 reports the results. For the firm-initiated subsample, models 1-3 show that news sentiment has no effect on LVF. This finding suggests that the positive or negative tone of firm- initiated press releases that are disseminated by presswires is not related to subsequent liquidity volatility, possibly because these disclosures lack credibility (e.g., Kothari et al. 2009) or because any reaction to the firm-initiated news is short-lived. Regarding the latter, Solomon (2011) finds that returns for firms that use investor relation firms are higher around news announcements, but are lower around earnings announcements, consistent with high expectations followed by disappointment. While not all firms use investor relation firms, investors may react to the broader set of firm-initiated news in a similar way. Since we focus on news sentiment and

24 month-ahead liquidity volatility, our tests are designed to detect the long-term effects of news sentiment. The results in models 1-3 indicate that firm-initiated news has no enduring implications for liquidity volatility.

Insert Table 6

Interestingly, NStor is not significant in models 1 and 3 and is negative and significant in model 2. In all other models reported before and after, NStor is positive and significant. This confirms that firm-initiated news stories are viewed differently by the market.

Models 4-6 in Table 6 are estimated using press-initiated news. The results are similar to the full sample results in Table 4. PosSent and NegSent are negatively and significantly related to LVF, while PosSent*AQ and NegSent*AQ are positively and significantly related to LVF.

Kothari et al. (2009) argue that the business press is not subject to the same agency problems and incentives that face managers and financial analysts, leading to greater credibility. Of course, the business press has its own unique incentives (e.g., Dyck and Zingales 2003, Solomon and Soltes

2011).

Several recent papers examine the relation between product market competition and accruals quality. For example, Darrough and Stoughton (1990) find analytically that, when entry barriers are low, firms with good news and bad news have incentive to provide high quality information to dissuade potential entrants and to ensure accurate valuations of their firms.

Balakrishnan and Cohen (2011) expect that by imposing discipline and encouraging monitoring, product market competition will improve accruals quality. They argue that when competing firms release information, a firm that misreports runs the risk that the competitor’s report will allow investors to uncover the distortions. The more competitors there are, the more likely that at least one will provide the highest quality financial reports. As others in the industry raise their

25 reporting quality to meet this standard, it becomes easier to identify the poor quality reporters.

Balakrishnan and Cohen (2011) find that the frequency of earnings restatements is lower in more competitive industries. Similarly, Dhaliwal, Huang, Khurana, and Pereira (2011) find greater accounting conservatism in competitive industries.

On the other hand, proprietary costs make it costly to provide full information (e.g., Dye

1985), and analytical models by Gal-Or (1985) and Gertner, Gibbons, and Scharfstein (1988) predict greater misreporting in competitive industries. Moreover, Shleifer (2004) argues that greater competition leads to unethical behavior and aggressive accounting practices.

Consequently, we partition the sample based on product market competition. Consistent with DeFond and Park (1999) and Balakrishnan and Cohen (2011), we use the Herfindahl-

Hirschman Index (HHI) as our measure of competition where smaller (larger) HHI values indicate higher (lower) industry competition. We rank the firm-months based on HHI and divide into quintiles. We classify firm-months in the lowest quintiles as the high product market subsample. The remaining quintiles comprise the low product market subsample.

Table 7 provides the results for the two subsamples. Models 1-3 are estimated using the low product market competition subsample. In these models, PosSent and NegSent are negatively signed and significant similar to the full sample results (Table 3). However, both interactions are insignificant whether they are included separately or together. This is consistent with these firms having lower accruals quality. Low accruals quality implies high formation asymmetry. Thus, the impact of news sentiment on liquidity volatility is greater for these firms because they start with a greater level of information asymmetry.

Insert Table 7

26

In contrast, for the high product market competition, the two interactions continue to be significant with positive coefficients in models 4 and 5. This is consistent with the view that intense product market competition can lead to better accruals quality which improves the transparency and results in a smaller quantum of information asymmetry that can be reduced by news sentiment.

Lang and Maffett (2011) and Ng (2011) consider whether the direct effect of accruals quality on variations in liquidity was different during crises and non-crises period. We extend their work by including news sentiment and investigating whether the moderating effect of accruals quality on the news sentiment-LVF relation differs before and during the recent financial crisis.

There is no general agreement on when the global financial crisis began, but Elliott

(2011) identifies August 9, 2007 as the starting date. He writes:

Phase one on 9 August 2007 began with the seizure in the banking system precipitated by BNP Paribas announcing that it was ceasing activity in three hedge funds that specialised in US mortgage debt. This was the moment it became clear that there were tens of trillions of dollars worth of dodgy derivatives swilling round which were worth a lot less than the bankers had previously imagined. Nobody knew how big the losses were or how great the exposure of individual banks actually was, so trust evaporated overnight and banks stopped doing business with each other.

Lang and Maffett (2011) argue that in such an environment, liquidity is fragile because of a paucity of capital and overall uncertainty. Also, risk adversity among liquidity providers increases because of increasing uncertainty about fundamental values. Greater accruals quality can increase transparency, reduce uncertainty, and slow or reverse withdrawals of liquidity by liquidity providers.

We define a pre-financial crisis period from January 2003 to July 2007 and a financial crisis period from August 2007 to December 2011, and re-estimate our regressions for each

27 subperiod. Table 8 presents the results. We find the positive and negative news sentiment have a negative effect on LVF in both the non-financial crisis period and the financial crisis period.

This is a significant finding in itself as prior studies examining news coverage or news content do not include the financial crisis. For example, Bushee et al.’s (2010) sample period ends in

2004, Fang and Peress’ (2009) period ends in 2002, Kothari et al.’s (2009) period ends in 2001, and Tetlock et al.’s (2008) period ends in 2004. Our results indicate that news sentiment can affect liquidity volatility in periods with normal or extreme liquidity concerns.

Insert Table 8

However, the role of accruals quality does vary between the subperiods. We find that the moderating effect of accruals quality is limited to the financial crisis period. Conversely, accruals quality has no effect on how news sentiment impacts LVF in the non-crisis. Our results are consistent with Ng (2011) who finds that accounting quality reduces liquidity risk in periods with large decreases in market liquidity but has no effect in periods with large increases in market liquidity. He conjectures that during periods of extreme market illiquidity, investors view firms with poor accounting quality as being riskier so they prefer to exit these stocks consistent with a flight to quality. Similarly, Lang and Maffett (2011) find that transparency

(which includes accounting quality) is more important during market downturns and that this effect is greatest for large downturns.

V. Conclusion

The recent financial crisis heightened concerns about liquidity volatility. In particular, episodes of extreme illiquidity could limit investors’ ability to sell their shares when they want to. When liquidity is less predictable, the probability that investors may find themselves in such

28 a situation increases, exposing these investors to a form of liquidity risk. This would be particularly a concern for liquidity-motivated traders.

We extend prior research on the role of the business press by examining how news sentiment affects liquidity volatility. We also extend recent research on the relation between accruals quality and liquidity risk by examining whether accruals quality moderates the effect of news sentiment on liquidity volatility. Using data from 2003-2011, we find that both positive and negative news sentiment reduce liquidity volatility, suggesting that news sentiment provides information that allows investors to make more precise estimates of fundamental value (i.e., a variance effect). We also find that accruals quality moderates this effect, consistent with firms with high quality accounting having less information asymmetry to start with. We find our results are strongest in months that do not follow an earnings announcement, for press-initiated news, in competitive industries, and during the recent financial crisis, consistent with information asymmetry, the credibility of the news source, and the relevance of accounting information having a role in the interplay between news sentiment and accruals quality.

Our study is also notable as we use news sentiment scores from Thompson Reuter’s

News Analytics database. To our knowledge, we are the first accounting study to do so. Prior studies examining news content (e.g., Tetlock et al. 2008, Kothari et al. 2009) have downloaded a huge number of news items from Factiva and have employed content-analysis software to rate each item based on its positivity and negativity. Thus, prior researchers incur non-trivial data collection costs which may explain why the research on the business press is so limited

(especially compared to the research on financial analysts, another information intermediary). In addition to being hard to replicate, the news items are rated ex post. As a result, it is not clear how well these ratings align with the views of investors who read and rated these items in real-

29 time. In contrast, News Analytics data is less costly, is amenable to replication, and is the same information that is delivered to investors and trading desks in real-time. We believe the News

Analytic data could be used to examine a variety of important research questions in accounting.

30

REFERENCES

Acharya, V., and L. Pedersen. 2005. Asset pricing with liquidity risk. Journal of Financial Economics 77: 375-410. Akbas, F., W. Armstrong, and R. Petkova. 2011. Idiosyncratic volatility of liquidity and expected stock returns. Working paper, Purdue University. Allen, F., and E. Carletti (2008), The role of liquidity in financial crises. 2008 Jackson Hole Conference Proceedings, Federal Reserve Bank of Kansas City: 379-412. Amihud, Y. 2002. Illiquidity and stock returns: cross-section and time-series effects. Journal of Financial Markets 5: 31-56. Atkins, A., and E. Dyl. 1997. Transactions costs and holding periods for common stocks. Journal of Finance 52: 309-325. Baker, M., and J. Stein. 2004. Market liquidity as a sentiment indicator. Journal of Financial Markets 7: 271-299. Balakrishnan, K., and D. Cohen. 2011. Product market competition and financial accounting misreporting. Working paper, University of Pennsylvania. Barry, C., and S. Brown. 1984. Differential information and the small firm effect. Journal of Financial Economics 13: 283-294. Berger, P. 2011. Challenges and opportunities in disclosure research – A discussion of ‘The financial reporting environment: Review of the recent literature’. Journal of Accounting and Economics 51: 204-218. Bhattacharya, N., H. Desai, and K. Venkataraman. 2012. Does accruals quality affect information asymmetry? Evidence from trading costs. Contemporary Accounting Research 29: forthcoming. Brunnermeier, M. 2009. Deciphering the liquidity and credit crunch 2007–2008. Journal of Economic Perspectives 23: 77-100. Brunnermeier, M., and L. Pedersen. 2009. Market liquidity and funding liquidity. Review of Financial Studies 22: 2201-2238. Bushee, B., J. Core, W. Guay, and S. Hamm. 2010. The role of the business press as an information intermediary. Journal of Accounting Research 48: 1-19. Chordia, T., R. Roll, and A. Subrahmanyam. 2000. Commonality in liquidity. Journal of Financial Economics 56 (2000): 3-28. Darrough, M., and N. Stoughton. 1990. Financial disclosure policy in an entry game. Journal of Accounting and Economics 12: 219-243. Dechow, P., and I. Dichev. 2002. The quality of accruals and earnings: The role of accrual estimation errors. The Accounting Review 77 (Supplement): 35-59. DeFond, M., and C. Park. 1999. The effect of competition on CEO turnover. Journal of Accounting and Economics 27: 35-56. Dhaliwal, D., S. Huang, I. Khurana, and R. Pereira. 2011. Product market competition and accounting conservatism. Working paper, University of Arizona. Diamond, D., and R. Verrecchia. 1991. Disclosure, liquidity, and cost of capital. Journal of Finance 46: 1325-1355. Dyck, A., and L. Zingales. 2003. The media and asset prices. Working paper, Harvard University. Dye, R. 1985. Disclosure of nonproprietary information. Journal of Accounting Research 23: 544-574. Dzielinski, M. 2011. Short-term reactions to news announcements. Working paper, University of Zurich. Edelen, R. 1999. Investor flows and the assessed performance of open-end mutual funds. Journal of Financial Economics 53: 439-466. Elliott, L. 2011. Global financial crisis: Five key stages 2007-2011. The Guardian (August 7): http://www.guardian.co.uk/business/2011/aug/07/global-financial-crisis-key-stages. Engelberg, J., and C. Parsons. 2011. The causal impact of media in financial market. Journal of Finance 66: 67-97.

31

Fama, E., and K. French. 1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33: 3-56. Fang, L., and J. Peress. 2009. Media coverage and the cross-section of stock returns. Journal of Finance 64: 2023-2052. Francis, J., R. LaFond, P. Olsson, and K. Schipper. 2005. The market pricing of accruals quality. Journal of Accounting and Economics 39: 295-327. Frankel, R., and X. Li. 2004. Characteristics of a firm’s information environment and the information asymmetry between insiders and outsiders. Journal of Accounting and Economics 37: 229-259. Gal-Or, E. 1985. Information sharing in oligopoly. Econometrica 53: 329-343. Gertner, R., R. Gibbons, and D. Scharfstein. 1988. Simultaneous signalling to the capital and product markets. Rand Journal of Economics 19: 173-190. Glosten, L., and L. Harris. 1988. Estimating the components of the bid/ask spread. Journal of Financial Economics 21: 123-142. Gow, I., G. Ormazabal, and D. Taylor. 2010. Correcting for cross‐sectional and time‐series dependence in accounting research. The Accounting Review 85: 483-512. Groβ-Kluβmann, A., and N. Hautsch. 2011. When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions. Journal of Empirical Finance 18: 321-340. Hameed, A., W. Kang, and W. Viswanathan. 2010. Stock market declines and liquidity. Journal of Finance 65: 257-293. Healy, P., A. Hutton, and K. Palepu. 1999. Stock performance and intermediation changes surrounding increases in disclosure. Contemporary Accounting Research 16: 485-520. Huang, R., and H. Stoll. 1996. Dealer versus auction markets: A paired comparison of execution costs on NASDAQ and NYSE. Journal of Financial Economics 41: 313-357. Klein, R., and V. Bawa. 1976. The effect of estimation risk on optimal portfolio choice. Journal of Financial Economics 3, 215-231. Korajczyk, R., and R. Sadka. 2008. Pricing the commonality across alternative measures of liquidity. Journal of Financial Economics 87: 45-72. Kothari, S., X. Li, and J. Short. 2009. The effect of disclosure by management, analysts, and business press on cost of capital, return volatility, and analysts forecasts: A study using content analysis. The Accounting Review 84: 1639-1670. Lambert, R., C. Leuz, and R. Verrecchia. 2007. Accounting information, disclosure, and the cost of capital. Journal of Accounting Research 45: 385-420. Lang, M., and M. Maffett. 2011. Transparency and liquidity uncertainty in crisis periods. Journal of Accounting and Economics 52: 101-125. Li, E., K. Ramesh, and M. Shen. 2011. The role of newswires in screening and disseminating value- relevant information in periodic SEC reports. The Accounting Review 86: 669-701. McNichols, M. 2002. Discussion of ‘The quality of accruals and earnings: The role of accrual estimation errors’. The Accounting Review (Supplement): 61-69. Merton, R. 1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42: 483-510. Moorthy, S. 2003. Liquidity black holes: Testing the theory’s predictions. In Persaud, A. (Ed.), Liquidity Black Holes: Understanding, Quantifying and Managing Financial Liquidity Risk, Vol. I. Haymarket House, London: 21-40. Ng, J. 2011. The effect of information quality on liquidity risk. Journal of Accounting and Economics 52: 126-143. Ng, J., R. Verrecchia, and J. Weber. 2009. Firm performance measures and adverse selection. Working paper, MIT Sloan School of Management. Pastor, L., and R. Stambaugh. 2003. Liquidity risk and expected stock returns. Journal of Political Economy 111: 642-685.

32

Reinganum, M. 1990. Market microstructure and asset pricing. Journal of Financial Economics 28: 127- 147. Sadka, R. 2006. Momentum and post-earnings announcement drift anomalies: The role of liquidity risk. Journal of Financial Economics 80: 309-349. Sadka, R. 2011. Liquidity risk and accounting information. Journal of Accounting and Economics 52: 144-152. Shleifer, A. 2004. Does competition destroy ethical behaviour? American Economic Review 94: 414-418. Sinha, N. 2011. News articles and momentum. Working paper, University of Illinois, Chicago. Solomon, D. 2012. Selective publicity and stock prices. Journal of Finance 67: 599-638. Solomon, D., and E. Soltes. 2011. The determinants of coverage in the business press. Working paper, University of Southern California. Strahan, P. 200 Tetlock, P. 2007. Giving content to investor sentiment: The role of media in the stock market. Journal of Finance 62: 1139-1168. Tetlock, P., M. Saar-Tsechansky, and S. Mackassy. 2008. More than words: Quantifying language to measure firms’ fundamentals. Journal of Finance 63: 1437-1467. Vayanos, D. 2004. Flight to quality, flight to liquidity, and the pricing of risk. Working paper, London School of Economics. Welker, M. 1995. Disclosure policy, information asymmetry, and liquidity in equity markets. Contemporary Accounting Research 11: 801-827.

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TABLE 1 Summary Statistics

Variable Mean Std. dev. 25% Median 75% LVF -0.216 0.982 -0.773 -0.396 0.093 NStor 15.709 41.254 1.000 5.000 14.000 PosSent 0.300 0.214 0.140 0.304 0.447 NegSent 0.195 0.167 0.056 0.172 0.292 AQ -0.042 0.035 -0.051 -0.032 -0.020 Lev 0.212 0.177 0.065 0.191 0.314 Ami 0.107 1.757 0.000 0.001 0.010 Turn 0.191 0.209 0.077 0.139 0.237 ROA 0.035 0.109 0.011 0.040 0.078 Size 14.124 1.807 12.954 14.157 15.253 StdRet 0.025 0.018 0.014 0.020 0.030 Mom 0.082 0.371 -0.112 0.045 0.212

Variable definitions: LVF is the first factor from a principle components analysis (Korajczyk and Sadka 2008) of three liquidity volatility measures: 1) monthly standard deviation of the Amihud (2002) price impact measure, Liqi,d , where Liqi,d = | Ri,d |/ DVoli,d and Ri,d is the return on the stock of firm i on day d and DVoli,d is the dollar trading volume for stock of firm i on day d in millions, 2) skewness of Liqi,d for each firm-month, and 3) idiosyncratic liquidity volatility estimated using the model from Akbas, Armstrong and Petkova (2011): Liqi,d = αi + β1,i LiqM,d + β2,i (RM,d – rf,d) + εi,d where Liqi,d is the Amihud measure for firm i on day d, LiqM,d is the aggregate market illiquidity on day t, RM,d is the CRSP total market return on day d, rf,d is the risk-free rate on day d, and idiosyncratic volatility of liquidity is the standard deviation of εi,d in a month scaled by the average level of illiquidity in that month, Liqi,t. The three news-related variables are based on data from Thomson Reuters News Analytics. NStori,t is the total number of news items for firm i in month t. PosSenti,t is the aggregate positive news sentiment score for all news items for firm i in month t scaled by the total number of news items for firm i in month t. NegSenti,t is the aggregate negative news sentiment score for all news items for firm i in month t scaled by the total number of news items for firm i in month t. We use lagged values for the news-related variables, i.e., NStor i,t-1, PosSent i,t-1, and NegSent i,t-1, in all our tests. AQi,t is accruals quality estimated using Dechow and Dichev’s (2002) model as modified by McNichols (2002), i.e., the following model on a cross-sectional basis for each of 48 Fama-French industry groups with at least 20 observations in year t: TCAi,t = φ0,j + φ1,jCFOi,t-1 + φ2,jCFOi,t + φ3,jCFOi,t+1 + φ4,jΔRevi,t + φ5,jPPEi,t + νi,t where TCAi,t is total current accruals equal to the change in current assets (ΔCAi,t) less the change in current liabilities (ΔCLi,t) less the change in cash (ΔCashi,t) plus the change in short-term debt (ΔSTDEBTi,t); CFOi,t is the cash flow from operations in year t equal to net income before extraordinary items (NIBEi,t) less total accruals [ΔCAi,t – ΔCLi,t – ΔCashi,t – ΔSTDEBTi,t – depreciation and amortization expense (DEPNi,t)]; ΔRevi,t is the change in revenues from year t-1 to year t; and PPEi,t is the firm’s gross value of plant, property, and equipment in year t. AQi,t is the standard deviation of firm i’s residuals over the years t-4 to t. We multiply the standard deviation by -1 so our measure AQi,t is increasing in accruals quality. We use the lagged accruals quality in all our tests. Following Bhattacharya et al. (2012), we define the lagged measure as AQi,t-1 for months 4-12 of the fiscal year and as AQi,t-2 for months 1-3 of the fiscal year. Ami is the monthly average of liquidity, Liqi,d. STDRET is return variability, measured as the standard deviation of daily return in a month. TURN is turnover, calculated as the total traded volume in a month divided by the number of outstanding shares at the beginning of the year. SIZE is natural log of market capitalization, measured as the stock price at the end of the previous month multiplied by the number of shares outstanding. MOM is the past six-month returns, measured as the cumulative monthly returns from month t-7 through t-1. LEV is leverage, computed as the ratio of the total long-term debt to total assets in the previous fiscal year. ROA is return on assets, computed as income before extraordinary items over the average assets. Sample size is 150,681.

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TABLE 2 Correlation Matrix

LVF PosSent NegSent AQ NStor StdRet Turn Ami Size Mom Lev PosSent -0.141*** NegSent -0.135*** -0.570*** AQ -0.121*** 0.077*** 0.055*** NStor -0.133*** -0.098*** 0.117*** 0.130*** StdRet 0.020*** -0.119*** 0.045*** -0.181*** 0.014*** Turn -0.164*** -0.108*** 0.037*** 0.002*** 0.134*** 0.408*** Ami 0.423*** -0.002 0.013*** -0.034*** -0.012*** 0.051*** -0.045*** Size -0.390*** -0.130*** 0.036*** 0.342*** 0.500*** -0.170*** 0.128*** -0.082*** Mom 0.007*** 0.006** -0.006** -0.034*** -0.004 0.001 0.009*** 0.000*** -0.045*** Lev -0.040*** -0.045*** 0.017*** 0.149*** -0.008*** 0.023*** 0.061*** -0.016*** 0.135*** 0.015*** ROA -0.076*** 0.022*** -0.049*** 0.237*** 0.017*** -0.175*** 0.001 -0.033*** 0.089*** -0.069*** -0.106*** **, *** indicate significance at 0.05 and 0.01 level (two-tailed), respectively.

Variable definitions: LVF is the first factor from a principle components analysis (Korajczyk and Sadka 2008) of three liquidity volatility measures: 1) monthly standard deviation of the Amihud (2002) price impact measure, Liqi,d , where Liqi,d = | Ri,d |/ DVoli,d and Ri,d is the return on the stock of firm i on day d and DVoli,d is the dollar trading volume for stock of firm i on day d in millions, 2) skewness of Liqi,d for each firm-month, and 3) idiosyncratic liquidity volatility estimated using the model from Akbas, Armstrong and Petkova (2011): Liqi,d = αi + β1,i LiqM,d + β2,i (RM,d – rf,d) + εi,d where Liqi,d is the Amihud measure for firm i on day d, LiqM,d is the aggregate market illiquidity on day t, RM,d is the CRSP total market return on day d, rf,d is the risk-free rate on day d, and idiosyncratic volatility of liquidity is the standard deviation of εi,d in a month scaled by the average level of illiquidity in that month, Liqi,t. The three news-related variables are based on data from Thomson Reuters News Analytics. NStori,t is the total number of news items for firm i in month t. PosSenti,t is the aggregate positive news sentiment score for all news items for firm i in month t scaled by the total number of news items for firm i in month t. NegSenti,t is the aggregate negative news sentiment score for all news items for firm i in month t scaled by the total number of news items for firm i in month t. We use lagged values for the news-related variables, i.e., NStor i,t- 1, PosSent i,t-1, and NegSent i,t-1, in all our tests. AQi,t is accruals quality estimated using Dechow and Dichev’s (2002) model as modified by McNichols (2002), i.e., the following model on a cross-sectional basis for each of 48 Fama-French industry groups with at least 20 observations in year t: TCAi,t = φ0,j + φ1,jCFOi,t-1 + φ2,jCFOi,t + φ3,jCFOi,t+1 + φ4,jΔRevi,t + φ5,jPPEi,t + νi,t where TCAi,t is total current accruals equal to the change in current assets (ΔCAi,t) less the change in current liabilities (ΔCLi,t) less the change in cash (ΔCashi,t) plus the change in short-term debt (ΔSTDEBTi,t); CFOi,t is the cash flow from operations in year t equal to net income before extraordinary items (NIBEi,t) less total accruals [ΔCAi,t – ΔCLi,t – ΔCashi,t – ΔSTDEBTi,t – depreciation and amortization expense (DEPNi,t)]; ΔRevi,t is the change in revenues from year t-1 to year t; and PPEi,t is the firm’s gross value of plant, property, and equipment in year t. AQi,t is the standard deviation of firm i’s residuals over the years t-4 to t. We multiply the standard deviation by -1 so our measure AQi,t is increasing in accruals quality. We use the lagged accruals quality in all our tests. Following Bhattacharya et al. (2012), we define the lagged measure as AQi,t-1 for months 4-12 of the fiscal year and as AQi,t-2 for months 1-3 of the fiscal year. Ami is the monthly average of liquidity, Liqi,d. STDRET is return variability, measured as the standard deviation of daily return in a month. TURN is turnover, calculated as the total traded volume in a month divided by the number of outstanding shares at the beginning of the year. SIZE is natural log of market capitalization, measured as the stock price at the end of the previous month multiplied by the number of shares outstanding. MOM is the past six-month returns, measured as the cumulative monthly returns from month t-7 through t-1. LEV is leverage, computed as the ratio of the total long-

35 term debt to total assets in the previous fiscal year. ROA is return on assets, computed as income before extraordinary items over the average assets. Sample size is 150,681.

36

TABLE 3 Regressions of Liquidity Volatility Factor on News and Control Variables

Variable (1) (2) (3) PosSent -0.245 -0.257 (-11.560)*** (-11.880)*** NegSent -0.277 -0.295 (-11.740)*** (-12.200)*** NStor 0.032 0.035 0.050 (5.396)*** (5.861)*** (7.840)*** StdRet -4.773 -4.582 -4.855 (-5.984)*** (-5.846)*** (-6.176)*** Turn -0.320 -0.313 -0.315 (-8.295)*** (-8.207)*** (-8.276)*** Ami 0.208 0.208 0.208 (13.129)*** (12.999)*** (13.363)*** Size -0.203 -0.206 -0.207 (-23.960)*** (-24.130)*** (-24.620)*** Mom 0.036 0.023 0.028 (2.132)** (1.405) (1.696)* Lev -0.085 -0.08 -0.081 (-2.708)*** (-2.411)** (-2.595)** ROA 0.167 0.149 0.163 (3.311)*** (2.977)** (3.274)*** Intercept 2.823 2.831 2.914 (21.849)*** (21.79)*** (22.756)***

Adj. R2 0.324 0.324 0.327 N 150,681 150,681 150,681 *, **, *** indicate significance at 0.10, 0.05 and 0.01 level (two-tailed), respectively.

Variable definitions: LVF is the first factor from a principle components analysis (Korajczyk and Sadka 2008) of three liquidity volatility measures: 1) monthly standard deviation of the Amihud (2002) price impact measure, Liqi,d , where Liqi,d = | Ri,d |/ DVoli,d and Ri,d is the return on the stock of firm i on day d and DVoli,d is the dollar trading volume for stock of firm i on day d in millions, 2) skewness of Liqi,d for each firm-month, and 3) idiosyncratic liquidity volatility estimated using the model from Akbas, Armstrong and Petkova (2011): Liqi,d = αi + β1,i LiqM,d + β2,i (RM,d – rf,d) + εi,d where Liqi,d is the Amihud measure for firm i on day d, LiqM,d is the aggregate market illiquidity on day t, RM,d is the CRSP total market return on day d, rf,d is the risk-free rate on day d, and idiosyncratic volatility of liquidity is the standard deviation of εi,d in a month scaled by the average level of illiquidity in that month, Liqi,t. NStori,t is based on data from Thomson Reuters News Analytics. NStori,t is the total number of news items for firm i in month t. PosSenti,t is the aggregate positive news sentiment score for all news items for firm i in month t scaled by the total number of news items for firm i in month t. NegSenti,t is the aggregate negative news sentiment score for all news items for firm i in month t scaled by the total number of news items for firm i in month t. We use lagged values for the news-related variables, i.e., NStor i,t-1, PosSent i,t-1, and NegSent i,t-1, in all our tests. AQi,t is accruals quality estimated using Dechow and Dichev’s (2002) model as modified by McNichols (2002), i.e., the following model on a cross- sectional basis for each of 48 Fama-French industry groups with at least 20 observations in year t: TCAi,t = φ0,j + φ1,jCFOi,t-1 + φ2,jCFOi,t + φ3,jCFOi,t+1 + φ4,jΔRevi,t + φ5,jPPEi,t + νi,t where TCAi,t is total current accruals equal to the change in current assets (ΔCAi,t) less the change in current liabilities (ΔCLi,t) less the change in cash (ΔCashi,t) plus the change in short-term debt (ΔSTDEBTi,t); CFOi,t is the cash flow from operations in year t equal to net income before extraordinary items (NIBEi,t) less total accruals [ΔCAi,t – ΔCLi,t – ΔCashi,t – ΔSTDEBTi,t – depreciation and amortization expense (DEPNi,t)]; ΔRevi,t is the change in revenues from year t-1 to year t; and PPEi,t is the firm’s gross value of plant, property, and equipment in year t. AQi,t is the standard deviation of firm i’s residuals over the years t-4 to t. We multiply the standard deviation by -1 so our measure AQi,t is increasing in accruals quality. We use the lagged accruals quality in all our tests. Following Bhattacharya et al. (2012), we define the lagged measure as AQi,t-1 for months 4-12 of the fiscal year and as AQi,t-2 for months 1-3 of the fiscal year. Ami is the monthly average of liquidity, Liqi,d. STDRET is return variability, measured as the standard deviation of daily return in a month. TURN is turnover, calculated as the

37

total traded volume in a month divided by the number of outstanding shares at the beginning of the year. SIZE is natural log of market capitalization, measured as the stock price at the end of the previous month multiplied by the number of shares outstanding. MOM is the past six-month returns, measured as the cumulative monthly returns from month t-7 through t-1. LEV is leverage, computed as the ratio of the total long-term debt to total assets in the previous fiscal year. ROA is return on assets, computed as income before extraordinary items over the average assets.

38

TABLE 4 Regressions of Liquidity Volatility Factor on News, Accruals Quality, and Control Variables

Variable (1) (2) (3) PosSent -0.287 -0.280 (-6.487)*** (-6.480)*** PosSent*AQ 0.017 0.013 (2.072)** (1.635) NegSent -0.361 -0.350 (-7.836)*** (-7.903)*** NegSent*AQ 0.023 0.017 (2.632)*** (2.034)*** AQ -0.001 -0.000 -0.003 (-0.216) (-0.084) (-0.671) NStor 0.042 0.046 0.058 (6.802)*** (7.331)*** (8.790)*** StdRet -5.170 -4.965 -5.245 (-7.335)*** (-7.246)*** (-7.569)*** Turn -0.416 -0.414 -0.412 (-8.313)*** (-8.254)*** (-8.345)*** Ami 0.204 0.204 0.204 (15.514)*** (15.415)*** (15.741)*** Size -0.220 -0.223 -0.224 (-25.050)*** (-25.230)*** (-25.540)*** Mom 0.031 0.021 0.025 (1.820)* (1.234) (1.463) Lev -0.081 -0.072 -0.077 (-2.141)** (-1.890)* (-2.054)** ROA 0.261 0.247 0.254 (4.466)*** (4.259)*** (4.384)*** Intercept 3.058 3.07 3.145 (23.604)*** (23.488)*** (24.515)***

Adj. R2 0.373 0.372 0.375 N 103,284 103,284 103,284 *, **, *** indicate significance at 0.10, 0.05 and 0.01 level (two-tailed), respectively.

Variable definitions: LVF is the first factor from a principle components analysis (Korajczyk and Sadka 2008) of three liquidity volatility measures: 1) monthly standard deviation of the Amihud (2002) price impact measure, Liqi,d , where Liqi,d = | Ri,d |/ DVoli,d and Ri,d is the return on the stock of firm i on day d and DVoli,d is the dollar trading volume for stock of firm i on day d in millions, 2) skewness of Liqi,d for each firm-month, and 3) idiosyncratic liquidity volatility estimated using the model from Akbas, Armstrong and Petkova (2011): Liqi,d = αi + β1,i LiqM,d + β2,i (RM,d – rf,d) + εi,d where Liqi,d is the Amihud measure for firm i on day d, LiqM,d is the aggregate market illiquidity on day t, RM,d is the CRSP total market return on day d, rf,d is the risk-free rate on day d, and idiosyncratic volatility of liquidity is the standard deviation of εi,d in a month scaled by the average level of illiquidity in that month, Liqi,t. The three news-related variables are based on data from Thomson Reuters News Analytics. NStori,t is the total number of news items for firm i in month t. PosSenti,t is the aggregate positive news sentiment score for all news items for firm i in month t scaled by the total number of news items for firm i in month t. NegSenti,t is the aggregate negative news sentiment score for all news items for firm i in month t scaled by the total number of news items for firm i in month t. We use lagged values for the news-related variables, i.e., NStor i,t-1, PosSent i,t-1, and NegSent i,t-1, in all our tests. AQi,t is accruals quality estimated using Dechow and Dichev’s (2002) model as modified by McNichols (2002), i.e., the following model on a cross-sectional basis for each of 48 Fama-French industry groups with at least 20 observations in year t: TCAi,t = φ0,j + φ1,jCFOi,t-1 + φ2,jCFOi,t + φ3,jCFOi,t+1 + φ4,jΔRevi,t + φ5,jPPEi,t + νi,t where TCAi,t is total current accruals equal to the change in current assets (ΔCAi,t) less the change in current liabilities (ΔCLi,t) less the change in cash (ΔCashi,t) plus the change in short-term debt (ΔSTDEBTi,t); CFOi,t is the cash flow from operations in year t equal to net income before extraordinary items (NIBEi,t) less total accruals [ΔCAi,t –

39

ΔCLi,t – ΔCashi,t – ΔSTDEBTi,t – depreciation and amortization expense (DEPNi,t)]; ΔRevi,t is the change in revenues from year t-1 to year t; and PPEi,t is the firm’s gross value of plant, property, and equipment in year t. AQi,t is the standard deviation of firm i’s residuals over the years t-4 to t. We multiply the standard deviation by -1 so our measure AQi,t is increasing in accruals quality. We use the lagged accruals quality in all our tests. Following Bhattacharya et al. (2012), we define the lagged measure as AQi,t-1 for months 4-12 of the fiscal year and as AQi,t-2 for months 1-3 of the fiscal year. Ami is the monthly average of liquidity, Liqi,d. STDRET is return variability, measured as the standard deviation of daily return in a month. TURN is turnover, calculated as the total traded volume in a month divided by the number of outstanding shares at the beginning of the year. SIZE is natural log of market capitalization, measured as the stock price at the end of the previous month multiplied by the number of shares outstanding. MOM is the past six-month returns, measured as the cumulative monthly returns from month t-7 through t-1. LEV is leverage, computed as the ratio of the total long-term debt to total assets in the previous fiscal year. ROA is return on assets, computed as income before extraordinary items over the average assets.

40

TABLE 5 Regressions of Liquidity Volatility Factor on News, Accruals Quality, and Control Variables for Post-EA/Other Months

Post-EA Months Other Months Variable (1) (2) (3) (4) (5) (6) PosSent -0.248 -0.278 -0.247 -0.231 (-2.832)*** (-3.125)*** (-6.062)*** (-5.892)*** PosSent*AQ 0.009 0.005 0.014 0.010 (0.563) (0.338) (1.710)* (1.259) NegSent -0.207 -0.232 -0.374 -0.351 (-2.661)** (-2.979)*** (-7.494)*** (-7.527)*** NegSent*AQ 0.003 -0.003 0.025 0.019 (0.202) (-0.189) (2.584)*** (2.112)** AQ 0.002 0.003 0.003 0.000 -0.000 -0.002 (0.248) (0.734) (0.388) (0.016) (-0.046) (-0.497) NStor 0.022 0.019 0.039 0.041 0.045 0.053 (2.238)** (1.875)* (3.608)*** (6.137)*** (6.675)*** (7.596)*** StdRet -6.213 -5.893 -6.276 -5.185 -5.019 -5.213 (-7.814)*** (-7.378)*** (-7.746)*** (-6.676)*** (-6.639)*** (6.854)*** Turn -0.355 -0.355 -0.361 -0.375 -0.367 -0.365 (-3.996)*** (-4.056)*** (-4.027)*** (-8.912)*** (-8.656)*** (-8.782)*** Ami 0.195 0.195 0.195 0.372 0.374 0.369 (39.376)*** (39.188)*** (39.830)*** (9.8687)*** (9.737)*** (9.700)*** Size -0.212 -0.214 -0.219 -0.205 -0.205 -0.205 (-19.200)*** (-18.990)*** (-19.470)*** (-21.390)*** (-21.180)*** (-21.410)*** Mom 0.015 -0.002 0.009 0.041 0.033 0.036 (0.701) (-0.082) (0.418) (2.170)* (1.753)* (1.880)* Lev -0.080 -0.067 -0.077 -0.063 -0.057 -0.061 (-1.657) (-1.370) (-1.580) (-1.695)* (-1.520) (-1.636) ROA 0.261 0.229 0.253 0.233 0.227 0.227 (3.447)*** (3.053)*** (3.337)*** (3.927)*** (3.841)*** (3.847)*** Intercept 2.970 2.965 3.098 2.811 2.791 2.847 (19.426)*** (18.712)*** (19.602)*** (19.596)*** (19.435)*** (19.980)***

Adj. R2 0.524 0.524 0.525 0.269 0.269 0.271 N 34,305 34,305 34,305 68,979 68,979 68,979 *, **, *** indicate significance at 0.10, 0.05 and 0.01 level (two-tailed), respectively.

41

Variable definitions: LVF is the first factor from a principle components analysis (Korajczyk and Sadka 2008) of three liquidity volatility measures: 1) monthly standard deviation of the Amihud (2002) price impact measure, Liqi,d , where Liqi,d = | Ri,d |/ DVoli,d and Ri,d is the return on the stock of firm i on day d and DVoli,d is the dollar trading volume for stock of firm i on day d in millions, 2) skewness of Liqi,d for each firm-month, and 3) idiosyncratic liquidity volatility estimated using the model from Akbas, Armstrong and Petkova (2011): Liqi,d = αi + β1,i LiqM,d + β2,i (RM,d – rf,d) + εi,d where Liqi,d is the Amihud measure for firm i on day d, LiqM,d is the aggregate market illiquidity on day t, RM,d is the CRSP total market return on day d, rf,d is the risk-free rate on day d, and idiosyncratic volatility of liquidity is the standard deviation of εi,d in a month scaled by the average level of illiquidity in that month, Liqi,t. The three news-related variables are based on data from Thomson Reuters News Analytics. NStori,t is the total number of news items for firm i in month t. PosSenti,t is the aggregate positive news sentiment score for all news items for firm i in month t scaled by the total number of news items for firm i in month t. NegSenti,t is the aggregate negative news sentiment score for all news items for firm i in month t scaled by the total number of news items for firm i in month t. We use lagged values for the news-related variables, i.e., NStor i,t-1, PosSent i,t-1, and NegSent i,t-1, in all our tests. AQi,t is accruals quality estimated using Dechow and Dichev’s (2002) model as modified by McNichols (2002), i.e., the following model on a cross-sectional basis for each of 48 Fama-French industry groups with at least 20 observations in year t: TCAi,t = φ0,j + φ1,jCFOi,t-1 + φ2,jCFOi,t + φ3,jCFOi,t+1 + φ4,jΔRevi,t + φ5,jPPEi,t + νi,t where TCAi,t is total current accruals equal to the change in current assets (ΔCAi,t) less the change in current liabilities (ΔCLi,t) less the change in cash (ΔCashi,t) plus the change in short-term debt (ΔSTDEBTi,t); CFOi,t is the cash flow from operations in year t equal to net income before extraordinary items (NIBEi,t) less total accruals [ΔCAi,t – ΔCLi,t – ΔCashi,t – ΔSTDEBTi,t – depreciation and amortization expense (DEPNi,t)]; ΔRevi,t is the change in revenues from year t-1 to year t; and PPEi,t is the firm’s gross value of plant, property, and equipment in year t. AQi,t is the standard deviation of firm i’s residuals over the years t-4 to t. We multiply the standard deviation by -1 so our measure AQi,t is increasing in accruals quality. We use the lagged accruals quality in all our tests. Following Bhattacharya et al. (2012), we define the lagged measure as AQi,t-1 for months 4-12 of the fiscal year and as AQi,t-2 for months 1-3 of the fiscal year. Ami is the monthly average of liquidity, Liqi,d. STDRET is return variability, measured as the standard deviation of daily return in a month. TURN is turnover, calculated as the total traded volume in a month divided by the number of outstanding shares at the beginning of the year. SIZE is natural log of market capitalization, measured as the stock price at the end of the previous month multiplied by the number of shares outstanding. MOM is the past six-month returns, measured as the cumulative monthly returns from month t-7 through t-1. LEV is leverage, computed as the ratio of the total long-term debt to total assets in the previous fiscal year. ROA is return on assets, computed as income before extraordinary items over the average assets.

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TABLE 6 Regressions of Liquidity Volatility Factor on News, Accruals Quality, and Control Variables Based on News Source

Firm-initiated Press-Initiated Variable (1) (2) (3) (4) (5) (6) PosSent -0.074 -0.068 -0.347 -0.325 (-1.467) (-1.387) (-7.688)*** (-7.515)*** PosSent*AQ 0.004 0.003 0.024 0.019 (0.359) (0.357) (2.878)*** (2.326)*** NegSent -0.084 -0.069 -0.390 -0.356 (-1.137) (-0.964) (-8.371)*** (-8.187)*** NegSent*AQ 0.003 0.001 0.026 0.017 (0.181) (0.098) (2.856)*** (2.017)** AQ 0.006 0.006 0.006 -0.002 -0.000 -0.004 (1.178) (1.405) (1.098) (-0.490) (-0.045) (-0.793) NStor -0.015 -0.026 -0.011 0.037 0.041 0.053 (-1.071) (-1.706)* (-0.742) (6.169)*** (6.684)*** (8.236)*** StdRet -7.051 -7.046 -7.044 -4.962 -4.748 -5.032 (-7.158)*** (-7.150)*** (-7.151)*** (-7.082)*** (-6.964)*** (-7.320)*** Turn -0.449 -0.449 -0.448 -0.406 -0.409 -0.401 (-3.090)*** (-3.094)*** (-3.094)*** (-8.138)*** (-8.102)*** (-8.159)*** Ami 0.197 0.197 0.197 0.203 0.203 0.203 (25.965)*** (25.947)*** (25.944)*** (16.288)*** (16.170)*** (16.539)*** Size -0.326 -0.327 -0.326 -0.212 -0.216 -0.215 (-27.860)*** (-28.080)*** (-27.910)*** (-24.930)*** (-25.120)*** (-25.480)*** Mom 0.015 0.016 0.015 0.034 0.022 0.027 (0.611) (0.628) (0.609) (1.965)* (1.290) (1.545) Lev -0.003 -0.002 -0.004 -0.086 -0.079 -0.081 (-0.053) (-0.032) (-0.065) (-2.294)** (-2.097)** (-2.160)** ROA 0.330 0.327 0.329 0.221 0.204 0.216 (3.662)*** (3.637)*** (3.651)*** (3.593)*** (3.337)*** (3.522)*** Intercept 4.459 4.467 4.465 2.959 2.977 3.039 (28.402)*** (28.451)*** (28.526)*** (23.411)*** (23.315)*** (24.343)***

Adj. R2 0.452 0.452 0.452 0.382 0.382 0.384 N 34,657 34,657 34,657 95,884 95,884 95,884 *, **, *** indicate significance at 0.10, 0.05 and 0.01 level (two-tailed), respectively.

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Variable definitions: LVF is the first factor from a principle components analysis (Korajczyk and Sadka 2008) of three liquidity volatility measures: 1) monthly standard deviation of the Amihud (2002) price impact measure, Liqi,d , where Liqi,d = | Ri,d |/ DVoli,d and Ri,d is the return on the stock of firm i on day d and DVoli,d is the dollar trading volume for stock of firm i on day d in millions, 2) skewness of Liqi,d for each firm-month, and 3) idiosyncratic liquidity volatility estimated using the model from Akbas, Armstrong and Petkova (2011): Liqi,d = αi + β1,i LiqM,d + β2,i (RM,d – rf,d) + εi,d where Liqi,d is the Amihud measure for firm i on day d, LiqM,d is the aggregate market illiquidity on day t, RM,d is the CRSP total market return on day d, rf,d is the risk-free rate on day d, and idiosyncratic volatility of liquidity is the standard deviation of εi,d in a month scaled by the average level of illiquidity in that month, Liqi,t. The three news-related variables are based on data from Thomson Reuters News Analytics. NStori,t is the total number of news items for firm i in month t. PosSenti,t is the aggregate positive news sentiment score for all news items for firm i in month t scaled by the total number of news items for firm i in month t. NegSenti,t is the aggregate negative news sentiment score for all news items for firm i in month t scaled by the total number of news items for firm i in month t. We use lagged values for the news-related variables, i.e., NStor i,t-1, PosSent i,t-1, and NegSent i,t-1, in all our tests. AQi,t is accruals quality estimated using Dechow and Dichev’s (2002) model as modified by McNichols (2002), i.e., the following model on a cross-sectional basis for each of 48 Fama-French industry groups with at least 20 observations in year t: TCAi,t = φ0,j + φ1,jCFOi,t-1 + φ2,jCFOi,t + φ3,jCFOi,t+1 + φ4,jΔRevi,t + φ5,jPPEi,t + νi,t where TCAi,t is total current accruals equal to the change in current assets (ΔCAi,t) less the change in current liabilities (ΔCLi,t) less the change in cash (ΔCashi,t) plus the change in short-term debt (ΔSTDEBTi,t); CFOi,t is the cash flow from operations in year t equal to net income before extraordinary items (NIBEi,t) less total accruals [ΔCAi,t – ΔCLi,t – ΔCashi,t – ΔSTDEBTi,t – depreciation and amortization expense (DEPNi,t)]; ΔRevi,t is the change in revenues from year t-1 to year t; and PPEi,t is the firm’s gross value of plant, property, and equipment in year t. AQi,t is the standard deviation of firm i’s residuals over the years t-4 to t. We multiply the standard deviation by -1 so our measure AQi,t is increasing in accruals quality. We use the lagged accruals quality in all our tests. Following Bhattacharya et al. (2012), we define the lagged measure as AQi,t-1 for months 4-12 of the fiscal year and as AQi,t-2 for months 1-3 of the fiscal year. Ami is the monthly average of liquidity, Liqi,d. STDRET is return variability, measured as the standard deviation of daily return in a month. TURN is turnover, calculated as the total traded volume in a month divided by the number of outstanding shares at the beginning of the year. SIZE is natural log of market capitalization, measured as the stock price at the end of the previous month multiplied by the number of shares outstanding. MOM is the past six-month returns, measured as the cumulative monthly returns from month t-7 through t-1. LEV is leverage, computed as the ratio of the total long-term debt to total assets in the previous fiscal year. ROA is return on assets, computed as income before extraordinary items over the average assets.

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TABLE 7 Regressions of Liquidity Volatility Factor on News, Accruals Quality, and Control Variables for Low/High Product Market Competition Industries

Low Product Market Competition High Product Market Competition Variable (1) (2) (3) (4) (5) (6) PosSent -0.188 -0.185 -0.341 -0.332 (-3.606)*** (-3.588)*** (-5.009)*** (-5.054)*** PosSent*AQ 0.006 0.003 0.026 0.021 (0.630) (0.339) (2.159)* (1.832)* NegSent -0.286 -0.287 -0.410 -0.388 (-5.344)*** (-5.502)*** (-5.702)*** (-5.805)*** NegSent*AQ 0.016 0.013 0.027 0.018 (1.480) (1.263) (2.066)** (1.477) AQ 0.003 0.002 0.001 -0.004 -0.002 -0.006 (0.540) (0.382) (0.130) (-0.732) (-0.361) (-0.902) NStor 0.025 0.030 0.040 0.045 0.050 0.060 (3.217)*** (3.675)*** (4.945)*** (6.145)*** (6.697)*** (7.571)*** StdRet -5.999 -5.907 -6.052 -4.817 -4.575 -4.914 (-7.900)*** (-7.855)*** (-8.054)*** (-5.881)*** (-5.714)*** (-6.091)*** Turn -0.346 -0.341 -0.342 -0.395 -0.394 -0.390 (-4.771)*** (-4.754)*** (-4.775)*** (-7.391)*** (-7.213)*** (-7.356)*** Ami 0.345 0.346 0.344 0.197 0.197 0.197 (7.215)*** (7.253)*** (7.229)*** (29.387)*** (29.321)*** (29.995)*** Size -0.221 -0.225 -0.224 -0.199 -0.201 -0.202 (-20.640)*** (-20.820)*** (-20.980)*** (-18.870)*** (-18.810)*** (-19.150)*** Mom 0.035 0.026 0.030 0.016 0.005 0.007 (1.696)* (1.296) (1.492) (0.804) (0.239) (0.363) Lev -0.015 -0.005 -0.009 -0.165 -0.160 -0.168 (-0.340) (-0.113) (-0.192) (-3.032)*** (-2.928)*** (-3.100)*** ROA 0.327 0.319 0.325 0.143 0.119 0.128 (4.423)*** (4.275)*** (4.400)*** (1.733)* (1.501)*** (1.589) Intercept 3.045 3.076 3.119 2.771 2.757 2.863 (20.064)*** (19.992)*** (20.719)*** (17.176)*** (17.155)*** (17.607)***

Adj. R2 0.269 0.269 0.270 0.489 0.489 0.491 N 57,129 57,129 57,129 46,155 46,155 46,155 *, **, *** indicate significance at 0.10, 0.05 and 0.01 level (two-tailed), respectively.

45

Variable definitions: LVF is the first factor from a principle components analysis (Korajczyk and Sadka 2008) of three liquidity volatility measures: 1) monthly standard deviation of the Amihud (2002) price impact measure, Liqi,d , where Liqi,d = | Ri,d |/ DVoli,d and Ri,d is the return on the stock of firm i on day d and DVoli,d is the dollar trading volume for stock of firm i on day d in millions, 2) skewness of Liqi,d for each firm-month, and 3) idiosyncratic liquidity volatility estimated using the model from Akbas, Armstrong and Petkova (2011): Liqi,d = αi + β1,i LiqM,d + β2,i (RM,d – rf,d) + εi,d where Liqi,d is the Amihud measure for firm i on day d, LiqM,d is the aggregate market illiquidity on day t, RM,d is the CRSP total market return on day d, rf,d is the risk-free rate on day d, and idiosyncratic volatility of liquidity is the standard deviation of εi,d in a month scaled by the average level of illiquidity in that month, Liqi,t. The three news-related variables are based on data from Thomson Reuters News Analytics. NStori,t is the total number of news items for firm i in month t. PosSenti,t is the aggregate positive news sentiment score for all news items for firm i in month t scaled by the total number of news items for firm i in month t. NegSenti,t is the aggregate negative news sentiment score for all news items for firm i in month t scaled by the total number of news items for firm i in month t. We use lagged values for the news-related variables, i.e., NStor i,t-1, PosSent i,t-1, and NegSent i,t-1, in all our tests. AQi,t is accruals quality estimated using Dechow and Dichev’s (2002) model as modified by McNichols (2002), i.e., the following model on a cross-sectional basis for each of 48 Fama-French industry groups with at least 20 observations in year t: TCAi,t = φ0,j + φ1,jCFOi,t-1 + φ2,jCFOi,t + φ3,jCFOi,t+1 + φ4,jΔRevi,t + φ5,jPPEi,t + νi,t where TCAi,t is total current accruals equal to the change in current assets (ΔCAi,t) less the change in current liabilities (ΔCLi,t) less the change in cash (ΔCashi,t) plus the change in short-term debt (ΔSTDEBTi,t); CFOi,t is the cash flow from operations in year t equal to net income before extraordinary items (NIBEi,t) less total accruals [ΔCAi,t – ΔCLi,t – ΔCashi,t – ΔSTDEBTi,t – depreciation and amortization expense (DEPNi,t)]; ΔRevi,t is the change in revenues from year t-1 to year t; and PPEi,t is the firm’s gross value of plant, property, and equipment in year t. AQi,t is the standard deviation of firm i’s residuals over the years t-4 to t. We multiply the standard deviation by -1 so our measure AQi,t is increasing in accruals quality. We use the lagged accruals quality in all our tests. Following Bhattacharya et al. (2012), we define the lagged measure as AQi,t-1 for months 4-12 of the fiscal year and as AQi,t-2 for months 1-3 of the fiscal year. Ami is the monthly average of liquidity, Liqi,d. STDRET is return variability, measured as the standard deviation of daily return in a month. TURN is turnover, calculated as the total traded volume in a month divided by the number of outstanding shares at the beginning of the year. SIZE is natural log of market capitalization, measured as the stock price at the end of the previous month multiplied by the number of shares outstanding. MOM is the past six-month returns, measured as the cumulative monthly returns from month t-7 through t-1. LEV is leverage, computed as the ratio of the total long-term debt to total assets in the previous fiscal year. ROA is return on assets, computed as income before extraordinary items over the average assets.

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TABLE 8 Regressions of Liquidity Volatility Factor on News, Accruals Quality, and Control Variables for Financial Crisis/Non-Financial Crisis Periods

Non-Financial Crisis Financial Crisis Variable (1) (2) (3) (4) (5) (6) PosSent -0.156 -0.143 -0.279 -0.303 (-3.186)*** (-3.051)*** (-4.251)*** (-4.548)*** PosSent*AQ 0.003 -0.001 0.025 0.026 (0.356) (-0.114) (2.342)** (2.387)** NegSent -0.261 -0.254 -0.288 -0.323 (-4.501)*** (-4.609)*** (-4.966)*** (-5.455)*** NegSent*AQ 0.012 0.009 0.027 0.028 (1.172) (0.887) (2.553)*** (2.519)** AQ 0.003 0.001 0.002 -0.006 -0.003 -0.012 (0.600) (0.339) (0.450) (-1.218) (-0.928) (-1.983)* NStor 0.027 0.031 0.042 0.014 0.018 0.024 (3.795)*** (4.168)*** (5.542)*** (2.064)** (2.626)*** (3.322)*** StdRet -7.683 -7.569 -7.781 -3.987 -3.787 -4.032 (-7.745)*** (-7.640)*** (-7.876)*** (-5.364)*** (-5.208)*** (-5.501)*** Turn -0.528 -0.524 -0.521 -0.277 -0.276 -0.270 (-4.477)*** (-4.496)** (-4.468)*** (-6.936)*** (-6.830)*** (-6.881)*** Ami 0.530 0.531 0.527 0.196 0.196 0.196 (12.924)*** (12.898)*** (12.872)*** (34.630)*** (34.252)*** (35.271)*** Size -0.222 -0.224 -0.224 -0.160 -0.162 -0.163 (-27.080)*** (-27.050)*** (-27.480)*** (-17.070)*** (-17.070)*** (-17.270)*** Mom 0.025 0.018 0.020 0.052 0.043 0.047 (1.195) (0.850) (0.968) (1.958)* (1.699)* (1.809)* Lev -0.041 -0.032 -0.038 -0.103 -0.099 -0.100 (-0.911) (-0.707) (-0.837) (-2.577)** (-2.457)** (-2.490)** ROA 0.211 0.195 0.200 0.215 0.213 0.219 (2.813)*** (2.633)*** (2.715)*** (2.946)*** (2.940)*** (2.987)*** Intercept 3.089 3.113 3.150 2.202 2.190 2.305 (25.909)*** (25.610)*** (26.351)*** (14.391)*** (14.798)*** (14.926)***

Adj. R2 0.289 0.289 0.290 0.530 0.530 0.531 N 57,672 57,672 57,672 45,612 45,612 45,612 *, **, *** indicate significance at 0.10, 0.05 and 0.01 level (two-tailed), respectively.

47

Variable definitions: LVF is the first factor from a principle components analysis (Korajczyk and Sadka 2008) of three liquidity volatility measures: 1) monthly standard deviation of the Amihud (2002) price impact measure, Liqi,d , where Liqi,d = | Ri,d |/ DVoli,d and Ri,d is the return on the stock of firm i on day d and DVoli,d is the dollar trading volume for stock of firm i on day d in millions, 2) skewness of Liqi,d for each firm-month, and 3) idiosyncratic liquidity volatility estimated using the model from Akbas, Armstrong and Petkova (2011): Liqi,d = αi + β1,i LiqM,d + β2,i (RM,d – rf,d) + εi,d where Liqi,d is the Amihud measure for firm i on day d, LiqM,d is the aggregate market illiquidity on day t, RM,d is the CRSP total market return on day d, rf,d is the risk-free rate on day d, and idiosyncratic volatility of liquidity is the standard deviation of εi,d in a month scaled by the average level of illiquidity in that month, Liqi,t. The three news-related variables are based on data from Thomson Reuters News Analytics. NStori,t is the total number of news items for firm i in month t. PosSenti,t is the aggregate positive news sentiment score for all news items for firm i in month t scaled by the total number of news items for firm i in month t. NegSenti,t is the aggregate negative news sentiment score for all news items for firm i in month t scaled by the total number of news items for firm i in month t. We use lagged values for the news-related variables, i.e., NStor i,t-1, PosSent i,t-1, and NegSent i,t-1, in all our tests. AQi,t is accruals quality estimated using Dechow and Dichev’s (2002) model as modified by McNichols (2002), i.e., the following model on a cross-sectional basis for each of 48 Fama-French industry groups with at least 20 observations in year t: TCAi,t = φ0,j + φ1,jCFOi,t-1 + φ2,jCFOi,t + φ3,jCFOi,t+1 + φ4,jΔRevi,t + φ5,jPPEi,t + νi,t where TCAi,t is total current accruals equal to the change in current assets (ΔCAi,t) less the change in current liabilities (ΔCLi,t) less the change in cash (ΔCashi,t) plus the change in short-term debt (ΔSTDEBTi,t); CFOi,t is the cash flow from operations in year t equal to net income before extraordinary items (NIBEi,t) less total accruals [ΔCAi,t – ΔCLi,t – ΔCashi,t – ΔSTDEBTi,t – depreciation and amortization expense (DEPNi,t)]; ΔRevi,t is the change in revenues from year t-1 to year t; and PPEi,t is the firm’s gross value of plant, property, and equipment in year t. AQi,t is the standard deviation of firm i’s residuals over the years t-4 to t. We multiply the standard deviation by -1 so our measure AQi,t is increasing in accruals quality. We use the lagged accruals quality in all our tests. Following Bhattacharya et al. (2012), we define the lagged measure as AQi,t-1 for months 4-12 of the fiscal year and as AQi,t-2 for months 1-3 of the fiscal year. Ami is the monthly average of liquidity, Liqi,d. STDRET is return variability, measured as the standard deviation of daily return in a month. TURN is turnover, calculated as the total traded volume in a month divided by the number of outstanding shares at the beginning of the year. SIZE is natural log of market capitalization, measured as the stock price at the end of the previous month multiplied by the number of shares outstanding. MOM is the past six-month returns, measured as the cumulative monthly returns from month t-7 through t-1. LEV is leverage, computed as the ratio of the total long-term debt to total assets in the previous fiscal year. ROA is return on assets, computed as income before extraordinary items over the average assets.

48