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The Cost Benefit Trade-off of Analysts’ Incorporation of

Firm-Specific Information into Stock Prices

Shimin Chen

China Europe International Business School

Ferdinand A Gul

Monash University Sunway Campus

Jing Zhou

Shanghai University of Finance and Economics

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1. Introduction

Financial analysts are important information intermediaries and information providers

in the capital market. They collect and disseminate information, as demonstrated in

extensive accounting and finance literature. However, past literature on financial

analysts generally focus on the overall information role rather than break down the

information role into market level, industry level or firm-specific until Piotroski and

Roulstone (2004) who find that analysts’ comparative advantage lies in interpreting

specific industry or market sector trends and improving intra-industry information

transfer. They further find and explain that analysts accelerate incorporation of both

industry- and firm-specific components of future earnings news into stock price, but

being more successful in pre-empting industry news. Chan and Hameed (2006)

document similar findings by using data from emerging markets.

This finding seems ‘contrary to the conventional wisdom that security analysts

specialize in the production of firm-specific information’ (Chan and Hameed 2006,

p.115). Furthermore, Hong et al. (2000) document that analysts increase the speed

of diffusion of firm-specific information across market participants. A typical

analyst report also contains more firm news than industry news (see detailed

description of a typical analyst report in Chen et al. 2010 and Asquith et al. 2005).

We believe analysts incorporate both industry- and firm-specific information into

stock prices as Piotroski and Roulstone (2004) documented and both roles are

important. An important but unexplored issue is the factors affecting the content of

information produced by analysts. Analysts’ incentive of producing market/industry

or firm-specific information depends on their cost benefit trade-off of providing that

particular information.

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One important factor affecting the cost benefit trade-off is the transparency of

accounting information. Publicly available firm disclosure is the premier information

source the market can access. Ferreira and Laux (2007) find that more transparent

accounting information will encourage the collection of firm-specific information by

investors. However, it is unclear whether it will encourage analysts’ collection of

firm-specific information. Compared with individual investors, financial analysts

are generally considered as sophisticated market participants who have superior

analytical ability and various information sources. Justice William O. Douglas is an

early believer of analysts’ activities improving the efficiency of capital markets.

Douglas (1933) observes,’ even though an investor has neither the time, money, or

intelligence to assimilate the mass of information in the registration statement, there

will be those who can and who will do so, whenever there is a broad market. The

judgment of those experts will be reflected in the market prices.’ Financial analysts

generally devote more time, effort and intelligence to extract information from

various sources than individual investors and therefore, analysts’ cost and benefit of

producing firm-specific information is different from the trade-off of individual

investors. While more transparent accounting information will lower analysts’ cost of

producing firm-specific information, it is also likely to reduce the market demand of

analysts’ services since the general public are better informed. If the outward shift in

supply of firm-specific information more than offsets the reduced demand, the

encouragement effects prevail.

Prior studies found mixed results on whether more transparent accounting information

will encourage analysts’ collection and production of information or crowd it out, and

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this stream of literature generally does not distinguish the content of information

produced by analysts. High quality earnings are generally associated with low

information risk and low information cost (Francis et al. 2004). Analysts are less

likely to follow firms with potential incentives to withhold or manipulate information

(Lang et al. 2004). Analysts’ reports are more informative when the information

process cost is low (Frankel et al. 2006). Lang and Lundhom (1996) suggest that more

informative financial statements are associated with an increase in the net benefits

available to information intermediaries and increased resources devoted to

information discovery. On the other hand, some analytical models predict that

investors are expected to place lower weight on analyst reports in setting prices when

corporate accounting information is timely (Holthausen and Verrecchia 1988, Demski

and Feltham 1994). Therefore, high quality firm provided information may deter

analysts’ incentive to actively collect and disseminate information to investors.

Frankel and Li (2004) find that firms with financial statements that are less value

relevant tend to have higher analyst following and more news coverage, which means

analyst following and news available each substitutes for financial statement

informativenss. Chen et al. (2010) suggest that the relation between financial analysts

and corporate disclosures depends on the information roles played by analysts,

namely information discovery vs. information interpretation. If analysts primarily

discover and publish private information, then analyst reports will tend to pre-empt

subsequent corporate disclosures. If analyst reports mainly interpret existing public

information, then a more informative corporate disclosure will lead to a more

informative analyst report. They find both roles are important. Our study belongs to

this stream of literature investigating the impact of firm disclosure quality on the

analysts’ services, but our study is different in a sense that the aforementioned studies

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focus on the overall information provided by analysts while we disaggregate

information into market/industry and firm level. The disaggregation will help our

understanding in the content of information provided by analysts.

To measure accounting transparency, we focus on earnings quality because earnings

information is one of the most important inputs of analyst reports1 (Previts and

Bricker 1994, Block 1999). More specifically, it is measured as the absolute value

of firm-specific residuals from an industry regression of total accruals on lagged,

contemporaneous, and leading cash flow from operations (Dechow and Dichev 2002)2.

We use stock price synchronicity to measure the relative amount of firm-specific

information incorporated in stock price as demonstrated in prior literature (Morck et

al. 2000, Piotroski and Roulstone 2004, Durnev et al. 2003, 2004).

Using data of US listed firms from 1990-2009, we find that stock price synchronicity

is positively associated with analyst following indicating analysts incorporate

relatively more market/industry-level information than firm-specific information into

prices, which is consistent with Piotroski and Roulstone (2004). We further find that

the above relationship is moderated by earnings quality. When the earnings quality is

high, stock price synchronicity is less positively associated with analyst following,

suggesting an increase in the amount of firm-specific information (relative to

market/industry-level information) analysts bring to prices. Informative accounting

1 We understand that earnings information is only one of the various information items disclosed by firms, however it is one of the most important ones. A survey by Block (1999) asks analysts to rank their key valuation inputs and the respondents rank earnings first. 2 Although Dechow and Dichev (2002) use the standard deviation of firm-specific residuals as the measure of earnings quality, an alternative measure is the absolute value of the residual. It is also the main earnings quality measure in Ferreira and Laux (2007).

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information encourages (rather than crowds out) financial analysts to produce more

firm-specific information to the market.

Furthermore, we find some interesting results regarding to the effect of information

complexity measured by the readability of annual reports. The readability of annual

reports (i.e. 10-K) measures the overall complexity of firms’ financial communication,

that is, the cost of processing and interpreting the entirety of firm’s disclosure. We

find that less readable 10-K reports will encourage analysts bringing more

firm-specific information into stock prices, suggesting that the benefit of serving the

increasing demand for analyst services is greater than the increasing cost of

processing complex information. However, the early mentioned result on earnings

quality suggests a ‘complement’ effect of disclosure quality and informativeness of

analysts’ reports, suggesting the benefit of lower information processing costs resulted

from better earnings quality in greater than the decreasing demand analyst services.

The seemingly conflict results are actually consistent with the literature that examines

the impact of various properties of firm’s disclosure on analyst behaviors. While the

literature generally documents that better disclosure quality (earning-based measures)

tend to attract greater analyst following, Lehavy et al. (2011) document increasing

demand for analyst services for firms with less readable communication and a greater

collective effort by analysts for firms with less readable disclosure. Moreover, Lehavy

et al. (2011) use the same research design of Frankel et al. (2006) and find that the

informativeness of analysts’ reports are greater for firms with less readable annual

reports while Frankel et al. (2006) find that accounting reports’ informativeness and

information in analyst reports are complements. The results suggest that different

dimensions of disclosure quality affect analysts’ cost benefit trade-off differently. The

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outward shift in supply of firm information more than offsets the effect of reduced

demand of analyst services when the accounting transparency is high, however, the

effect of increased demand more than offsets the inward shift in supply when the

annual report is more difficult to read.

Additionally, we find that the encouragement effect of more transparent accounting

information and less readable 10-K reports is less prominent in years when the market

sentiment is high than in years when the market sentiment is low. Investor sentiment

contains a market-wide component with the potential to influence prices on many

securities in the same direction at the same time. The capital market is overwhelmed

by market-wide information and analysts’ incentive to provide firm-specific

information is deterred because sentiment-driven investors do not trade stocks based

on firms’ fundamentals. Stambough et al. (2011) suggest that the presence of

sentiment-driven investors can cause prices to depart from fundamental values. Since

the demand for firm-specific information is low, analysts are more likely to focus on

dissemination of market-wide and industry-level information in the capital markets.

Last but not the least, we examine whether there exist differences between analysts

with industry expertise (defined as analysts who follow more than 5 firms in one

industry) and those without in helping incorporate firm-specific information into stock

price. We find that the effect of better earnings quality and less complexity of 10-K

reports on analysts’ incorporation of firm-specific information into stock prices only

exists in the group of analysts without industry expertise. The results are consistent

with the argument that the comparative advantage of industry specialist lies in

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interpreting specific industry or market sector trends and the information quality of an

individual firm is not likely to affect their information dissemination activities.

Our study extends and complements the existing literature. First, consistent with

Piotroski and Roulstone (2004), we find stock price synchronicity is positively

associated with analyst activities. Additionally, we further document that the

magnitude of this association depends on the transparency of accounting information.

More transparent accounting information will encourage analysts to incorporate more

firm-specific information into stock prices resulting in a less positive association. Our

study contributes to our understanding of the information role played by financial

analysts to provide firm-specific or industry/market level information. Second, we add

evidence to the debate over the relationship between corporate disclosure and analysts

information production activities. We support the view that high quality corporate

disclosure will encourage analysts to provide more information. Moreover, we

provide new evidence that high quality corporate disclosure will encourage more

firm-specific information incorporated into stock price by analysts. To our best

knowledge, this evidence does not exist in prior literature. Last but not the least, we

find that different dimensions of disclosure quality may have different impacts on

analyst services. More specifically, while high quality earnings will encourage

analysts’ collection of firm specific information, the less readable 10-K reports will

also encourage analysts to do so.

The rest of the paper is organized as follows. Section 2 reviews the related literature.

Section 3 describes the tests and reports the results. Additional test results are given in

Section 4 and we conclude the paper in Section 5.

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2. Literature Review

2.1 Stock Price Synchronicity as an Inverse Measure of Firm-specific Information

Stock prices signal information to the financial market. A stream of research

establishes a link between stock price synchronicity and the informativeness of stock

prices. According to Roll (1988), individual stocks in the U.S. exhibit low R2 statistics

from the market model because much of the firm-specific information has already

been incorporated in stock prices. As a result, idiosyncratic price movements mainly

reflect private firm-specific information being impounded into stock prices by

informed trading. This suggests that stock price synchronicity, an inverse measure of

idiosyncratic volatility, is a good summary measure for the lack of firm-specific

information flow. Both country-level and firm-level evidence support this

interpretation.3

At the country level, Morck et al. (2000) observe that stock prices move together

more in poor than in rich economies. They find that this phenomenon is not due to

market size and is only partially explained by higher fundamental correlations in the

low-income economies. They further find that the government disrespect of private

property rights and the lack of shareholder protection laws explain the low levels of

firm-specific return variations in those economies. Wurgler (2000) shows that the

efficiency of capital allocation is positively correlated with low levels of stock price

synchronicity in domestic stock returns, which suggests that countries with stock

3 This information interpretation is not without controversy as several working papers have produced contrary evidence (Ashbaugh-Skaife et al. 2006; Rajgopal and Venkatachalam 2011; Yang and Zhang 2006). However, as reviewed below, published evidence overwhelmingly leans towards supporting this interpretation.

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markets characterized by higher levels of firm specific information flow exhibit more

efficient capital allocation.

At the firm-level, Durnev et al. (2003) investigate whether idiosyncratic price

movements reflect the flow of private information or noise trading. Consistent with

the information interpretation, they find that stock prices with higher levels of

idiosyncratic volatility contain more information about future earnings. Further,

Durnev et al. (2004) investigate the link between corporate capital investment and

stock price informativeness. They document a positive association between

investment efficiency and low levels of stock price synchronicity. Ferreira and Laux

(2007) provide evidence that openness to the market for corporate control (fewer

anti-takeover provisions) leads to more informative stock prices as measured by

idiosyncratic volatility, which improves the efficiency of corporate investment. Gul et

al. (2011) show that stock prices of firms with gender-diverse boards reflect more

firm-specific information and this relationship is stronger for firms with weak

corporate governance.

Piotroski and Roulstone (2004) examine the impact of three market participants,

namely, insiders, analysts, and institutional investors on the synchronicity of stock

prices in the U.S. They find that insider transactions reduce stock price synchronicity,

which is consistent with insiders’ firm specific information advantage. However, they

report a positive association between stock price synchronicity and analyst forecasting

activities, suggesting analyst advantage in improving intra-industry information

transfers. Although their results on the effect of institutional investors are mixed, they

find the level of institutional holdings positively related to synchronicity similar to

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analysts. Chan and Hameed (2006) confirm the role of analysts in helping the market

and industry level information transfer in emerging markets.

2.2 Analysts’ Information Role in the Capital Market

Analysts are prominent information intermediaries in the capital market. They engage

in private information search, perform prospective analysis aimed at forecasting a

firm’s future earnings and cash flows, and conduct retrospective analysis that

interprets past events (Beaver 1998). Hong et al. (2000) find that momentum

strategies work better among stocks with low analyst coverage, indicating that

analysts increase the speed of diffusion of firm information across market participants.

Gleason and Lee (2003) find that the post revision price drift associated with analyst

forecast revisions is lower for firms followed by more analysts, suggesting that

coverage by multiple analysts helps to facilitate the price discovery process. Other

researches (Brennan et al. 1999; Bhattacharya 2001; Zhang 2008) also find supporting

evidence that the level of financial analyst coverage influences the efficiency with

which the market processes information. This stream of research does not explicitly

disaggregate the information into firm, industry, and market level but focuses on the

overall information role of analyst in the capital market.

Piotroski and Roulstone (2004) document a positive relation between stock price

synchronicity and analyst forecast revision, suggesting analysts’ comparative

advantage lies in interpreting specific industry or market sector trends and they

facilitate industry and market level information transfer. Chan and Hameed (2006) use

data from 25 emerging markets and find similar results as Piotroski and Roulstone

(2004). These findings seem ‘contrary to the conventional wisdom that security

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analysts specialize in the production of firm-specific information’ (Chan and Hameed

2006, p.115). The ‘conventional wisdom’ comes from the above mentioned

evidence that analysts improve the market information efficiency. In addition,

research reports issued by analysts tend to focus more on firm news than industry

news as revealed in Chen et al. (2010) (also see a detailed description of a typical

analyst report in Asquith et al. 2005). We believe that analysts help incorporate firm,

industry and market level information and there exist various factors affecting

analysts’ cost benefit trade-off of incorporating each type of information.

2.3 Earnings Quality, Analysts, and Firm Specific Information Incorporated

Accounting disclosure is a key element of information flow. More transparent

accounting information will ‘encourage’ or ‘crowd out’ the private information

collection effort by informed investors.

One view of the effect of earnings quality is that as more information is channeled

into public reporting, it crowds out private information (Kim and Verrecchia 2001).

Under this view, higher quality earnings increase public information but decrease

private information.

More evidence in the literature supports the ‘encouragement’ view. Morck et al. (2000)

conjecture that there will be more firm-specific price variation (less stock price

synchronicity) in countries with better accounting standards. If accounting data are

more useful, more firm-specific information is available to all investors, which allows

risk arbitrageurs to make more precise predictions about firm-specific stock price

movements. Jin and Myers (2006) formally model this link between transparency and

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synchronicity, and verify the model using data from 40 markets around the world

from 1990 to 2001. They report negative relations between stock price synchronicity

and several measures of opaqueness including auditing and accounting disclosure.

More relevant to our study is Ferreira and Laux (2007) who provide evidence on the

relation between accounting transparency at the firm level as measured by

discretionary accruals and stock price synchronicity in addition to the impact of

corporate governance. Consistent with an encouragement effect on private

information production, they find that both openness to the market for corporate

control and accounting transparency lead to low levels of stock price synchronicity,

which further verifies the prediction of Jin and Myers (2006).

Whether high quality accounting disclosure will ‘encourage’ or ‘crowd out’ analysts’

firm-specific information collection effort depends on the different roles that analysts

play in the capital market. Lang and Lundholm (1996) identify two roles analysts play

in the capital market. They state ‘If analysts are primarily information intermediaries-

the principle flow of information goes from the firm to the analysts, who process the

information and transmit it to the capital market-then an increase in firm provided

information means that analyst has a more valuable report to sell… If analysts are

primarily information providers who compete with firm provided disclosures made

directly to investors, then an increase in firm provided information will substitute for

the analyst report. In this case increase disclosure reduces the aggregate demand for

analyst services (Lang and Lundholm 1996, P. 470-471). There two roles are

sometimes called ‘substitutes’ or ‘complements’ in later studies.

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Supporting the substitution view comes from some analytical models predicting that

investors are expected to place lower weight on analyst reports in setting prices when

corporate accounting information is timely (Holthausen and Verrecchia 1988; Demski

and Feltham 1994). Frankel and Li (2004) find that firms with financial statements

that are less value relevant tend to have higher analyst following and more news

coverage, which means analyst following and news available each substitute for

financial statement informativeness.

Lang and Lundhom (1996) find that more analysts follow firms and greater consensus

among analysts with more informative disclosure practices. Frankel et al. (2006) find

that more timely financial information is associated with more informative analysts’

reports. Francis et al. (2002) find a positive relation between the price reaction to

analyst report and to earnings announcement and they conclude that their results ‘do

not in general support the predicted substitution relation between earnings

announcements and competing information sources’ (Francis et al. 2002 p. 137).

2.4 Sentiment and Stock Price Synchronicity

Investor sentiment contains a market-wide component with a potential to influence

prices on many securities in the same direction at the same time. Perhaps ’irrational

exuberance’ (Shiller 2000) drives prices above fundamental values. (also see Brown

and Cliff 2005) Baker and Wurgler (2006) argue that market-wide sentiment should

exert stronger impacts on stocks that are difficult to value and hard to arbitrage. Yu

and Yuan (2011) show that the correlation between the market’s expected return and

its conditional volatility is positive during low-sentiment periods and nearly flat

during high-sentiment periods, indicating the market is less rational during

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high-sentiment periods due to higher participation by noise trader in such periods.

Stambaugh et al. (2011) find that anomalies, to the extent they reflect mispricing, are

stronger following high sentiment. Kumar and Lee (2006) find that there exists a

systematic component of trading activities of retail investors and this so called retail

investor sentiment has incremental explanation power in explaining return

comovement. We believe sentiment is another important factor that will affect

analysts’ information role in the capital markets.

3. Tests and Results

3.1 Sample and Data Description

The initial sample consists of all the US listed firms with available data on Compustat

from 1990-2009. We delete firms with: (1) insufficient data to estimate our

accounting transparency measure, absolute size of abnormal accruals; (2) insufficient

data to estimate our synchronicity measure; and (3) insufficient data to calculate

control variables. We also exclude firms from the financial service industry (SIC code

6000-6999) and utility industry (SIC code 4900-4999) because disclosure

requirements and accounting rules are significantly different for these industries. The

missing values for number of analyst following is treated as no analyst following the

firm (Hong et al. 2000; Piotroski and Roulstone 2004). After the above adjustments,

the final sample consists of 79,009 firm year observations.

Financial statement data are collected from the Compustat Annual Industrial and

Research files. Returns and stock prices data are collected from CRSP daily and

monthly stock return files. Analysts’ data are collected from I/B/E/S detail tape. We

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use market sentiment data of Baker and Wurgler (2007), which is available from their

website.

3.2 Measurement of Stock Price Synchronicity

Each year, we estimate firm-specific measures of return synchronicity using the

methodology outlined in Piotroski and Roulstone (2004). For each firm-specific

observation, we regress firm j’s daily return on the current and last day’s

value-weighted market return and the current and last day’s value-weighted industry

return:

Rj,t = φ0+ φ1R_MARKETj,t-1+ φ2R_MARKETj,t+ φ3R_INDj,t-1+ φ4R_INDj,t+ νj,t (1)

where the market return (R_MARKETj,t) is the value-weighted average of all the

firms in the market. The industry return (R_INDj,t) for a specific day is created using

all firms within the same industry, with firm j’s daily return omitted, and the return is

then computed as the value-weighted average of these firms’ daily returns. The

industry is classified based on Fama-French 48 industry specifications (Fama and

French, 1997). In addition, we include lag period industry and market returns to

control for potential autocorrelation problems. Following Durnev et al. (2004), we

estimate this regression for each firm-year with a minimum of 200 daily observations.

Following the definition of Morck et al. (2000), stock price synchronicity (SYNC) is

defined as:

SYNCj,t =Ln[R2j,t(eq1)/(1-R2

j,t(eq1))] (2)

where R2 is the adjusted R-square value from regression (1) for firm j in year t. The

log transformation of R2 creates an unbounded continuous variable out of a variable

originally bounded by 0 and 1, yielding a dependent variable with a more normal

distribution (Piotroski and Roulstone 2004).

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3.3 Descriptive Statistics

The final sample consists of 79,009 firm-year observations. Table 1 presents the

descriptive statistics for variables.

[Insert Table 1 here]

Table 1 reports the descriptive statistics for the full sample. The dependent variable

(SYNC) has a mean (median) value of -2.5151 (-2.6024) with standard deviation of

1.5373. The absolute size of abnormal accruals has a mean (median) value of 0.1121

(0.0640) with standard deviation of 0.1903.

Out of the79, 009 observations, 30,668 observations (38.82% of the full sample) in

our full sample are not covered by I/B/E/S and thus are assigned a value zero for NAF.

Compared with Hong et al. (2000) who report 36.9% of their sample is not covered by

I/B/E/S tape, the I/B/E/S coverage for our sample is 2% lower. The mean and median

for NAF is 5.0431 and 2, respectively.

3.4 Earnings Quality, Analyst Following and Price Synchronicity

We use the following model to test the relationship between analyst following and

stock price synchronicity, and the moderating effect of earnings quality on this

relationship.

SYNCj,t=λ0+λ1Aj,t+λ2Ln(NAF)j,t + λ3Ln(NAF)j.t*Aj,t +λ4 Ln(MV)j,t + λ5 Levj,t+

λ6BMj,t+λ7ROAj,t+λ8stdROAj,t+λ9 Ln(Herf)j,t +λ10 Ln(Nind)j,t+λ11 DDj,t+λ12

MERGERj,t+λ13 DIVERj,t+μj,t (3)

A is the absolute value of firm-specific residuals from an industry regression of total

accruals on lagged, contemporaneous, and leading cash flows from operations, all

scaled by lagged total assets. The larger A indicates poor earnings quality of the firm.

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Ln(NAF) is the natural log of the number of analysts making annual earnings

forecast for that firm during the year. We expect λ1 to be significantly positive,

indicating poor earnings quality is less likely to encourage private information

collection effort of investors (Ferrarei and Laux 2007). In addition, we expect λ2 to be

significantly positive, indicating analyst help more in industry-level information

transfer (Piotroski and Roulstone 2004). If better earnings quality will encourage

analysts bringing more firm-specific information into stock prices then we expect λ3

to be significantly positive. If better earnings quality will crowd out analysts’ private

information collection, then we expect λ3 to be significantly negative. Following

Piotroski and Roulstone (2004), Ln(Herf) is included to control for industry

concentration. The more concentrated an industry is, the more likely firms in it

perform inter-dependently and the synchronicity of these firms will be larger. We

calculate industry concentration (Herf) as the Fama-French 48 industry specification

(Fama and French, 1997) Herfindahl index for the year. We expect Ln(Herf) to be

positively related to SYNC. We include a number of other control variables drawn

from literature, including firm size (Ln(MV)), firm profitability (ROA), standard

deviation of ROA (stdROA), leverage (Lev), and book to market ratio (BM). We

also include the number of firms in the industry (Ln(Nind)) to control for other

cross-sectional differences (Piotroski and Roulstone 2004). We also include three

dummy variables to control for other firm characteristics, whether the firm pays

dividend (DD), whether the firm operates in multiple business segments (DIVER),

and whether the firm engages in merger and acquisition (MERGER). Industry

dummies are included based on Fama-French 48 industry specifications (Fama and

French, 1997).

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We estimate Equation (3) for each of the 20 years in our sample and the coefficient

estimates presented in Table 2 are calculated using these 20 yearly coefficient

estimates, and the statistical significance using the time-series standard errors of these

estimates (Fama and MacBeth 1973). Industry dummies based on Fama-French 48

industry classifications are included but not reported. We also estimate pooled

regressions as a sensitivity test and obtain similar results as the Fama MacBeth

regression.

[Insert Table 2 here]

Table 2 presents the results of the association between analyst following and stock

price synchronicity and the moderating effect of earnings quality on this association.

The coefficient on A is positive 0.2664 at the 1% significance level as predicted. This

result suggests that a larger (smaller) size of accruals is associated with a more (less)

synchronized stock price, indicating the more transparent accounting numbers, the

more informative the stock prices, which is consistent with the interpretation of

Ferreira and Laux (2007). The coefficient on Ln(Naf) is 0.2789 at 1% significance

level, which is consistent with Piotroski and Roulstone (2004). When we add the

interaction of A and Ln(Naf) into the model, the coefficient on the interaction is

significantly positive, indicating poor (better) earnings quality will discourage

(encourage) analyst bringing more firm specific information into stock prices. The

result is consistent with the argument that earnings quality and analyst

informativeness are complements rather than substitutes. The coefficients on control

variables are generally consistent with the literature (Piotroski and Roulstone 2004,

Ferreira and Laux 2007, Gul et al. 2011). We also test the relation between analyst

following and stock price synchronicity by splitting the full sample based on the

median of variable A. The subsample analysis confirms the findings of the interaction

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model. For the low earnings quality subsample, the coefficient on Ln(Naf) is larger

than the coefficient on the high earnings subsample, suggesting more industry level

and/or market level information is incorporated into stock prices by analysts when the

firm’s earnings quality is poor.

3.5 Alternative Information Measures

3.5.1 Incremental Explanatory Power of Industry-Level Returns to Explain Firm

Returns

Following Piotroski and Roulstone (2004), we use the incremental explanatory power

of industry-level returns to explain firm return (DIFF) as an alternative information

measure. DIFF is defined as the difference between R2 of eq (1) and the R2 of the

following regression:

Rj,t = φ0+ φ1R_MARKETj,t-1+ φ2R_MARKETj,t + νj,t (4)

We replace SYNC in eq (3) by DIFF and re-estimate the model. We report the results

in Table 4. The findings are generally consistent with those reported in Table 2. The

results suggest that analysts improve the firm’s information environment by using

their industry expertise. When the earnings quality is poor, less firm information and

more industry information is incorporated into prices by analysts.

[Insert Table 3 here]

3.5.2 PIN

The information interpretation of stock price synchronicity is not without controversy,

therefore, we use an alternative private information measure (PIN4) developed by

4 The PIN data is downloaded from http://sites.google.com/site/hvidkjaer/data

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Easley et al. (2002). PIN is also used in Ferreira and Laux (2007) as an alternative

measure of information flow. Since the PIN date downloaded covers years up to 2001,

our sample size is reduced accordingly. After merged with PIN data, our sample

reduced to 12,303 observations from 1990-2001.

We replace SYNC in eq (3) by PIN and re-estimate the model. PIN measure the

probability of informed trading, the larger value indicates more informed trading and

thus more private information incorporated into stock prices. Therefore, we expect the

coefficient on earnings quality, on analyst following and on the interaction of earnings

quality and analyst following to be significantly negative, which is opposite to the

estimates of the SYNC regression, eq (3). We report the results in Table 4. The results

are generally consistent with the expectation, except the coefficient on earnings

quality is negative but not significant.

In all, the results reported in Table 3 suggest that the results reported in Table 2 is

generally robust to alternative information measures, i.e. the incremental explanation

power of industry-level returns in explaining firm returns and probability of informed

trading (PIN).

3.6 Change Model

We use the change model to address the potential endogeneity problem. Specifically,

we estimate the following model for the full sample and for the high/low earnings

quality subsample:

22

ΔSYNCj,t=λ0+λ1ΔAj,t+λ2ΔLn(NAF)j,t+λ3 ΔLn(MV)j,t + λ4 ΔLevj,t+ λ5ΔBMj,t+

Λ6ΔROAj,t+λ7ΔstdROAj,t+λ8ΔLn(Herf)j,t+λ9ΔLn(Nind)j,t+λ10ΔDDj,t+λ11ΔMERGERj,t

+λ12Δ DIVERj,t+μj,t (5)

[Insert Table 4 here]

The change in the analyst following is significantly positively related to the change in

stock price synchronicity, suggesting increase (decrease) in analyst following will

help bring more (less) industry level information to the stock prices. The coefficient

on the change in earnings quality is positive but not significant, maybe because the

earnings quality of the firm stays relatively stable across years for a firm. We then run

the regression separately for the high/low earnings quality subsample, and find that

the coefficient on the change of analyst following is larger in the low earnings quality

subsample than in the high earnings subsample. The result suggests that for firms with

low earnings quality, the effect of increase (decrease) of analyst following on the

incorporation of more (less) industry/market information into stock prices is more

prominent.

3.7 Unexplained Earnings Quality

Earnings quality is predicted by firm characteristics and analyst following. Since we

interact earnings quality with analyst following, we would like to separate the impact

of analyst following on earnings quality. We build a prediction model of earnings

quality as follows.

Aj,t=λ0+λ1Ln(Asset)j,t+λ2stdSalesj,t + λ3stdCFOj,t +λ4OperCyclej,t + λ5Negj,t+

λ6Int_intensityj,t+λ7Cap_intensityj,t+λ8Ln(NAF)j,t +μj,t (6)

23

The model is largely build on Dechow and Dechiv (2002), who reveal that accrual

quality is likely to be systematically related to observable and recurring firm

characteristics like volatility of operations, volatility of sales, volatility of cash flow,

and operation cycle etc. The reason behind is higher volatility is associated with

higher incidence of unavoidable estimation errors. Following Dechow and Dichev

(2002), we include firm size (Ln(Asset)), cash flow volatility (stdCFOj,t) , sales

variability (stdSalesj,t), length of operation cycle (OperCyclej,t), incidence of negative

earnings realization (Negj,t ), RD and advertising expense (Int_intensityj,t) and capital

intensity (Cap_intensityj,t). We also include the number of analyst following in the

prediction model. The residual (Res_A) of the model represents the unexplained part

of earnings quality. We replace A with Res_A and re-estimate eq(3). The results are

reported in Table 5. The results are qualitatively the same as those reported in Table 2.

[Insert Table 5 here]

3.8 Earnings Quality and the Impact of Analyst Following on the Timing of Industry-

versus Firm-Specific Earnings Information Incorporation into Prices.

Ayer and Freeman (1997) decompose annual earnings innovations into market,

industry and firm components and conclude that industry component of earnings is

incorporated into stock price earlier than the firm-specific component. Piotroski and

Roulstone (2004) extend Ayer and Freeman (1997)’s model by adding the effect of

analyst activities and find that analyst accelerate incorporate of both industry and firm

component of future earnings news into stock price but analysts are more successful

in pre-empting industry news versus firm-specific news. We replicate Piotroski and

Roulstone (2004)’s following model:

24

CARj,t=λ0+λ1Ij,t+λ2 Fj.t +λ3 Ij,t+1 +λ4Fj.t+1 +λ5Ij,t* Ln(NAF)j.t +λ6 Fj.t* Ln(NAF)j.t +λ7

Ij,t+1* Ln(NAF)j.t +λ8Fj.t+1* Ln(NAF)j.t + λ9CARj,t+1+λ10 Ln(NAF)j.t +λ11Ln(MV)j,t+λ12

Ln(BM)j,t +μj,t (7)

where CARj,t is the value-weighted market adjusted return for firm j for year t. Firm

and market index returns are measured from the start of the fourth month of year t to

the end of the third month of year t+1. Industry- and firm-specific components of each

year’s earnings innovation are as defined in Ayers and Freeman (1997). The industry

component of the current earnings innovation, Ij,t, is measured as ΔIEj,t-ΔMEj,t, where

ΔIEj,t is the median annual change in firm earnings for all firms in firm j’s industry in

year t and ΔMEj,t is the median ΔIE for all industries in year t. ΔFEj,t is the first

difference in firm j’s earnings divided by its beginning-of-year-year market value. Fj,t

which represents the firm-specific component of firm j’s change in earnings is

measured as ΔFEj,t-ΔIEj,t. Following Piotroski and Roulstone (2004), we control for

known cross sectional determinants of annual returns.

Consistent with Ayer and Freeman (1997) and Piotroski and Roulstone (2004), we

include December 31 year-end firms only. Firm-year observations containing an

absolute earnings realization greater than 1.5 are excluded from the sample. The

sample is 27,962 firm-year observations.

[Insert Table 6 here]

The results are reported in Table 6. Consistent with Piotroski and Roulstone (2004),

we find λ7 and λ8 are both significantly positive and λ7 is greater than λ8, indicating

analyst following help incorporation of both components of future earnings news into

stock prices, but more for industry component. In addition, we find the coefficient (λ6)

on contemporaneous firm-specific earnings interacted with analyst following is

25

significantly positive, while the coefficient (λ5) on contemporaneous industry earnings

interacted with analyst following is positive but not significant5, which is consistent

with analysts being more successful in pre-empting industry news rather than firm

news.

We then estimate eq (7) for the high/low earnings quality subsample. We find that

similar to the results for the full sample, λ7 and λ8 are both significantly positive and

λ7 is greater than λ8 for both the high and low earnings subsample. However, we find

that the coefficient (λ7) on leading industry earnings interacted with analyst following

is larger in the low earnings subsample and the coefficient (λ8) on leading

firm-specific earnings interacted with analyst following is larger in the high earnings

quality subsample, indicating that analysts help the incorporation of relative more

firm component of future earnings news into prices when the earnings quality is high.

The results confirm our main findings in Table 2.

4. Additional Tests

4.1 Information Complexity: Annual Report Readability

In the main test, we focus on earnings quality as a general measure of accounting

transparency because earnings figure is one of the most important items disclosed by a

firm. However, we believe other properties of firm disclosure may also have potential

impact on analysts’ cost benefit trade-off of incorporation of firm, industry and

market information into stock prices. In this section, we examine the impact of annual

report readability which is a measure of information complexity.

5 Piotroski and Roulstone (2004) find λ5 to be significantly negative.

26

Lehavy et al. (2011) find that less readable communication will increase the demand

for analyst services and induce greater collective effort by analysts, suggesting a

substitution effect of information complexity and analyst activities. Following

Lehavy et al. (2011), we use the Fog Index to measure the overall complexity of

firms’ disclosure. The Fog Index is also used to examine the relationship between

annual report readability and earning persistence (Li 2008), timel price adjustment

(Callen et al. 2009), and investment efficiency (Biddle et al. 2009). Specifically, we

add the Fog Index and the interaction of the Fog Index and analyst following into eq

(3) and estimate the following model;

SYNCj,t=λ0+λ1Ln(NAF)j,t +λ2Aj,t + λ3Ln(NAF)j.t*Aj,t+λ4Ln(FOG)j,t

+λ5Ln(NAF)j,t*Ln(FOG)j,t+λ6Ln(MV)j,t+λ7 Levj,t+ λ8BMj,t+λ9ROAj,t+λ10stdROAj,t+λ11

Ln(Herf)j,t +λ12 Ln(Nind)j,t+λ13 DDj,t+λ14 MERGERj,t+λ15 DIVERj,t+μj,t (8)

The Fog Index6 covers year starting from 1993, so our sample years are reduced

accordingly. After merged with the Fog Index, our sample size is reduced to 40,305

firm-year observations from 1993-2009.

The results are reported in Table 7. The interaction of earnings quality and analyst

following remains significantly positive after we add the Fog Index into the model,

suggesting the encouragement effect of earnings quality on analysts’ incorporation of

firm-specific information. We find the interaction of the Fog Index and analyst

following is significantly negative. The result suggests that when a firm discloses

complex information, analysts have stronger incentive to help to incorporate firm

specific information into stock price because the benefit of increase demand of analyst

6 The Fog Index is available at http://webuser.bus.umich.edu/feng/.

27

services more than offsets the increased cost of information processing, as

documented in Lehavey et al. (2011). The results suggest that different properties of

firm disclosure (i.e. earnings quality and annual report readability) have different

impact on analysts’ cost benefit trade-off of incorporation of firm, industry and

market information into prices.

[Insert Table 7 here]

4.2 Sentiment

In this section, we investigate whether the yearly difference in market sentiment will

affect our findings in Section 3.4 and Section 4.1. The literature generally suggests

that the sentiment driven investors will drive the stock prices away from the firm

fundamentals. In that sense, when the market sentiment is high, the demand for

firm-specific information is low. Analysts’ cost benefit trade-off of providing

firm-specific information may be affected by the general market sentiment.

We split the full sample based on the yearly difference in market sentiment. The

sentiment data is updated to 2005, so the sample year is 1990-2005. For the model

using fog index as a measure of corporate disclosure, the sample year is 1993-2005

because the fog data covers year starting from 1993. For the model using accruals as a

measure of corporate disclosure, the high sentiment subsample includes years above

median market sentiment, i.e. 1996, 1997, and 1999-2004. The low sentiment year is

1990-1995, 1998, and 2005. For the model using fog index as the measure of

corporate disclosure, the high sentiment year is 1996, 1997, and 1999-2003 and the

low sentiment year is 1993-1995, 1998, 2004 and 2005. We find that for both models,

in high sentiment years, the coefficient on the interaction of disclosure and analyst

following is not significant, indicating that when the market is overwhelmed by the

28

market sentiment, the information quality is less important in investors’ decision

making. In the low sentiment subsample, the results are consistent with those reported

in Table 2 and Table 7.

[Insert Table 8 here]

4.3 Industry Specialized Analysts

Different analysts may have different cost benefit trade-off of incorporation of

different level of information into stock prices. Industry specialization is particularly

relevant in our setting since our main results indicate that analysts’ information

advantage lies in incorporating industry-level information and firm’s disclosure has

moderating effect on the incorporation process. Gilson et al. (2001) show that analysts

forecast accuracy improves after spin-offs and equity carve-outs and the

improvements is more prominent for industry specialized analysts. The comparative

industry information advantage is more for industry specialist than for non-specialist

and the moderating effect of firm disclosure may also be different. Ramnath (2002)

shows that analysts revise their earnings forecasts in response to the earnings

announcements of other firms in the same industry. We conjecture that industry

specialists possess more information across different firms in one industry so that the

disclosure quality of one particular firm is less likely to affect their relative amount of

information incorporated into prices. We classified analysts as industry specialist if

they follow more than 5 firms in one industry (Gilson et al. 2001) and modified eq (3).

Specifically, we estimate the following model:

29

SYNCj,t=λ0+λ1Discj,t+λ2Ln(IF)j,t +λ3Ln(NIF)j,t+λ4Ln(IF)j.t*Discj,t

+λ5Ln(NIF)j.t*Discj,t+λ6Ln(MV)j,t+λ7Levj,t+ λ8BMj,t+λ9ROAj,t+λ10stdROAj,t

+λ11Ln(Herf)j,t + λ12 Ln(Nind)j,t+λ13 DDj,t+λ14 MERGERj,t+λ15 DIVERj,t+μj,t (9)

Disc represents the two firm disclosure measures we used in previous test, i.e.

earnings quality (A) and annual report readability (Ln(FOG)). Ln(IF) is the log of the

number of industry specialist analyst following. Ln(NIF) is the log of the number of

non-industry specialist analyst following. We present the results in Table 9.

[Insert Table 9 here]

We find that both industry specialist and non-specialist help incorporate industry level

information into stock prices. In addition, we find the encouragement effect of

earnings quality and the crowding out effect of annual report readability we

documented earlier only exists in non-specialist analyst group. The results confirm

our conjecture that industry specialists have industry advantage and the information

quality of a particular firm is no as important to them as to non-specialist in the

incorporation of information into stock prices.

5. Conclusion

This study examines the impact of the quality of a firm’s disclosure, i.e. earnings

quality on analysts’ incorporating of firm, industry, and market information into stock

prices. We find that analysts help incorporate relatively more industry level

information (than firm level information) into prices as documented in Piotroski and

Roulstone (2004) and better (poor) earnings quality will encourage analysts

incorporate more (less) firm specific information relative to industry level information

into prices. The result suggests that analysts are encouraged by the low information

30

process cost associated with better earning quality to provide more firm specific

information to the market. In addition, we find that less readable 10-K reports will

encourage analysts bringing more firm-specific information into stock prices,

suggesting that the benefit of serving the increasing demand for analyst services is

greater than the increasing cost of processing complex information. Last but not the

least, we find that only non-industry specialist analysts will be encourage by better

earnings quality and more complex disclosure to incorporate more firm-specific

information, suggesting that industry specialists possess information across different

firms in one industry and are less likely to be affected by the disclosure quality of a

single firm.

This study enhances our understanding of the complex relations among accounting

transparency, analyst following, and stock price synchronicity in general, and the

information role of financial analysts in particular. While Piotroski and Roulsone

(2004) show a positive association of stock price synchronicity with analyst following,

Ferreira and Laux (2007) find synchronicity to be negatively associated with

accounting transparency. We bring all three variables together in a single study setting

and present evidence that helps to reconcile some apparent inconsistency in the

literature regarding the information role of financial analysts. Specifically, the result

of Piotroski and Roulsone (2004) that financial analysts play a primary role in

transferring market/industry-wide information is not consistent with what we know

from the literature about how analysts help to produce firm-specific information (e.g.,

Ayers and Freeman 2003). By bring accounting transparency into the investigation,

we are able to uncover dual information roles for financial analysts. At the same time

analysts help to produce market/industry-wide information, they also encourage the

31

collection and flow of firm-specific information to the market in firms where the

earnings quality is higher and where the annual report readability is lower.

32

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35

Table 1 Descriptive Statistics mean stddev p5 p25 median p75 p95 SYNC -2.5151 1.5373 -4.9235 -3.6568 -2.6024 -1.3718 0.0513 A 0.1172 0.1903 0.0052 0.0281 0.0640 0.1325 0.3845 NAF 5.0431 7.2850 0.0000 0.0000 2.0000 7.0000 21.0000 Ln(NAF) 1.1732 1.1177 0.0000 0.0000 1.0986 2.0794 3.0910 Ln(MV) 5.3794 2.1474 2.0620 3.8079 5.2178 6.8069 9.2542 Lev 0.1949 0.2354 0.0000 0.0028 0.1235 0.3017 0.6458 BM 0.6269 0.6301 0.0514 0.2642 0.4826 0.8044 1.7963 ROA -0.0193 0.2296 -0.4743 -0.0401 0.0356 0.0868 0.2053 stdROA 0.1814 0.4075 0.0101 0.0297 0.0642 0.1501 0.6856 Ln(Herf) 0.0578 0.0454 0.0208 0.0314 0.0462 0.0625 0.1571 Ln(Nind) 5.7272 0.8884 4.3041 5.1240 5.8348 6.2615 7.2071 DD 0.3262 0.4688 0.0000 0.0000 0.0000 1.0000 1.0000 MERGER 0.1989 0.3991 0.0000 0.0000 0.0000 0.0000 1.0000 DIVER 0.6248 0.4842 0.0000 0.0000 1.0000 1.0000 1.0000 SYNC =the natural logarithmic transformation of R2 of Equation (1), defined as Ln(R2/(1-R2)); A = the absolute value of firm-specific residuals from an industry

regression of total accruals on lagged, contemporaneous, and leading cash flows from operations, all scaled by lagged total assets;

NAF = the number of analysts making annual earnings forecast for that firm during the fiscal year. If there is no analyst reported in I/B/E/S tape, NAF=0; Ln(NAF) is the natural log of NAF, defined as Ln(NAF+1);

Ln(MV) = the natural log of the market capitalization at the beginning of the year; Lev =leverage ratio as total liabilities divided by total asset; BM =book value of equity divided by market value of equity; the market value is calculated as the market price of shares at fiscal year end times the number of shares outstanding; ROA = return on assets as net income divided by average total asset; stdROA = the standard deviation of ROA measured over the years 1990 through 2009; Ln(Herf) = the natural log of Herfindahl index of industry-level concentration; Ln(Nind) = the natural log of the number of firms in the industry; DD = annual dividend dummy, which equals 1 if the firm pays dividend, and 0 otherwise; MERGER =dummy variable which equals 1 if Compustat item AFTNT1= ‘AA’, ‘AB’, ‘AR’, ‘AS’, ‘FA’, ‘FB’, ‘FC’, ‘FD’, ‘FE’, ‘FF’, and 0 otherwise; and DIVER =annual dummy variable that equals 1 when a firm operates in multiple segments, and 0 otherwise.

36

Table 2 Earnings Quality, Analyst Following and Stock Price Synchronicity This table presents average coefficients from 20 annual estimates of the following model: SYNCj,t=λ0+λ1Aj,t+λ2Ln(NAF)j,t + λ3Ln(NAF)j.t*Aj,t +λ4 Ln(MV)j,t + λ5 Levj,t+ λ6BMj,t+λ7ROAj,t+λ8stdROAj,t+λ9 Ln(Herf)j,t +λ10 Ln(Nind)j,t+λ11 DDj,t+λ12 MERGERj,t+λ13 DIVERj,t+μj,t Full Sample Low EQ High EQ Intercept -4.8798*** -48.47 -4.8764*** -48.47 -5.0129*** -46.73 -4.7200*** -43.06 A 0.2664*** 3.74 0.2359*** 3.58 0.2202*** 3.44 0.7345*** 2.99 Ln(NAF) 0.2789*** 13.01 0.2735*** 12.03 0.3010*** 14.60 0.2601*** 11.42 Ln(NAF)*A 0.0716*** 2.56 Ln(MV) 0.3940*** 82.60 0.3940*** 82.18 0.3779*** 55.17 0.4080*** 52.43 Lev -0.7151*** -16.94 -0.7142*** -16.91 -0.6283*** -16.65 -0.8234*** -15.27 BM -0.3858*** -21.45 -0.3860*** -21.37 -0.3644*** -19.91 -0.4079*** -17.88 ROA 0.1907*** 2.91 0.1861*** 2.89 0.1967*** 3.18 0.1836 1.84 stdROA 0.1176*** 5.37 0.1186*** 5.36 0.0924*** 4.62 0.1849*** 4.76 Ln(Herf) 0.5760*** 2.68 0.5747*** 2.67 0.9974*** 3.30 0.0857 0.45 Ln(Nind) 0.035*** 2.84 0.0352*** 2.82 0.0616*** 4.20 0.0041 0.34 DD -0.0489 -1.22 -0.0466 -1.17 -0.0476 -1.32 -0.0526 -1.10 MERGER 0.0142 1.21 0.0144 1.22 0.0365*** 2.98 -0.0071 -0.48 DIVER 0.0448*** 2.90 0.0455*** 2.96 0.0265 1.03 0.0554*** 3.06 Adj.R2 54.48% 55.49% 52.84% 57.01% All variables are previously defined in Table1. Each year, t =1990-2009, we estimate the above regression and we report the mean of the annual coefficient estimates; t-statistics are based on the standard errors of the time-series of 20 estimates. Industry dummies are included but not reported. Low EQ (High EQ) subsamples consists of observations above (below) median of variable A.

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Table 3 Earnings Quality, Analyst Following and Alternative Information Measures This table presents average coefficients from 20 annual estimates of the following DIFF model and from 12 annual estimates of the PIN model: DIFFj,t=λ0+λ1Aj,t+λ2Ln(NAF)j,t + λ3Ln(NAF)j.t*Aj,t +λ4 Ln(MV)j,t + λ5 Levj,t+ λ6BMj,t+λ7ROAj,t+λ8stdROAj,t+λ9 Ln(Herf)j,t +λ10 Ln(Nind)j,t+λ11 DDj,t+λ12 MERGERj,t+λ13 DIVERj,t+μj,t

PINj,t=λ0+λ1Aj,t+λ2Ln(NAF)j,t + λ3Ln(NAF)j.t*Aj,t +λ4 Ln(MV)j,t + λ5 Levj,t+ λ6BMj,t+λ7ROAj,t+λ8stdROAj,t+λ9 Ln(Herf)j,t +λ10 Ln(Nind)j,t+λ11 DDj,t+λ12 MERGERj,t+λ13 DIVERj,t+μj,t DIFF PIN Intercept -0.0423*** -7.03 -6.2637*** -68.52 0.3712*** 27.12 0.3256*** 32.55 A 0.0102*** 3.37 0.2482*** 4.00 -0.0111 -0.73 0.0207 1.04 Ln(NAF) 0.0072*** 4.85 0.1488*** 11.31 -0.0118*** -6.59 -0.0079*** -5.35 Ln(NAF)*A 0.1538*** 3.71 -0.0195** -2.03 Ln(MV) 0.0166*** 27.39 0.3609*** 46.89 -0.0246*** -29.79 -0.0215*** -25.84 Lev -0.0199*** -9.57 -0.5606*** -18.40 0.0388*** 12.32 0.0330*** 12.87 BM -0.0085*** -8.08 -0.2532*** -10.76 0.0183*** 13.01 0.0136*** 6.85 ROA -0.0033 -1.55 0.0347 0.82 0.0143 1.30 -0.0106 -0.75 stdROA 0.0049*** 4.52 0.1301*** 6.24 -0.0161** -2.24 -0.0320*** -2.57 Ln(Herf) 0.0212 0.77 0.3229 0.96 -0.0137 -1.74 0.0207 1.57 Ln(Nind) 0.0001 0.11 -0.0068 -0.52 -0.0032*** -3.70 0.0000 0.03 DD 0.0069*** 4.61 -0.0736*** -2.77 0.0011 0.62 -0.0036 -1.67 MERGER -0.0082*** -5.76 -0.0797*** -3.39 -0.0002 -0.14 0.0028*** 3.43 DIVER -0.0089*** -5.41 -0.0304 -1.41 0.0001 0.04 0.0001 0.05 Adj.R2 17.58% 22.67% 42.38% 45.62% DIFF is the incremental explanatory power of industry returns (over the market), defined as the difference between R2 of eq (1) and the

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R2 of the following regression: Rj,t = φ0+ φ1R_MARKETj,t-1+ φ2R_MARKETj,t + νj,t

PIN is the annual probability of information-based trading of Easley et al. (2002). The PIN data is downloaded from hvidkjaer website. All other variables are previously defined in Table1. Each year, we estimate the above regression and we report the mean of the annual coefficient estimates; t-statistics are based on the standard errors of the time-series of 20 estimates (for PIN model, 12 yearly estimates from 1990 to 2001). Industry dummies are included but not reported.

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Table 4 Change in Analyst Following and Change in Stock Price Synchronicity This table presents average coefficients from 19 annual estimates of the following model: ΔSYNCj,t=λ0+λ1ΔAj,t+λ2ΔLn(NAF)j,t+λ3 ΔLn(MV)j,t + λ4 ΔLevj,t+ λ5ΔBMj,t+ Λ6ΔROAj,t+λ7ΔstdROAj,t+λ8ΔLn(Herf)j,t+λ9ΔLn(Nind)j,t+λ10ΔDDj,t+λ11ΔMERGERj,t+λ12Δ DIVERj,t+μj,t Full Sample Low EQ High EQ Intercept 0.0102 0.11 0.0128 0.14 0.0508 0.58 ΔA 0.0303 0.93 0.0456 1.41 0.0544 0.76 ΔLn(Naf) 0.0782*** 4.38 0.0915*** 4.82 0.0488*** 3.75 ΔLn(MV) 0.4306*** 11.79 0.4228*** 11.48 0.3126*** 4.55 Δlev -0.2684*** -6.70 -0.2667*** -8.08 -0.1535*** -2.78 ΔBM -0.2071*** -8.14 -0.2066*** -8.91 -0.2277*** -8.45 ΔROA 0.2337*** 4.66 0.2475*** 4.74 0.2845*** 4.96 ΔstdROA -0.1241*** -2.95 -0.0840* -1.93 -0.0424 -0.29 ΔLn(Herf) 0.5528 0.25 0.9844 0.53 0.4406 0.36 ΔLn(Nind) 0.3400 1.04 0.2913 1.06 -0.0334 -0.15 ΔDD -0.0343 -0.97 -0.0216 -0.71 -0.0050 -0.21 ΔMERGER 0.0099 0.73 -0.0010 -0.11 -0.0113 -1.28 ΔDIVER 0.0649 1.68 0.0805*** 2.76 0.0812*** 3.44 Adj.R2 4.68% 4.61% 2.50% All variables are previously defined in Table1. Each year, t =1991-2009, we estimate the above regression and we report the mean of the annual coefficient estimates; t-statistics are based on the standard errors of the time-series of 19 estimates. Low EQ (High EQ) subsamples consists of observations above (below) median of variable A.

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Table 5 Unexplained Part of Earnings Quality, Analyst Following and Stock Price Synchronicity This table presents average coefficients from 20 annual estimates of the following model: SYNCj,t=λ0+λ1 Res_Aj,t+λ2Ln(NAF)j,t + λ3Ln(NAF)j.t* Res_Aj,t +λ4 Ln(MV)j,t + λ5 Levj,t+ λ6BMj,t+λ7ROAj,t+λ8stdROAj,t+λ9 Ln(Herf)j,t +λ10 Ln(Nind)j,t+λ11 DDj,t+λ12 MERGERj,t+λ13 DIVERj,t+μj,t

Intercept -4.8488*** -47.63 -4.8490*** -47.71 Res_A 0.2605*** 3.77 0.2252*** 4.53 Ln(NAF) 0.2853*** 13.07 0.2851*** 12.99 Ln(NAF)*Res_A 0.1001** 2.12 Ln(MV) 0.3924*** 78.57 0.3924*** 78.30 Lev -0.7220*** -15.62 -0.7218*** -15.60 BM -0.3765*** -21.24 -0.3765*** -21.16 ROA 0.1967*** 2.81 0.1927*** 2.78 stdROA 0.1454*** 5.73 0.1435*** 5.69 Ln(Herf) 0.4668*** 2.60 0.4711*** 2.62 Ln(Nind) 0.0335*** 2.58 0.0335*** 2.58 DD -0.0534 -1.28 -0.0518 -1.25 MERGER 0.0225 1.82 0.0232 1.83 DIVER 0.0474*** 3.27 0.0472*** 3.26 Adj.R2 55.97% 55.99% Res_A, the unexplained component of earnings quality, is defined as the residual of the following model Aj,t=λ0+λ1Ln(Asset)j,t+λ2stdSalesj,t + λ3stdCFOj,t +λ4OperCyclej,t + λ5Negj,t+ λ6Int_intensityj,t+λ7Cap_intensityj,t+λ8Ln(NAF)j,t +μj,t Ln(Asset) is the natural log of total assets at the beginning of the year; OperCycle is the natural log of the sum of the firm’s days accounts receivable plus days inventory; NegEarn is the proportion of losses over the prior the years; StdCFO is the standard deviation of the firm’s rolling ten-year cash flows from operations; StdSales is the standard deviation of the firm’s rolling ten-year sales revenue; Int_Intensity is the sum of the firm’s reported R&D and advertising expenses as a proportion of its sales revenue; and Cap_Intensity is the ratio of the net book value of PPE to total assets. All other variables are previously defined. Each year, t =1990-2009, we estimate the above regression and we report the mean of the annual coefficient estimates; t-statistics are based on the standard errors of the time-series of 20 estimates. Industry dummies are included but not reported.

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Table 6 Earnings Quality and the relation between Analyst Following and the Timing of the Incorporation of Industry and Firm-Specific Earnings components into Prices. This table presents average coefficients from 20 annual estimates of the following models: CARj,t=λ0+λ1Ij,t+λ2 Fj.t +λ3 Ij,t+1 +λ4Fj.t+1 +λ5Ij,t* Ln(NAF)j.t +λ6 Fj.t* Ln(NAF)j.t +λ7 Ij,t+1* Ln(NAF)j.t +λ8Fj.t+1* Ln(NAF)j.t + λ9CARj,t+1+λ10 Ln(NAF)j.t +λ11Ln(MV)j,t+λ12 Ln(BM)j,t +μj,t

Full Sample Low EQ High EQ Intercept 0.1299 0.99 0.1272 0.97 0.1112 0.71 0.0288 0.31 It 5.3614*** 3.61 5.1619*** 3.34 4.2241*** 3.05 4.0715*** 3.27 Ft 0.1839*** 2.73 0.1512** 2.14 0.1569* 1.84 0.1441 1.27 It+1 2.2334* 1.83 1.0562 0.90 -0.3615 -0.24 1.8451 1.40 Ft+1 0.0833*** 3.21 0.0401 1.19 0.0793 1.51 -0.1684 -0.80 It* Ln(NAF) 0.3992 0.50 0.5219 0.52 0.2093 0.39 Ft* Ln(NAF) 0.1196*** 2.57 0.1079** 2.09 0.2427** 2.53 It+1* Ln(NAF) 0.9564*** 2.54 1.2113** 2.46 1.0296** 2.18 Ft+1* Ln(NAF) 0.1519*** 2.89 0.0953** 2.25 0.3355*** 2.32 Ln(NAF) 0.0176 1.39 0.0186 1.41 0.0367** 2.14 0.0149 1.03 CAR t+1 0.0412 1.09 0.0375 1.02 0.0262 0.70 -0.0139 -0.47 Ln(MV) -0.0289 -1.50 -0.0284 -1.49 -0.0373 -1.53 -0.0117 -0.84 Ln(BM) -0.1737*** -7.25 -0.1722*** -7.08 -0.1955*** -6.88 -0.1332*** -6.54 Adj.R2 10.94% 11.47% 11.37% 13.18% CARj,t is the summation of market-adjusted monthly return for year t; firm and value-weighted market returns are measured from the fourth month of year t to the third month of year t+1. Ij,t is the industry component of firm j’s change in earnings. Fj,t is the firm-specific component of firm j’s change in earnings. All other variables are previously defined in Table 1. Each year, t =1990-2009, we estimate the above regression and we report the mean of the annual coefficient estimates; t-statistics are based on the standard errors of the time-series of 20 estimates. Low EQ (High EQ) subsamples consists of observations above (below) median of variable A.

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Table 7 Annual Report Readability, Earnings Quality, Analyst Following and Stock Price Synchronicity This table presents average coefficients from 17 annual estimates of the following model: SYNCj,t=λ0+λ1Ln(NAF)j,t +λ2Aj,t + λ3Ln(NAF)j.t*Aj,t+λ4Ln(FOG)j,t +λ5Ln(NAF)j,t*Ln(FOG)j,t+λ6Ln(MV)j,t+λ7 Levj,t+ λ8BMj,t+λ9ROAj,t+λ10stdROAj,t+λ11 Ln(Herf)j,t +λ12 Ln(Nind)j,t+λ13 DDj,t+λ14 MERGERj,t+λ15 DIVERj,t+μj,t Intercept -5.1088*** -12.94 -5.2277*** -13.26 Ln(NAF) 0.8380*** 4.05 0.9137*** 5.15 A 0.1213 1.36 Ln(NAF)*A 0.0966 1.23 Ln(FOG) 0.1595 1.46 0.1537** 2.16 Ln(NAF)*Ln(FOG) -0.1911*** -2.93 -0.2218*** -3.72 Ln(MV) 0.4038*** 15.25 0.4071*** 15.33 Lev -0.7039*** -10.76 -0.7076*** -10.83 BM -0.3606*** -10.63 -0.3539*** -10.52 ROA 0.2841 1.26 0.3315 1.54 stdROA 0.3067* 1.83 0.2896 1.68 Ln(Herf) 0.0089 0.05 0.0257 0.14 Ln(Nind) 0.0125 1.14 0.0101 0.94 DD -0.0201 -0.41 -0.0109 -0.23 MERGER 0.0251 1.72 0.0180 1.32 DIVER 0.0730*** 4.19 0.0737*** 4.30 Adj.R2 58.04% 58.29% FOG is the fog index of 10-k filing of Lehavy et al. (2011) All other variables are previously defined in Table 1. Each year, t =1993-2009, we estimate the above regression and we report the mean of the annual coefficient estimates; t-statistics are based on the standard errors of the time-series of 17 estimates.

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Table 8 This table presents average coefficients from 8 annual estimates in high (low) sentiment year of the following model: SYNCj,t=λ0+λ1Discj,t+λ2Ln(NAF)j,t + λ3Ln(NAF)j.t*Discj,t +λ4 Ln(MV)j,t + λ5 Levj,t+ λ6BMj,t+λ7ROAj,t+λ8stdROAj,t+λ9 Ln(Herf)j,t +λ10 Ln(Nind)j,t+λ11 DDj,t+λ12 MERGERj,t+λ13 DIVERj,t+μj,t Disc=A Disc=LN(FOG) High Sentiment Low Sentiment High Sentiment Low Sentiment Intercept -5.1835*** -57.29 -4.8079*** -35.74 -5.8215*** -9.39 -5.6218*** -15.93 Disc 0.1903* 1.87 0.4406*** 6.34 0.3438 1.37 0.0397 0.29 Ln(NAF) 0.3153*** 7.33 0.2294*** 8.15 0.9164 1.97 0.7604*** 3.24 Ln(NAF)*Disc 0.0352 0.94 0.1379** 2.13 -0.2143 -1.44 -0.1623** -2.11 Ln(MV) 0.3856*** 45.94 0.3980*** 59.45 0.4313*** 30.83 0.4303*** 15.76 Lev -0.7849*** -18.69 -0.7509*** -10.12 -0.9340*** -18.52 -0.6250*** -10.27 BM -0.4177*** -13.91 -0.3799*** -22.98 -0.4440*** -10.57 -0.3849*** -33.22 ROA -0.0999 -1.38 0.3644*** 9.32 -0.2231** -2.46 0.9215 1.89 stdROA 0.0981*** 3.98 0.1566*** 3.47 0.1455*** 3.53 0.7040 1.83 Ln(Herf) 0.5407** 2.35 0.0301 0.10 -0.5256*** -2.61 0.2463 0.80 Ln(Nind) 0.0699*** 3.31 0.0056 0.37 -0.0093 -0.56 0.0433 2.54 DD -0.0501 -0.87 -0.1467*** -2.81 -0.0710 -0.91 -0.0650 -0.75 MERGER 0.0368*** 2.99 0.0255 1.36 0.0759*** 4.01 -0.0222 -1.30 DIVER 0.0952*** 4.23 -0.0011 -0.08 0.1054*** 2.96 0.0550*** 3.13 Adj.R2 55.05% 53.32% 56.28% 57.30% The high low sentiment year is classified based on the sentiment index of Baker and Wurgler (2006); updated version of Eq. (3) in that paper. For the model Disc=A, the high sentiment year is 1996, 1997, and 1999-2004 and the low sentiment year is 1990-1995, 1998, and 2005. For the model Disc=LN(FOG), the high sentiment year is 1996, 1997, and 1999-2003 and the low sentiment year is 1993-1995, 1998, 2004 and 2005. All variables are previously defined in Table 1. Each year, we estimate the above regression and we report the mean of the annual coefficient estimates; t-statistics are based on the standard errors of the time-series of annual estimates. Industry dummies are included but not reported.

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Table 9 Annual Report Readability, Earnings Quality, Industry Specialist Analyst Following and Stock Price Synchronicity This table presents average coefficients from 20 annual estimates of the following model: SYNCj,t=λ0+λ1Discj,t+λ2Ln(IF)j,t +λ3Ln(NIF)j,t +λ4Ln(IF)j.t*Discj,t+λ5Ln(NIF)j.t*Discj,t+λ6Ln(MV)j,t+λ7Levj,t+ λ8BMj,t+λ9ROAj,t+λ10stdROAj,t+λ11 Ln(Herf)j,t +λ12 Ln(Nind)j,t+λ13 DDj,t+λ14 MERGERj,t+λ15 DIVERj,t+μj,t Disc=A Disc=LN(FOG) Intercept -4.8154*** -50.44 -4.8196*** -52.19 -4.7421*** -8.8 -6.5283*** -10.79 Disc 0.2616*** 3.72 0.2225*** 3.57 -0.2248* -1.94 0.3694** 2.41 Ln(IF) 0.2172*** 11.29 0.1875*** 3.36 0.1905*** 9.92 0.5297 1.23 Ln(NIF) 0.2120*** 9.37 0.1746** 2.12 0.1733*** 16.53 0.4095*** 6.80 Ln(IF)*Disc 0.0223 0.50 -0.0869 -0.75 Ln(NIF)*Disc 0.0945** 2.16 -0.4078*** -5.70 Ln(MV) 0.3904*** 82.1 0.3888*** 81.81 0.4361*** 41.85 0.4370*** 42.65 Lev -0.7068*** -16.81 -0.7084*** -17.52 -0.6329*** -11.98 -0.6351*** -11.95 BM -0.3856*** -21.23 -0.3841*** -21.04 -0.3463*** -8.49 -0.3461*** -8.46 ROA 0.2018*** 3.06 0.1942*** 2.99 0.1283 1.07 0.1249 1.05 stdROA 0.1151*** 5.43 0.1148*** 5.45 0.0660 1.45 0.0663 1.48 Ln(Herf) 0.5818*** 2.95 0.6287*** 2.98 22.3680*** 3.51 22.0540*** 3.48 Ln(Nind) 0.0304** 2.41 0.0305*** 2.49 -0.0065 -0.53 -0.0050 -0.39 DD -0.0488 -1.27 -0.0400 -1.04 0.1462*** 4.34 0.1470*** 4.39 MERGER 0.0195 1.57 0.0203 1.60 0.0542*** 3.07 0.0538*** 3.04 DIVER 0.0468*** 3.06 0.0439*** 2.92 0.1902*** 6.50 0.1919*** 6.30 Adj.R2 55.75% 55.98% 62.48% 62.51% IF is the number of industry specialist analyst following. Industry specialist is defined as the analyst following more than 5 firms in one industry. NIF is the number of non-industry specialist analyst following. All other variables are previously defined. Each year, t =1990-2009, we estimate the above regression and we report the mean of the annual coefficient estimates; t-statistics are based on the standard errors of the time-series of 20 estimates. Industry dummies are included but not reported.