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The Benefits of Firm Comparability

Gus De Franco

Rotman School of Management, University of Toronto

105 St. George StreetToronto, Canada, M5S 3E6

Phone: (416) 978-3101

Email: [email protected]

S.P. Kothari

MIT Sloan School of Management50 Memorial Drive E52-325

Cambridge, MA 02142-1347

Phone: (617) 253-0994Email: [email protected]

Rodrigo S. Verdi

MIT Sloan School of Management50 Memorial Drive E52-325

Cambridge, MA 02142-1347

Phone: (617) 253-2956Email: [email protected]

August 27, 2008

ABSTRACT

We develop a new metric of and study the capital market consequences of firm comparability.

Investors, regulators, academics, and researchers all emphasize the importance of comparability.

However, an empirical construct of financial statement comparability is typically not specified.

More importantly, little evidence exists on the benefits of comparability to users. We fill thesegaps. We find that analyst following is increasing in comparability, and that comparability is

positively associated with forecast accuracy and negatively related to bias and dispersion in

earnings forecasts. Our results suggest comparability enhances a firms informationenvironment, a benefit to capital market participants.

______________________________

We appreciate the helpful comments of Stan Baiman, Rich Frankel, Wayne Guay, Thomas Lys, Jeffrey Ng, Ole-

Kristian Hope, Shiva Rajgopal (a discussant), Shiva Shivramkrishnan, Shyam Sunder, and workshop participants atBarclays Global Investors, Columbia University, University of Florida, University of Houston, London Business

School, MIT, and the University of Toronto. We thank I/B/E/S Inc. for the analyst data, available through the

Institutional Brokers Estimate System. I/B/E/S offers access to data as a part of their broad academic program toencourage earnings expectation research. We gratefully acknowledge the financial support of MIT Sloan and the

Rotman School, University of Toronto. Part of the work on this article was completed while Gus De Franco was a

Visiting Assistant Professor at the Sloan School of Management, MIT.

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1

The Benefits of Firm Comparability

1. Introduction

Several factors point toward the importance of comparability of financial information

across firms in financial analysis. According to the Securities and Exchange Commission (SEC)

(2000), when investors judge the merits of investments and comparability of investments,

efficient allocation of capital is facilitated and investor confidence nurtured. The usefulness of

comparable financial statements is underscored in the Financial Accounting Standards Board

(FASB) accounting concepts statement. Specifically, the FASB (1980, p. 40) states that

investing and lending decisions essentially involve evaluations of alternative opportunities, and

they cannot be made rationally if comparative information is not available (our emphasis).1

Financial statement analysis textbooks almost invariably stress the importance of comparability

across financial statements in judging a firms performance using financial ratios, including

ratios for the same firm in prior years, ratios for selected firms in the same industry, or ratios

based on industry averages.

2

For instance, Stickney and Weil (2006, p. 189) conclude that,

Ratios, by themselves out of context, provide little information. Analyst reports routinely

include a list of comparable or peer firms (see evidence in section 2 below). In these

reports, analysts typically evaluate the firms current valuation and/or predicted valuation on the

basis of a comparative analysis of the (past, current, and projected) financial performance of a set

of comparable, peer, or similar firms.

1 As an additional example of the importance of comparability in a regulatory context, comparability is one of

three qualitative characteristics of accounting information included in the accounting conceptual framework (alongwith relevance and reliability). Further, according to the FASB (1980, p. 40), The difficulty in making financial

comparisons among enterprises because of the different accounting methods has been accepted for many years as

the principal reason for the development of accounting standards. Here, the FASB argues that users demand for

comparable information drives accounting regulation.2

See, e.g., Libby, Libby and Short (2004, p. 707), Stickney, Brown, and Wahlen (2007, p. 199), Revsine,Collins, and Johnson (2004, pp. 213-214), Wild, Subramanyam, and Halsey (2006, p. 31), Penman (2006, p. 324),

White, Sondhi, and Fried (2002, p. 112), and Palepu and Healy (2007, p. 5-1).

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Despite the importance of comparability: i) The literature lacks an empirical measure of

financial statement comparability; and, ii) Little evidence exists on the benefits of financial

statement comparability to users. This paper fills these gaps. We develop two measures of and

study the benefits of comparability to investors (proxied by sell-side analysts). A key innovation

is the construction of empirical, firm-specific, output-based, quantitative measures of

comparability. Our measures contrast with qualitative, input-based definitions of comparability

such as business activities or accounting methods. Further, our measures are intended to capture

comparability from the perspective of users, such as investors or analysts, who evaluate

historical firm performance, predict future firm performance, or make other decisions using

financial statement information. As a proxy for the users benefits, we study the properties of the

observable outputs (i.e., earnings forecasts) of sell-side analysts, which are publicly available for

a long period.

We measure comparability based on financial statement outputs. In particular, our

comparability measures use (arguably) the primary output of financial reporting: earnings. The

first measure, which we label accounting comparability, is based on the idea that comparable

firms experiencing similar economic events, as proxied by stock returns, should report similar

accounting earnings. The second measure, which we label earnings comparability, is based on

the strength of the historical covariance between a firms earnings and the earnings of other firms

in the same industry, as evidenced by theR2

values. If firms experience similar economic shocks

and account for the economic transactions in a similar way, then such firms earnings should

covary over time. To focus on the similarity in accounting for the events, we control for

similarity of business models and economic shocks when using earnings comparability. While

our primary focus is on creating comparability measures at the firm level, we also produce

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measures of relative comparability at the firm-pair level, in which a measure is calculated for

all possible pairs of firms in the same industry. These measures are described in detail below.

Before proceeding to the tests of our hypotheses, because they are new, we study the

construct validity of our earnings comparability measure via an analysis of the textual contents of

a hand-collected sample of analysts reports. We find that the likelihood of an analyst using

another firm in the industry (say, firm j) as a benchmark when analyzing a particular firm (say,

firm i) is increasing in the comparability between the two firms. This shows that our measures of

comparability are correlated with the actual mention of comparable firms in analyst reports,

bolstering the construct validity of our comparability metrics.

We then document the effect of comparability on the properties of analysts outputs.

Given a particular firm, we hypothesize that the availability of information about comparable

firms (as captured by our comparability measures) lowers the cost of acquiring information, and

increases the overall quantity and quality of information available about the firm. Our results are

consistent with the hypothesis. We find that comparability facilitates analyst following.

Specifically, the likelihood that an analyst covering a particular firm (e.g., firm i) would also be

covering another firm in the same industry (e.g., firm j) is increasing in the earnings

comparability between the two firms. Further, firms classified as more comparable are also

covered by more analysts. This result suggests that analysts indeed benefit, i.e., face lower costs,

from higher comparability.

We also find that comparability enables analysts to issue more accurate and less biased

earnings forecasts. Thus, comparability helps analysts more accurately forecast earnings and that

improvement comes, at least in part, through a reduction in forecast bias (i.e., optimism). These

results are consistent with analysts facing lower costs of acquiring information from sources

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other than management. This would reduce analysts reliance on managements private

information, and hence decrease their incentive to strategically include optimistic bias in their

forecasts. Last, we document that one of our comparability measures, accounting comparability,

is negatively related to analysts forecast dispersion, consistent with the availability of superior

public information about highly-comparable firms and an assumption that analysts use similar

forecasting models. There is no evidence of a relation between dispersion and the other

comparability measure, earnings comparability.

Our study contributes to the literature in a number of ways. We develop a measure of

comparability that likely captures users notions of comparability and the benefits of

comparability to them. The ability to forecast future earnings is a common task for users such as

investors and analysts, particularly those engaged in valuation. Improved accuracy and reduced

bias, for example, represent tangible benefits to this user group. Further, the results of increased

analyst following, greater forecast accuracy, lower bias, and less dispersion collectively are

consistent with comparability enriching firms information environment, which provides a

tangible benefit for firms with higher comparability. While comparability is generally accepted

as a valuable attribute, there is little evidence beyond this study that would empirically confirm

this widely-held belief.

The paper proceeds as follows. Section 2 outlines our predictions that comparability

provides benefits to analysts. Section 3 defines our comparability measures. We provide

descriptive statistics and construct validity tests of our measures in Section 4. Section 5 presents

the results of our empirical tests. The last section concludes.

2. Hypotheses: The effect of comparability on analysts

In this section, we develop hypotheses about the effect of comparability on analysts and

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therefore on the properties of their forecasts. As mentioned above, any lesson on financial

statement analysis emphasizes the difficulty in drawing meaningful inferences about a financial

measure unless there is a comparable benchmark. FASB (1980, p. 40) echoes this point.

Implicit is the idea that better benchmarks enable superior inferences (e.g., better evaluation of

firm performance, better prediction of next years performance, etc.). More comparable firms

constitute better benchmarks for each other and lead to a higher quality information set for these

firms. As an additional consideration, information transfer among comparable firms is likely to

be greater. We thus expect the effort exerted by analysts to understand and analyze the financial

statements of firms with other comparable firms to be lower than for firms without other

comparable firms. As a result of this change in analysts cost of analyzing a firm, we investigate

two dimensions of changes in analysts behavior the number of analysts following a firm and

the properties of analyst forecasts.

Our first hypothesis examines whether comparability enhances analyst coverage. As

discussed in Bhushan (1989) and in Lang and Lundholm (1996), the number of analysts

following a firm is a function of the analysts costs and benefits. We argue that, ceteris paribus,

since the cost to analyze firms with other comparable firms is lower, more analysts should cover

these firms. Our first hypothesis (in alternate form) is:

H1: Ceteris paribus, comparability is positively associated with analyst coverage.

The null hypothesis is that the better information environment associated with higher-

comparability firms will decrease the investor demand for analyst coverage. That is, the benefits

to analysts will decrease as well. However, the literature on analysts suggests that analysts

primarily interpret information as opposed to convey new information to the capital markets

(Lang and Lundholm 1996; Francis, Schipper, and Vincent 2002; Frankel, Kothari, and Weber,

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2006; De Franco, 2007). Further, Lang and Lundholm (1996) and others in the literature find

that analyst coverage is increasing in firm disclosure quality. These empirical findings suggest

that an increase in the supply of information results in higher analyst coverage, consistent with

the lower costs of more information outweighing the potentially lower benefit of decreased

demand. These findings in the literature support our signed prediction.

Our second set of hypotheses examines the association between comparability and the

properties of analyst earnings forecasts. The first property we examine is forecast accuracy. The

higher quality information set associated with higher comparability should facilitate analysts

ability to forecast firm i's earnings and lead to improved forecast accuracy. For example, the

existence of comparable firms could allow analysts to better explain firms historical

performance or to use information from comparable firms as an additional input in their earnings

forecasts. Our hypothesis 2a (in alternative form) is:

H2a: Ceteris paribus, comparability is positively associated with analyst forecast

accuracy.

Second, prior research finds analysts long-horizon forecasts are optimistic on average

(e.g., OBrien, 1988, and Richardson et al., 2004).3

Francis and Philbrick (1993), Das et al.

(1998), and Lim (2001) show that part of the bias in analysts forecasts is explained by analysts

adding optimism to their forecasts to gain access to managements private information, which

helps improve forecast accuracy.4

If information from comparable firms serves as a substitute

for information from management, then the incentive to strategically add optimistic bias to gain

access to management is reduced. Further, if more objective information from comparable firms

is available, it is easier to identify when analysts act strategically (i.e., to catch them) regardless

3Analyst optimism has decreased over time and is more-pronounced for longer-horizon forecasts and it seems

to be driven by relatively few observations (Brown 2001; Lim 2001; Gu and Wu 2003; Richardson et al. 2004).4 Recent research by Eames et al. (2002) and Eames and Glover (2003) question these results.

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of the reason for the optimism, which hence increases the cost to the analyst of this optimistic

behavior. Therefore analysts forecasts of higher-comparability firms should be less optimistic.

We state this prediction as hypothesis 2b (in alternate form):

H2b: Ceteris paribus, comparability is negatively related to analyst forecastoptimism.

For both these accuracy and optimism predictions, a counter argument is that if forecasts

for the comparable firms are noisy or biased, then increased comparability could lead to less

accurate and more biased forecasts. We expect this effect to reduce the ability of our tests to

support these two predictions.

Third, we investigate the relation between comparability and analyst forecast dispersion.

If analysts have the same forecasting model, and if higher comparability implies the availability

of superior public information, then an analysts optimal forecast will place more weight on

public information and less on her private information. This implies comparability will reduce

forecast dispersion. Our hypothesis 2c (in alternative form) is:

H2c: Ceteris paribus, comparability is negatively associated with analyst forecastdispersion.

We acknowledge that superior public information via higher comparability could

generate more dispersed forecasts, which would support the null of hypothesis 2c. The intuition

here is that if some analysts process a given piece of information differently from other analysts,

then the availability of greater amounts of public information for comparable firms will generate

more highly-dispersed forecasts. A number of theoretical studies predict such a phenomenon.

Harris and Raviv (1993) and Kandel and Pearson (1995) develop models in which disclosures

promote divergence in beliefs. Kim and Verrecchia (1994) allow investors to interpret firm

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disclosures differently, whereby better disclosure is associated with more private information

production.

3. Empirical measures of comparability

The term comparability in accounting textbooks, in regulatory pronouncements, and in

academic research is defined in broad generalities rather than precisely. In the first two

subsections, we motivate and explain how we compute our two empirical measures of

comparability, respectively. In the third subsection, we discuss our comparability measures and

contrast it with other measures used in the literature that are indirectly related to ours.

3.1. Measure of accounting comparability

Accounting is the mapping from economic transactions to financial statements. As such,

it can be represented as follows:

Financial Statementsit= it(Economic Transactionsit) (1)

wherefit( ) represents the accounting of firm i during period t.

We define accounting comparability as the similarity with which two firms translate the

same economic events to the financial statements. That is, two firms with comparable

accounting should have similar functionsf( ) such that, given a set of economic transactions X,

firm j produces similar financial statements to firm i. To operationalize this idea, we examine

the relation between earnings, one important summary financial statement measure, and returns,

a proxy for the net economic effect of transactions. For each firm we estimate the following

equation using 16 previous quarters of data:

Earningsit= i + i Returnsit+ it. (2)

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where Earnings is the ratio of quarterly net income before extraordinary items (data8) to the

average total assets (data44), taken from the Compustat Quarterly file, andReturns is the stock

price return during the quarter, taken from CRSP.

Under the framework in Equation 1, i and i proxy for the accounting function f( ) for

firm i. Similarly, the accounting function for firmj is proxied by j and j (using earnings andreturns from firmj). Next we calculate the predicted earnings for firm i, given the same set of

economic transactions, using the accounting functions of both firm i andj respectively.

E(Earnings)iit= i + i Returnsit (3)

E(Earnings)ijt= j + j Returnsit (4)

where E(Earnings)iit is the expected earnings of firm i givenfirm is function and firm is returns

in period tand E(Earnings)ijt is the expected earnings of firm i given firm js function and firm

is returns in period t. We restrict the sample to firms whose fiscal year ends in March, June,

September, or December. This ensures that i andj firms earnings are measured at the end of the

same fiscal quarter.

We then define accounting comparability between firm i and j as the negative value of the

average difference between the expected earnings for firm i under firm is andjs functions.

|)()(|*16/115

ijt

t

t

iitijt EarningsEEarningsECompAcct =

(5)

Higher values indicate higher accounting comparability. We estimate accounting comparability

for each firm i firm j combination for J firms within the same SIC 2-digit industry

classification at the end of December for each year. We exclude holding firms. In some cases,

Compustat contains financial statements for both the parent and the subsidiary company, and we

want to avoid matching such two firms. We exclude ADRs and limited partnerships because our

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focus is on corporations domiciled in the United States.5

We also exclude firms that have names

highly similar to each other using an algorithm that matches five-or-more-letter words in the firm

names, but avoids matching on generic words such as hotels, foods, semiconductor, etc.

Finally, we restrict the sample to industries with at least 20 firms per year based on the SIC two-

digit classification.

In addition, we provide a firm-year measure of accounting comparability by aggregating

the firm i firm j CompAcctijt for a given firm i. Specifically, after estimating accounting

comparability for each firm i firmj combination, we rank allJvalues ofComp Acctijt for each

firm i from the highest to lowest. We then define Comp4 Acctit as the average CompAcctijt of the

four firmjs with the highest comparability for firm i during period t. (Results are similar if we

use the top ten firmjs instead.) Similarly, we define CompInd Acctit as the average CompAcctijt

for all firms in the same industry as firm i during period t. Firms with high Comp4 Acctand

CompInd Acct are firms for which the accounting function is more similar to a peer group of

firms and to the industry respectively.

3.2. Measure of earnings comparability

If two firms are comparable, they are more likely to experience similar economic shocks.

For instance, a change in input prices or shifts in consumer demand for firms with similar

business models should translate into similar changes in economic profitability. Comparable

firms are also likely to account for economic transactions in a similar way. This leads to an

expectation that earnings for comparable firms will covary over time. While this scenario leads

to positive covariance in earnings, it is also possible that comparable firms could have a negative

earnings covariance over time. For example, if two competitors compete for market share, one

5 Specifically if the word Holding, Group, ADR, or LP (and associated variations of these words) appear in the

firm name on Compustat, the firm is excluded.

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firms economic gain could be the other firms economic loss. In contrast, if business models are

different, if firms are sensitive to different types of shocks, or if their accounting policies differ,

then we would expect such firms earnings not to covary over time.

To operationalize this intuition and quantify the degree of similarity between pairs of

firms, we calculate the historical covariance of quarterly earnings among all possible pairs of

firms in the same industry. Ceteris paribus, firms with higher comparability are firms whose

earnings covary more with the earnings of its peers firms. More specifically, using 16 quarters

of earnings data we estimate:

Earningsit= 0ij + 1ij Earningsjt+ ijt. (6)We define our firm i firmj measure of earnings comparability (Comp Earnijt) as the adjusted

R2

from this regression. (Hereafter, we use R2

to mean adjusted R2.) Higher values indicate

higher comparability. In order to avoid the influence of outliers on theR2

measure, we remove

observations in whichEarnings for firm i is more than three standard deviations from the mean

value of the 16Earnings observations for firm i used to estimate Equation 6. Following a similar

procedure and scope to our development of the Comp Acctvariables above we obtain a Comp

Earnijt for firm i firm j pair forJfirms in the same 2-digit SIC industry with available data.

Comp4Earnit is the averageR2

for the four firmjs with the highestR2s. CompIndEarnit is the

average R2

for all firms in the industry. Firms with high Comp4 Earn and CompInd Earn are

firms for which earnings covary more with earnings from a peer group of firms and from the

industry respectively.

An issue with ourComp Earn variable is that theR2s we measure could be mainly due to

similar business models and economic shocks, rather than comparable financial statements, our

primary construct of interest. When we use Comp Earn in our tests, we hence include controls

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for similar business model and economic shocks. One control is based on cash flow from

operations, which captures covariation in near-term economic shocks. Comp CFO is created in

an identical manner to Comp Earn except that in Equation 6 we replace Earnings with CFO,

which is the ratio of cash flow from operations quarterly (data108) to the average total assets

(data44). Our second control is stock returns, which capture covariation in economic shocks

related to cash flow expectations over long horizons. Comp Retis also defined in a manner that

parallels the construction ofComp Earn. In Equation 6, instead ofEarnings we use monthly

stock returns taken from the CRSP Monthly Stock file, and instead of 16 quarters we use 48

months.We then calculate pairwise firm i firm j values (Comp CFOij and Comp Retij) and

aggregated firm level values (Comp4 CFO, CompInd CFO, Comp4 Ret, and CompInd Ret) for

these measures.

3.3. Discussion of measures

In developing our two measures, we adopt the perspective of financial statements users

in particular, analysts and focus on earnings, a financial statement output. Other research has

examined comparable inputs such as similar accounting methods, business activities, or industry

membership. As an example, Bradshaw and Miller (2007) study whether international firms that

intend to harmonize their accounting with US GAAP adopt US GAAP accounting methods.

DeFond and Hung (2003) argue that accounting choice heterogeneity (e.g., differences in

inventory methods such as LIFO versus FIFO) increases the difficulty in comparing earnings

across firms.

Other existing measures of comparability are based mainly on similarities in cross-

sectional levels of contemporaneous measures (e.g., return on equity, firm size, price multiples)

at a single point in time. Joos and Lang (1994) study the comparability of accounting data in a

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European setting. They expect that improved accounting comparability between countries will

result in smaller differences between accounting measures of profitability (i.e., ROE), between

valuation multiples of accounting data (i.e., Earnings/Price; Book value/Market value of equity),

and between the degree of association between accounting and stock data (i.e., value relevance).

Land and Lang (2002) also focus on comparing valuation multiples across countries. These

measures are typically estimated in aggregate at the country level. Notable exceptions are

studies that examine returns to pairs trading, such as Papadakis and Wysocki (2007). They

identify pairs of similar firms using the average difference in daily normalized price over a 12-

month period. In effect, their measure captures a blend of similar levels andsimilar covariation

over time. Compared to the aggregated measures described above, our measure is dynamic,

capturing similarities over time, and isfirm-specific.

OurComp Earn measure in particular is indirectly related to four others measures. First,

relative performance evaluation theory suggests filtering out of noise caused by factors unrelated

to managements actions on performance (see, e.g., Holmstrm (1979), Antle and Smith (1986),

Banker and Datar (1989), and Dikolli, Hofmann, and Pfeiffer (2007)). One way to identify these

common shocks is to examine the correlation in performance (e.g., annual stock returns) between

a firm and its industry or peer group.

Second, the literature (e.g., Lipe 1990) has established the time-series concept of earnings

predictability in which earnings are regressed on previous-period earnings. OurComp Earn

measure is a cross-sectional version of predictability. While earnings predictability or persistence

measures have been around in the literature for quite some time (see, e.g., Lipe 1990, and

Francis, LaFond, Olsson, and Schipper, 2004), their use in developing a comparability measure

in this study is unique.

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Third, Piotroski and Roulstone (2004), and Chan and Hameed (2006), among others,

study stock price synchronicity, which is based on the R2

from a regression of firms stock

returns on market and industry stock returns. They are inherently interested in the type of

information (firm, industry, or market) impounded in stock prices. OurComp Earn measure is

based solely on accounting data, and is hence not sensitive to flows of non-accounting

information, to investors interpretation, or to assumptions about market efficiency. Our

measure could also be applied to private firms.

Fourth, an older literature studies the accounting beta, which captures the covariance

between a firms earnings with the earnings at the industry or market level. Brown and Ball

(1967) show that firm earnings can be explained in part by the earnings of other firms in the

same industry and the earnings of all firms in the market. This research focuses on documenting

that the market beta (i.e., covariance with the market portfolio in the Sharpe-Lintner Capital

Asset Pricing Model) is positively related to the accounting beta (see, e.g., Beaver, Kettler, and

Scholes 1970; Beaver and Manegold 1975; Gonedes 1973, 1975). In contrast, ourComp Earn

measure focuses on the covariance of earnings between i-j firm pairs within an industry.

4. Estimating and validating a measure of comparability

4.1. Estimating comparability

Our sample period spans the years 1993 to 2006. Table 1 presents descriptive statistics

for our measures of comparability. Panels A and B (C and D) present descriptive statistics and

correlations for the pairwise firm i firmj (aggregated firm i) comparability measures. In Panel

A the sample size for the pairwise comparability is 3,592,745 firm ifirm j-year observations.

The mean value forComp Acctij is -1.55 suggesting that the average error in quarterly earnings

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between firm i and firmj functions is 1.55% of total of assets. The mean value forComp Earnij

is 11.19 suggesting that on average firmjs earnings explains 11% of firm is earnings. Similar

values forComp CFOij and Comp Retij are 8% and 6%. Panel B presents correlations among

these variables. The correlations are all positive and significant although the magnitudes are

small, ranging from 0.01 between Comp Acctij and Comp Retij to 0.08 between Comp Earnij and

Comp CFOij.

Panels C and D present the descriptive statistics for the firm i comparability measures.

The sample size is 27,972 firm-year observations. The mean value forComp4 Acct is -0.26

suggesting that the average error in quarterly earnings for the top four firms with the highest

accounting comparability to firm i is 0.26% of total of assets. By construction, this value is

higher than the mean value forCompInd Acctwhich is -1.14. The mean value forComp4 Earn

is 52.36 meaning that the earnings of the top four comparable firms explain, on average, 52% of

firm i's earnings. Mean values for Comp4 CFO and Comp4 Ret are 42.83% and 24.76%

respectively. Panel D presents pairwise correlations among these variables. The correlations are

generally positive particularly for the top 4 firm and average industry versions of the same

measure. For example, the Pearson correlation between Comp4 Acctand CompInd Acctis 0.89.

[Table 1]

4.2. Validating our comparability measures

In this section, we test the construct validity of our comparability measures. The test

implicitly assumes that, for any given firm, analysts know the identity of comparable firms (if

any). This seems reasonable because analysts have access to a broad information set about each

firm, which goes beyond the historical financial statements, and includes firms business models,

competitive positioning, markets, products, etc.

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We make a testable prediction to fortify our measures construct validity. The prediction

relates to the important assumption underlying our measures, namely, that the relative ranking of

firm i - firmj comparability identifies a set of firms that analysts view as comparable to firm i.

We predict that if an analyst issues a report about firm i, then we expect the analyst to more

likely use firms that are comparable to firm i in her reports. The typical analyst report context

is that the analyst desires to evaluate the current, or justify the predicted, firm valuation multiple,

(e.g., Price/Earnings ratio), using a comparative analysis of peer firms valuation multiples as

benchmarks. Evidence from a test of this prediction suggests our measures of comparability are

reasonable.

The comparable firms that an analyst uses in her analysis are not available in a machine-

readable form in existing databases. We hand collect a sample of analyst reports from Investext

and manually extract this information from the reports. The cost of collecting this information

limits this analysis to one year of data. Reports are chosen as follows. We begin with all firms

(i.e., firm is) in our sample with available data for the year 2005. For these firms, we search

Investext to find up to three reports per firm i, each written by a different analyst and each

mentioning comparable or peer firms (i.e., potential firm js) in the report. We then record

the name and ticker of all firms used by the analyst as a peer or comparable firm for firm i. We

match these peer firms with Compustat using the firm name and ticker. In total, we obtain 1,000

reports written by 537 unique analysts for 634 unique firms. Each report mentions one or more

firms as comparable to the firm for which the analyst has issued the report.6

For our tests, we estimate the following logistic regression:

6 Part of the reason this process is labor intensive is because we do not know ex ante whether Investext covers

firm i, and because not all analysts discuss comparable firms in their analysis. For example, many reports represent

simple updates with no discussion of valuation methods. In other cases, analysts rely more heavily on a discountedflow analysis or use historical valuation multiples to predict future multiples. We exclude reports on Investext that

are computer generated or not written by sell-side analysts.

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UseAsCompij = +1 Compijij +Controlsj + ij. (7)

UseAsComp is an indicator variable that equals one if an analyst who writes a reportabout firm i

refers to firmj as a comparable firm in her report, and equals zero otherwise. Compijij is one of

the comparability measures for each firm i firmj pair in our sample (i.e., Comp Acctij, Comp

Earnij, Comp CFOij, orComp Retij). We predict that the probability of an analyst using firmj

in her report is increasing in Compij. We use Size, Volume, Book-Market, ROA, and industry

fixed effects as control variables (results are the same if we use firm i fixed effects). Our choice

of these controls follows their common use by other researchers who match control firms with

treatment firms along these three dimensions (see, e.g., Barber and Lyon, 1996, 1997; Kothari,

Leone, and Wasley, 2005). In addition to the levels of these variables, we control for the

differences in characteristics between firm i and firmj. Differences are measured by the absolute

value of the difference between firm is and firmjs respective variables. The intuition for using

both levels and differences is as follows: An analyst who reports on firm i is more likely to use a

firm as a peer if the firm has similar (comparable) characteristics (e.g., similar size, growth

potential, and profitability) to firm i. This implies the larger the difference between firm i and

firmj, the less likely it is to be covered by the analyst. However, large, high-growth, and highly-

profitable firms are more likely to be covered by an analyst and recognized by investors, which

motivates us to also include the levels of these firm characteristics in the regression. Finally, we

include industry fixed-effects at the 2-digit SIC industry classification and cluster the standard

errors at the firm i level (results are similar if we use firm i fixed-effects instead).

Table 2 presents logistic regressions for model (7). In the first model, we include

accounting comparability (Comp Acctij) and the controls. The coefficient on Comp Acctij is

positive and marginally significant, suggesting that as the Comp Acctij increases, the odds of an

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analyst using firmj as a peer firm in a report about firm i increases. In the next three models we

include earnings comparability (Comp Earnij), controls for economic comparability (Comp

CFOij orComp Retij), and the remaining controls. In all cases the coefficient on Comp Earnij is

positive and statistically significant. Similar to Comp Acctij, this result suggests that, as Comp

Earnij increases, the odds of an analyst using firm j as a peer firm in a report about firm i

increases. In addition, Comp CFOij and Comp Retij also increase the likelihood that a firm is

used as a comparable firm in the report.7

In terms of economic significance, an increase from the

10th

to the 90th

percentile in Comp Acctij (Comp Earnij) is associated with an increase in the

probability of a firm being used as comparable firm in the report from 1.27% to 1.39% (1.11% to

1.27%), a relative increase of 10% (14%). For comparison, the same increase forComp Retij

(the strongest predictor in Table 2) is associated with an increase in probability from 0.73% to

2.49% suggesting that the effect is modest but also economically significant. Overall, the results

in Table 2 support the notion that an analyst who writes a report about a firm more likely chooses

benchmark firms that have higher values of comparability. This bolsters the construct validity of

our comparability measures.

[Table 2]

5. Empirical tests

5.1. Conditional analyst coverage

In this section, continuing with our previous analysis in which we use pairwise firm i -

firm j level comparability (as opposed to aggregated firm i level comparability), we provide

7In Table 2 we present three specifications for Comp Earnij depending on the inclusion of Comp CFOij or

Comp Retij. In the subsequent analysis we present only the full model but the results are robust to the other

specifications (i.e., including eitherComp CFOij orComp Retij).

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initial evidence of our first hypothesis that higher comparability facilitates analyst coverage.

This test is similar in spirit to the test in the previous section but now we use actual analyst

coverage instead of analyst use of comparable firms in analysts reports. We expect that the

likelihood of an analyst covering a particular firm (e.g., firm i) also covering another firm in the

same industry (e.g., firmj) is increasing in the comparability between these two firms. Hence, we

not only predict that higher earnings comparability leads to more analysts covering the firm (as

we do in the next section), but also specifically predict which other firms the analyst will follow.

We estimate the following logistic regression for each year of our sample:

CondCoverageikj = +1 Compijij +Controlsj + ikj. (8)CondCoverage is an indicator variable that equals one if analyst kwho covers firm i also covers

firmj, and equals zero otherwise. An analyst covers a firm if she issues at least one annual

forecast about the firm. As before, Compijij is one of the four comparability measures for each

firm i firmj pair in our sample (i.e., Comp Acctij, Comp Earnij, Comp CFOij, orComp Retij).

We predict that the probability of covering firmj is increasing in Compijij (i.e., 1 > 0).

In estimating Equation 8, we control for other factors motivating an analyst to cover firm

j by including determinants of analyst coverage previously documented in the literature (e.g.,

Bhushan, 1989, OBrien and Bhushan, 1990, Brennan and Hughes, 1991, Lang and Lundholm,

1996, and Barth, Kasznik, and McNichols, 2001). Size is the logarithm of the market value of

equity measured at the end of the year. Volume is the logarithm of trading volume in millions of

shares during the year. Issue is an indicator variable that equals one if the firm issues debt or

equity securities during the years t-1, t, or t+1, and zero otherwise. Book-Market is the ratio of

the book value to the market value of equity. R&D is research and development expense scaled

by total sales. Depreciation is depreciation expense scaled by total sales. Following Barth et al.

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(2001), we industry adjust the R&D and depreciation measures by subtracting the respective 2-

digit SIC industry mean value. Earn Volatility is the standard deviation of 16 quarterly earnings

(deflated by total assets), consistent with the horizon used to estimate earnings comparability.

We also control for earningspredictability. Predictability is the R2

from a firm-specific AR1

model with 16 quarters of data. In addition to the levels of these variables, we control for the

differences in characteristics between firm i and firm j, following the analysis in Table 2. In

particular we control for the differences in size, trading volume, and book-to-market.

The annual sample for this test is quite large. For firm i, there areKanalysts who cover

the firm. For each firm i analyst kpair there areJfirms in the same industry as firm i. Hence,

our sample consists ofIfirms Kanalysts Jfirms. In addition to requiring valid data for all

our measures, we require each analyst kto cover at least five firms. In estimating the model, we

rely on the coverage choice of an analyst within an industry, and therefore require the availability

of at least a few observations per analyst per industry for which CondCoverage equals one. This

restriction should exclude junior analysts, analysts in transition, and data-coding errors. We

exclude analysts who cover more than 40 firms. Covering greater than 40 firms is rare (less than

one percent of analysts) and could be a data-coding error in that the observations could refer to

the firm employing the analyst rather than an individual analyst at the firm.

The large sample size (average annual sample used in our tests consists of 1.2 million

firm i analyst k firmj observations) prohibits us from estimating a panel regression. Thus, in

Table 3, we provide the mean coefficient from the 14 annual logistic regressions. The t-statistic

is based on the distribution of the 14 annual coefficients using the Fama and MacBeth (1973)

procedure. Further, we adjust for potential time-series dependence in the estimates using the

Newey-West (1987) correction with one lag. (In untabulated tests, we find that higher lags lead

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to highert-statistics.)

The mean coefficient on Comp Acctij is positive and statistically significant as predicted.

In untabulated analysis we find that the coefficient is positive in all 14 years. Further, the mean

coefficient on Comp Earnij is positive and statistically significant (as well as the coefficients on

Comp CFOij and Comp Retij). The result suggests that the firmjs we identify as comparable

to firm i are more likely to be followed by the analysts who also cover firm i. In terms of

economic significance, using the year of 2000 as a sample year (this year is selected because it

reflects the mean effect over the whole sample period), an increase from the 10th

to the 90th

percentile in Comp Acctij (Comp Earnij) is associated with an increase in the probability of

being covered by an analyst from 1.01% to 1.12% (0.94% to 1.14%), a relative increase of 10%

(21%). For comparison, the same increase forComp Retij (the strongest predictor in Table 2) is

associated with an increase in probability from 0.70% to 1.89%. Overall, we conclude that the

likelihood of an analyst covering firmj, conditional on the analyst covering firm i, increases in

the comparability between firms i and j. This is consistent with higher comparability reducing

the cost of covering the firm. It also suggests the benefits of covering firms with high

comparability (due to their associated higher-quality information set) outweigh the potential

decreased benefit from investors reduced demand for information about highly-comparable

firms.8

[Table 3]

5.2. Firm level comparability

In the previous sections we investigated the consequences of pairwise firm i - firmj level

8This result complements a study by Ramnath (2002), who shows that there is information transfer between

firms covered by the same analyst. He shows that among these firms, the earnings announcement surprises of firms

that announce first are systematically related to forecast revisions for the other firms that the analysts cover.

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comparability. In the following sections we examine the benefits to analysts of aggregated firm i

level comparability.

5.2.1. Sample and dependent variables

As seen in Table 1, there are 27,972 firm years with firm-level comparability scores. In

order to test the hypotheses using firm comparability, we further restrict the sample to firms with

available data to compute the dependent variables and the control variables. In particular, the

main restriction is positive analyst coverage, which biases the sample towards larger and more-

frequently-traded firms. These restrictions reduce the sample to a maximum number of 13,037

firm-year observations (this is the sample for the analyst coverage tests; the sample is smaller for

the remaining dependent variables).

The four dependent variables in the tests below are defined as follows. Coverage is the

logarithm of the number of analysts issuing an annual forecast for firm i in year t. Analyst

forecast accuracy is the absolute value of the forecast error:

Accuracy (%)it= |Fcst EPSitActual EPSit|/Priceit-1 -100. (9)

Fcst EPSitis analysts mean I/B/E/S forecast of firm-is annual earnings for yeart. For a given

fiscal year (e.g., December of yeart+1) we collect the earliest forecast available during the year

(i.e., we use the earliest forecast from January to December of year t+1 for a December fiscal

year-end firm). Actual EPSit is the actual amount announced by firm i for fiscal period t+1 as

reported by I/B/E/S. Price is the stock price at the end of the prior fiscal year. Because the

absolute forecast error is multiplied by -100, higher values ofAccuracy imply more accurate

forecasts. We measure optimism in analysts forecasts using the signed forecast error:

Optimism (%)it= (Fcst EPSitActual EPSit)/Priceit-1 100. (10)

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Dispersion (%) is the cross-sectional standard deviation of individual analysts annual forecasts

for a given firm, scaled by price, multiplied by 100.

Table 4, Panel A presents descriptive statistics for the dependent variables and the

comparability measures. The mean (median) Coverage (i.e., the logarithm of the number of

analysts covering a firm) is 1.53 (1.61) implying that a median firm is covered by 5 analysts.

Mean forecast accuracy is 4.6% of share price. Mean forecast optimism is 2.6% of share price,

which is consistent with prior research that analysts tend to be optimistic on average. However,

the median is only 0.3%, also consistent with previous research. The mean forecast dispersion is

0.9% of share price. Panel B presents the correlation matrix. Analyst coverage and forecast

accuracy are positively correlated with the comparability measures whereas forecast optimism is

negatively associated with firm comparability. The correlations between forecast dispersion and

the comparability measures are not in a consistent direction.

[Table 4]

5.2.2. Analyst coverage tests

To test whether analyst coverage and comparability are positively related, our first

hypothesis, we estimate the following regression:

Coverageit+1 = +1 Comparabilityit+Controlsit+ it+1. (11)

Comparability is one of the firm-level comparability measures (e.g., Comp4 Acct,

CompInd Acct, Comp4 Earn, or CompInd Earn). Throughout the remaining analysis, for

continuous variables that we do not take the logarithm of, we delete observations if these

variable values fall in the lowest or highest percentile of their respective distributions, calculated

annually (i.e., we trim the data annually at the 1% and 99% percentile). We also include industry

and year fixed effects. Because the estimation of Equation 11 is likely to suffer from time-series

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dependence, we estimate the model as a panel and cluster the standard errors at the firm level (in

addition to the year fixed-effects). In estimating Equation 11, we control for other factors

motivating an analyst to cover firmj as described in the prior section - Size, Volume,Issue,Book-

Market,R&D,Depreciation,Earn Volatility andPredictability.

Table 5 presents the regression results. Both of the accounting comparability measures

(Comp4 Acctand CompInd Acct) are positively associated with analyst coverage. In terms of

economic significance, an increase from the 10th

to the 90th

percentile in CompInd Acct is

associated with an increase in the logarithm of analyst following of 0.043 (= 0.0146 x 2.93).

Given that the median firm in our sample is covered by 5 analysts, this effect translates to a

percentage increase in analyst coverage of 4.4%, suggesting that the effect is also modestly

significant on an economic basis. Similarly, Comp4 Earn and CompInd Earn are also positively

associated with analyst coverage (in this case an 80th

percentile increase in Comp4 Earn would

translate to an increase in coverage of 5%). Finally, we note that CFO and return comparability

are also positively associated with analyst coverage. Overall, the regression results in Table 5

confirm the conditional analyst coverage findings in Table 3, and are consistent with hypothesis

1 that predicts a positive association between analyst coverage and comparability.

[Table 5]

5.2.3 Forecast accuracy, optimism, and dispersion tests

To test our hypotheses about whether comparability affects forecast accuracy, optimistic

bias, and dispersion, we estimate the following specification:

Forecast Metricit+1 = +1 Comparabilityit+Controlsit+ it+1. (12)

Forecast Metric is Accuracy, Optimism, orDispersion. Hypothesis 2 predicts that accuracy is

increasing in comparability, and that optimism and dispersion are decreasing in comparability.

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We control for other determinants of these forecast metrics as previously documented in

the literature. SUEis the absolute value of firm is unexpected earnings in yeartscaled by the

stock price at the end of the prior year. Unexpected earnings are actual earnings minus the

earnings from the prior year. Firms with greater variability are more difficult to forecast, so

forecast errors should be greater (e.g., Kross, Ro and Schroeder, 1990, and Lang and Lundholm,

1996). Consistent with Heflin, Subramanyam and Zhang (2003), earnings with more transitory

components should also be more difficult to forecast. We include the following three variables

to proxy for the difficulty in forecasting earnings. Neg UEequals one if firm is earnings are

below the reported earnings a year ago, zero otherwise. Loss equals one if the current earnings is

less than zero, zero otherwise. Neg SIequals the absolute value of the special item deflated by

total assets if negative, zero otherwise. We expect these three variables to be positively related

to optimism given that optimism is greater when realized earnings are more negative.

Daysitis a measure of the forecast horizon, calculated as the logarithm of the number of

days from the forecast date to firm-is earnings announcement date. The literature shows that

forecast horizon strongly affects accuracy and optimism (Sinha et al., 1997, Clement, 1999, and

Brown and Mohd, 2003). We also control for Size because firm size is related to analysts

forecast properties (e.g., Lang and Lundholm, 1996). Last, we include industry and year fixed

effects. Similar to the estimation of Equation 11, we estimate the model as a panel and cluster

the standard errors at the firm level.

Table 6 presents the regression results for analyst forecast accuracy. With respect to the

comparability measures, our primary variables of interest, we find that both accounting and

earnings comparability are positively associated with accuracy. In terms of economic

significance, an increase from the 10th

to the 90th

percentile in Comp4 Acct (Comp4 Earn) is

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associated with an increase in accuracy of about 0.36% (0.71%), which represents an

improvement in accuracy of about 7% (15%) for the average firm in the sample. This result

supports hypothesis 2a that higher earnings comparability increases the accuracy of analysts

forecasts. Finally, the measures of CFO and return comparability are negatively related with

forecast accuracy in the Column 3 (Comp4 Earn) regression and not significant in the Column 4

(CompInd Earn) regression, contrary to the findings with accounting and earnings comparability.

[Table 6]

Table 7 presents the results for forecast optimism. In support of hypothesis 2b, we find a

consistent negative relation between our measures of comparability and analyst optimism. As

with forecast accuracy, the result is also economically significant suggesting a reduction in

analyst optimism for the average firm in the sample that ranges from 8% with CompInd Earn to

28% with Comp4 Earn. Together with the findings using forecast accuracy, these results suggest

that one way earnings comparability improves forecast accuracy is via a reduction of analyst

optimism.

[Table 7]

The results for forecast dispersion are presented in Table 8. In this case the results are

more mixed. While accounting comparability is negatively associated with forecast dispersion,

we fail to find a significant relation between earnings comparability and forecast dispersion.

Still, the result with accounting comparability suggests a reduction in forecast dispersion

between 11% and 37% for a change from 10th

to the 90th

percentile in Comp4 Acctand CompInd

Acctrespectively.

[Table 8]

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In sum, the above results support the hypotheses that analysts accuracy is increasing in

comparability and decreasing in analysts optimism. In addition, they provide some evidence

that forecast dispersion is decreasing in comparability.

6. Conclusion

This paper develops two measures of comparability and then studies the effect of these

comparability measures on analysts. A key innovation is the development of empirical, firm-

specific, output-based, quantitative measures of comparability. The first measure, accounting

comparability, is based on the idea that comparable firms with similar economic events, as

proxied by returns, should report similar accounting earnings. The second measure, earnings

comparability, is based on the strength of the historical covariance between a firms earnings and

the earnings of other firms in the same industry, as evidenced by the R2

values. We first provide

construct validity of our measures. The likelihood of an analyst using firm j as a benchmark

when analyzing firm i in a report is increasing in the comparability between firm i and j, as

defined using our measures. This suggests that our measures are correlated with actual use of

comparable firms in analysts reports.

We then test whether comparability manifests any benefits to the capital markets as

gleaned from the effect on analyst coverage and the properties of analyst forecasts. With respect

to analyst coverage, coverage increases in comparability. Tests also indicate that the likelihood

of an analyst covering firm i also covering firmj is increasingin the comparability between firm

i and j. Hence, we not only show that comparability leads to greater analyst following, but also

specifically predict which other firms an analyst will follow. These results are consistent with

comparability leading to richer information sets, which more than offsets the potential decreased

benefit due to reduced investor demand for information about high-comparability firms. In

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addition, analysts who follow firms with higher comparability issue more accurate and less

biased earnings forecasts. These results suggest that earnings comparability helps analysts to

forecast earnings and that the improvement comes, at least in part, through a reduction in

forecast optimism. Last, we document that accounting comparability is negatively related to

analysts forecast dispersion, consistent with the availability of superior public information about

highly-comparable firms and an assumption that analysts uses similar forecasting models.

In sum, we develop a measure of comparability that likely captures users notions of

comparability and the benefits of comparability to them. The ability to forecast future earnings

is a common task for users such as investors and analysts, particularly those engaged in

valuation. Improved accuracy and reduced bias, for example, represent tangible benefits to this

user group. Further, the results of increased analyst following, greater forecast accuracy, lower

bias, and less dispersion collectively are consistent with comparability enriching firms

information environment, which provides a tangible benefit for firms with higher comparability.

While comparability is generally accepted as a valuable attribute, there is little evidence beyond

this study that would empirically confirm this widely-held belief.

We believe our comparability measure could be used in a number of contexts, with

modifications to the measure tailored to suit the needs. Our measure could be used to help assess

the changes in comparability as a result of changes in accounting measurement rules or reporting

standards, accounting choice differences, or of adjustments. For example, according to the

International Accounting Standards Committee Foundation (IASCF), the primary objective of

the International Financial Reporting Standards (IFRS) is to develop a single set of global

accounting standards that require high quality, transparent and comparable information in

financial statements and other financial reporting (our emphasis) (IASCF 2005). Our measure

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could be used to assess whether IFRS achieves the intended consequence of enhanced financial

statement comparability (see e.g., Beuselinck, Joos, and Van der Meulen, 2007).

Our measure could also assist practitioners, such as analysts and boards, in their objective

selection of comparable firms. For example, new executive compensation rules issued by the

SEC (2006) advise those companies who engage in compensation benchmarking to identify the

peer companies used as benchmarks. Further, choosing comparables is often considered an art

form (see Bhojraj and Lee, 2002) and the inherent discretion in this choice can lead to strategic

behavior. For instance, Lewellen, Park, and Ro (1996) find firms choices of industry and peer-

company benchmarks are self serving. Thus, our measure could be used internally by firms or

externally by investors to assess or validate this choice.

Notwithstanding the above benefits, some caveats are in order. We do not study the

determinants of comparability. Our analysis is silent on what firms can do to improve cross-

sectional comparability. Certainly, firms could choose to have more comparable accounting

choices. We speculate, however, that economic innovations, which by definition distinguish

firms from their peers, could lead to decreased economic comparability. Further, our results are

silent on the effects of comparability to risk. For example, an investor interested in diversifying

a portfolio might not desire comparability if that means holding securities with a positive return

covariance. Hence from a diversified investors perspective, comparability may lead to negative

risk effects that offset the benefits we document. Last, while earnings are arguably the most

important summary measure of accounting performance, it captures only one financial-statement

dimension. An opportunity exists to create a multi-dimensional financial statement measure. We

leave these issues to future research.

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APPENDIX - Variable definitions

Variable Definition

UseAsComp = Indicator variable that equals one if analyst kwho writes a reportabout firm i refers to firmj as a

comparable firm in her report, and equals zero otherwise.

CondCoverage = Indicator variable that equals one if analyst kwho covers firm i also covers firmj, and equals zerootherwise. An analyst covers a firm if she issues at least one annual forecast about the firm.

Coverage = Logarithm of the number of analysts issuing a forecast for the firm.

Accuracy (%) = Absolute value of the forecast error multiplied by -1, scaled by the stock price at the end of the prior

fiscal year, where the forecast error is the I/B/E/S analysts mean annual earnings forecast less theactual earnings as reported by I/B/E/S.

Optimism (%) = Signed value of the forecast error, scaled by the stock price at the end of the prior fiscal year, where

the forecast error is the I/B/E/S analysts mean annual earnings forecast less the actual earnings asreported by I/B/E/S.

Dispersion (%) = Cross-sectional standard deviation of individual analysts annual forecasts, scaled by the stock priceat the end of the prior fiscal year.

Comp Acctij = The absolute value of the difference of the predicted value of a regression of firm is earnings on

firm is returns using the estimated coefficients for firm i andj respectively. It is calculated for eachfirm i firmj pair, (ij),j = 1 toJfirms in the same 2-digit SIC industry as firm i.

Comp Earnij =R2 from a regression of firm is quarterly earnings on the quarterly earnings of firmj. It is calculated

for each firm i firmj pair, (ij),j = 1 toJfirms in the same 2-digit SIC industry as firm i.

Comp CFOij =R2 from a regression of firm is quarterly CFO on the quarterly CFO of firm j. It is calculated for

each firm i firmj pair, (ij),j = 1 toJfirms in the same 2-digit SIC industry as firm i.

Comp Retij =R2 from a regression of firm is monthly returns on the monthly returns of firmj. It is calculated for

each firm i firmj pair, (ij),j = 1 toJfirms in the same 2-digit SIC industry as firm i.

Comp4Acct = Average of the four highest Comp Acctij for firm i.

Comp4 Earn = Average of the four highest Comp Earnij for firm i.

Comp4 CFO = Average of the four highest Comp CFOij for firm i.

Comp4 Ret = Average of the four highest Comp Retij for firm i.

CompInd Acct = Average Comp Acctij for firm i for all firms in the industry.

CompInd Earn = Average Comp Earnij for firm i for all firms in the industry.

CompInd CFO = Average Comp CFOij for firm i for all firms in the industry.

CompInd Ret = Average Comp Retij for firm i for all firms in the industry.

Book-Market = Ratio of the book value to the market value of equity.

Days = Logarithm of the number of days from the forecast date to the earnings announcement date.

Depreciation = Firms depreciation expense scaled by total sales, less the respective 2-digit SIC industry mean

value of depreciation expense scaled by total sales.

Earn Volatility = Standard deviation of 16 quarterly earnings.

Issue = Indicator variable that equals one if the firm issues debt or equity securities during the preceding,current, or following year, and zero otherwise.

Loss = Indicator variable that equals one if the current earnings is less than zero, and equals zero otherwise.

Neg SI = Absolute value of the special item deflated by total assets if negative, and equals zero otherwise.

Neg UE = Indicator variable that equals one if firm is earnings are below the reported earnings a year ago, andequals zero otherwise.

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APPENDIX (Continued)

Variable Definition

Predictability =R2

of a regression of annual earnings on prior-year annual earnings for the same firm.

R&D = Firms research and development expense scaled by total sales, less the respective 2-digit SIC

industry mean value of research and development expense scaled by total sales.

Size = Logarithm of the market value of equity measured at the end of the year.

Size-$ = Market value of equity measured at the end of the year.

SUE = Absolute value of unexpected earnings, scaled by the stock price at the end of the prior year, where

unexpected earnings is actual earnings less a forecast based on a seasonal-adjusted random walk

time-series model.

Volume = Logarithm of trading volume in millions of shares during the year.

Difference = Absolute value of the difference between firm is and firmjs respective variables.

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TABLE 1 Comparability: Descriptive statistics

Panels A and B (C and D) provides descriptive statistics of the firm i firm j pair (firm i) level comparability

metrics. Panels A and C present descriptive statistics. Panels B and D present Pearson (Spearman) correlations

above (below) the main diagonal. Variables are defined in the Appendix.

Panel A: Pairwise firm i firmj level comparability - Descriptive statistics (all numbers in %)

Variable No. of Obs Mean STD 10th Percent Median 90th Percent

Comp Acctij 3,592,745 -1.55 2.80 -3.62 -0.65 -0.09

Comp Earnij 3,592,745 11.19 13.85 0.19 5.56 31.26

Comp CFOij 3,592,745 8.24 10.34 0.14 4.10 22.75

Comp Retij 3,592,745 6.16 7.91 0.11 3.04 16.80

Panel B: Pairwise firm i firmj level comparability - Correlations

Comp Acctij Comp Earnij Comp CFOij Comp Retij

Comp Acctij 1.000 0.026 0.013 0.011

Comp Earnij 0.039 1.000 0.082 0.081

Comp CFOij 0.024 0.049 1.000 0.058

Comp Retij 0.033 0.042 0.030 1.000

Panel C: Firm level comparability - Descriptive statistics (all numbers in %)


Comp4 Acct 27,972 -0.26 0.95 -0.43 -0.03 0.00

CompInd Acct 27,972 -1.14 1.99 -2.36 -0.53 -0.20

Comp4 Earn 27,972 52.36 14.62 33.23 52.02 72.12

CompInd Earn 27,972 6.69 4.21 2.73 5.58 11.84

Comp4 CFO 27,972 42.83 11.55 27.64 42.69 57.73

CompInd CFO 27,972 4.63 2.15 2.66 4.10 7.17

Comp4 Ret 27,972 24.76 12.25 11.28 22.24 42.29

CompInd Ret 27,972 4.25 4.27 1.01 2.77 9.36

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TABLE 1 (Continued)

Panel D: Firm level comparability - Correlations

Comp4 Acct CompInd Acct Comp4 Earn CompInd Earn Comp4 CFO CompInd CFO

Comp4 Acct 1.000 0.889 0.045 0.030 0.075 0.002

CompInd Acct 0.750 1.000 -0.006 0.051 0.024 0.041

Comp4 Earn 0.200 0.000 1.000 0.564 0.331 0.027

CompInd Earn 0.055 0.084 0.570 1.000 0.055 0.210

Comp4 CFO 0.247 0.040 0.316 0.072 1.000 0.403

CompInd CFO 0.002 0.116 0.047 0.149 0.418 1.000

Comp4 Ret 0.251 0.061 0.306 0.103 0.287 0.029

CompInd Ret 0.077 0.034 0.167 0.132 0.118 0.083

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TABLE 2 Use of comparable firm in analysts reports

This table reports an analysis of the relation between the pairwise comparability measures and analysts use in their

reports of firms in the same industry as the sample firm for the year 2005. The sample includes the combination of

analysts reports about sample firms multiplied by the number of firms in each sample-firms industry with availabledata. We estimate various specifications of the following pooled logistic regression:

UseAsCompij

= +1Compij

ij+Controls

j+

ij.

Industry fixed effects are included but not tabulated. Coefficientz-statistics are in italics and are clustered at the firm

level. Significance levels are based on two-tailed tests. ***, **, and * denotes significance at the 1%, 5%, and 10%

levels, respectively. Variables are defined in the Appendix.

Prediction (1) (2) (3) (4)

Comp Acctij + 3.40*

1.89

Comp Earnij + 0.77*** 0.51*** 0.43**

4.77 3.05 2.53

Comp CFOij + 1.10*** 0.86***

5.22 4.16

Comp Retij + 4.99*** 4.96***

18.35 18.00

Size + 0.21*** 0.21*** 0.16*** 0.16***

8.46 8.56 6.26 6.37

Volume + 0.31*** 0.31*** 0.24*** 0.24***

10.20 9.80 8.01 7.64

Book-Market ? 0.33** 0.30** 0.14 0.16

2.54 2.44 1.13 1.30

ROA ? 0.88 0.45 1.54* 1.40*

1.05 0.52 1.86 1.68

Size Difference - -0.27*** -0.28*** -0.23*** -0.22***

-11.31 -11.10 -9.18 -8.78

Volume Difference - -0.14*** -0.14*** -0.11*** -0.12***

-4.51 -4.40 -3.40 -3.61

Book-Market Difference - -0.69*** -0.66*** -0.50*** -0.51***

-4.62 -4.43 -3.48 -3.51

ROA Difference -0.39 -0.51 0.11 0.18

-0.49 -0.59 0.14 0.23

Industry FE Yes Yes Yes Yes

Pseudo R2

3.53% 3.41% 3.67% 3.59%No. of Obs. 139,767 139,767 139,767 139,767

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TABLE 3 Conditional Analysts coverage of comparable firms

This table reports an analysis of the relation between the pairwise comparability measures and analyst coverage of

firms in the same industry as the sample firm. For each of the years 1993 to 2006 in our sample, we estimate the

following logistic regression:

CondCoverageikj = +1 Compijij +Controlsj + ikj.

Industry fixed effects are included but not tabulated. The number of observations used in the annual estimation is thecombination of sample firms multiplied by the analysts covering the sample firms multiplied by the number of firms

in each sample-firms industry with available data. The table presents the mean, maximum, and minimum

coefficients, pseudo R2, and number of observations from the 14 annual logistic regressions. Mean coefficient t-

statistics (in parentheses) are based on the distribution of the 14 annual coefficients and adjusted for time-series

dependence using the Newey-West (1987) correction with one lag. Significance levels are based on two-tailed tests.***, **, and * denotes significance at the 1%, 5%, and 10% levels, respectively. Variables are defined in the

Appendix.

Prediction Estimate T-statistic Estimate T-statistic

Comp Acctij + 4.78*** 8.85

Comp Earnij + 0.59*** 22.06

Comp CFOij + 0.63*** 5.98

Comp Retij + 4.97*** 48.12

Size + 0.36*** 15.65 0.32*** 17.85

Volume + 0.28*** 27.72 0.23*** 10.50

Book-Market 0.51*** 5.35 0.32*** 2.71

R&D + 0.65*** 5.21 0.46*** 3.89

Depreciation + 2.01*** 6.14 1.74*** 3.42

Issue + -0.02 -0.58 0.02 1.28

Predictability + -0.23*** -3.08 -0.28*** -3.38Earn Volatility -1.19** -2.08 -2.00*** -4.98Size Difference -0.19*** -10.18 -0.13*** -5.53Volume Difference -0.18*** -24.83 -0.14*** -17.87

Book-Market Difference -0.44*** -6.50 -0.22*** -3.67Industry FE Yes Yes

Pseudo R2 7.22% 6.56%

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TABLE 4 Firm comparability: Descriptive statistics

This table reports descriptive statistics for the dependent variables and the comparability metrics. The sample is

restricted to observations with available data to calculate all the variables in this analysis. Panel A presents

descriptive statistics and Panel B reports Pearson correlations.

Panel A: Descriptive statistics (all number are in %)


Coverage 13,037 1.53 1.05 0.00 1.61 2.89

Accuracy 11,945 -4.64 13.25 -10.04 -1.23 -0.11

Optimism 11,861 2.61 11.76 -2.04 0.30 7.92

Dispersion 8,292 0.87 1.71 0.06 0.32 2.07

Comp4 Acct 13,037 -0.12 0.37 -0.22 -0.02 0.00

CompInd Acct 13,041 -0.78 1.08 -1.65 -0.45 -0.19

Comp4 Earn 12,550 54.15 14.15 35.36 54.00 73.47

Comp4 CFO 12,550 43.94 11.19 29.08 43.89 58.63

Comp4 Ret 12,550 26.88 12.09 13.14 24.56 44.56

CompInd Earn 12,544 7.12 4.28 2.92 6.01 12.51CompInd CFO 12,544 4.79 2.07 2.80 4.31 7.39

CompInd Ret 12,544 4.69 4.22 1.18 3.31 10.04

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TABLE 4 (Continued)

Panel B: Correlation matrix

Coverage Accuracy Optimism DispersionComp4

Acct

CompInd

Acct

Comp4

Earn

Comp4

CFO

Comp4

Ret

Coverage 1.000 0.190 -0.143 -0.162 0.097 0.181 0.065 0.105 0.264

Accuracy 1.000 -0.921 -0.473 0.112 0.175 0.008 0.010 0.020

Optimism 1.000 0.302 -0.065 -0.101 -0.020 -0.025 -0.053

Dispersion 1.000 -0.146 -0.256 0.026 0.000 0.098

Comp4 Acct 1.000 0.758 0.059 0.080 0.053

CompInd Acct 1.000 -0.029 -0.014 -0.038

Comp4 Earn 1.000 0.268 0.225

Comp4 CFO 1.000 0.227

Comp4 Ret 1.000

CompInd Earn

CompInd CFO

CompInd Ret

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TABLE 5 Firm comparability and analyst coverage

This table reports an analysis of the relation between firm comparability and analyst coverage. The sample is

restricted to observations with available data to calculate all the variables in this analysis. The table reports the

results of various specifications of the following regression:

Coverageit+1 = +1 Comparabilityit+Controlsit + it+1.

Industry and year fixed effects are included for each model but not tabulated. We estimate each model as a panel andcluster the standard errors at the firm level. Coefficient t-statistics are in italics. Significance levels are based on

two-tailed tests. ***, **, and * denotes significance at the 1%, 5%, and 10% levels, respectively. Varia

comp sep 2 2008

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