Changes in Analysts Coverage and Future Returns: Does the ...€¦ · compensation and career prospects are closely tied to high standings in the annual polls conducted by Institutional

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Changes in Analysts Coverage and Future Returns:

Does the Market Overreact?

AMBRUS KECSKÉS and KENT L. WOMACK*

Preliminary Version 1.0, October 19, 2006

ABSTRACT

A sell-side analyst’s decision to add or drop coverage of a firm typically reflects better or

worse operating performance, respectively, both in the year of the decision and the next

year. But the stock market overreacts to analysts’ coverage decisions. When the number

of analysts following a firm increases, future returns are lower, and, conversely, when the

number of analysts decreases, future returns are higher. The decrease-increase return

spread is 6.4 percentage points. The overreaction is more pronounced when changes in

analyst following are “confirmed” by changes in analysts’ consensus recommendations or

changes in institutional ownership and the overreaction depends on valuation levels.

* Kecskés is from the Joseph L. Rotman School of Management, University of Toronto. Womack is from

the Amos Tuck School of Business, Dartmouth College.

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1. Introduction

There are numerous reasons for brokerage analysts, so-called “sell-side” analysts,

to add or drop research coverage of stocks in the industries they follow. Analysts’

compensation and career prospects are closely tied to high standings in the annual polls

conducted by Institutional Investor and The Wall Street Journal. Within their industry,

analysts strive to identify up-and-coming firms as well as to cover the large capitalization

firms that are of interest to most institutional investors. Analysts also tend to cover firms

with solid stock market and operating performance.1 Indeed, Jegadeesh, Kim, Krische,

and Lee (2004) show that analysts tilt their recommendations towards glamour firms, i.e.,

firms with past market outperformance, high market valuations, high trading volume, etc.

Another motivation for analysts’ coverage decisions is the generation of trading

commissions stemming from the information content in analysts’ earnings estimates and

investment recommendations. For example, Irvine (2003) finds that initiations of analyst

coverage are followed by increases in liquidity.

Analysts are also expected to assist their bank’s investment banking division by

covering firms with possible securities issuance needs and/or mergers and acquisitions

prospects. Analysts are supposed to stimulate investor interest in these firms, which

generates lucrative banking fees if it leads to the analyst’s bank being chosen to

intermediate these deals. As Krigman, Shaw, and Womack (2001) report from a survey of

firms that switch underwriters, 88% of executives cite research coverage as one of the top

three reasons for switching. Moreover, Michaely and Womack (1999) show that pressure

from investment bankers may be sufficiently great as to bias analysts’ investment

1 See Bhushan (1989) and O’Brien and Bhushan (1990).

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recommendations in favor of firms that their bank has recently taken public, particularly

when these firms have performed poorly. In sum, analysts’ coverage decisions seem to

cater to the market’s demand for information as well as to generating trading

commissions and investment banking fees.

Analysts’ decisions to drop coverage of certain stocks stem from related causes.

Analysts have finite resources and so they generally cannot cover all of the firms in their

industry group. For example, Boni and Womack (2006) show that the typical analyst

covers 10 firms even though the typical industry has 177 firms. Therefore, an analyst’s

decision to add coverage of one firm tends to imply a decision to drop another firm.

Firms for which analysts drop coverage have poorer past stock market and operating

performance and demand fewer investment banking services. Generally speaking,

analysts will add coverage of stocks about which they are bullish and drop coverage of

stock about which they are bearish (e.g., McNichols and O’Brien (1997)).

This study examines how the market reacts to analysts’ decisions to add and drop

coverage. Does firm value increase when analysts add coverage and vice versa? Does the

market efficiently impound the information contained in the change in coverage or is

there a subsequent return drift or reversal? We measure changes in analyst following and

returns over calendar years and find that excess returns are higher in the year in which the

number of analysts following a stock increases, and excess returns are lower in the year

in which analyst following decreases. Surprisingly, however, in the year after a change in

analyst following, excess returns are negative (-1.7 percentage points) when following

has increased and positive (-4.7 percentage points) when following has decreased. In

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other words, the market appears to overreact in the year of a change in analyst following

and reverses itself the next year. This finding stands up to a battery of robustness tests.

To put our findings in a rational theoretical framework, we consider the investor

recognition model of Merton (1987). Suppose that the cost of capital for a firm is

decreasing in the number of investors who include the firm in their portfolio choice

problem. If investor recognition increases, the cost of capital falls, and so realized returns

are higher than expected today but expected returns are lower going forward. Our results

are consistent with this investor recognition model.

Nevertheless, investor recognition cannot be the whole story. Changes in investor

recognition impact the cost of capital but they should not systematically impact cash

flows. However, we find that firms for which analysts add coverage have significantly

better operating performance than firms for which analysts drop coverage. This

performance difference persists beyond the year of the change in coverage. Moreover,

when increases in analyst following are confirmed by changes in analysts’ consensus

recommendations, operating performance is even better than when changes in analyst

following are contradicted. The converse is also true for decreases in analyst following.

In other words, it appears that analysts add (drop) coverage of firms that have better

(worse) operating performance, both in the year of the change in coverage and the next

year. The puzzle, then, is that excess returns reverse in the year after a change in analyst

coverage.

Whether for rational or behavioral reasons, the bottom line is that firm value

increases (decrease) when analyst following increases (decreases). Further results suggest

that the market gets carried away by the information value of changes in analyst

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following, resulting in mispricing that is subsequently reversed. If the market does

overreact, then, for a given change in analyst following (an increase, no change, or

decrease), firms with higher valuations (e.g., as measured by book-to-market) should

have lower excess returns next year relative to firms with lower valuations. Indeed,

extreme glamour firms for which analyst following increases have the worst excess

returns the next year. Extreme value firms for which analyst following increases have

much better excess returns but their excess returns are only slightly worse than for

glamour firms for which analyst following decreases. Extreme value firms for which

analyst following decreases have the highest excess returns the next year. The evidence

from the glamour-value distinction lends support to the market overreaction explanation

of analysts’ decisions to add or drop coverage.

The rest of this paper is organized as follows. Section 2 outlines the sample

selection and data sources. Section 3 describes the sample. Section 4 presents the main

results. Section 5 examines explanations of the main result. Section 6 presents robustness

tests. Section 7 concludes.

2. Sample Selection and Data Sources

This study primarily examines the time-series variation of the number of analysts

following firms (providing earnings forecasts to IBES). Typically, an analyst will at least

provide one-year-ahead earnings estimates on the firms he follows whether or not he

provides anything else. Accordingly, we assume that an analyst “follows” a firm if he

provides a one-year-ahead earnings estimate on that firm. We extract the number of

analysts providing earnings estimates (“analyst following”) from the monthly I/B/E/S

Summary History – Summary Statistics file every December for every firm. Most of our

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returns and operating performance statistics are for calendar years unless otherwise

stated. We only retain a firm-year observation for which there is at least one analyst

following the firm in two consecutive years. This restriction filters out potentially large

changes in firms’ information environment resulting from changes in analyst following of

some to zero analysts and from zero to some analysts. Since I/B/E/S earnings estimates

coverage becomes comprehensive in 1983 and since our analysis requires one year of

future returns data, we compute the change in analyst following for every year between

1984 and 2004 inclusive. We extract the mean recommendation for every firm from the

monthly I/B/E/S Recommendations – Summary Statistics file for the same time period.

We use these data to study the impact of a change in analyst following that is confirmed

or contradicted by analysts’ consensus recommendation changes. We merge our extracts

from the estimates summary and recommendations summary files.

We extract the return, closing price, shares outstanding, volume, exchange code,

and share code from the CRSP monthly stock file for every firm, for every month. As is

common practice, we retain only firms with share codes 10, 11, and 12 (operating

companies). We match the remaining CRSP firms to our I/B/E/S firms, and we retain

only firms that have both I/B/E/S earnings estimates data and CRSP data. Our final

sample consists of a maximum of 66,627 firm-years and 10,619 firms with available

earnings estimates and a maximum of 38,358 firm-years and 7,946 firms with both

earnings estimates and recommendations. Additionally, we extract from CRSP the value-

weighted index, which we use as our market index. We also extract the first date of

listing for every firm as well as a list of S&P 500 constituents from CRSP.

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We study how changes in analyst following relate to firm characteristics, market

and operating performance, financing and investment activity, and valuations. Therefore,

we extract operating performance data from the Compustat industrial annual and

quarterly files. We extract annual data on total assets (item #6), sales (item #12), book

value of equity (item #60), capital expenditures (item #128), and income before

extraordinary items (item #237). We extract quarterly data on sales (item #2),

depreciation and amortization (item #5), income before extraordinary items (item #8),

and book value of equity (item #59). We obtain yearly NYSE capitalization decile

breakpoints from Ken French’s website.2 We extract from Securities Data Company

equity offering dates and M&A transaction dates. We extract from the CDA/Spectrum

Institutional (13f) Holdings database, at the fourth quarter of every calendar year, for

every firm, the number of institutional shareholders as well as the total number of

institutions in the database.

3. Sample Description

We begin by examining the characteristics of firms that analysts follow and why

changes in analyst following occur. Therefore, we first examine analyst following

conditional upon market capitalization and institutional ownership for a typical year,

1994. We sort all sample firms into deciles based on analyst following, based on NYSE

market capitalization decile breakpoint, and based on the number of institutional

shareholders of the stock. Panels A and B of Table I show separately for all firms as well

as only for S&P 500 firms, respectively, the well-known strong positive relation between

analyst following and market capitalization. Panel C shows that there is also a strong 2 http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/

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positive relation between analyst following and institutional ownership and Panel D

shows that there is a strong positive relation between market capitalization and

institutional ownership. These relationships are a consequence of the fact that analysts

follow firms for which they can generate commission and banking revenues. The results

for other years are similar. In summary, Table I shows that both institutions and analysts

follow larger firms.

[Insert Table I about here]

Next, we examine how the number of analysts following a firm changes from year

to year. Figure 1-A shows the mean market capitalization of firms followed by analysts

and of S&P 500 firms. The typical firm followed by analysts is about a quarter the size of

the typical S&P 500 firm throughout the years. Figure 1-B shows the distribution of

analyst following (for firms followed by at least one analyst). This distribution is stable

over the years. Mean (median) following is about 6.5 (4) analysts and very few firms are

followed by more than 20 analysts.

[Insert Figure 1 about here]

We now turn to the distribution of changes in analyst following from year to year.

Figure 2-A shows the distribution of changes in analyst following and Figure 2-B shows

the relative percentage of the changes in analyst following (increases, no changes, and

decreases). The distribution of changes in analyst following is stable over the years, with

the typical change being close to zero. However, this stability belies the substantial

fluctuations in aggregate analyst following. The percentage of firms with no changes is

fairly stable from year to year, between 24% and 32% of changes. By contrast, increases

and decrease fluctuate substantially from year to year at each other’s expense.

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Oftentimes, increases (decreases) occur in years of high (low) market returns (not

tabulated). Armed with a sketch of the dynamics of analyst following and the change in

analyst following, we now turn to examining the determinants of the change in analyst

following for a firm from year to year.


We rely primarily on the literature on analyst following to guide our choice of the

determinants of changes in analyst following. Determinants include:

• Market capitalization decile change: Market capitalization is a well-known

determinant of analyst following (e.g., Bhushan (1989)).

• Change in institutional breadth: We define change in institutional breadth for a

given stock in a given year as the change in the number of 13f filers that hold that

stock between last year and this year, all divided by the total number of 13f filers

(for all stocks) last year. Thus change in institutional breadth is simply scaled

changes in institutional holdings. Scaling is necessary because the number of

institutions in our data source increases several fold during our sample period.

O’Brien and Bhushan (1990) find that the number of institutions is positively

related to the number of analysts. We emphasize that throughout the paper, except

in Panel A of Table IV, we take change in institutional breadth at face value as

change in institutional ownership.

• Change in turnover percentile: Greater analyst following may be a profitable

activity if it generates more trading and hence commissions for banks. Every year,

for every exchange, we sort all firms in CRSP with a full year of monthly

turnover data into turnover percentiles. We do so separately for the NYSE,

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AMEX, and NASDAQ because volume measurement differs substantially

between exchanges.

• Raw stock return and market return: O’Brien and Bhushan (1990) find that an

increase in analyst following is associated with higher excess of market returns,

and at the yearly frequency aggregate analyst following is positively related to the

level of the market. Accordingly, we use both the raw stock return this year and

last year and the market return.

• Change in book-to-market: Book-to-market measures valuation. Higher valuation

may capture greater growth opportunities and/or greater mispricing, but in both

cases information production by analysts may be valuable.

• Equity issuance dummy and acquirer dummy: These measure whether the firm

has issued equity and acquired another firm, respectively, this year. A firm may

be more likely to choose as underwriter a particular bank if an influential analyst

at that bank follows the firm. The equity issuance dummy variable equals one if a

given firm in a given year issues equity according to SDC and zero otherwise.

The acquirer dummy variable equals one if a given firm in a given year acquires

another firm according to SDC and zero otherwise.

• Change in return on equity: As already noted, O’Brien and Bhushan (1990) find

that an increase in analyst following is associated with higher excess of market

returns, so we consider the operating performance counterpart of stock return,

return on equity. Return on equity is computed as income before extraordinary

items this year scaled by the mean of book value of equity this year and last year.

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• Change in sales growth: O’Brien and Bhushan (1990) find that the number of

firms entering an industry is positively related to the number of analysts following

firms in that industry. This finding inspires our somewhat simpler choice of sales

growth, which is computed for every firm as the growth rate of sales this year

versus last year.

• Change in capital expenditures: As a measure of real investment, capital

expenditures may capture growth opportunities that are not captured by sales

growth since sales growth is derived from assets in place. Both firms that invest

heavily themselves and institutions interested in these firms may benefit from

greater analyst following of these firms. Capital expenditures are scaled by the

mean of total assets this year and last year.

We regress changes in analyst following on contemporaneous determinants listed

above. We also calculate the effect of a one standard deviation increase in each of our

explanatory variables on change in analyst following. Since our sample consists of a

cross-section of firms across time, we implement a firm fixed effects regression. The

Appendix describes why we choose firm fixed effects and explains this methodology.

Table II presents the results. All of the relations are in the predicted direction, except that

book-to-market change, the acquirer dummy, return on equity change, and sales growth

change are not statistically significant. The results overall are fairly intuitive. Firms for

which size, institutional ownership, turnover, returns, and investment increase also

experience an increase in analyst following as is the case for firms that issue equity.

Following also increases for firms for which size, valuation, and trading activity increase.

Additionally, analyst following increases when the broader market performs better and

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when the firm is involved in financings and acquisitions. The most important determinant

of change in analyst following is equity issuance, followed by raw stock return last year,

change in institutional breadth, and the market return. Equity issuance is associated with

an increase of 0.49 analysts. A one standard deviation increase in the raw stock return last

year, change in institutional breadth, and the market return are associated with increases

of 0.42, 0.34, and 0.26 analysts, respectively. Changes in analyst following do reflect

changes in the market and operating performance of firms. Are changes in analyst

following related to future returns?

[Insert Table II about here]

4. Main Result

We now examine the relation between changes in analyst following and returns.

We are mindful of the possibility that an additional analyst may have less of an impact

for a firm followed by 10 analysts than for a firm followed by one analyst. As Figure 3

shows, the number of analysts following a firm this year for a firm that was followed by

two analysts last year (the 25th percentile of the distribution of analyst following) ranges

roughly from one to six analysts. For a firm followed by four analysts last year (the

median of the distribution), the range is wider, roughly one to ten analysts. By sharp

contrast, for a firm followed by nine analysts last year (the 75th percentile of the

distribution), the range is very wide, roughly one to 17 analysts. Therefore, in all multiple

regressions that involve regressing returns on changes in analyst following, we control

for the logarithm of the number of analysts following the firm last year and the

interaction between the logarithm of the number of analysts following the firm last year

and the change in the number of analysts this year. Furthermore, when we refer to mean

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returns, we mean excess of market returns, and when we refer to returns in regressions,

we mean excess of the risk free rate and we include controls for the Fama-French three

factors plus momentum. Finally, we always use a firm fixed effects regression when

return is a dependent variable.


Table III presents this year’s changes in analyst following related to returns last

year, this year, and next year. Panel A presents sample means and Panel B presents

regressions of returns on change in analyst following. From Panel A, the decrease-

increase return spread is 4.7--1.7 = 6.4 percentage points. From Panel B, an additional

analyst this year is associated with incremental returns of 7.0, 1.7, and -4.6 percentage

points last year, this year, and next year, respectively. Note that when we express

relations between some variable and incremental returns, we hold constant the logarithm

of analyst following last year at its mean value of about 1.5 analysts. In other words,

∂(excess returnt) ( ) ( ) 5.1/1ln ×+=Δ∂−×ΔΔ ttt followingfollowingfollowingtfollowing ββ .

[Insert Table III about here]

Not surprisingly, we find strong evidence that more analysts follow firms that

have performed well in the past. Nothing appears to attract market participants like stellar

past returns. Nevertheless, it is striking that returns reverse after changes in analyst

following. The interested reader can peek ahead at Section 6 where we present a battery

of tests that suggest the results in Table III are robust.

5. Explanations

5.1. Investor Recognition

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We have found that changes in analyst following are positively related to returns

this year and negatively related to returns next year. There is a rational explanation for

this. Suppose that investors only include a firm in their portfolio choice problem if they

know about it, and that not all firms are known to all investors. We can think of greater

“investor recognition” as reducing the cost of capital for the firm. Merton (1987) models

a capital market equilibrium with incomplete information. His model predicts that (1)

changes in investor recognition are positively related to present returns and (2) negatively

related to future returns, that (3) the foregoing two relationships are more pronounced for

riskier firms, and that (4) both financing and investing are increasing in changes in

investor recognition.

We assume that analyst following proxies for investor recognition. Accordingly,

the results in Table III are consistent with Merton (1987)’s predictions (1) and (2).

Lehavy and Sloan (2006) also find evidence supporting Merton (1987)’s predictions.

They use change in institutional breadth as a proxy for investor recognition.3 To get a

sense of how our findings overlap with theirs, we replicate the results in Panel B of Table

III but we add change in institutional breadth as an explanatory variable. Panel A of

Table IV presents the results. A one standard deviation analyst following increase (about

2.5 analysts) is associated with incremental returns next year of -10.5 percentage points.

3 Institutional breadth change is a somewhat controversial proxy for investor recognition. First, the number

of investors that own a stock is a lower bound of the number of investors that know about the stock, but

beyond this it is unclear how the two are related. Second, institutional breadth change may be interpreted as

a noisy stock level proxy for mutual fund flows, which are a well-known proxy for investor sentiment.

Conceptually at least the rational and behavioral explanations for the reduction in the cost of capital are

very different (greater investor recognition versus more optimistic sentiment, respectively).

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By contrast, a one standard deviation increase in change in institutional breadth (about

1.65 percent) is associated with incremental returns next year of -3.8 percentage points.

Insofar as change in institutional breadth is also a proxy for investor recognition, change

in analyst following and change in institutional breadth are more like complementary

rather than competitive proxies for investor recognition change.

[Insert Table IV about here]

We continue testing Merton (1987)’s predictions. To test prediction (3), we repeat

our tests for predictions (1) and (2) but we separate changes in analyst following for high

and low risk firms. Every year, we sort all firms in CRSP with twelve months of monthly

returns data based on their annualized standard deviation of monthly returns. We then

classify a given firm in a given year as high (low) risk if its standard deviation is above

(below) the median standard deviation that year. Panel B of Table IV presents the results.

An additional analyst this year is associated with incremental returns of 2.7 (0.6)

percentage points this year for high (low) risk firms, while for next year this association

is -5.3 (-3.8) percentage points for high (low) risk firms. Hence the relation between

changes in analyst following and present and future returns is more pronounced for high

risk firms, which is consistent with prediction (3).

To test prediction (4), we test whether changes in analyst following are related to

financing and investment, controlling as usual for lagged analyst following and its

interaction. We measure financing with our equity issuance dummy variable. We

measure investment with our acquirer dummy variable as well as change in capital

expenditures. Panel C of Table IV presents the results. Change in analyst following is

positively related to financing and investment, which is consistent with prediction (4).

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In summary, we find that changes in analyst following are positively related to

present returns and negatively related to future returns, that the foregoing two

relationships are more pronounced for riskier firms, and that both financing and investing

are increasing in analyst following. If analyst following is a proxy for investor

recognition, Merton (1987)’s predictions are confirmed.

5.2. Market Overreaction

Investor recognition is not the only possible explanation for the return reversal

after changes in analyst following. The obvious alternative explanation is that the market

overreacts to changes in analyst following and eventually corrects its excesses. This

explanation is consistent with the positive relation between change in analyst following

and present returns and the negative relation between change in analyst following and

future returns being more pronounced for more risky firms. More risky firms are harder

to value and to arbitrage so they are more likely to be mispriced. It is also consistent with

firms issuing more (less) equity and investing more (less) when following increases

(decreases). Investors and managers alike may correctly perceive the direction of the

change in the cost of capital induced by the following change but they may overshoot

with the magnitude, resulting in too much equity issuance and real investment. We

cannot distinguish between the rational and behavioral explanations on the basis of

Merton (1987)’s predictions alone.

Before testing whether or not a behavioral explanation is consistent with our main

results, we examine whether the market reaction to changes in analyst following is

different when confirmed or contradicted by other votes of confidence. Specifically, we

examine whether the magnitude of returns this year and next year is bigger (smaller)

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when change in analyst following this year are confirmed (contradicted) by changes in

analysts’ consensus recommendation this year and by changes in institutional ownership

this year. The intuition behind these tests is simple. We have found that increases

(decreases) in analyst following are greeted by positive (negative) returns. Womack

(1996) finds that recommendation upgrades (downgrades) are greeted by positive

(negative) market returns. When more of the analyst community pays attention to a firm

and these analysts view the firm more favorably, there should be a greater market

reaction than when these analysts view the firm less favorably. The logic for changes in

institutional ownership is analogous, provided that the market responds more (less)

favorably to institutional ownership increases (decreases).

To implement our tests, we separate changes in analyst following into four

dummy variables. The first dummy variable is for analyst following increases that are

confirmed by more optimistic consensus recommendation changes (↑↑). The second

dummy variable is for analyst following increases that are contradicted by more

pessimistic consensus recommendation changes (↑↓). The third dummy variable is for

analyst following decreases that are contradicted by more optimistic consensus

recommendation changes (↓↑). The fourth dummy variable is for analyst following

decreases that are confirmed by more pessimistic consensus recommendation changes

(↓↓). We follow the same setup when using changes in institutional ownership.

Table V presents mean excess returns this year and next year for increases and

decreases in analyst following that are confirmed and contradicted by other relevant

signals. First, we examine returns next year. If following increases and is confirmed by a

consensus increase, incremental returns next year are -1.6 percentage points compared to

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only -0.6 if contradicted by a consensus decrease. If following decreases and is confirmed

by a consensus decrease, incremental returns next year are 8.4 percentage points

compared to only 4.3 if contradicted by a consensus increase. If following increases and

is confirmed by an ownership increase, incremental returns next year are -1.6 percentage

points compared to only -1.2 if contradicted by an ownership decrease. If following

decreases and is confirmed by an ownership decrease, incremental returns next year are

7.5 percentage points compared to only 2.2 if contradicted by an ownership increase. The

magnitude of the return reversal is bigger (smaller) when change in analyst following are

confirmed (contradicted).

[Insert Table V about here]

Next, we examine returns this year. If following increases (decreases) and is

confirmed by a consensus increase (decrease), incremental returns this year are positive

(negative). However, if following increases (decreases) and is contradicted by a

consensus decrease (increase), incremental returns this year are negative (positive). The

results for changes in institutional ownership tell the same story. By contrast to the results

for returns next year, it appears that the consensus change effect dominates the following

change effect for returns this year. Taken together, the results in Table V suggest that the

market reacts more strongly to changes in analyst following that are confirmed by other

votes of confidence than to changes that are contradicted.

We now turn to distinguishing between rational and behavioral explanations of

our main results. We ask whether or not analysts are justified in making their following

decisions by fundamentals. Specifically, we examine whether or not analysts add

coverage of firms that are better performers on an operating basis and drop coverage of

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worse performers, both contemporaneous with and subsequent to changes in analyst

following. As we have already argued, analysts should rationally add coverage of “good”

firms and drop coverage of “bad” firms. We extend this analysis by separating changes in

analysts following according to whether these changes are confirmed or contradicted by

changes in analysts’ consensus recommendation and changes in institutional ownership.

We wish to ensure that firms that get a double dollop vote of confidence through greater

analyst attention and more glowing analyst accolades are actually “better” firms. We also

look at how changes in analyst following are corroborated by changes in institutional

ownership, i.e., by how investors are voting with their dollars.

Our measures of operating performance are return on equity, sales growth, and

capital expenditures. We first test how these variables this year and next year are

explained by changes in analyst following this year. We then test how these variables this

year and next year are explained by the same four dummy variables created for Table V

based on change in analyst following and analysts’ consensus recommendation changes.

Table VI presents the results. From Panel A, both this year and next year,

profitability, growth, and investment are all monotonically higher for analyst following

increases. From Panel B, with minor exceptions not only does following change in the

direction of operating performance but the results are even more pronounced when we

incorporate the additional information provided by consensus recommendation changes.

In other words, operating performance is higher for firms for which analyst following

increases than decreases and is even higher for firms for which analyst following

increases and is confirmed by a consensus recommendations increase. Our findings for

growth and investment are consistent with the findings of Jegadeesh, Kim, Krische, and

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Lee (2004), who find that individual analysts tilt their recommendations towards glamour

stocks. Our results suggest that analysts gravitate towards (away from) more (less)

profitable, faster growing, and more heavily investing firms.

[Insert Table VI about here]

If analysts follow and recommend good firms as measured by both present and

future operating performance, why do these firms turn out to be bad investments? The

investor recognition explanation speaks only to changes in the cost of capital and is silent

on the subject of changes in cash flows. The answer that suggests itself is that these firms

are overvalued. We test the overvaluation explanation by examining whether or not firm

valuations at the time of changes in analyst following are related to returns thereafter. For

following increases, the return reversal should be exacerbated for firms with higher

valuations because they have a longer way to fall. For following decreases, the return

reversal should be moderated for firms with higher valuations because they are already

richly valued.

We use four valuation measures. Following Lakonishok, Shleifer, and Vishny

(1994), we use book-to-market, cash flow-to-price, and sales growth, and following Lee

and Swaminathan (2000), we use turnover. We form these variables as follows. We form

book-to-market ratios using book value from the fiscal quarter ending during the third

calendar quarter and market value from the last trading day of the calendar year. We form

cash flow-to-price ratios by summing earnings before extraordinary items plus

depreciation and amortization for the four fiscal quarters ending during the third calendar

quarter and dividing the total by market value from the last trading day of the calendar

year. We only use strictly positive cash flow-to-price ratios. We form sales growth using

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sales from the fiscal quarter ending during the third calendar quarter of the current year

compared to five years prior and we calculate the geometric mean growth rate. We form

turnover as the mean of the monthly turnover ratios (total monthly volume divided by

month end shares outstanding) during the calendar year. Next, for each of these four

variables, every year, we sort all firms in Compustat into quintiles. Finally, for each of

these four variables, we sort our sample firms into three groups of change in analyst

following (increases, no changes, and decreases) by five valuation quintiles. For every

cell, we calculate the mean excess of market return.

Table VII reports the results. Before interpreting them, several pointers are in

order. First, there are typically at least a few thousand observations in each cell, and any

concentration in particular cells does not appear to be systematic within or across panels.

Second, the mean return for each change in analyst following group is different in Panels

A and D from Panels B and C because there are considerably fewer observations in the

latter than the former panels. Third, in Panel D, since Lee and Swaminathan (2000) find

that high turnover stocks have glamour characteristics, the progression of quintiles is

reversed, running from five (glamour) to one (value).

[Insert Table VII about here]

We now interpret the results. For every valuation measure, for a given valuation

quintile, returns are lower for following increases than for no changes and lower for no

changes than for decreases. The analyst following decrease-increase spread (about five

percentage points) is always statistically significant. In other words, for a given valuation

level, returns reverse after following changes, which is consistent with our previous

results. Moreover, in Panels A, B, and D, eight of the nine extreme value-glamour

21

spreads (about five percentage points) are statistically significant. (For comparison,

without conditioning on change in analyst following, the t-statistics for the value-glamour

premium for Panels A, B, C, and D are 5.68, 5.71, -0.29, and 8.41, respectively.) It is not

surprising that results in Panel C are weak because by construction we only include firms

that have at least six years of sales data, which eliminates a lot of young and growing

firms, and we thus lose about 40 percent of our sample firms. The results indicate that for

a given change in analyst following, returns next year are lower when valuations this year

are higher, which is consistent with the market overreacting to changes in analyst

following and subsequently correcting its excesses. Future returns for firms with

extremely low valuations and for which following increases are similar to future returns

for firms with extremely high valuations and for which following decreases. This further

corroborates the market overreaction explanation. We conclude that while analysts’

decisions to add or drop coverage of stocks are sound in that their decisions are solidly

grounded in fundamentals, market overreaction to changes in analyst following means

that blindly implementing these decisions makes for a bad investment strategy.

6. Robustness Tests

We subject our main result to a battery of robustness tests. We wish to ensure that

our results are general rather than being mechanical or being driven by a particular or

unrepresentative group of firms. To this end, we replicate the results in Panel B of Table

III in various incarnations and we present the results in Table VIII.

[Insert Table VIII about here]

First, we run regressions of excess returns next year on changes in analyst

following this year by five year intervals to test whether or not the return reversal is

22

consistent across time. From Panel A, the return reversal appears to be a stable

phenomenon throughout our sample period.

Second, we test the relation between excess returns next year and changes in

analyst following this year separately by analyst following quintiles constructed using

analyst following last year. Our objective is to group together firms such that firms in

each group attract roughly the same amount of analyst attention. From Panel B, no matter

how much or little analyst attention firms receive, returns reverse after changes in analyst

following.

Third, we test whether or not the return reversal is transitory or persistent by

regressing returns two and three years into the future on change in analyst following this

year. From Panel C, though smaller in magnitude (-2.5 and -1.6 percentage points for two

and three years, respectively, rather than -4.6), the return reversal persists for several

years.

Fourth, we test whether or not the return reversal is a mechanical result of not

controlling for the positive relation between analyst following next year and returns next

year. This is a possibility since the change in analyst following is positively related to

returns this year but negatively related to returns next year. We run regressions of returns

next year on changes in analyst following this year for even and odd years separately,

thereby necessarily destroying any mechanical relation between adjacent years. From

Panel D, the return reversal persists and is clearly not generated mechanically.4

4 We cannot control for following as usual because if we did we would have to control for following both

this year and next year, which would induce perfectly multicollinearity with change in analyst following

this year.

23

Fifth, we test whether or not it is primarily large changes in the information

environment of firms that have a large impact on returns. To do this, we exclude all large

changes in analyst following, namely those below the 5th percentile (-4 analysts) and

above the 95th percentile (4 analysts), and restricting attention to firm-years during which

following changes by (the absolute value of) zero to four analysts in one instance and by

zero to one analysts in another. From Panel E, an additional analyst this year is associated

with incremental returns of -4.6, -4.9, and -5.6 percentage points when we include all

changes in following, changes of only zero to four analysts, and changes of only zero to

one analyst in succession. Thus the return reversal is not primarily driven by large

changes in analyst following.

Finally, we briefly describe other robustness tests the results of which are not

tabulated. First, lower priced stocks are harder to arbitrage so they are more likely to be

mispriced and may experience larger swings in returns. To test whether this affects the

return reversal, we only include stocks that have a month-end closing price of at least $5

throughout our sample period. Second, firms that go public underperform for several

years thereafter, and IPOs tend to receive analyst coverage immediately after listing. To

test whether this affects the return reversal, we include only firm-years for which the firm

has been followed in IBES for at least five years. Third, the marginal impact of change in

analyst following may be greater for younger versus older firms. To test whether this

affects the return reversal, we control for the logarithm of the number of years listed. In

all three cases above, the return reversal persists, though the relations between change in

analyst following this year and returns this year and next year are smaller in magnitude

when we exclude low priced stocks, IPOs, and younger firms, as we would expect.

24

7. Conclusion

We examine the market reaction to sell-side analysts’ decisions to add or drop

coverage. Firms for which analyst following increases (decreases) have higher (lower)

returns during the year in which coverage changes. However, returns reverse in the next

year, leading to a decrease-increase return spread of 6.4 percentage points. In contrast to

the market reaction, operating performance is better for firms for which analyst following

increases than for firms for which following decreases, both in the year of the change in

coverage and the next year. The market reaction and operating performance results are

even stronger when changes in analyst following are confirmed by changes in analysts’

consensus recommendation and changes in institutional ownership. Moreover, for a given

change in analyst following, firms with higher valuations have lower returns next year.

Glamour stocks for which analyst following increases have the worst returns next year

whereas value stocks for which following decreases have the highest returns. Taken

together, our results suggest that the market overreacts to changes in analyst following

and subsequently corrects itself.

25

REFERENCES

Bhushan, Ravi, 1989, Firm characteristics and analyst following, Journal of Accounting

and Economics 11, 255-274.

Boni, Leslie, and Kent L. Womack, 2006, Analysts, industries, and price momentum,

Journal of Financial and Quantitative Analysis 41, 85-109.

Irvine, Paul J., 2003, The incremental impact of analyst initiation of coverage, Journal of

Corporate Finance 9, 431-451.

Jegadeesh, Narasimhan, Joonghyuk Kim, Susan D. Krische, and Charles M. C. Lee,

2004, Analyzing the analysts: When do recommendations add value?, Journal of

Finance 59, 1083-1124.

Krigman, Laurie, Wayne H. Shaw, and Kent L. Womack, 2001, Why do firms switch

underwriters?, Journal of Financial Economics 60, 245-284.

Lakonishok, Josef, Andrei Shleifer, and Robert W. Vishny, 1994, Contrarian investment,

extrapolation, and risk, Journal of Finance 49, 1541-1578.

Lee, Charles M. C., and Bhaskaran Swaminathan, 2000, Price momentum and trading

volume, Journal of Finance 55, 2017-2069.

Lehavy, Reuven, and Richard G. Sloan, 2006, Investor recognition and stock returns,

working paper.

McNichols, Maureen, and Patricia C. O’Brien, 1997, Self-selection and analyst coverage,

Journal of Accounting Research 35, 167-199.

Merton, Robert C., 1987, A simple model of capital market equilibrium with incomplete

information, Journal of Finance 42, 483-510.

26

Michaely, Roni, and Kent L. Womack, 1999, Conflict of interest and the credibility of

underwriter analyst recommendations, Review of Financial Studies 12, 653-686.

O’Brien, Patricia C., and Ravi Bhushan, 1990, Analyst following and institutional

ownership, Journal of Accounting Research 28, 55-76.

Womack, Kent L., 1996, Do brokerage analysts’ recommendations add value?, Journal of

Finance 51, 137-167.

27

Appendix. Fixed Effects Regressions

Our sample consists of a cross-section of firms across time, an unbalanced panel.

We can approach our data in the familiar linear regression setting,

itiitit vXy εβα +++= , where, for each individual i, we have multiple observations, one

for each time period t. In our application, we examine the effect of change in analyst

following across time for a given firm and for multiple firms, so the impact of change in

analyst following on returns may be different from firm to firm (e.g., riskier firms may

have higher returns). With observations on each individual at different time periods, the

residual may be a compound residual with an individual specific component itiv ε+ ,

where itε is the least squares residual and iv is a residual that is constant across time and

specific to individual i.

For this reason, we take the fairly general approach of implementing a firm fixed

effects model. Specifically, we run ordinary least squares regressions on the equation

( ) ( )εεεβα +−+++−+=+− iitiitiit vXXXyyy , where ( ) ∑ =×= iT

t itii yTy1

1 ,

( ) ∑ ∑= =×=

N

i

T

t itii yNTy

1 11 , and iX , X , v , iε , and ε are analogously defined. This

yields the same coefficient estimates as running an ordinary least squares regression on

itiitit vXy εβα +++= and including firm dummy variables. The standard errors from

ordinary least squares and firm fixed effects are identical after adjusting for the extra N-1

estimated firm means. The within R2s from fixed effects (“within” firms, as opposed to

“between” firms and “overall”) are identical to the ordinary least squares R2s. Since with

fixed effects we are estimating, among other things, one mean per firm, we can only

include other regressors that are not constant across time. The correlation between iX

28

and iv is zero since iv is fixed by assumption. While fixed effects always give consistent

estimates, they may not be efficient relative to random effects. However, the Hausman

test for the equality of the coefficients from fixed and random effects indicates that

random effects are not appropriate (p-value 0.0000), so we stick to fixed effects.

29

Table I Characteristics of Firms Followed by Analysts

This table presents the relations between the number of analysts following a stock, the number of institutions owning a stock, and market capitalization. The sample consists of all firms that are followed by at least one analyst, both this year and the previous year. For ease of interpretation, results for a single representative year, 1994, are presented. Panel A: Number of firms by analyst following and market capitalization quintiles, 1994 only

Market capitalization quintile 1 2 3 4 5

Median number of analysts

1 751 143 25 9 2 2 238 115 34 6 3 3 240 201 72 24 2 4 4 90 245 244 149 26 8 A

naly

st

follo

win

g qu

intil

e

5 2 32 119 235 328 18 Median capitalization 59.4 239.8 604.9 1,408.9 4,967.8

Panel B: Number of S&P 500 firms by analyst following and market capitalization quintiles, 1994 only



1 1 2 2 2 2 3 3 3 6 6 5 4 7 19 35 18 10 A

naly

st

follo

win

g qu

intil

e

5 5 18 75 285 22 Median capitalization 122.0 301.9 658.6 1,713.3 5,578.6

Panel C: Number of firms by analyst following and institutional ownership quintiles, 1994 only

Institutional ownership quintile 1 2 3 4 5


1 403 296 183 44 1 2 2 72 134 133 50 5 3 3 43 134 190 152 20 4 4 10 43 184 350 166 8 A

naly

st

follo

win

g qu

intil

e

5 11 133 572 18 Median managers 9 20 34 66 165

Panel D: Number of firms by institutional ownership and market capitalization quintiles, 1994 only


Median number of institutions

1 512 9 3 2 9 2 515 83 7 1 20 3 283 345 60 13 34 4 9 289 330 94 7 66

Inst

itutio

nal

owne

rshi

p qu

intil

e

5 8 94 313 349 165 Median capitalization 59.4 239.8 604.9 1,408.9 4,967.8

30

Table II Calendar Year Regression of Change in Analyst Following On Its Determinants

This table presents a calendar year firm fixed effects regression of the change in the number of analysts following a firm on the determinants of change in analyst following. The sample consists of all firms-years between 1984 and 2004 such that there is at least one analyst following each firm each year and the previous year. Δcap decile is measured relative to NYSE capitalization deciles. Δinstitutional breadth is the change in the number institutions that own the stock each year relative to the previous year and scaled by the total number of institutions the previous year. Δturnover percentile is measured relative to other firms listed on the same exchange as the firm. The equity issuance dummy equals one if the firm issues equity this year and zero otherwise. The acquirer dummy equals one if the firm completes an acquisition this year and zero otherwise. Return on equity is scaled by the mean of book value of equity this year and last year. Capital expenditures are scaled by the mean of total assets this year and last year. For comparability, the impact on the change in the number of analysts following a firm from a one standard deviation increase in each of the explanatory variables is also presented.

Δnumber of analysts

b se(b)

Δnumber of analysts change from a one standard deviation increase in variable

Δcap decile 0.137*** (7.60) 0.12 Δinstitutional breadth 18.891*** (25.29) 0.34 Δturnover percentile 0.006*** (7.19) 0.08 raw stock return this year 0.090*** (2.86) 0.05 raw stock return last year 0.701*** (33.67) 0.42 market return 1.534*** (21.65) 0.26 Δbook-to-market 0.026 (1.47) 0.02 equity issuance dummy 0.493*** (10.63) acquirer dummy 0.023 (0.61) Δreturn on equity 0.048 (1.55) 0.02 Δsales growth -0.039 (1.59) -0.02 Δcapex 0.540** (2.41) 0.03 Constant -0.419*** (25.95) Number of firm-years 49247 Number of firms 7890 R-squared 0.091

31

Table III Calendar Year Change in Analyst Following and Returns

This table presents sample means in Panel A and firm fixed effects regressions in Panel B examining change in analyst following and returns last year, this year, and next year. The sample consists of all firms-years between 1984 and 2004 such that there is at least one analyst following each firm each year and the previous year. Panel A presents mean excess of market returns last year, this year, and next year by analyst following increases, no changes, and decreases. Panel B presents the results of regressions of returns last year, this year, and next year on change in analyst following this year. The Fama-French three factors plus momentum are included as explanatory variables in Panel B. Panel A: Mean returns conditional on change in analyst following

excess returnt-1 excess returnt excess returnt+1 Increase 19.3 9.3 -1.7

No change 0.6 -1.1 0.2

Ana

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fo

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ing

Decrease -8.4 -8.0 4.7 Decrease-increase return spread -27.7 -17.2 6.4 t-statistic for H0: increase - decrease = 0 53.38 34.63 -12.26 Number of firm-years 62543 66627 61892

Panel B: Regressions of returns on change in analyst following

excess returnt-1 excess returnt excess returnt+1 b se(b) b se(b) b se(b) Δfollowingt 0.121*** (40.53) 0.032*** (11.88) -0.080*** (28.59) ln(followingt-1) 0.014*** (2.77) -0.160*** (32.70) -0.174*** (34.67) Δfollowingt×ln(followingt-1) -0.034*** (28.25) -0.010*** (8.68) 0.023*** (20.63) Rm-Rf 1.015*** (65.56) 0.957*** (63.40) 0.933*** (60.16) SMB 0.779*** (38.57) 0.715*** (37.31) 0.687*** (34.62) HML 0.233*** (13.61) 0.233*** (14.22) 0.259*** (15.30) UML 0.059*** (2.95) -0.039** (2.05) -0.063*** (3.19) Constant -0.020** (2.18) 0.237*** (27.47) 0.289*** (32.26) Number of firm-years 62543 66627 61892 Number of firms 9948 10619 9723 R-squared 0.167 0.148 0.138

32

Table IV Analyst Following As Investor Recognition

This table presents firm fixed effects regressions examining change in analyst following and returns last year, this year, and next year. The sample consists of all firms-years between 1984 and 2004 such that there is at least one analyst following each firm each year and the previous year. Panel A presents the results of regressions of returns this year and next year on change in analyst following this year and change in institutional breadth this year. Panel B presents the results of regressions of returns this year and next year on changes in analyst following this year separated for high risk and low risk firms, where the risk of a firm is determined by whether the firm’s annualized standard deviation of monthly return this year is above or below the median for all CRSP firms this year. Panel C presents the results of regressions of equity issuance, acquisitions, and capital expenditures change on contemporaneous change in analyst following. The equity issuance dummy equals one if the firm issues equity this year and zero otherwise. The acquirer dummy equals one if the firm completes and acquisition this year and zero otherwise. The Fama-French three factors plus momentum are included as explanatory variables in all panels, though for expositional simplicity they are not reported. Panel A: Regressions of returns on change in analyst following and change in institutional breadth

excess returnt excess returnt+1 b se(b) b se(b) Δfollowingt 0.013*** (5.33) -0.076*** (26.58) ln(followingt-1) -0.145*** (32.31) -0.175*** (34.43) Δfollowingt×ln(followingt-1) -0.010*** (9.55) 0.023*** (20.04) Δinstitutional breadtht 13.462*** (111.83) -2.270*** (16.82) Constant 0.151*** (18.89) 0.303*** (33.12) Number of firm-years 64445 60102 Number of firms 10431 9595 R-squared 0.309 0.142

Panel B: Regressions of returns on changes in analyst following separated for high risk and low risk firms

excess returnt excess returnt+1 b se(b) b se(b) Δfollowingt×high riskt 0.037*** (13.28) -0.084*** (29.38) Δfollowingt×low riskt 0.016*** (5.04) -0.069*** (21.52) ln(followingt-1) -0.161*** (32.97) -0.173*** (34.54) Δfollowingt×ln(followingt-1) -0.007*** (5.87) 0.021*** (18.29) Constant 0.240*** (27.82) 0.286*** (31.99) Number of firm-years 66627 61892 Number of firms 10619 9723 R-squared 0.150 0.139

Panel C: Regressions of financing and investment on change in analyst following

equity issuance dummyt acquirer dummyt Δcapext b se(b) b se(b) b se(b) Δfollowingt 0.047*** (34.86) 0.016*** (9.94) 0.003*** (8.07) ln(followingt-1) -0.004* (1.81) 0.034*** (11.64) -0.003*** (5.23) Δfollowingt×ln(followingt-1) -0.015*** (26.73) -0.004*** (5.48) -0.001*** (5.89) Constant 0.077*** (19.54) 0.073*** (15.50) -0.000 (0.35) Number of firm-years 66650 66650 50977 Number of firms 10621 10621 8065 R-squared 0.035 0.004 0.005

33

Table V Calendar Year Changes in Analyst Following Confirmed or Contradicted By Changes in Analysts’

Consensus Recommendations or Changes in Institutional Ownership and Returns This table presents mean excess of market returns this year and next categorized by confirmed or contradicted change in analyst following. The sample consists of all firms-years between 1984 and 2004 such that there is at least one analyst following each firm each year and the previous year. Firms are first grouped by analyst following increases, no changes, and decreases. Then, firms are separated into strict changes by whether they are confirmed or contradicted by changes in analysts’ consensus recommendations in one instance and by changes in institutional ownership in another. Mean returns are also presented for increases and decreases. Pairs indicated by a, b, c, d, and e denote statistical significance at the 1% level for a difference in means test.

Mean excess returns conditional on whether changes in analyst following are confirmed or contradicted

by changes in analysts’ consensus recommendation by changes in institutional ownership

This year Next year This year Next year Following increase mean return 10.5 -1.1 9.2 -1.6 Following increase, confirmed (↑↑) 23.8a,c -1.6e 22.6a,c -1.6e Following increase, contradicted (↑↓) -0.4a -0.6 -27.3a -1.2 No following change 0.9 1.8 -2.2 0.3 Following decrease mean return -6.6 6.8 -8.0 4.7 Following decrease, contradicted (↓↑) 12.1b 4.3d 14.3b 2.2d Following decrease, confirmed (↓↓) -19.1b,c 8.4d,e -31.6b,c 7.5d,e Number of firm-years 38358 35164 64445 60102

34

Table VI Calendar Year Change in Analyst Following and Operating Performance

This table presents sample means of operating performance measures this year and next categorized by confirmed or contradicted change in analyst following. The sample consists of all firms-years between 1984 and 2004 such that there is at least one analyst following each firm each year and the previous year. Panel A presents sample mean operating performance measures this year and next year by analyst following increases, no changes, and decreases. Panel B presents sample mean operating performance measures this year and next year by changes in analyst following this year, where increases and decreases are separated by whether they are confirmed or contradicted by analysts’ consensus recommendation changes. Return on equity is scaled by the mean of book value of equity this year and last year. Capital expenditures are scaled by the mean of total assets this year and last year. Pairs indicated by a, b, c, d, and e denote statistical significance at the 1% level for a difference in means test. Panel A: Mean operating performance measures conditional on change in analyst following

Return on equity Sales growth Capital expenditures

This year Next

year This year Next

year This year Next

year Increase 9.4% 7.5% 29.0% 20.8% 8.3% 7.8%

No change 3.0% 2.5% 17.2% 13.9% 6.9% 6.4%

Ana

lyst

fo

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ing

Decrease 1.8% 2.5% 12.0% 9.7% 6.7% 6.1%

t-statistic for H0: increase - decrease = 0 28.31 17.05 36.14 28.04 20.83 23.70

Number of firm-years 62690 59516 62181 59118 55975 53143

Panel B: Mean operating performance measures conditional on changes in analyst following confirmed or contradicted by analysts’ consensus recommendation changes

return on equityt

return on equityt+1

sales growtht

sales growtht+1

capext capext+1

Following increase, confirmed (↑↑) 8.7a,c 7.5d,f 35.0c 27.8d,f 7.7a,c 7.7d,f

Following increase, contradicted (↑↓) 6.5a 4.6d 34.8 21.1d 8.0a 7.1d

No following change 1.5 1.4 19.9 16.6 6.5 6.1 Following decrease, contradicted (↓↑) 4.5b 5.6e 15.0b,c 14.3e 6.5 6.2e

Following decrease, confirmed (↓↓) -1.0b,c -0.7e,f 12.8b,c 8.0e,f 6.5c 5.4e,f

Number of firm-years 37035 34651 36811 34496 32919 30875

35

Table VII Mean Returns Next Year Conditional on Change in Analyst Following This Year and Valuation Proxies This

Year This table presents mean excess of market returns by fifteen categories of three change in analyst following groups and valuation quintiles. The sample consists of all firms-years between 1984 and 2004 such that there is at least one analyst following each firm each year and the previous year. Valuation quintile breakpoints are based on all Compustat firms in a given year. Sales growth is measured over the last five years. Turnover is mean monthly ratio of monthly volume to month end shares outstanding. Panel A: Mean returns next year conditional upon change in analyst following this year and book-to-market quintile this year

Book-to-market quintile Glamour Value 1 2 3 4 5

Mean for change in analyst following

t-statistic for H0: value = glamour

Increase -3.2 -2.6 -0.5 0.9 -1.9 -1.6 0.82

No change -3.4 -2.7 2.4 1.9 1.6 0.3 2.82

Ana

lyst

fo

llow

ing

Decrease 2.2 1.5 4.9 7.5 8.1 4.9 3.37 Increase minus decrease -5.3 -4.1 -5.4 -6.6 -10.0 -6.5

t-statistic for H0: increase - decrease = 0 3.75 4.09 5.65 5.78 5.27

Number of firm-years 58121

Panel B: Mean returns next year conditional upon change in analyst following this year and cash flow-to-price quintile this year

Cash flow-to-price quintile Glamour Value 1 2 3 4 5



Increase -3.2 0.3 -0.7 0 1.1 -0.7 3.33

No change -2.8 -0.6 1.2 3.2 1.9 0.7 2.66

Ana

lyst

fo

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ing




36

Panel C: Mean returns next year conditional upon change in analyst following this year and sales growth quintile this year

Sales growth quintile Glamour Value 1 2 3 4 5



Increase 0.7 1.3 1.1 1.4 0 0.9 -0.41

No change 3 1.7 2.6 2.4 1.7 2.3 -0.63

Ana

lyst

fo

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ing




Panel D: Mean returns next year conditional upon change in analyst following this year and turnover quintile this year

Turnover quintile Glamour Value 5 4 3 2 1



Increase -4.0 -2.1 0.0 1.0 0.5 -1.7 3.99

No change -7.9 -1.8 1.6 4.0 4.2 0.2 8.66

Ana

lyst

fo

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ing




37

Table VIII Robustness Tests

This table presents various firm fixed effects robustness tests of the main result presented in Panel B of Table III. The sample consists of all firms-years between 1984 and 2004 such that there is at least one analyst following each firm each year and the previous year. Panel A presents regressions of excess of market return next year for four intervals of five years each. Panel B presents regressions of excess of market return next year on change in analyst following this year scaled by analyst following last year. Panel C presents regressions of excess of market return one, two, and three years into the future on change in analyst following this year. Panel D presents regressions of excess of market return next year on analyst following this year and next. Panel E presents regressions of excess of market returns next year on change in analyst following this year but with various restrictions on the magnitude of change in analyst following. The Fama-French three factors plus momentum are included as explanatory variables in all panels but for expositional simplicity they are not reported. Panel A: Calendar year regressions of returns on change in analyst following by five year intervals

excess returnt+1 1985-1989 1990-1994 1995-1999 2000-2004 b se(b) b se(b) b se(b) b se(b) Δfollowingt -0.059*** (10.70) -0.118*** (15.69) -0.102*** (13.51) -0.117*** (19.92) ln(followingt-1) -0.161*** (12.26) -0.268*** (16.15) -0.213*** (12.19) -0.172*** (12.52) Δfollowingt×ln(followingt-1) 0.016*** (7.70) 0.033*** (11.31) 0.030*** (10.01) 0.033*** (14.13) Constant 0.248*** (9.99) 0.462*** (16.35) 0.377*** (10.13) 0.014 (0.23) Number of firm-years 11967 13898 18354 15485 Number of firms 3744 4193 5989 4413 R-squared 0.206 0.142 0.059 0.264

Panel B: Calendar year regressions of returns on relative change in analyst following by lagged analyst following quintiles

excess returnt+1 Least followed Most followed 1 2 3 4 5 b se(b) b se(b) b se(b) b se(b) b se(b) Δfollowingt/followingt-1 -0.091*** (12.93) -0.201*** (11.01) -0.212*** (13.93) -0.256*** (13.80) -0.147*** (7.16) Constant 0.031*** (3.13) 0.032** (2.24) 0.001 (0.06) 0.007 (0.80) 0.013* (1.91) Number of firm-years 18192 8406 11812 11401 12081 Number of firms 6509 4334 4537 3312 1829 R-squared 0.122 0.176 0.137 0.151 0.129

38

Panel C: Calendar year regressions of returns one, two, and three years into future years on change in analyst following this year excess returnt+1 excess returnt+2 excess returnt+3 b se(b) b se(b) b se(b) Δfollowingt -0.080*** (28.59) -0.047*** (15.31) -0.029*** (8.75) ln(followingt-1) -0.174*** (34.67) -0.110*** (19.90) -0.079*** (13.26) Δfollowingt×ln(followingt-1) 0.023*** (20.63) 0.015*** (12.02) 0.009*** (6.85) Constant 0.289*** (32.26) 0.201*** (20.59) 0.152*** (14.46) Number of firm-years 61892 54459 47848 Number of firms 9723 8607 7637 R-squared 0.138 0.126 0.119

Panel D: Calendar year regression of returns next year on change in analyst following this year and next year

excess returnt+1 Even years only Odd years only b se(b) b se(b) Δfollowingt -0.089*** (19.85) -0.074*** (18.97) ln(followingt-1) -0.185*** (24.91) -0.172*** (26.21) Δfollowingt×ln(followingt-1) 0.025*** (13.93) 0.022*** (14.23) Constant 0.329*** (23.10) 0.253*** (20.69) Number of firm-years 32041 29851 Number of firms 8987 8810 R-squared 0.114 0.127

Panel E: Calendar year regressions of returns on change in analyst following by magnitude of change in analyst following

excess returnt+1 Δfollowingt not restricted |Δfollowingt|∈[0,1,2,3,4] |Δfollowingt|∈[0,1] b se(b) b se(b) b se(b) Δfollowingt -0.080*** (28.59) -0.092*** (25.93) -0.114*** (13.74) ln(followingt-1) -0.174*** (34.67) -0.175*** (33.48) -0.163*** (24.16) Δfollowingt×ln(followingt-1) 0.023*** (20.63) 0.029*** (18.15) 0.039*** (8.43) Constant 0.289*** (32.26) 0.277*** (30.76) 0.210*** (20.80) Number of firm-years 61892 57801 37632 Number of firms 9723 9618 8864 R-squared 0.138 0.136 0.123

39

Figure 1-A: Size of typical firm followed by analysts each year

560.

2 732.

0

799.

8

749.

8

802.

6

963.

5

925.

0 1,24

8.3

1,29

6.7

1,30

9.6

1,16

3.1

1,49

9.8

1,68

7.1

2,05

7.5

2,58

8.4

3,45

6.2

3,48

0.5

3,45

1.2

2,93

3.4

3,91

7.6

4,26

6.3

2,33

4.5

2,99

3.9

3,38

7.7

3,44

0.2

3,77

5.8

4,68

1.7

4,35

5.6

5,63

9.9

6,00

5.6

6,54

0.6

6,64

1.5

9,11

3.0

11,1

83.2

14,9

94.5

19,8

26.5

24,3

70.5

23,2

52.5

20,9

13.3

16,0

87.6

20,5

13.6

22,5

78.1

100

1,000

10,000

100,000

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004

Year

Mar

ket c

apita

lizat

ion

($ m

illion

)

Mean market capitalization of firms followed by analysts Mean market capitalization of S&P 500 firms Figure 1-B: Distribution of analyst following each year

0

5

10

15

20

25

30

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004

Year

Num

ber o

f ana

lyst

s

5th percentile Median Mean 95th percentile Figure 1. Summary statistics for analyst following each year. The sample consists of all firms-years between 1984 and 2004 such that there is at least one analyst following each firm each year and the previous year. Panel A shows each year the mean market capitalization of firms followed by analysts and of S&P 500 firms. Panel B shows each year the mean and median and 5th and 9th percentile of the number of analysts following firms.

40

Figure 2-A: Distribution of change in analyst following each year

-6

-4

-2

0

2

4

6

8

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004

Year

Cha

nge

in n

umbe

r of a

naly

sts

5th percentile Median Mean 95th percentile Figure 2-B: Relative percentage of changes in analyst following by year

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Year

Anal

yst f

ollo

win

g ch

ange

Decrease No change Increase Figure 2. Summary statistics for change in analyst following each year. The sample consists of all firms-years between 1984 and 2004 such that there is at least one analyst following each firm each year and the previous year. Panel A shows each year the mean and median and 5th and 9th percentile of the change in the number of analysts following firms. Panel B shows each year the relative percentage of increases, no changes, and decreases in the number of analysts following firms.

41

Figure 3-A: Number of analysts following a firm this year if 2 analysts followed the firm last year

Figure 3-B: Number of analysts following a firm this year if 4 analysts followed the firm last year

Figure 3-C: Number of analysts following a firm this year if 9 analysts followed the firm last year

Figure 3. Number of analysts following a firm this year conditional on analyst following last year. The sample consists of all firms-years between 1984 and 2004 such that there is at least one analyst following each firm each year and the previous year. Panels A, B, and C show the distribution of the number of analysts following a firm this year conditional on 2, 4, and 9 analysts following the same firm last year, respectively, these being the 25th, 50th, and 75th percentiles of the distribution of analyst following last year, respectively.

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Changes in Analysts Coverage and Future Returns: Does the ...€¦ · compensation and career prospects are closely tied to high standings in the annual polls conducted by Institutional