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Living in the Moment:

Short-Term Investors and the Informativeness of Prices for Future Fundamentals

Eric R. Holzman Kelley School of Business

Indiana University [email protected]

December 2016

ABSTRACT

Conventional theory suggests that the market reaction to a public disclosure reveals investors’ revised evaluations about the future fundamentals of the firm. However, emerging theory predicts that investors with short investment horizons are likely to be more concerned with predicting near-term price changes than with trading on expectations about future fundamentals around a public disclosure. Consistent with this theory, I predict and find that the informativeness of stock returns for future earnings news around corporate earnings disclosures is materially attenuated when a high proportion of firm shares are held by short-term investors. I find this reduction in the forward-looking fundamental information content of prices is strongest when the current earnings disclosure is more precise and during periods of extreme market sentiment. Overall, the findings suggest that prices more slowly anticipate future fundamentals when a firm’s investor base includes a significant portion of short-term investors.

Keywords: Investor Horizon, Price Informativeness, Price Formation, Earnings Announcements

I would like to thank the members of my dissertation committee: Teri Yohn (chair), Daniel Beneish, Matt Billett, Brian Miller, and Jim Wahlen for their insightful feedback and support in the development of this paper. Further, I would like to thank Michael Baye, Patricia Dechow, Craig Holden, Nathan Marshall, Neil Morgan and my fellow PhD students at Indiana University for helpful comments and suggestions. I also gratefully acknowledge the financial support of the Deloitte Foundation.

1. INTRODUCTION

A significant working assumption relied upon in a large number of capital markets studies

is that the short-window market response around earnings announcements reflects not only an

updating for the explicit information content of the disclosure, but also investors’ revised

expectations about the fundamentals of the firm. That is, an earnings announcement quickly spurs

trading that reveals investors’ private assessments about the implications of the earnings news for

the future fundamentals of the firm. Lee (2001, pp. 235-236) challenges the validity of this

assumption, and notes that the “speed and accuracy of price adjustment to new information is a

continuous process, and do[es] not occur instantaneously,” and calls for research that deepens our

understanding of the market forces that shape the processes by which stock prices incorporate

fundamental information.

One aspect of the price discovery process that has received little empirical attention is

whether, and how, the horizon of a firm’s investor base affects the speed at which fundamental

information is anticipated by prices. An evolving stream of theoretical research, based on

Keynes’s (1936) “beauty contest hypothesis”, recognizes that the decisions of short-term investors

are governed by different dynamics than the decisions of long-term investors. Specifically, theory

predicts that short-term investors will trade based on expectations of beliefs about a firm’s near-

term stock price, as opposed to expectations about the firm’s underlying fundamental value and

that this can reduce the informativeness of prices for future fundamentals.1 This paper examines

whether stock prices around earnings announcements more slowly anticipate future fundamentals

when a firm’s investor base contains a significant contingent of short-term investors.

1 See, for example theoretical predictions in, DeLong, Shleifer, Summers, and Waldmann (1990); Froot, Scharfstein, and Stein (1992); Dow and Gorton (1994); Allen, Morris, and Shin (2006); Gao (2008); Chen, Huang, and Zhang (2014).

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Many institutional traders, such as hedge funds, investment banks, and active mutual

funds, trade frequently and do not maintain long-term stock positions. Prior research provides

mixed evidence on how short-term, or transient, institutional investors affect price formation. On

the one hand, findings suggest that transient investors may improve price informativeness by more

quickly impounding disclosed information into price (“the arbitrageur hypothesis”) (Collins,

Gong, and Hribar 2003; Ke and Ramalingegowda 2005). On the other hand, studies have also

found that transient ownership is associated with mispricing (Bushee 2001; Elliott, Krische, and

Peecher 2010), and stock price volatility (Bushee and Noe 2000; Gallo 2015). Overall, given this

mixed evidence, it is not clear how transient ownership affects the speed with which prices

incorporate news about fundamentals.

Allen et al. (2006) provide an analytical model suggesting that beliefs-about-beliefs, rather

than expectations about fundamentals, can play a role in setting prices when investment horizons

are short. Intuitively, short-term investors are concerned with share price in the near-term. Thus,

they have less incentive than long-term investors to trade on their estimates of fundamental value

because these estimates may not be realized in price in the near-term. Allen et al. (2006) predict

short-term traders will base their trading decisions on public information because it is known by

all investors and is, thus, more useful to them than private evaluations of value in forming

expectations about near-term prices. If so, short-term investors are predicted to be more sensitive

to public news and less sensitive to expectations about future fundamentals, potentially leading to

a reduction in the informativeness of prices for future fundamentals.

To assess whether short-term owners act as arbitrageurs and improve price informativeness

or act as predicted by the beauty contest hypothesis and impair price informativeness, I examine

how the relation between earnings announcement returns and future earnings news varies with

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transient investor ownership.2 Prior research has shown that market prices anticipate future

earnings news incremental to information in prior earnings (Beaver, Lambert, and Morse 1980),

and, thus, current returns can be used as a predictor of future earnings. Based on this finding,

research has examined the extent to which the informativeness of stock prices varies with

disclosure practices (Lundholm and Myers 2002) and changes in accounting standards (Ettredge,

Kwon, Smith, and Zarowin 2005). Moreover, stock price informativeness has been shown to have

real economic effects in shaping firm investment practices (Chen, Goldstein, and Jiang 2007), and

governance choices (Ferreira, Ferreira, and Raposo 2011).

I use the categorization of institutional investors developed in Bushee (2001) to separate

institutions on investor horizon. Using these measures, I examine over 84,000 firm-quarters during

the period of 1991 to 2013 to test for variation in the degree to which returns at earnings

announcements predict next period unexpected earnings. Employing a firm fixed effect multiple

regression model, and controlling for current earnings news, I find that announcement returns

predict less information about next period earnings when the investor base contains a high

proportion of transient investors (i.e., typically institutions with shorter investment horizons), but

not a high proportion of dedicated or quasi-indexer investors (i.e., typically institutions with

longer investment horizons). Moreover, the results are economically significant as the price-leads-

earnings relationship is reduced by approximately 25% during the announcement window when a

high proportion of firm shares are held by transient investors. Additional tests indicate that the

informativeness of returns for future earnings is not impaired in the days prior to the

announcement, consistent with the predictions of beauty contest theory that public information

plays a role in setting near-term price expectations for short-horizon investors.

2 I use the terms “short-term”, “short-horizon”, and “transient” investors interchangeably throughout the manuscript.

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To further address concern that unobserved heterogeneity and/or investment self-selection

by transient investors may bias the results, I demonstrate the robustness of the results using time-

varying firm fixed effects and a first differences specification. I also document similar results with

a propensity-score-matched model where I match on transient ownership. Further, I examine a

setting where variation in transient ownership is largely exogenous due to stock index membership

cut-offs. As described in more detail later in the paper, recent findings (Boone and White 2015)

identify a discontinuity in transient ownership for those firms around the Russell 2000/1000

threshold. Exploiting this threshold as a source of variation in transient ownership exogenous to

firm earnings characteristics, I document a similar reduction in price informativeness around

earnings announcements for those firms with greater transient ownership.

I provide more direct evidence of the underlying mechanism by examining daily estimated

institutional order flow (i.e., net trade imbalances) around a subsample of earnings

announcements. The results from these analyses indicate that there is a strong positive relation

between institutional order flow around firm earnings announcements and future earnings news,

consistent with the findings of Campbell, Ramadorai, and Schwartz (2009) that institutional order

flow anticipates future earnings news. However, this relation is attenuated around earnings

announcements when a firm’s institutional investor base includes a high proportion of transient

owners.

I also examine whether the attenuation of the relation between announcement returns and

future earnings is more pronounced when the earnings announcement is more precise. Theory

predicts that the precision of the public disclosure influences the extent to which investors update

higher-order beliefs. Gao (2008) provides a model predicting that short-term investors will

overuse more precise public disclosures when forming expectations of near-term prices. Relying

on one market-based and two content-based proxies for variation in the precision of the

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announcement, I find that the attenuation of the announcement return-future earnings relation

associated with transient ownership is more pronounced when the current earnings disclosure is

more precise.

Additionally, findings in prior research suggest there are periods where trading on

expectations about near-term price development rather than future fundamentals may be more

important to short-term investors. Specifically, prior research has shown that near-term price

development is more weakly linked to fundamentals when market sentiment is both higher and

lower than normal (Baker and Wurgler 2006). Accordingly, I examine whether the strength of the

relation between firm announcement returns and future earnings news varies with short-term

ownership in these market conditions. I find that earnings announcement returns contain less

information about period ahead earnings news for high transient ownership firms compared to low

transient ownership firms when market sentiment is lower or higher than normal. I also find that

the strength of the relation between announcement returns and future earnings is significantly

weaker during four financial crisis periods among firms with high transient ownership but not for

those firms with low transient ownership. These findings suggest market sentiment may increase

short-term investors sensitivity to trading on expected near-term changes in price, and reduce their

sensitivity to trading on evaluations of future fundamentals.

Lastly, beauty contest theory predicts that short-term investors will overuse (underuse)

public (private) information. Consequently, I examine returns after an earnings announcement in

order to identify patterns consistent with these predictions. I find evidence of a reversal in post-

announcement returns associated with the earnings surprise when a firm has a high proportion of

transient investors. Additionally, I find that post-announcement returns are more sensitive to

future earnings news, measured before the next announcement, for those firms with a high degree

of transient investors. These patterns are consistent with an initial over-reaction to current earnings

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news and a relative reduction in forward-looking fundamental information being impounded into

price during the announcement window for those firms with a high proportion of transient

investors.

This paper is one of the first studies to test theories that suggest higher-order beliefs shape

price formation around public disclosures and that prices are less informative for future

fundamentals among firms with a greater preponderance of short-horizon investors. These findings

should be of interest to academic researchers studying the economic forces that shape price

formation (Lee and So 2015). Additionally, corporate executives have expressed concern that firm

stock prices misrepresent intrinsic value when their shareholder bases are comprised of short-term

investors (CGRI 2014), and, because of this, they spend considerable effort attracting longer-term

investors. Beyer, Larcker, and Tayan (2014) ask whether these concerns are justified. My findings

provide initial evidence that investor horizon influences the price formation process.

Lastly, market commentators suggest that the SEC has made regulatory decisions over the

last decade that favor short-term investor interests (e.g., Michaels and Mamudi 2015; Macey and

Swensen 2015), despite guidance in their rules and regulations indicating that the SEC has the

“responsibility to uphold the interests of long-term investors.” (SEC Rule 34-51808). My evidence

indicates short-horizon investors adversely affect price informativeness about future fundamentals

around important market disclosures which helps to inform the debate on the costs and benefits of

regulation that favors investors with different horizons.

The remainder of the paper progresses as follows: Section 2 reviews relevant literature and

develops hypotheses, Section 3 discusses sample selection, variable construction, descriptive

statistics, and research design, Section 4 reviews the results, and Section 5 concludes.

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2. LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT

2.1 Public Disclosure, Private Information Generation, and Prices

Theory demonstrates that the market response to earnings disclosures is a function of

investor interpretations of the announcement news (e.g., Kim and Verrecchia 1991, 1994, 1997).

Prior empirical research finds that the interpretation of current period earnings impounds

information into price about not only this period’s revision in expected cash flows, but also

expected revisions in future expected cash flows (e.g., Beaver, Cornell, Landsman, and Stubben

2008). However, as noted by Lee (2001), “prices do not adjust to fundamental value instantly by

fiat.” Rather, the costly process of trading aggregates private views, or information, into price.

Kim and Verrecchia (1997) observe that investors likely generate both pre-event and event private

information that they use to interpret the value implications of an information release. Consistent

with this, evidence suggests that investors develop private information as a result of earnings

announcements (Barron, Byard, and Kim 2002), which then stimulates trading that impounds

information about future fundamentals (Barron, Harris, and Stanford 2005).

This price updating process helps to explain findings from prior research which indicate

that stock prices impound information about future fundamental news (Beaver et al. 1980).

However, the role that investor horizon plays in shaping how quickly future fundamental

information is anticipated by prices has received little empirical attention.

2.2 Higher-Order Beliefs and Short-Term Investors

Keynes (1936) famously compared professional investors to entrants in a hypothetical

newspaper contest, in which participants select the most beautiful women from a large set of

photographs. Winners of the contest were chosen based upon whether they selected the most

popular photos. Keynes observed that sophisticated contestants should base their opinions not on

their own assessments of beauty, but rather on the expected assessments of the other entrants,

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suggesting that predicting the beliefs of other participants (forming beliefs-about-beliefs) is

conceivably more valuable than relying on one’s own opinion. Consequently, in the extreme, a

sophisticated player should attempt to anticipate what other contestant’s assessments of the

average opinion will be, in order to make an informed choice.

Relying on this powerful metaphor (“the beauty contest”), economic models have

examined what role beliefs-about-beliefs, or higher-order beliefs, may serve in setting market

prices. In the model of Froot et al. (1992) short-horizon investors liquidate their positions before

the liquidation date of the asset. Consequently, these traders worry about short-run price

developments because they can only profit from information that is reflected in price in the short

run. Because prices reflect the average expectation, each trader’s optimal information acquisition

depends on others’ information acquisition. Thus, short-term traders care more about the

information of others than expectations about future fundamentals. In DeLong et al. (1990),

rational investors foresee demand from momentum traders. If there is good news today, rational

actors buy and push the price beyond fundamental value because feedback traders are willing to

buy at a higher price in the subsequent period. Additionally, in the model of Dow and Gorton

(1994), myopia leads traders to focus on information that will affect prices in the near-term.3

Allen et al. (2006), similarly account for the beauty contest effect as a consequence of

short investment horizons. Given that short-term investors plan to exit the stock before the

fundamental value is realized, they are more attuned to predicting near-term stock prices than

information about future fundamentals.4 These investors are shown to weight public, rather than

3 Industry groups and practitioners have asserted that the short-term incentive structure (e.g., quarterly incentives) for fund managers leads them to myopically seek quick trading gains rather than focus on identifying high quality investments (e.g., CFA Institute 2008). 4 A key observation in the model of Allen et al. (2006) is that the law of iterated expectations generally does not hold when there is a place for asymmetric information between investors. This can have asset price implications when traders have finite investment horizons.

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private, information more in their decisions, given that public information serves two purposes.

The first is an informational role, in that public information conveys new information about the

fundamental value of the traded asset. The second is a commonality role, meaning that public

information is common in the information set of all investors. Therefore, public information is

useful to short-term investors in forecasting average beliefs. Thus, Allen et al. (2006) find that it

can be rational for short horizon traders to at least partially disregard their private information, and

to rely more heavily on public information.

The basic theoretical prediction in studies motivated by Keynes’s insight is that investors

that prefer short holding periods make investment decisions that are not solely a function of

expected asset values, which has the potential to impair the informativeness of prices.

Additionally, the predictions of Allen et al. (2006) indicate that this effect is plausibly amplified

by public information. Given these theoretical predictions and decades of research suggesting that

earnings announcements are significant public information events that help shape investor

expectations, I hypothesize:

H1: Stock returns at an earnings announcement will predict less information about future earnings when short-term investors own a high proportion of a firm’s shares.

Further, theory predicts that the precision of the public disclosure itself should influence

the extent to which investors rely on it to update higher-order beliefs. Gao (2008) extends the

findings in Allen et al. (2006) by showing that there is an endogenous link between the

commonality role and the informational role of public information via the quality of the

information. Intuitively, short-term investors rely on public information to forecast near-term

prices because others will rely on it to update their assessments of fundamental value.

Consequently, if the information is more precise, then short-term investors will rely on it more

than a noisy disclosure. However, this has the effect of “anchoring” short-term investors’ beliefs

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about other investors’ beliefs, and leads them to rely more on the public signal (Gao 2008),

compared to longer term investors.

Relatedly, in an experimental setting, Elliott et al. (2010) find that when a firm’s investor

base contains a significant proportion of transient investors and the firm provides a more

transparent disclosure, security analysts anticipate a greater degree of stock mispricing. As noted

in the corresponding discussion by Sapra (2010), if transparency improves the strength of the

accounting signal, then transient investors may place more weight on it rather than private

estimates of value in forecasting near-term average beliefs. Based upon these theoretic predictions

and empirical observations, I predict:

H2: The reduction in the informativeness of earnings announcement returns for future earnings associated with high short-term investor ownership is exacerbated when the current earnings disclosure is more precise.

Empirical evidence suggests that there are occasions where prices are more likely to

deviate from fundamental values. Specifically, a growing body of research suggests that prices

sometimes diverge from fundamental values due to overall market sentiment (e.g., Brown and

Cliff 2005; Baker and Wurgler 2006).5 Research in accounting documents increased market

responses to earnings information when market sentiment is either low or high (Mian and

Sankaguruswamy 2012) despite finding no difference in the implications of current earnings for

future earnings in these market conditions. In addition, Fischer, Heinle, and Verrecchia (2016)

predict that higher-order beliefs can drive time-varying stock price sensitivity to earnings news

even when the valuation implications of earnings do not vary over time. Moreover, their model

assumes that all investors have short investment horizons. Accordingly, investors that trade

frequently and do not maintain stock positions for long may rationally be more concerned with

5 Market sentiment is broadly defined as beliefs about cash flows and investment risk that are not justified by existing facts (Baker and Wurgler 2006, 2007).

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predicting near-term price movements rather than future fundamentals during these market

episodes. 6 Consequently, I hypothesize as follows:

H3: The reduction in the informativeness of earnings announcement returns for future earnings associated with high short-term investor ownership is exacerbated when overall market sentiment is higher or lower than normal.

Lastly, beauty contest theory predicts that short-term investors will overuse (underuse)

public (private) information (Allen et al. 2006; Gao 2008). In these cases, prices are exceedingly

sensitive to public information, and underweight private information regarding future fundamental

information. However, if longer-horizon agents have sufficient incentives to identify these

instances of mispricing on average (Grossman and Stiglitz 1980), then subsequent price

development would reverse out any initial over reliance by short-term agents on the public

disclosure. Moreover, the private information regarding future fundamentals initially omitted from

price would ultimately be impounded in the ensuing period leading up to and including the

disclosure of the future fundamental. Consequently, I hypothesize as follows:

H4a: Post-announcement stock returns are negatively associated with current period earnings news when short-term investors own a high proportion of a firm’s shares. H4b: Post-announcement stock returns are more strongly positively associated with future period earnings news when short-term investors own a high proportion of a firm’s shares.

3. SAMPLE SELECTION, VARIABLE DEFINITIONS AND RESEARCH DESIGN

3.1 Sample Selection

Table 1 provides the details of my sample selection. I begin with all firm-quarter

observations in the intersection of the COMPUSTAT unrestated quarterly file, I/B/E/S analyst

detailed file, and the CRSP daily file from 1991 to 2013 (301,550 observations).7 I drop 87,572

6 Statements made by Keynes (1936) are consistent with the notion that short-term investors trade with mispricing, “They [professional speculators] are concerned, not with what an investment is really worth to a man who buys it 'for keeps', but with what the market will value it at, under the influence of mass psychology …”. 7 I begin collecting data in 1991 because my primary proxy for earnings expectations and realizations are based upon analyst forecasts and I/B/E/S actual EPS amounts. Prior research has found that I/B/E/S standardized the way in which

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observations that lack 13F filing (i.e., institutional ownership) data in the 90 days prior to the

earnings announcement, as timely ownership data is critical to the analysis. Moreover, I drop

3,878 firm-quarter observations where the firm has a quarter-end stock price less than $2 to avoid

any confounds related to non-liquid stocks. Given that meaningful earnings consensus

expectations are necessary to minimize noise in the measurement of unexpected earnings news, I

require that at least three analyst forecasts for the current and next quarter earnings were issued in

the ninety days prior to an earnings announcement. This requirement leads me to drop 101,297

observations. Finally, I drop 24,442 observations that are missing necessary COMPUSTAT and

CRSP data to calculate control variables, resulting in a final sample size of 84,361 firm-quarter

observations. In addition, several of the analyses require different sets of variables, these data

requirements lead to variances between the primary and subsequent samples as detailed in Table 1.

3.2 Empirical Proxy for Short-Term Investors

Some investors trade within short investment horizons. While retail investors likely have

heterogeneous trading horizons, I follow prior research and base my proxy for short horizon

investors upon categorizations of institutional investors. Bushee (2001) categorizes institutions as

transient, quasi-indexers, or dedicated based on portfolio turnover and diversification.8 Transient

institutions typically have shorter investment horizons than quasi-indexer or dedicated institutions.

I merge this classification data with the Thomson Reuters Form 13F database. The SEC requires

that all investment managers with equity security holdings over $100 million file quarterly

reports.9 These filings occur at the end of each calendar quarter and thus it is not possible to match

it calculated actual EPS starting in 1991 (Bradshaw and Sloan 2002; Abarbanell and Lehavy 2007; Beaver et al. 2008). 8 I thank Brian Bushee for making this dataset available. 9 https://www.sec.gov/divisions/investment/13ffaq.htm.

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ownership characteristics precisely to the date of the earnings announcement.10 It is important to

capture ownership as of the announcement date as closely as possible; therefore, I match

institutional ownership to the earnings announcement date based on the filing of the most recent

calendar-quarter end prior to the announcement. I use the percentage of shares owned by transient

institutions (Traown) as the proxy for the degree of short-horizon traders in a firm’s stock.

Because some of the specifications examine the interaction of the investor base with returns, or

make use of a matching methodology, I use a median split (Hitran) based on the calendar quarter

distribution of transient share ownership.

3.3 Primary Research Design

The primary research design is motivated by theory and empirical evidence that earnings

announcements trigger market reactions and that this market activity impounds into price

information about the current earnings innovation as well as revised expectations for future

earnings innovations. I employ an empirical design that examines the extent to which the market

reactions to current earnings news predict innovations in future earnings. Specifically, I estimate

the following cross-sectional linear regression,

Ueit+1 = α + βEacarit + γmEacarit*Xmit + δSixbharit + θmSixbharit*Xm

it + λmXmit

+ Current Earnings News Controls

+ Earnings Characteristics Controls + Firm Characteristic Controls

+ Risk and Disagreement Controls + Firm Fixed Effects

+ Calendar Quarter Fixed Effects +εit, (1)

where Ueit+1 represents unexpected earnings per share (EPS) for quarter t+1, measured as the

actual I/B/E/S EPS for quarter t+1 less the analyst consensus EPS one day prior to the earnings

announcement for quarter t, scaled by price at the end of quarter t.11 In the primary analyses, I

10 The average sample lag between firm earnings announcements and the 13F shareholdings filing date is 27 calendar days. 11 Results are robust to scaling by the absolute consensus forecast, as well as using unscaled measures and including a firm fixed effect every six quarters to control for scale.

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examine a one quarter ahead horizon to proxy for future fundamental information because it is the

shortest possible measure of future fundamental information. Accordingly, using quarter-ahead

earnings mitigates the likelihood that macro uncertainty shocks differentially affect future earnings

realizations in the cross-section.12 Eacar is the cumulative market-adjusted return during the four-

day announcement window starting on the day of the earnings announcement and continuing for

three days after the announcement.13 Xmit represents a vector of indicator variables set to one when

a high proportion of firms shares are owned by transient (Hitran), quasi-indexer (Hiqix), or

dedicated (Hided) institutional investors prior to the earnings announcement for quarter t, and zero

otherwise. A firm is identified as having a high degree of transient, quasi-indexer, or dedicated

ownership based on a median split of the calendar quarter distribution of share ownership by

transient, quasi-indexer, and dedicated institutions, respectively.14

The coefficient on the interaction between the announcement return and high transient

ownership, Eacarit*Hitranit, is the primary variable of interest. A negative and significant

coefficient on this term would support the first hypothesis: high transient ownership attenuates the

relation between announcement returns and future earnings news. To control for the possibility

that an analyst-based expectation of next period earnings omits information already in the market

(e.g., Abarbanell 1991), I include the buy-and-hold return six months prior to the earnings

announcement for quarter t (Sixbhar). Additionally, I permit this coefficient to vary by the

ownership variables (e.g., Sixbharit* Hitranit). Hypothetically, if high transient investor ownership

accelerates the amount of information related to next period’s unexpected earnings into price

12 Using next quarter earnings, however, also biases against finding results as short-term investors may have anticipated horizons that exceed one quarter in length. 13 The inferences are robust to using shorter (i.e., [0,2]) or longer (i.e., [0,5]) announcement windows as well as unadjusted (raw) or four-factor adjusted announcement returns. 14 The results are consistent when continuous variables or scaled decile ranks (i.e., from 0 to 1) are used for the interaction terms instead of a median split.

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before the disclosure of quarter t earnings, then the coefficient on this term would be positive (i.e.,

a stronger relationship). However, because these investors are likely to implement momentum

strategies, or trade on non-fundamental information as suggested by theoretic research, I expect

this coefficient to be insignificant or negative.

In addition, I include firm and calendar quarter fixed effects, and I control for a large

number of factors identified in prior literature shown to be associated with the reaction to earnings

news to better isolate the influence of investor base composition on the amount of future earnings

news anticipated at the quarter t announcement. Importantly, I control for the current period

earnings surprise (Ueit) in order to ensure the predictiveness of the market reaction for future

earnings is orthogonal to the implications of the current surprise for changes in firm value and to

control for autocorrelation in earnings innovations. I include the product of squared Ueit and the

sign of Ueit (i.e., Ueit * |Ueit|) to control for non-linearities in the implications of the current

earnings surprise for future earnings (Freeman and Tse 1992). I also control for the information

content of a voluntary earnings forecast for subsequent period earnings (Bundlenews).

Additionally, I control for whether current earnings report a net loss (Loss), the persistence

of past earnings innovations (Persist), the predictability of past earnings (Predict), and the number

of days between the end of the quarter to the earnings announcement date (Report lag). Prior

research has shown that losses are generally less persistent (Hayn 1995), firms with more (less)

persistent (predictable) earnings are associated with higher (lower) market responses (Kormendi

and Lipe, 1987; Lipe 1990), and delayed (expedited) earnings announcements are predictably

associated with bad (good) news (Chambers and Penman 1984; Atiase et al. 1989). Additionally, I

include several controls commonly used in the literature to ensure the results are not attributable to

differences in important firm characteristics. Specifically, I control for firm size (Size), the book-

to-market ratio (Btm), asset growth (Growth), analyst coverage (Analysts), and leverage

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(Leverage).15 Lastly, recognizing that in the cross-section, firms are subject to varying levels of

risk and disagreement regarding future earnings, I control for analyst forecast dispersion for the

current (Andispt) and subsequent (Andispt+1) quarter measured just prior to the announcement for

quarter t, as well as stock return volatility (Retvol) over the 60 days prior to the earnings

announcement for quarter t. 16

3.4 Descriptive Statistics

Table 2 Panel A presents descriptive statistics for the primary model variables. I winsorize

the sample variables at the 1 percent and 99 percent levels to mitigate the impact of outliers. Panel

A indicates that earnings announcement returns are on average slightly positive (0.13%),

consistent with prior research that documents net positive returns in large samples around earnings

announcements (e.g., Ball and Kothari 1991). Additionally, the median firm-quarter has positive

buy-and-hold returns over the six months prior the earnings announcement (5.9%), a market

valuation that is approximately two times the accounting book value (i.e., Btm of .44), and an asset

base financed with a moderate amount of debt (≈18% leverage ratio). The average firm is followed

by approximately nine sell-side analysts, and their investor base consists largely of institutions

(45% indexers, 8% dedicated, and 17% transient investors). Lastly, sample firms are generally

large (median total assets of $2 billion), and growing (median annual asset growth of ≈7.7%).

Table 2 Panel B displays the mean and standard deviation for each of the primary model

variables for the subsamples of firm-quarter observations that have a high (low) degree of transient

ownership just prior to the earnings announcement for quarter t. Additionally, the final column

15 Additionally, in those tests where Hiqix and Hided are not included as main effects or in interaction terms, I control for the percentage of firm shares held by dedicated (Dedown) and quasi-indexers (Qixown) institutions. 16 In untabulated tests, I also examine whether the results are sensitive to including additional controls for lagged earnings announcement returns, average absolute earnings announcement returns, average prior firm earnings responses, and a proxy for accounting conservatism (i.e., the Khan and Watts 2009 c_score). The results were not materially different in any of these alternative specifications.

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displays the differences in the variable means for those observations in the high and low transient

ownership subsamples.

4. EMPIRICAL RESULTS

4.1 Tests of H1

4.1.1 Primary Analyses

The first hypothesis predicts when transient ownership is high, earnings announcement

returns will predict less information about future earnings. Table 3 presents the results of

estimating Eq. (1) where I allow the predictiveness of earnings announcement returns for next

quarter’s unexpected earnings to vary by classifications of institutional owners (i.e., Hitran, Hiqix,

and Hided).17 Column (1) indicates that earnings announcement returns (Eacart), lagged period

returns (Sixbhar), current period earnings news (Uet), and bundled earnings guidance

(Bundlenews) are all positively associated with next quarter unexpected earnings (Uet+1).

Consistent with H1, I find that the association between earnings announcement returns and future

earnings news is attenuated when a firm’s investor base includes a high proportion of transient

owners (Eacart * Hitran; t-statistic:-3.17).18 Moreover, by comparing the coefficient on the

interaction term to the coefficient on the main effect for Eacar it is clear that the magnitude of this

attenuation is economically meaningful (approximately a 25% reduction). I do not, however, find

a similar attenuation when a firm’s investor base includes a high proportion of quasi-indexers

(Eacart * Hiqix; t-statistic: -0.11) or dedicated investors (Eacart * Hided; t-statistic: -0.72).

Additionally, I document a negative and significant coefficient on the interaction between lagged

returns and the high transient ownership indicator variable (Sixbhar * Hitran) (t-statistic: -2.68).

17 The results are also robust to using a jointly estimated model (i.e., seemingly unrelated estimation) where the model partition is based upon high and low transient ownership prior to the earnings announcement. 18 I document consistent results when using year-ahead rather than quarter-ahead unexpected future earnings. Additionally, these results are robust to using naïve expectations and GAAP or operating (as defined by COMPUSTAT) EPS.

17

Importantly, this interaction term is not significantly positive, which would have suggested that

when transient ownership is high lagged returns may have substituted for announcement returns in

predicting information about future fundamentals.

Table 3 columns (2) through (4) examine the robustness of the results to the inclusion of

additional interactions between quarter t’s earnings news, the announcement returns, and control

variables. Column (2) includes interactions between this period’s earnings surprise (Uet) and the

earnings characteristics controls (Loss, Persist, Predict, and Report lag) given findings from prior

literature that the implications of this period’s earnings news may have different implications for

future earnings based upon these characteristics. Column (3) includes interactions between Eacar

and the risk and disagreement controls (Retvol, Andispt, and Andispt+1) to rule out the possibility

that differences in the risk-profile or uncertainty regarding fundamentals of firms in which

transient investors are highly invested accounts for the results in column (1). Lastly, column (4)

tabulates the result of including the interactions from both columns (2) and (3). In sum, I find that

the association between earnings announcement returns and future earnings news is attenuated

when a firm’s investor base includes a high proportion of transient investors and this finding is not

materially affected by the inclusion of additional interactions between control variables.19

In the spirit of falsification tests, Table 4 further examines whether the price-leads-earnings

relationship is attenuated outside of the announcement window for those firms with high transient

ownership, to assess whether omitted variables could be driving the results. If correlated omitted

variables are driving the results rather than short-term investor trading around public disclosures,

then I would expect the relationship between current returns and future earnings to be attenuated

19 Additionally, I examine the robustness of these results to a permutations framework where I randomly assign transient ownership each quarter and estimate equation (1), and repeat this 1,000 times. The resulting distribution of randomly generated coefficients indicate that the coefficient obtained from the actual empirical data is significant at the 1% level. I also document consistent results using median regression or a decile ranked specification.

18

prior to the earnings announcement. I increase the length of time over which I examine the price-

leads-earnings relationship to the 31-day event window centered on the earnings announcement

date (i.e., [-15,15] event window). Table 4 re-estimates Eq. (1) after re-setting the earnings

expectations for quarter t and t+1 to start fifteen days prior to the earnings announcement and

examines day-by-day the information content of returns for next period earnings over the [-15, 15]

day event window. The results indicate that the vast majority of daily stock returns (λt+n) during

this event window are positively associated with next period earnings news (i.e., the coefficient on

27 of 31 days is at least marginally significant). Moreover, all of the interaction terms between

daily returns and an investor base with high transient ownership (ωt+n) are insignificant in the

period prior to the earnings announcement, and the estimated coefficients on the interaction terms

during the -15 to -1 day window are approximately evenly distributed between positive and

negative. Consequently, the evidence suggests that prior to a public disclosure firm stock returns

anticipate earnings information at a similar rate for firms whose shares are owned by a high and

low degree of transient investors. Around the announcement, however, the positive relation is

significantly attenuated, as the interaction terms at days zero, one, and two are negative and

significant (t-statistics: -2.00, -1.82. and -3.61). Collectively, the evidence suggests that the

strength of the current returns-future earnings relation attenuates at, but not before, the earnings

announcement when transient investors hold a high proportion of firm shares.

Figure 1 illustrates how the strength of the announcement return-future earnings news

relation varies over the announcement period with variation in transient ownership. Figure 1 plots

the cumulative coefficient estimates over the [-15,15] event window for firms with below median

(λt+n) and above median (λt+n + ωt+n) transient ownership. Additionally, Figure 1 plots similar

curves for those firms in the top and bottom decile of transient ownership (results not tabulated).

As shown in Figure 1 the current returns-future earnings relation varies little with transient

19

ownership prior to the earnings announcement. However, starting at the announcement and

continuing for several days thereafter, the relation becomes stronger (stays about the same) for

those firms with low (high) amounts of short-term ownership. This is consistent with a relative

reduction in the informativeness of prices for future earnings at the disclosure of public

information as short-term ownership increases.

Table 5 examines the robustness of the initial results to alternative specifications designed

to mitigate concerns related to potential endogeneity between a firm’s earnings generation process

and its investor base.20,21 First, while the analyses in Table 3 control for time-invariant firm fixed

effects in order to rule out the possibility that results could be attributed to unspecified “firm type”

omitted variables, it is possible that firms change slowly over time and that these firm-specific

time-variant omitted factors could drive the results. Consequently, I examine the robustness of

these associations to using a modified firm fixed effect approach. In order to rule out slow moving

firm-specific trends, I include a firm-specific dummy variable every six fiscal quarters (“time-

varying firm fixed effects”).22 Column (1) of Panel A in Table 5 reports these results. Importantly,

the interaction between announcement returns and high transient ownership remains negative and

significant (t-statistic: -3.47).

Next, in column (2) of Table 5 Panel A, I examine a first differences specification as an

alternative method to rule out unobserved heterogeneity. This analysis provides greater insight

into whether changes in a firm’s investor base are associated with changes in the informativeness

20 In additional analyses (untabulated), I test for a difference in the informativeness of current earnings news for next quarter and next year earnings news for those firms with high transient ownership, but find no evidence of a difference. 21 Additionally, to ensure the results are not attributable to earnings management (Bushee 1998), I re-estimate the analyses in Table 3 after dropping all observations where firm earnings realizations for quarter t or quarter t+1 just met or beat analyst expectations. The results of these tests (untabulated) continue to indicate a material reduction in the informativeness of prices for future fundamentals for those firms with a high degree of transient ownership. 22 I document similar results employing a comparable analysis that includes a firm-specific fixed effect every eight or four consecutive firm quarters.

20

of announcement returns for future earnings. Consistent with the Table 3 findings, I document a

negative and statistically significant association between the change in the Eacar * Hitran

interaction term and the change in next quarter unexpected earnings (t-statistic: -2.50). Given that

the choice of firm to invest in is endogenously determined, I explore the robustness of the results

to using a propensity-matched model. To examine whether investment self-selection significantly

biases the documented associations, I predict the likelihood of high transient ownership in firm i

during quarter t using determinants of transient investment examined in prior work (e.g., Bushee

2001), and match high transient ownership firm-quarter observations to low transient ownership

firm-quarter observations based on the propensity for high transient ownership. Then, I re-estimate

Eq. (1) with a propensity score-matched sample of treatment (Hitran=1) and control (Hitran=0)

observations that have the same predicted probabilities of having high transient investor

ownership based on observable characteristics with covariate balance. The details of the

propensity match model are discussed in Appendix B and the associated Table B-1. Consistent

with previous results, I find a negative and significant coefficient on Eacar * Hitran (t-statistic: -

2.76).

Lastly, I exploit a natural setting where a discontinuity in transient ownership arises due to

index membership among a set of relatively similar firms. Specifically, Boone and White (2015)

provide evidence of this discontinuity and show it is sharpest for the 50 firms on either side of the

firm size threshold between the Russell 2000 (i.e., smaller firms) and 1000 (i.e., larger firms)

indices, and it is more pronounced during the months immediately after the annual rebalancing at

the end of June.23 They attribute this at least partly to the greater (poorer) liquidity experienced by

these firms due to their relatively high (low) value-weight in the Russell 2000 (1000) index. This

23 See Boone and White (2015) Figure 1 and Table 1 pgs. 515-516. Further, Appendix A (pgs. 530-531) of Boone and White (2015) provides detail on how the Russell 1000 and 2000 indices are constructed.

21

is consistent with the conclusions of Bushee and Noe (2000), which suggest all else equal transient

investors prefer a more liquid environment in order to minimize their trade impact. Accordingly, I

collect data on those firms in the top (bottom) 50 positions in the Russell 2000 (1000) index for

the period of 1998 – 2013. I match this data to my sample and identify 299 (188) firm-quarter

earnings announcement observations where the firm was in the top (bottom) set of 50 firms

included in the Russell 2000 (1000) during six months (i.e., July to December) after the annual

rebalancing.

Boone and White (2015) show that firms listed at the very top of the Russell 2000 index

have approximately 9% more transient ownership than those firms listed at the very bottom of the

Russell 1000 index. Consistent with these findings, I find (untabulated) that there is approximately

a 10% difference in total share ownership by transient institutions between these two subsamples

of firms, and importantly there is no statistical difference between these firms on many important

earnings characteristics such as the level of earnings (i.e., return on assets), persistence of prior

earnings innovations, predictability of prior earnings innovations, and the magnitude of the

earnings surprise for quarter t. Thus, relying on this threshold as an exogenous source of variation

in transient ownership for this set of firms, I re-estimate a modified version of Eq. (1), and display

the results in Table 5 Panel B. Similar to the previous findings, I document a positive and

statistically significant relation between Eacar and Uet+1 (t-statistic: 3.37). Importantly, I find that

the interaction between the announcement return and an indicator for those firms just below the

Russell 1000 firm size threshold (i.e., Eacart * BelowThreshold) is negative and significant (t-

statistic: -2.27).24 In column (2) of Table 5 Panel B, I also show that the results are robust to the

24 Given that the subsamples are unbalanced (i.e., 188 and 299 observations for firms just above and below the Russell 1000 index size threshold, respectively), I perform an analysis (untabulated) where I randomly throw out 111 observations from the subsample of observations just below the index cut-off and re-estimate the regression on this balanced sample. I repeat this procedure 1,000 times and document a median t-statistic of -2.28 on the interaction term, which is highly consistent with the reported results.

22

inclusion of industry fixed effects. In summary, the analyses in Table 5 indicate that the results are

robust to using different statistical specifications designed to mitigate omitted variable and

investment selection concerns. Moreover, I examine a setting where differences in transient

ownership arise due to index membership rather than firm earnings characteristics and continue to

draw similar inferences.

4.1.2 Additional H1 Analysis

In this section, I report the results of an analysis that provides additional support for H1.

To provide more direct identification that it is the trading activities of transient institutions that

leads to the attenuation in the relation between earnings announcement returns and future earnings

news, I analyze estimated institutional order flow around earnings announcements. Specifically,

Campbell et al. (2009) estimate the daily flow of institutional trading (i.e., institution driven

trading imbalances) in firm shares by tying daily trade volumes from TAQ to changes in quarterly

institutional holdings from 13F filings for a sample of firms from 1993 to 2000.25 I merge the

sample of daily institutional order flow data examined in Campbell et al. (2009) to my sample of

observations and identify 10,799 firm-quarter observations with daily order flow data coverage. I

re-estimate a modified version of Eq. (1) where I replace Eacart with InstFlowt, which represents

cumulative institutional order flow over the announcement window (i.e., [0,3] day event

window).26 The results from this analysis are tabulated in Table 6.

The first column in Table 6 shows that there is a strong positive relation between

institutional order flow around an earnings announcement for quarter t and future earnings news

reported in quarter t+1 (t-statistic: 5.45). This is consistent with the inferences of Campbell et al.

(2009) that daily institutional order flow is positively associated with future earnings news. The

25 I thank Tarun Ramadorai for making this dataset available. 26 Estimated daily order flow is expressed in basis points of market capitalization.

23

second column in Table 6 adds interactions terms (i.e., Hitran, Hiqix, and Hided) to permit the

strength of the relation between InstFlowt and Uet+1 to vary by the composition of the firms’

institutional investor base. The results indicate that the positive relation between institutional

trading at earnings announcements and future earnings news is significantly attenuated when the

institutional investor base includes a high degree of transient ownership (t-statistic: -2.70).

However, there is no attenuation of this relation for those firms with a high degree of quasi-

indexer or dedicated ownership (t-statistics: -0.17 and 0.94). The third column tabulates the results

after propensity score matching the sample following the same procedure detailed in Appendix B.

Consistent with the results in column (2), I continue to document a negative and significant

coefficient on the interaction between institutional order flow and high transient ownership (t-

statistic: -4.41). These results provide more direct identification that the trading of transient

institutions leads to the attenuated relation between announcement returns and future earnings

news.

4.2 Tests of H2

The second hypothesis examines whether the precision of the current earnings disclosure is

associated with the beauty contest effect as suggested in Gao (2008). To test the second

hypothesis, I perform a two-way 3x3 sort of the sample on a proxy for the precision of the quarter

t earnings announcement, and then the percentage of shares owned by transient institutions. Next,

I estimate the following modified version of Eq. (1) in each of the sample partitions,

Ueit+1 = α + βEacarit + δSixbharit + γnControls + Firm Fixed Effects

+ Calendar Quarter Fixed Effects +εit, (2)

where I refer to the coefficient estimate on Eacar as an earnings prediction coefficient (EPC). I

then test across the sample partitions using seemingly unrelated estimation to examine whether the

24

strength of the EPC varies based upon earnings disclosure precision and the degree of transient

ownership.

4.2.1 Disclosure Precision Proxies for Tests of H2

I rely on three proxies to measure variation in the precision of earnings disclosures. The

first is a market-based measure and the last two are announcement content-based measures.

Rogers (2008) uses the change in bid-ask spread around the announcement as a proxy for the

degree of precision of the announcement. The basis for this measure as a proxy for disclosure

precision is that in the presence of adverse selection, market makers reduce firm liquidity.

Therefore, disclosures that are more forthcoming should improve liquidity. Following prior

literature, I estimate average bid-ask spreads over the [-10,-2] and [2,10] event windows where

day zero is the earnings announcement day (e.g., Coller and Yohn 1997; Rogers 2008).27 For a

content-based precision proxy, I examine the degree of balance sheet information included in the

earnings release. Prior research finds that earnings announcements are more informative to users

when they contain more financial statement detail (Francis, Schipper, and Vincent 2002; Collins,

Li, and Xie 2009; Barron, Byard, and Yu 2015).28 Based upon these findings, I rely on the degree

of balance sheet information included in the earnings press release as a proxy for the precision of

the earnings disclosure. I follow D’Souza, Ramesh, and Shen (2010) and measure the amount of

balance sheet information in an earnings release as:

Balance Sheet Ratio = .

. /.29 (3)

27 To estimate the bid-ask spread over these windows I use end of day bid-ask spreads from CRSP. The results are similar when using the Corwin and Schultz (2012) spread estimator. 28 Additionally, increased levels of balance sheet information included in the earnings announcement plausibly signals increased commitment to transparency to shareholders (Evans 2016). 29 I obtain data for the numerator from COMPUSTAT’s preliminary history database, and for the denominator from COMPUSTAT’s “as first reported” database. Evans (2016) and Schroeder (2016) use the same procedure when measuring earnings announcement disclosure ratios.

25

My third proxy of disclosure precision is limited to instances where management provides a

forward-looking EPS estimate at the time of the announcement (i.e., bundled guidance).

Specifically, I exploit variation in the precision of those forecasts: point versus range. Prior

research has relied upon these classifications to test theory related to the implications of disclosure

precision and security pricing (e.g., Baginski, Conrad, and Hassell 1993), where a point forecast is

considered a more precise disclosure than a range forecast.

4.2.2 H2 Findings

Table 7 examines whether the precision of the current earnings disclosure is associated

with the beauty contest effect as suggested in Gao (2008) (H2). Panels A, B, and C display the

results when the change in bid-ask spread around the earnings announcement, the amount of

balance sheet information included in the earnings release, and the specificity of a bundled

forecast, proxy for the precision of the release, respectively. The results displayed in Panel A

indicate that the EPC declines (χ2-statistic: 9.36) while moving from low to high transient

ownership when the bid-ask spread decreases from before to after the earnings announcement (top

row of Panel A). However, the same is not true when the spread remains at a similar level after

the announcement compared to before (χ2-statistic: 0.66) or when the spread increases (χ2-statistic:

0.93). Additionally, holding transient investment at a high level (moving down column 3b), the

EPC (χ2-statistic: 2.87) is smaller when bid-ask spreads decrease versus when they increase after

the disclosure. Consequently, these results provide support for H2.

The results displayed in Panel B indicate that the EPC declines (χ2-statistic: 4.20) moving

from low to high transient ownership when the earnings release contains more balance sheet

information. Additionally, holding transient investment at a high level there is a statistically

significant (χ2-statistic: 2.97) decrease in EPCs when moving from little balance sheet information

26

to significant balance information disclosed in the earnings release. These results provide further

support for H2.

Lastly, the results tabulated in Panel C indicate that the EPC declines when moving from

low to high transient ownership in occasions where management bundles a point (χ2-statistic:

4.99) or range (χ2-statistic: 6.20) forecast with the earnings release. However, holding transient

investment at a high level there is a statistical (χ2-statistic: 4.15) decrease in the EPC when moving

from a range to point bundled forecast (moving up row 2b). This result further supports H2.

Collectively the results provide evidence that the informativeness of announcement returns for

future earnings information is attenuated when the precision of the earnings announcement is high

and transient investors hold a large proportion of firm shares. This is consistent with the prediction

from Gao (2008) that short-term investors overuse public information as the precision of that

information increases.

4.3 Tests of H3

The third hypothesis examines whether overall market sentiment is associated with the

beauty contest effect. To test the third hypothesis, I perform a sort of the sample on a proxy for

overall market sentiment, similar to the tests of H2, and then the percentage of shares owned by

transient institutions. I then test across the sample partitions using seemingly unrelated estimation

to examine whether the strength of the EPC varies based upon market sentiment and the degree of

transient ownership.

4.3.1 Market Sentiment Proxies for Tests of H3

I follow prior research (Mian and Sankaguruswamy 2012) and rely on the Baker and

Wurgler (2006) sentiment index value from the month before the firm’s earnings announcement as

my primary proxy for market sentiment. Additionally, given recent findings that transient

investors are more likely to shorten their investment horizons during adverse market shocks (Cella

27

et al. 2013), I also examine periods of market crisis as an alternative proxy for extremely negative

market sentiment. I identify four major crisis periods affecting U.S. equities during my sample

examined in prior research: the Asian financial crisis (Bharath, Jayaraman, and Nagar 2013), the

Russian financial crisis (Bharath et al. 2013), the dot-com crash for NASDAQ firms

(Brunnermeier and Nagel 2004), and the Lehmann brothers bankruptcy announcement period

(Cella et al. 2013).30

4.3.2 H3 Findings

Table 8 Panel A displays the results for the tests of H3 where the Baker and Wurgler

(2006) index is used as a proxy for market sentiment. The results indicate that EPCs decline (χ2-

statistic: 13.27) moving from low to high transient ownership when market sentiment is low (i.e.,

moving along the top row of Table 8 Panel A). Similarly, the results indicate that EPCs decline

(χ2- statistic: 5.95) when moving from low to high transient ownership when market sentiment is

high (i.e., moving along the bottom row of Table 8 Panel A). However, there is no difference in

EPCs when sentiment is at a normal level (χ2- statistic: 0.01).31,32

Table 8 Panel B employs a similar procedure to examine whether the relation between

announcement returns and future earnings news varies with transient ownership to a greater extent

during shocks to the market in the form of financial crises. Prior research suggests that transient

investors are more likely to reduce their positions during periods of market crisis (Cella et al.

2013), consistent with increased sensitivity to expected near-term price changes rather than future

fundamentals. However, it is not clear a priori whether these findings generalize to a disclosure

30 The crisis periods examined include July 1997 – December 1997, August 1998 – December 1998, March 2000 – December 2000, September 2008 – October 2008 for the Asian financial crisis, Russian financial crisis, dot-com crash, and Lehman bankruptcy, respectively. 31 Given the findings from Hribar and McInnis (2012) that analyst forecasts may become biased when sentiment is high or low, I confirm the results are robust to using a naïve-time series expectation. 32 I note the possibility that increased capital allocated to transient institutions could plausibly lead to changes in overall market sentiment. I leave this for examination in future research.

28

setting. As a period of economic crisis is a time when market sentiment is extremely low, I

examine whether the association between announcement returns and next period earnings is

attenuated for firms highly held by transient investors in these periods. The results indicate that

EPCs decline when moving from low to high transient ownership during non-crisis (χ2-statistic:

6.68) and crisis periods (χ2-statistic: 3.54). However, holding transient ownership at a high level

(moving down column 3b), I find that EPCs decrease further during crisis periods (χ2-statistic:

5.38). However, a similar pattern does not emerge when a small proportion (moving down column

1b) of firm shares are held by transient investors (χ2-statistic: 0.59). Collectively, the findings in

Table 8 suggest that overall market sentiment interacts with investor horizon to determine the

extent of forward looking fundamental information that is impounded in returns around corporate

disclosure events.

4.4 Tests of H4

Beauty contest theory predicts that short-term investors will rely more on public

information and less on private information compared to long-term investors. This suggests the

propensity for an over-reaction to public news is increasing when the firm has a high proportion of

short-term investors. However, if longer-term actors are able to identify this over-reaction, then I

would expect to find evidence of a reversal in the period after the announcement. Additionally, if

less forward-looking fundamental information is impounded into returns around the current

announcement as suggested by the evidence presented thus far, then I would expect post-

announcement returns to be more sensitive to future earnings news. Consequently, I perform two

tests to examine whether post-announcement returns reflect these predicted patterns. Specifically,

I estimate the following least squares regressions,

Post EAt Returnsit = α + βUeit + γUeit * Hitranit + δHitranit

+ Calendar Quarter Fixed Effects + εit, and, (4)

29

Post EAt Returnsit = α + βUeit+1 + γUeit+1*Hitranit + δHitranit

+ Calendar Quarter Fixed Effects + εit, (5)

where Post Eat Returns represents cumulative four-factor adjusted returns (Fama and French

1993; Carhart 1997), starting four days after the earnings announcement for quarter t and

continuing through sixty-eight trading days after the earnings announcement for quarter t.33 Table

9 Panel A reports the results of estimating Eq. (4). Column (1) of Table 9 Panel A indicates that

the interaction between the earnings surprise for quarter t and an indicator for high transient

ownership is negatively associated (t-statistic: -3.14) with post announcement returns. This is

consistent with a post-announcement reversal in returns associated with the quarter t earnings

news for those firms with a high proportion of transient investors. Columns (2), (3), and (4)

demonstrate the robustness of this result to the inclusion of additional risk controls (i.e., Size, Btm,

Sixbhar, and Beta), industry fixed effects, and firm fixed effects, respectively. Additionally, as

several of these specifications indicate that there is a positive association between the earnings

surprise and post-announcement returns (i.e., Bernard and Thomas 1990), the f-statistics tabulated

in Table 9 Panel A indicate that the reversal in post announcement stock prices for firms with high

transient ownership is greater than the positive main effect on the earnings surprise term. These

results support H4a, and suggest that the initial short-window reaction to an earnings

announcement is reflective of an over-reaction when a firm has a high proportion of short-term

investors.

Further, Table 9 Panel B reports the results of estimating Eq. (5). Not surprisingly, column

(1) of Table 9 Panel B indicates that post-announcement returns are strongly associated with

quarter t+1 earnings news (t-statistic: 11.07). Importantly, I find that the interaction between

33 I chose sixty-eight days after the earnings announcement as an endpoint in order to increase the likelihood that quarter t+1 earnings have been disclosed. A typical quarter is sixty-three trading days, however, earnings announcement report dates are not fixed, therefore I added an additional one week to the post-period.

30

quarter t+1 earnings news and an indicator for high transient ownership is also positively

associated with post announcement returns (t-statistic: 2.99). This is consistent with less forward-

looking fundamental information being impounded into price during the announcement window

for quarter t earnings and thus this information is impounded into price later in the quarter. Similar

to the Panel A results, columns (2), (3), and (4) demonstrate the robustness of this result to the

inclusion of additional risk controls, industry fixed effects, and firm fixed effects, respectively.

These results support H4b, and corroborate prior evidence provided in this paper suggesting that

less future fundamental information is impounded into prices around public disclosures when a

firm’s investor base contains a significant proportion of short-term investors.

5. CONCLUSION

In his famous economic work, The General Theory of Employment, Interest, and Money,

Keynes compared the stock market with a beauty contest, where contestants try to guess the

opinions of other judges rather than forming their own opinions. Their objective was not to choose

the prettiest face, but rather to choose the contest winners. Keynes (1936) and Allen et al. (2006)

liken this to the stock market where short-term investors search effort is not focused on

determining fundamental value, but rather on finding out or forecasting the information that other

traders will trade on in the near future. One mechanism that can help these investors make

forecasts about future trader actions is public information. As public information is common to the

information set of all investors, it is plausibly useful in forecasting near-term average expectations.

A predicted effect of this behavior, however, is that prices will be less reflective of future

fundamentals when short-term investors trade on beliefs about near-term prices.

Motivated by these predictions, I examine whether the relation between price changes at an

earnings announcement and period ahead earnings news is attenuated when transient investors

own a high proportion of firm shares. I document associations consistent with this prediction.

31

Additionally, I find stock prices form more slowly over the course of a quarter when a high

proportion of firm shares are held by transient investors consistent with future fundamental

information being recognized in price with a delay. I also examine circumstances in which I

anticipate transient investors to be more concerned with forecasting near-term price swings versus

assessing underlying firm fundamental value. I find that, for firms with more transient ownership,

the relation between earnings announcements returns and future earnings news is weaker when the

current earnings disclosure is more precise. I also find the association between earnings

announcement returns and period ahead earnings news is weaker for firms with high transient

ownership compared to low transient ownership when market sentiment is lower or higher than

normal. Additionally, I find the informativeness of announcement returns for future earnings for

firms with high transient ownership to be lower during crisis periods compared to non-crisis

periods, but I find no evidence that this is true for firms with relatively little transient ownership.

Lastly, consistent with an initial over-reaction to public information, I provide initial evidence of a

reversal in stock returns after an earnings announcement associated with the earnings news for

those firms with a high proportion of transient ownership.

This paper is one of the first studies to empirically test whether theories that incorporate

higher-order beliefs are descriptive of price formation around public disclosures. While assertions

from a number of capital market stakeholders have indicated that short-term investors alter the

price process, little previous empirical evidence exists. The implications of these findings are

potentially far-reaching. Does the interaction between public information and short investment

horizons significantly contribute to significant security mispricing or bubble formation? How can

managers best develop disclosure policies to maximize price informativeness when their investor

base includes significant short-term ownership? I leave these questions to be answered in future

research.

32

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36

APPENDIX AVariable Definitions

Variable DefinitionPrimary Model Variables

Ue t+1 = Actual earnings reported by I/B/E/S for firm quarter t+1 less the mean of qualifying individual analyst forecasts for quarter t+1 calculated one day prior to the earnings announcement for quarter t. The forecast error is scaled by price at the end of quarter t. Qualifying forecasts are made within 90 days of the earnings announcement. If an analyst makes multiple forecasts during the 90 day period, only the most recent forecast is used.

Eacar = The cumulative prediction error (i.e., market-adjusted returns), during the event window [0,3], from a standard market model, where the estimation period for the market model parameters is [t-200,t-21], and the earning announcement date is day 0.

Sixbhar = The six month buy and hold return for firm i ending three days prior to the earnings announcement for quarter t.

Ue t = Actual earnings reported by I/B/E/S for firm quarter t less the mean of qualifying individual analyst forecasts for quarter t calculated one day prior to the earnings announcement for quarter t. The forecast error is scaled by price at the end of quarter t. Qualifying forecasts are made within 90 days of the earnings announcement. If an analyst makes multiple forecasts during the 90 day period, only the most recent forecast is used.

Bundlenews = The difference between the EPS forecast provided by management for the next quarter and the consensus expectation for the next quarter at the earnings announcement (per the I/B/E/S guidance database), scaled by price at the end of quarter t. If a range forecast was provided the midpoint was used. If no forecast was provided then set equal to zero.

Characteristics of EarningsLoss = A binary variable set to one when earnings before extraordinary items for

quarter t are less than zero, otherwise set to zero.

Persist = The AR(1) coefficient from a regression of seasonally differenced return on assets estimated over the twenty quarters prior to quarter t, where at least 12 quarters of data was required to estimate the parameter.

Predict = The root mean squared error from the firm specific rolling persistence regressions described above.

Report lag = The difference between the earnings announcement date and the quarter end date.

Firm CharacteristicsSize = The natural logarithm of total assets at the end of quarter t.Btm = The book-to-market ratio at the end of quarter t.Growth = The percentage change in seasonally differenced firm total assets.Analysts = The number of analyst forecasts included in the consensus forecast calculated

one day prior to the earnings announcement for quarter t.

37


Variable DefinitionLeverage = The sum of long-term debt in non-current liabilities and the current portion

of long-term debt in current liabilities divided by total assets.

Dedown = The percentage of shares owned by dedicated investors as classified by Bushee (2001), from the 13F filing just prior to the earnings announcement for quarter t.

Qixown = The percentage of shares owned by quasi-indexer investors as classified by Bushee (2001), from the 13F filing just prior to the earnings announcement for quarter t.

Traown = The percentage of shares owned by transient investors as classified by Bushee (2001), from the 13F filing just prior to the earnings announcement for quarter t.

Risk and DisagreementAndisp t = The standard deviation of qualifying individual analyst forecasts for quarter t

earnings measured one day prior to the earnings announcement for quarter t. Dispersion is scaled by price at the end of quarter t.

Andisp t+1 = The standard deviation of qualifying individual analyst forecasts for quarter t+1 earnings measured one day prior to the earnings announcement for quarter t. Dispersion is scaled by price at the end of quarter t.

Retvol = The standard deviation of firm stock returns over the sixty-day period ending three days before the earnings announcement for quarter t.

Appendix B VariablesD_Mrktadjret = The decile rank (by calendar quarter) of the firm's market-adjusted one-year

buy-and-hold return concluding at the end of quarter t.

D_Earngrow = The decile rank (by calendar quarter) of earnings growth calculated for quarter t-1 as the seasonal difference in earnings before extraordinary items scaled by beginning total assets.

D_Turnover = The decile rank (by calendar quarter) of the firm's average monthly turnover ratio (shares traded/shares outstanding) for the twelve month period concluding at the quarter t end date.

D_Mve = The decile rank (by calendar quarter) of the firm's market value of equity at the end of quarter t.

D_Age = The decile rank (by calendar quarter) of the number of years the firm is listed in the CRSP header file at the end of quarter t.

Dividend = An indicator variable set to one if the firm paid dividends in the prior fiscal year, and zero otherwise.

D_Retvol = The decile rank (by calendar quarter) of the standard deviation of firm stock returns over the sixty day period ending three days before the earnings announcement for quarter t.

38


Variable DefinitionAdditional Variables

Hitran = A binary variable set to one if the level of transient ownership (Traown ) in firm i is greater than or equal to the quarterly median value, and zero otherwise.

Hiqix = A binary variable set to one if the level of quasi-indexer ownership (Qixown ) in firm i is greater than or equal to the quarterly median value, and zero otherwise.

Hided = A binary variable set to one if the level of dedicated ownership (Dedown ) in firm i is greater than or equal to the quarterly median value, and zero otherwise.

Adjret t+n = The daily market-adjusted return for firm i, where the day (n) is denoted with respect to the earnings announcement date for quarter t.

BelowThreshold = A binary variable set to one if a firm was listed in the top 50 positions of the Russell 2000 index during the six months after the annual rebalancing in June, and set to zero if a firm was listed in the bottom 50 positions of the Russell 1000 index during the six months after the annual rebalancing in June.

InstFlow = The cumulative daily institutional trading imbalances estimated in Campbell, Ramadorai, and Schwartz (2009), during the event window [0,3], where the earnings announcement date is day 0. The estimated daily order flow is expressed in basis points of market capitalization.

Post EA t Returns = Cumulative four-factor model adjusted returns, calculated starting four days after the earnings announcement for quarter t and continuing through sixty-eight trading days after the earnings announcement. The post announcement four-factor model adjusted returns are calculated as the prediction errors from a four-factor model (i.e., market beta, size, book-to-market, and momentum) estimated every firm-quarter over the [-30, 252] window where zero is the earnings announcement date.

Beta = The market model beta calculated using sixty months (minimum of 36 months) of firm and market returns ending at the end of quarter t.

39

Appendix B Propensity Score Matching

I use the following first stage probit model to predict the likelihood that a high proportion

of firm i shares are held by transient institutions prior to an earnings announcement for quarter t,

in order to predict the probabilities necessary to construct a 1-to-1 propensity score matched

(PSM) sample. Variable definitions for the model are noted in Appendix A with further discussion

below.

Pr(Hitran=1) = α + β1D_Mrktadjretit + β2D_Earngrowit + β3D_Turnoverit

+ β4D_Mveit + β5D_Ageit + β6Dividendit + β7D_Retvolit

+ Industry Fixed Effects + Calendar Quarter Fixed Effects +εit.

The dependent variable (Hitran) is calculated in the same manner as it is used throughout

the manuscript, where it is set to one if the level of transient ownership (Traown) in firm i is

greater than the quarterly median value, and zero otherwise. The covariates included in the model

are based upon prior research on the determinants of transient investment. The first two

determinants, market adjusted annual returns (D_Mrktadjret) and lagged earnings growth

(D_Earngrow), are included given that prior research finds that transient investors trade with

earnings growth and return momentum (Bushee 2001; Ke and Ramalingegowda 2005).34 The

third determinant, average annual share turnover (D_Turnover), is based upon the conclusions of

Bushee and Noe (2000) and Bushee (2001), that transient investors prefer liquid trading

environments. The next three determinants: market value of equity (D_Mve), firm age (D_Age),

and whether the firm pays dividends (Dividend) are included given transient investors propensity

to invest in smaller and less mature firms (Dikoli, Kulp, and Sedatole 2009). Lastly, I include

return volatility (D_Retvol) to control for any residual risk-profile differences between high and

low transient ownership firms.

34 Variables with a “D” prefix indicate that they are decile ranked variables calculated every calendar quarter. The results are robust to matching on the continuous variable analogs.

40

Table B1 presents the results of the first stage probit model. The model has a pseudo r-

squared of 22.1 percent and an area under the ROC curve of 80.2 percent, which is considered

excellent discrimination (Hosmer and Lemeshow 2000). I find that market-adjusted return,

earnings growth, and average share turnover are strongly positively associated with the likelihood

of having a high transient investor base. Additionally, I find that firm market value, age, dividend

policy, and return volatility are negatively associated with the likelihood of having a high transient

investor base.35 All of the predictors of high transient ownership have a coefficient with the

anticipated sign.

The PSM model produces a sample of treatment and control observations that have the

closest predicted probabilities of having a high transient investor base. This allows me to control

for observable determinants of transient investor stock selection so that the inferences about the

effect of transient ownership on earnings announcement return informativeness is not biased by

the endogenous investment choice. I construct the PSM sample by performing a 1-to-1 match,

without replacement, of treatment (Hitran=1) and control (Hitran=0) observations predicted

probabilities within a caliper range of one percent.36 This results in a total of 22,975 matched pairs

(45,950 observations) for the PSM sample analysis reported in Table 5. Per Table B1, I achieve

covariate balance from the first stage model, on the majority of covariates. While there is a noted

statistical difference on D_Turnover, the economic significance of the difference does not appear

large. However, I include D_Turnover as a control in the propensity-matched sample linear

regression tabulated in Table 5 in order to hold constant any differences in share turnover between

the treatment and matched control observations.

35 In untabulated tests where share turnover is omitted as a determinant I find that return volatility is positive and strongly associated with high transient ownership consistent with the findings of Bushee and Noe (2000). Inferences remain unchanged if share turnover is omitted as a determinant. 36 The inferences do not change if I increase the maximum distance in propensity score to five percent or ten percent.

41

TABLE B-1Propensity Score Match Model

Mean ComparisionDV = Hitran Treated Control t-statistic

D_Mrktadjret (+) 0.0424*** 5.41 5.40 0.22[13.41]

D_Earngrow (+) 0.0522*** 5.35 5.35 0.09[18.20]

D_Turnover (+) 0.2347*** 5.62 5.67 -2.15**[38.16]

D_Mve (-) -0.0488*** 5.39 5.40 -0.42[-5.82]

D_Age (-) -0.0266*** 5.31 5.28 1.00[-4.59]

Dividend (-) -0.2902*** 0.53 0.53 0.73[-7.99]

D_Retvol (+/-) -0.0250*** 5.57 5.60 -1.16[-4.09]

Intercept (+/-) -0.9181*** - - -[-2.86]

Observations 84,361

Psuedo R2 22.1%Area under ROC Curve (AUC) 0.802Industry FE YESQrtr FE YESSE Cluster Firm & Quarter

Table B-1 displays the results of estimating a probit regression where Hitran is the dependent variable with z-statistics reported in parentheses below each coefficient. Industry and calendar quarter fixed effects are included in the model and standard errors are clustered by firm and quarter. All variables are winsorized at the 1% and 99% levels. All variable definitions are provided in Appendix A. ***, **, * indicate two-tailed statistical significance of coefficient estimates at the 1%, 5%, and 10% level.

42

FIGURE 1

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

‐15 ‐14 ‐13 ‐12 ‐11 ‐10 ‐9 ‐8 ‐7 ‐6 ‐5 ‐4 ‐3 ‐2 ‐1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

ST

RE

NG

TH

OF

TH

E P

-L-E

RE

LA

TIO

NS

HIP

DAY RELATIVE TO EARNINGS ANNOUNCEMENT FOR QUARTER T

Cumulative Daily P-L-E CoefficientsDay by Day around Earnings Announcements

Bottom Decile Transient %

Below Median Transient %

Above Median Transient %

Top Decile Transient %

43

TABLE 1Sample Selection

Adjustments

301,550 301,550 301,550 301,550 301,550 301,550

(87,572) (87,572) (87,572) (87,572) (87,572) (87,572)

(3,878) (3,878) (3,878) (3,878) (3,878) (3,878)

(101,297) (101,297) (101,297) (101,297) (101,297) (101,297)

(24,442) (24,442) (24,442) (24,442) (24,442) (24,442)

84,361 84,361 84,361 84,361 84,361 84,361

(1,088)

83,273

(83,874)

487

(73,562)

10,799

(3,712)

80,649

(57,353)

27,008

Total firm-quarters from the intersection of COMPUSTAT Quarterly Unrestated Earnings Database, CRSP, and I/B/E/S from 1991 to 2013

Less: Firm-quarters without 13F filing within 90 days of earnings announcement & after the earnings announcement for quarter t-1

Less: Firm-quarters with stock prices less than $2

Less: Firm-quarters dropped because they have less than three analysts following them in quarters t and t+1

Less: Firm-quarter missing necessary COMPUSTAT or CRSP data to calculate control variables

Less: Firm-quarters without a bundled quarterly or annual EPS management forecast

Sample Examined in Table 7 Panel C

Primary Sample Examined

Less: Firm-quarters missing detailed preliminary earnings coverage

Sample Examined in Table 7 Panel B

Less: Firm-quarters lacking coverage in the daily estimated institutional trading flow data in the Campbell, Ramadorai, and Schwartz (2009) dataset

Sample Examined in Table 6

Less: Firm-quarters with less than 3 qualifying analysts forecasts for quarter t+1 fifteen days before the earnings announcement for quarter t

Sample Examined in Table 4

Less: Firm-quarters where the company was not listed in the first (last) fifty positions in the Russell 2000 (1000) index during the first six months after the annual index rebalancing in June of each year

Sample Examined in Table 5 Panel B

44

TABLE 2Descriptive Statistics

Panel A: Primary Sample

Variable N Mean Std. dev. 25th Pct Median 75th PctUe t+1 84,361 -0.0016 0.0112 -0.0024 0.0001 0.0017

Eacar 84,361 0.0013 0.0763 -0.0386 0.0009 0.0416Sixbhar 84,361 0.0769 0.3095 -0.1029 0.0592 0.2229Ue t 84,361 0.0003 0.0061 -0.0004 0.0004 0.0017

Ue t * |Ue t | 84,361 0.0000 0.0001 0.0000 0.0000 0.0000

Bundlenews 84,361 -0.0004 0.0017 0.0000 0.0000 0.0000Loss 84,361 0.1639 - - - -Persist 84,361 0.2914 0.2954 0.0612 0.2784 0.5202Predict 84,361 0.0256 0.0372 0.0051 0.0120 0.0292Reportlag 84,361 27.0000 9.2000 20.0000 26.0000 32.0000Total assets ($M) 84,361 11,150 31,014 641 2,049 7,126Btm 84,361 0.5202 0.3733 0.2689 0.4397 0.6754Growth 84,361 0.1451 0.2932 0.0029 0.0772 0.1929Analysts 84,361 9.5000 6.0000 5.0000 8.0000 13.0000Leverage 84,361 0.2092 0.1918 0.0340 0.1826 0.3213Andisp t 84,361 0.0020 0.0036 0.0004 0.0009 0.0020

Andisp t+1 84,361 0.0024 0.0037 0.0005 0.0012 0.0026

Retvol 84,361 0.0248 0.0136 0.0151 0.0213 0.0305Qixown 84,361 0.4509 0.1577 0.3468 0.4575 0.5593Dedown 84,361 0.0821 0.0711 0.0248 0.0665 0.1229Traown 84,361 0.1708 0.1003 0.0952 0.1556 0.2298

Table 2 Panel A displays descriptive statistics for the variables in the primary analysis. To minimize the effects of outliers, all continuous variables are winsorized at the 1% and 99% levels. All variable definitions are provided in Appendix A.

45

TABLE 2Descriptive Statistics

Panel B: Partitioned By High/Low Transient Ownership

Hitran=1 Hitran=0N Mean Std. dev. N Mean Std. dev. Difference

Variable (1) (2) (3) (4) (5) (6) (2) - (5)

Ue t+1 42,203 -0.0012 0.0108 42,158 -0.0020 0.0115 0.0007***

Eacar 42,203 0.0007 0.0828 42,158 0.0019 0.0692 -0.0012**Sixbhar 42,203 0.1066 0.3422 42,158 0.0471 0.2697 0.0595***Ue t 42,203 0.0006 0.0058 42,158 0.0000 0.0064 0.0006***

Ue t * |Ue t | 42,203 0.0000 0.0001 42,158 0.0000 0.0002 0.0000***

Bundlenews 42,203 -0.0004 0.0018 42,158 -0.0003 0.0015 -0.0001***Loss 42,203 0.1717 0.3771 42,158 0.1561 0.3629 0.0156***Persist 42,203 0.2909 0.2895 42,158 0.2919 0.3013 -0.0011Predict 42,203 0.0315 0.0409 42,158 0.0196 0.0320 0.0119***Reportlag 42,203 28.0 9.4 42,158 26.0 9.0 2.000***Total assets ($M) 42,203 5,700 19,000 42,158 17,000 39,000 -11,300***Btm 42,203 0.4811 0.3502 42,158 0.5593 0.3912 -0.0782***Growth 42,203 0.1844 0.3349 42,158 0.1058 0.2380 0.0785***Analysts 42,203 9.5000 5.8000 42,158 9.6000 6.2000 -0.1000Leverage 42,203 0.2023 0.2008 42,158 0.2160 0.1821 -0.0137***Andisp t 42,203 0.0020 0.0035 42,158 0.0021 0.0037 -0.0001***

Andisp t+1 42,203 0.0024 0.0037 42,158 0.0024 0.0037 0.0001**

Retvol 42,203 0.0269 0.0135 42,158 0.0228 0.0134 0.0041***Qixown 42,203 0.4695 0.1432 42,158 0.4323 0.1689 0.0372***Dedown 42,203 0.0858 0.0687 42,158 0.0784 0.0732 0.0074***Traown 42,203 0.2459 0.0830 42,158 0.0957 0.0441 0.1502***

Table 2 Panel B displays descriptive statistics for the variables in the primary analysis for the high and low transient ownership subsamples. To minimize the effects of outliers, all continuous variables are winsorized at the 1% and 99% levels. All variable definitions are provided in Appendix A. ***, **, * indicate two-tailed statistical significance of the difference between sample means at the 1%, 5%, and 10% level.

46

TABLE 3Tests of H1

The Informativeness of Announcement Returns for Future Earnings

DV= Ue t+1 w/o additional w/ add. Ue t w/ add. Eacar w/ all

Interactions Interactions Interactions Interactions

Variables Pred. (1) (2) (3) (4)

Announcement Returns and Investor OwnershipEacar t + 0.0174*** 0.0172*** 0.0071*** 0.0070***

[8.99] [8.65] [4.27] [3.96]Eacar t * Hitran - -0.0045*** -0.0044*** -0.0042*** -0.0040***

[-3.17] [-3.04] [-2.94] [-2.76]Eacar t * Hiqix +\- -0.0001 -0.0002 0.0017 0.0015

[-0.11] [-0.17] [1.27] [1.16]Eacar t * Hided +\- -0.0011 -0.0012 -0.0011 -0.0012

[-0.72] [-0.76] [-0.75] [-0.80]Lag Returns and Investor Ownership

Sixbhar + 0.0042*** 0.0041*** 0.0045*** 0.0045***[10.25] [10.15] [10.66] [10.55]

Sixbhar * Hitran - -0.0009*** -0.0009*** -0.0009*** -0.0009***[-2.68] [-2.62] [-2.70] [-2.62]

Sixbhar * Hiqix +\- 0.0007* 0.0007* 0.0005 0.0005[1.86] [1.93] [1.31] [1.38]

Sixbhar * Hided +\- -0.0003 -0.0004 -0.0004 -0.0004[-1.06] [-1.11] [-1.29] [-1.33]

Ownership Main EffectsHitran +\- 0.0001 0.0001 0.0001 0.0001

[0.64] [0.61] [1.00] [0.97]Hiqix +\- -0.0002 -0.0002 0.0000 0.0000

[-1.23] [-1.22] [0.03] [0.03]Hided +\- 0.0001 0.0001 0.0002 0.0002

[0.92] [0.99] [1.47] [1.52]Current Earnings News

Ue t + 0.5114*** 0.5189*** 0.4747*** 0.4875***

[13.83] [11.40] [12.45] [10.49]Ue t * |Ue t | - -4.7204*** -4.8419*** -2.7032 -3.2555*

[-2.77] [-2.83] [-1.52] [-1.83]Bundlenews + 0.4581*** 0.4566*** 0.4530*** 0.4526***

[10.26] [10.24] [10.15] [10.13]

Observations 84,361 84,361 84,361 84,361

Adjusted R2 28.8% 29.0% 28.2% 28.5%Calendar Qrtr FE & Intercept YES YES YES YESFirm FE YES YES YES YESCharacteristics of Earnings Controls YES YES YES YESFirm Characteristics Controls YES YES YES YESDisagreement and Risk Controls YES YES YES YESUe t * Characteristics of Earnings Controls NO YES NO YES

Eacar * Risk and Disagreement Controls NO NO YES YESSE Cluster Firm & Qrtr Firm & Qrtr Firm & Qrtr Firm & Qrtr

Table 3 reports linear regressions where Ue t+1 is the dependent variable with t-statistics reported in brackets below each coefficient. Firm fixed effects and calendar quarter fixed effects are included in each model and standard errors are clustered by firm and quarter. All variables are winsorized at the 1% and 99% levels. All variable definitions are provided in Appendix A. Column (2) displays regression results after adding the following interactions: Ue t * Loss , Ue t * Persist , Ue t * Predict , Ue t * Reportlag , where Persist , Predict , and Reportlag are transformed to dummy variables based upon quarterly median splits. Column (3) displays regression results after adding the following interactions: Eacar t * Andisp t, Eacar t * Andisp t+1, Eacar t * Retvol , where Andisp t, Andisp t+1, and Retvol are transformed to dummy variables based upon quarterly median splits. Column (4) displays regression results after adding the interaction terms from Columns (2) and (3). ***, **, * indicate two-tailed statistical significance of coefficient estimates at the 1%, 5%, and 10% level.

47

TABLE 4Tests of H1

Day by Day Informativeness of Returns for Future Earnings

Adjret t+n Adjret t+n * Hitran

Daily Return Around EA t Coef (λt+n) t-stat Coef (ωt+n) t-stat

Day t-15 0.0088** [2.34] -0.0072 [-1.53]Day t-14 0.0066** [2.04] -0.0045 [-1.04]Day t-13 0.0073* [1.86] 0.0063 [1.33]Day t-12 0.0067* [1.82] 0.0015 [0.31]Day t-11 0.0024 [0.57] 0.0054 [0.99]Day t-10 0.0073** [2.00] 0.0033 [0.80]Day t-9 0.0021 [0.68] 0.0062 [1.41]Day t-8 0.0109** [2.38] -0.0012 [-0.25]Day t-7 0.0158*** [4.47] -0.0066 [-1.43]Day t-6 0.0026 [0.67] 0.0060 [1.24]Day t-5 0.0102*** [2.99] -0.0060 [-1.43]Day t-4 0.0046 [1.43] -0.0021 [-0.59]Day t-3 0.0068* [1.68] -0.0041 [-0.77]Day t-2 0.0146*** [3.91] 0.0003 [0.06]Day t-1 0.0162*** [3.76] 0.0002 [0.04]Day t 0.0193*** [8.38] -0.0052** [-2.00]Day t+1 0.0161*** [10.28] -0.0029* [-1.82]Day t+2 0.0226*** [6.82] -0.0141*** [-3.61]Day t+3 0.0138*** [2.93] -0.0037 [-0.82]Day t+4 0.0109*** [2.98] -0.0013 [-0.29]Day t+5 0.0122*** [3.40] -0.0062 [-1.50]Day t+6 0.0138*** [3.27] -0.0063 [-1.25]Day t+7 0.0189*** [4.86] -0.0080* [-1.73]Day t+8 0.0098** [2.36] 0.0002 [0.04]Day t+9 0.0121*** [3.20] -0.0023 [-0.52]Day t+10 0.0106*** [2.77] 0.0008 [0.16]Day t+11 0.0079** [2.01] -0.0000 [-0.01]Day t+12 0.0089** [2.56] 0.0008 [0.14]Day t+13 0.0087** [2.12] -0.0025 [-0.64]Day t+14 0.0085** [2.21] -0.0001 [-0.03]Day t+15 0.0121** [2.57] -0.0018 [-0.39]

Observations 83,273

Adjusted R2 32.5%Calendar Qrtr FE & Intercept YESFirm FE YESControls YESSE Cluster Firm & Qrtr

Table 4 reports linear regressions where Ue t+1 is the dependent variable with t-statistics reported in brackets next to each coefficient. Firm fixed effects and calendar quarter fixed effects are included in the model and standard errors are clustered by firm and quarter. Controls include the characteristics of earnings, firm characteristics, and the disagreement and risk controls as well as the lagged return control (Sixbhar ) and its interaction with high transient ownership (Sixbhar*Hitran ). All variables are winsorized at the 1% and 99% levels. All variable definitions are provided in Appendix A. ***, **, * indicate two-tailed statistical significance of coefficient estimates at the 1%, 5%, and 10% level.

48

TABLE 5Tests of H1

The Informativeness of Announcement Returns for Future EarningsEndogeneity

Panel A: Alternative Specifications

DV= Ue t+1 Model: Time Vary FE First Diff PSM

Variables Pred. (1) (2) (3)

Returns and Investor OwnershipEacar t + 0.01740*** 0.01400*** 0.01744***

[12.04] [9.86] [11.01]Eacar t * Hitran - -0.00532*** -0.00354** -0.00479***

[-3.47] [-2.50] [-2.76]Sixbhar + 0.00341*** 0.00048 0.00403***

[9.39] [1.31] [9.95]Sixbhar * Hitran - -0.00094*** -0.00004 -0.00029

[-2.65] [-0.11] [-0.56]Hitran +\- -0.00014 -0.00057*** -0.00003

[-0.88] [-3.34] [-0.18]

Observations 84,361 61,597 45,950Adjusted R2 45.5% 7.8% 36.0%

Calendar Qrtr FE & Intercept YES YES YES

Firm FE YES NO YES

Controls YES YES YES

SE Cluster Firm & Qrtr Firm & Qrtr Firm & QrtrTable 5 Panel A reports linear regressions where Ue t+1 is the dependent variable with t-statistics reported in brackets below each coefficient. Calendar quarter fixed effects are included in each model and standard errors are clustered by firm and quarter. Controls include the characteristics of earnings, firm characteristics, and the disagreement and risk controls. All variables are winsorized at the 1% and 99% levels. All variable definitions are provided in Appendix A. Column (1) displays regression results where a dummy variable is included for each firm every six consecutive quarters. Column (2) displays regression results for a first difference estimator. Column (3) displays regression results for a one-to-one propensity score matched sample detailed in Appendix B. ***, **, * indicate two-tailed statistical significance of coefficient estimates at the 1%, 5%, and 10% level.

49

TABLE 5Tests of H1

The Informativeness of Announcement Returns for Future EarningsEndogeneity

Panel B: Russell 2000/1000 Threshold

DV= Ue t+1 Pred. (1) (2)

Returns and Investor OwnershipEacar t + 0.0554*** 0.0611***

[3.37] [3.40]Eacar t * BelowThreshold - -0.0377** -0.0356**

[-2.27] [-1.98]Sixbhar + 0.0038 0.0009

[0.82] [0.17]

Sixbhar * BelowThreshold - -0.0005 0.0033[-0.11] [0.56]

BelowThreshold +\- 0.0011 -0.0005[0.75] [-0.34]

Observations 487 487Adjusted R2 20.2% 21.1%

Calendar Qrtr FE & Intercept YES YES

Industry FE NO YES

Controls YES YES

SE Cluster Firm Firm

Table 5 Panel B reports linear regressions where Ue t+1 is the dependent variable with t-statistics reported in brackets below each coefficient. Calendar quarter fixed effects are included in each model and standard errors are clustered by firm. These analyses examine a subsample of earnings announcement observations where the corresponding firm was included in top (bottom) 50 positions in the Russell 2000 (1000) index during the first six months after the annual index rebalancing. BelowThreshold is a binary variable set to one if a firm was listed in the top 50 positions of the Russell 2000 index during the six months after the annual rebalancing in June, and set to zero if a firm was listed in the bottom 50 positions of the Russell 1000 index during the six months after the annual rebalancing in June. Controls include the characteristics of earnings, firm characteristics, and the disagreement and risk controls. All variables are winsorized at the 1% and 99% levels. All variable definitions are provided in Appendix A. ***, **, * indicate two-tailed statistical significance of coefficient estimates at the 1%, 5%, and 10% level.

50

TABLE 6Tests of H1

The Informativeness of Institutional Trade Flow for Future Earnings

DV= Ue t+1 w/o Inv Horiz w/ Inv Horiz

Interactions Interactions PSM

Variables Pred. (1) (2) (3)

Institutional Trading and Investor OwnershipInstFlow t + 0.0204*** 0.0309*** 0.0431***

[5.45] [3.19] [3.11]InstFlow t * Hitran - - -0.0212*** -0.0450***

[-2.70] [-4.41]InstFlow t * Hiqix +\- - -0.0014 -0.0031

[-0.17] [-0.21]InstFlow t * Hided +\- - 0.0079 0.0183

[0.94] [1.22]Ownership Main Effects

Hitran +\- - -0.0001 0.0001[-0.25] [0.16]

Hiqix +\- - 0.0006** 0.0005[2.56] [1.43]

Hided +\- - -0.0004 -0.0006[-1.36] [-1.35]

Current Earnings NewsUe t + 0.9798*** 0.9802*** 0.9871***

[10.25] [10.33] [7.18]Ue t * |Ue t | - -18.9348*** -19.0146*** -22.6833***

[-3.37] [-3.41] [-2.99]Bundlenews + 0.2637* 0.2662* 0.4142**

[1.83] [1.85] [2.11]

Observations 10,799 10,799 5,744

Adjusted R2 43.2% 43.3% 42.8%Calendar Qrtr FE & Intercept YES YES YESIndustry FE YES YES YESControls YES YES YESSE Cluster Firm & Qrtr Firm & Qrtr Firm & Qrtr

Table 6 reports linear regressions where Ue t+1 is the dependent variable with t-statistics reported in brackets

below each coefficient. InstFlow t represents the cumulative net order imbalance from institutional traders during

the [0,3] day announcement window (the coefficient estimates on InstFlow have been multiplied by 1,000 for ease of tabulation) estimated by Campbell et al. (2009). Controls include the characteristics of earnings, firm characteristics, and the disagreement and risk controls. Industry fixed effects and calendar quarter fixed effects are included in each model and standard errors are clustered by firm and quarter. All variables are winsorized at the 1% and 99% levels. All variable definitions are provided in Appendix A. ***, **, * indicate two-tailed statistical significance of coefficient estimates at the 1%, 5%, and 10% level.

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TABLE 7Tests of H2

Disclosure Precision, Transient Investors, and Price Informativeness

Panel A: Disclosure Precision Proxy - Change in Spreads Around the Earnings Announcement

Transient OwnershipLow Mid High

(1b) (2b) (3b) χ2 Test:Decrease (1a) 0.02146*** 0.01464*** 0.00959*** (1a,1b) = (1a,3b)

[5.09] [6.33] [4.42] 9.36***9,369 9,369 9,369

Small Chg (2a) 0.01232*** 0.01141*** 0.01015*** (2a,1b) = (2a,3b)[4.48] [5.08] [5.94] 0.669,381 9,381 9,380

Increase (3a) 0.01418*** 0.01615*** 0.01442*** (3a,1a) = (3a,3b)[3.87] [6.66] [5.97] 0.939,371 9,371 9,370

χ2 Test: (1a,1b) = (3a,1b) (1a,2b) = (3a,2b) (1a,3b) = (3a,3b)2.51 0.23 2.87*

Panel B: Disclosure Precision Proxy - Balance Sheet Disclosure Ratio

Transient OwnershipLow Mid High

(1b) (2b) (3b) χ2 Test:Near Full (1a) 0.01946*** 0.01475*** 0.01147*** (1a,1b) = (1a,3b)

[4.71] [7.70] [6.05] 4.20**8,803 8,802 8,802

Some (2a) 0.01535*** 0.01185*** 0.01046*** (2a,1b) = (2a,3b)[6.30] [5.68] [5.18] 2.599,028 9,027 9,027

Almost None (3a) 0.02060*** 0.01282*** 0.01544*** (3a,1a) = (3a,3b)[6.50] [4.62] [7.10] 2.449,054 9,053 9,053

χ2 Test: (1a,1b) = (3a,1b) (1a,2b) = (3a,2b) (1a,3b) = (3a,3b)0.11 0.39 2.97*

Bal

ance

Sh

eet R

atio

Δ in

Spr

eads

52

TABLE 7Tests of H2

Disclosure Precision, Transient Investors, and Price Informativeness

Panel C: Disclosure Precision Proxy - Bundled Forecast Precision

Transient OwnershipLow High(1b) (2b) χ2 Test:

Point (1a) 0.01205*** 0.00272 (1a,1b) = (1a,2b)[2.82] [1.10] 4.99**1,727 1,726

Range (2a) 0.01327*** 0.00795*** (2a,1b) = (2a,2b)[6.89] [5.69] 6.20**11,778 11,777

χ2 Test: (1a,1b) = (2a,1b) (1a,2b) = (2a,2b)0.10 4.15**

Ue it+1 = α + βEacar it + δSixbhar it + γ1Ue it + γ2Ue it * |Ue it| + γ3Bundlenews it

+ γ4Loss it + γ5Persist it + γ6Predict it +γ7Reportlag it + γ8Size it + γ9Btm it

+ γ10Growth it + γ11Analysts it + γ12Leverage it + γ13Dedown it + γ14Qixown it

+ γ15Andisp it +γ16Andisp it+1 + γ17Retvol it + Firm Fixed Effects + Cal Qtr Fixed Effects + εit.

Table 7 Panels A-C report the coefficient on β after estimating the following model within each cell resulting from the double sort of the primary sample:

In Panel A, the change in bid ask spread is calculated by subtracting the average spread during the [-10,-2] window from the average spread during the [2,10] window. In Panel B, the amount of balance sheet information included in the earnings announcement is calculated using the procedure established in D'Souza, Ramesh, and Shen (2010). In Panel C, the analysis is limited to only those observations where a point or range bundled forecast is provided with the earnings announcement. Standard errors are clustered by firm and quarter. Seemingly unrelated estimation is used to test whether β is statistically significant across the partitions. ***, **, * indicate two-tailed statistical significance of coefficient estimates at the 1%, 5%, and 10% level.

F

orec

ast P

reci

sion

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TABLE 8Tests of H3

Market Sentiment, Transient Investors, and Price Informativeness

Panel A: Sentiment Proxy - Baker and Wurgler Index

Transient Ownership

Low Mid High

(1b) (2b) (3b) χ2 Test:

Low (1a) 0.02644*** 0.01268*** 0.01310*** (1a,1b) = (1a,3b)

[7.59] [4.54] [6.68] 13.27***

9,648 9,648 9,648

Normal (2a) 0.01399*** 0.01100*** 0.01367*** (2a,1b) = (2a,3b)

[5.29] [4.53] [8.01] 0.01

9,390 9,390 9,389

High (3a) 0.01437*** 0.01034*** 0.00822*** (3a,1b) = (3a,3b)

[5.10] [6.22] [5.70] 5.95**

9,083 9,083 9,082

χ2 Test: (1a,1b) = (3a,1b) (1a,2b) = (3a,2b) (1a,3b) = (3a,3b)10.08*** 0.56 4.43**




+ γ15Andisp it +γ16Andisp it+1 + γ17Retvol it + Firm Fixed Effects + εit.

Mar

ket S

entim

ent

Table 8 Panel A reports the coefficient on β after estimating the following model within each cell resulting from the two-way sort of the primary sample:

Market sentiment is proxied for using the Baker and Wurgler (2006) index value for the month before the respective earnings announcement. Standard errors are clustered by firm and quarter. Seemingly unrelated estimation is used to test whether β is statistically significant across the partitions. ***, **, * indicate two-tailed statistical significance of coefficient estimates at the 1%, 5%, and 10% level.

54

TABLE 8Tests of H3

Market Sentiment, Transient Investors, and Price Informativeness

Panel B: Sentiment Proxy - Crisis Periods

Transient Ownership Low Mid High

(1b) (2b) (3b) χ2 Test:

Non-Crisis (1a) 0.01815*** 0.01363*** 0.01283*** (1a,1b) = (1a,3b)

Periods [7.70] [8.25] [8.59] 6.68**

22,456 22,456 22,456

Crisis (2a) 0.01515*** 0.00822*** 0.00755*** (2a,1b) = (2a,3b)

Periods [4.05] [2.66] [2.85] 3.54*5,665 5,664 5,664

(1a,1b) = (2a,1b) (1a,2b) = (2a,2b) (1a,3b) = (2a,3b)

χ2 Test: 0.59 3.51* 5.38**




+ γ15Andisp it +γ16Andisp it+1 + γ17Retvol it + Firm Fixed Effects + εit.

Table 8 Panel B reports the coefficient on β after estimating the following model within each cell resulting from the two-way sort of the primary sample:

The crisis periods examined include the Russian financial crisis, the Asian financial crisis, the dot-com crash for NASDAQ stocks, and the Lehman Brothers bankruptcy announcement period. The non-crisis periods include all other sample observations. Standard errors are clustered by firm and quarter. Seemingly unrelated estimation is used to test whether β is statistically significant across the partitions. ***, **, * indicate two-tailed statistical significance of coefficient estimates at the 1%, 5%, and 10% level.

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TABLE 9Tests of H4

Post Announcement Returns

Panel A: High Transient Ownership, Quarter t Earnings News, and Post Announcement Returns

w/o controls w/ controls Industry FE Firm FE

DV= Post EA t Returns Pred. (1) (2) (3) (4)

Ue t + 0.3354 0.3505* 0.3346* 0.0321

[1.53] [1.69] [1.65] [0.14] Ue t * Hitran - -0.7105*** -0.7300*** -0.7381*** -0.8222***

[-3.14] [-3.25] [-3.33] [-3.14]

Hitran +\- -0.0005 -0.0023 -0.0025 -0.0075***

[-0.22] [-1.18] [-1.47] [-3.59]

Size +\- - -0.0013** -0.0008 -0.0487***

[-1.99] [-1.10] [-15.59]

Btm +\- - -0.0010 0.0022 0.0558***

[-0.25] [0.61] [8.43]

Sixbhar +\- - 0.0006 0.0013 -0.0110**

[0.11] [0.24] [-1.98]

Beta +\- - 0.0026 0.0024 0.0046

[1.14] [1.27] [1.62]

F-test: Ue t + Ue t * Hitran =0 4.12** 4.43** 5.11** 16.24***

Observations 84,361 84,361 84,361 84,361

Adjusted R2 0.9% 0.9% 1.2% 4.8%Cal. Qtr FE & Intercept YES YES YES YESIndustry FE NO NO YES NOFirm FE NO NO NO YESSE Cluster Firm & Qrtr Firm & Qrtr Firm & Qrtr Firm & Qrtr

Table 9 Panel A reports linear regressions where Post EA t Returns is the dependent variable with t-statistics reported in brackets below each coefficient. Calendar quarter fixed effects are included in each model and standard errors are clustered by firm and quarter. Industry or firm fixed effects are included in the model when noted. All variables are winsorized at the 1% and 99% levels. All variable definitions are provided in Appendix A. ***, **, * indicate two-tailed statistical significance of coefficient estimates at the 1%, 5%, and 10% level.

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TABLE 9Tests of H4

Post Announcement Returns

Panel B: High Transient Ownership, Quarter t+1 Earnings News, and Post Announcement Returns

w/o controls w/ controls Industry FE Firm FE

DV= Post EA t Returns Pred. (1) (2) (3) (4)

Ue t+1 + 2.4181*** 2.6415*** 2.6269*** 2.6613***

[11.07] [12.98] [12.95] [12.25] Ue t+1 * Hitran + 0.4341*** 0.4090*** 0.4005*** 0.4164**

[2.99] [2.81] [2.77] [2.52]

Hitran +\- -0.0019 -0.0029 -0.0032* -0.0074***

[-0.88] [-1.54] [-1.89] [-3.79]

Size +\- 0.0150*** 0.0181*** 0.0753***

[3.74] [4.94] [10.57]

Btm +\- -0.0024*** -0.0020*** -0.0475***

[-3.67] [-2.70] [-15.42]

Sixbhar +\- -0.0161*** -0.0153*** -0.0257***

[-3.01] [-2.94] [-4.67]

Beta +\- 0.0037* 0.0035* 0.0044

[1.68] [1.92] [1.60]

Observations 84,361 84,361 84,361 84,361

Adjusted R2 4.1% 4.4% 4.6% 7.9%Cal. Qtr FE & Intercept YES YES YES YESIndustry FE NO NO YES NOFirm FE NO NO NO YESSE Cluster Firm & Qrtr Firm & Qrtr Firm & Qrtr Firm & Qrtr

Table 9 Panel B reports linear regressions where Post EA t Returns is the dependent variable with t-statistics reported in brackets below each coefficient. Calendar quarter fixed effects are included in each model and standard errors are clustered by firm and quarter. Industry or firm fixed effects are included in the model when noted. All variables are winsorized at the 1% and 99% levels. All variable definitions are provided in Appendix A. ***, **, * indicate two-tailed statistical significance of coefficient estimates at the 1%, 5%, and 10% level.

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Living in the Moment - Yale School of Management · Living in the Moment: Short-Term Investors and...

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