Essays in financial economics and applied econometrics

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ESSAYS IN FINANCIAL ECONOMICS AND
APPLIED ECONOMETRICS

Yingmei Cheng

A DISSERTATION
m

Economics

Presented to the Faculties of the University of Pennsylvania in Partial
Fulfillment of the Requirements for the Degree of Doctor of Philosophy

2001

A. Craig MacKimay, Co-s§pcrvisui TjFDisS5Rati'0TT

Francis X. Diebold, Co-supervisor of Dissertation

Steven A. Matthews, Graduate Group Chairperson

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To my parents, my husband and my children

a

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ACKNOWLEDGMENTS
First, I wish to thank the four people who have been guiding and supporting me in completing

this dissertation. My greatest debts are to Craig MacKinlay and Frank Diebold, my Dissertation
Supervisor and Co-supervisor, from whom I learned how to do research and teaching in financial
economics. I also appreciate their insightful comments, patience and continuing encouragement
throughout the process. Gary Gorton and Petra Todd have been enormously helpful in giving me
illuminating comments and directions o f my research. I could not have had a better team of
advisors.
Many faculty and staff members and fellow graduate students (former and current) at Penn
have helped me in different ways. In particular I am grateful to Professor Phillip Stocken for his
gracious help with the first chapter of the dissertation. I thank Sean Campbell, Jun (QJ) Qian,
Canlin Li and Brett Norwood for their helpful discussions and support. I would also like to thank
Professor Neil Wallace who taught me the first graduate class in economics at University of
Miami. Neil is a great economist, and an even better mentor.
I am indebted to University of Science and Technology of China (USTC) for its rigorous
training and academic freedom. I had the best experience as an undergraduate at USTC.
I would like to say thanks to my loving parents, Peiping Peng and Shengli Cheng. You gave
me the best home and the best education. More importantly you have always shown me the
enormous courage and strength. I also want to thank my older sister, Hongmei. You have always
been on my side. I am grateful to your confidence.
I want to thank my dear husband, Tianming Zhang, and my beautiful children, Jimmy and
Karina. You have made me a better person. You make me happy, gentle, and strong. I am grateful
to have you three in my life every day.
iii

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ABSTRACT
ESSAYS IN FINANCIAL ECONOMICS AND APPLIED ECONOMETRICS
Yingmei Cheng
A. Craig MacKinlay

In the first chapter, Informativeness o f Analysts’ Recommendations, we investigate the
informativeness of sell-side analysts’ recommendations by examining abnormal stock returns
before, during and after changes in analysts’ ratings. First, we show that the market derives
different information from the similar recommendations by different brokerage firms, especially
in the case of downgrading from “ buy”. The brokerage firms in the sample differ in terms of the
impact o f their analysts’ recommendations on subsequent stock returns, although they are all
ranked highly. Second, we document that the market reacts quickly to the analysts’
recommendations, which contradicts the continuation of abnormal returns for months after
recommendations, i.e., the so-called “ post-recommendation drift”, documented by the literature.
In the second chapter, A Model o f Inside and Outside Experts-the Example o f Buy-side and
Sell-side Equity Analysts, we model the information transmission from multiple equity analysts to
a mutual fund manager. The buy-side analyst has the same preference as the manager while the
sell-side has different preference. If the fund manager relies on only sell-side analysts, a subgame
equilibrium always exists in which the analysts’ opinions are independent of their private signals
and thus the information content is totally lost. With one buy-side analyst in the panel, however,
truth-telling is the only subgame equilibrium under a certain range o f parameters. The equilibrium
outcome is that the manager relies on both sell-side and buy-side equity analysts to make
investment decisions.
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In the third chapter, Evaluating Preschool Programs when Length o f Exposure to the
Program Varies—A Nonparametric Approach, we develop a nonparametric multi-dimensional
matching method and apply this method to a large, non-experimental data set to evaluate the
effects o f a preschool enrichment program. This generalized version of the matching method is
able to control for nonrandom selectivity into the program or into alternative program duration by
matching the group of interest to a comparison group on more than one dimension. It minimizes
the impact o f distributional assumptions. The third chapter is intimately related to the first two in

that it develops the nonparametric multi-dimensional matching method which is applicable to a
variety issues in corporate finance.

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C o ntents
1. Informativeness of Analysts’ Recommendations
1.1

Introduction

1

1.2

Information Transmission from Analysts to Investors

5

1.3

Data Construction and Sample Description

6

1.3.1

Data Construction

6

1.3.2

Sample Description

8

1.4

The Empirical Methodology

11

1.5

Brokerage Firms and Short-run Market Reactions

13

1.6

1.7

1.8

2.

1

1.5.1 Three-day Event Window

13

1.5.2

14

Impact of Brokerage Firms

Stock Returns Before and After the Recommendations

18

1.6.1

Pre-event and Post-event Abnormal Returns

18

1.6.2

Sensitivity of Pre-and Post-event Returns

19

Robustness to Alternative Models

22

1.7.1

Pre-event Abnormal Returns and Restricted Market Model

22

1.7.2

Nonparametric Model

25

1.7.3

Short-run Returns

27

1.7.4

Longer-term Returns

27

Discussion

28

1.8.1

The Models

28

1.8.2

Market Efficiency

29

1.9

Summary and Future Research

30

1.10

Appendix: Choice o f the Optimal Bandwidth

31

Inside and Outside Experts: the case of Buy-side and Sell-side Security Analysts

53

2.1

Introduction

53

22

The Basic Model

55

2.3

Characterization of the Equilibria

58

2.4

Extensions

66

2.4.1

Zero-cost Model

66

2.4.2

More General Utility Function for Sell-side Analysts

68

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3

2.5

Summary

70

2.6

Appendix —Proofs

70

Evaluating Preschool Program When the Length of Exposure Varies: A
Nonparametric Approach

90

3.1

Introduction

90

3.2

A Model o f the Program ParticipationDecision and Treatment Effects

3.3

Cumulative and Marginal Matching Estimators
3.3.1

Estimators

3.3.2

Comparison between Matching Methods and Traditionai

100
101

Regression-based Methodsof Evaluating Program impacts
3.4

96

Empirical Results

108
111

3.4.1

The data

111

3.4.2

Variables

114

3.4.3

Comparison of Group Mean Characteristics

115

3.4.4

Determinants of Program Participation

119

3.4.5

Impacts Estimated by Traditional Regression Methods

120

3.4.6

Cumulative Impacts Estimated by the Model of Matching

122

3.4.7

Marginal Program Impacts Estimated by the Model of Matching

125

3.5

Cost-Benefit Analysis

126

3.6

Conclusions

130

3.7

Appendix

132

3.7.1

Appendix A Data Appendix

132

3.7.2

Appendix B Technical Appendix on Local Linear Regression

132

3.7.3

Appendix C List of Variables Included in Program
Participation Model

3.7.4

133

Appendix D Evidence on Impact of Preschool Child Nutrition
and Cognitive Development on Post-schooling Earnings

vii

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134

L is t

of
1

T ables
The Rankings of the Brokerage Firms by Institutional Investor
from 1994 to 1997

33

2

Ratings Used by the Six Brokerage Firms.

33

3

Sample Description

34

4

Market Capitalization of the Stocks Included in the Data

35

5

Concurrent Quarterly Earnings Surprises and Forecast Revisions

35

6

ACARs during the Three-day Event Window

36

7

The Three-day ACARs for Stocks o f Different Sizes

37

8

ACARs for the Three-day Event Window by Different Brokerage
Firms for “Added to Buy” and “Removed from Buy”

9

38

Sizes of the Stocks followed by Different Brokerage Firms
for “Added to Buy” and “Removed from Buy”

39

10

Earnings News around Recommendations across Brokerage Firms

40

11

The Impact of Different Brokerage Firms on the Three-day ACARs

41

12

ACARs for Periods Before and After Recommendations

42

13

Estimated Alpha and Beta for all types of Recommendations

43

14

ACARs from Fama-French Three-factor Model

44

15

Comparison of some Major U.S. Child Care Intervention Programs

136

16

Sample Size of P, A and B Groups: rounds one and two

138

17a

Comparison of Mother’s Characteristics in Participant and Eligible
Nonparticipant Samples

17b

139

Comparison of Father’s Characteristics in Participant and Eligible
Nonparticipant Samples

17c

140

Comparison of Household’s Characteristics in Participant and Eligible
Nonparticipant Samples

18

141

Comparison of Difference in Raw Means and Cumulative Mean
Program Impacts After Adjusting for Selectivity Using
Matching Method: Group P and Eligible Group B

142

19

Estimated Cumulative Impacts by Duration and Age Classes

143

20

Comparison of Difference in Raw Means and Cumulative
Mean Program Impacts After Adjusting for Selectivity Using
Matching Method: Group P, Duration>=2 and Duration<=l
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144

21

Estimated Marginal Impacts by Duration and Age Classes: Group P,
Duration>=2 and Duration<=l

22

145

Costs and Estimated Benefits of the PEDI Program in U.S. Dollars
Under Different Hypothetical Impacts

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146


1. INFORMATIVENESS OF ANALYSTS’
RECOMMENDATIONS

1.1. Introduction 1
In A ugust 2000, after several m onths of controversy, the Securities and Exchange Com­
mission passed new fair-disclosure rules, known as R egulation FD: effective from Oc­
to b er 23, 2000, inform ation th a t a publicly trad ed com pany knows to be “m aterial’m ust be disclosed to the professional and th e public a t the sam e time. This regulation
is designed to make the inform ation of public com panies as accessible to investors as
it is to well-known analysts. It shows th a t the SEC tru ly believes th a t analysts have
privileged access to information. We know th a t one task of equity analysts2 is to issue

recom m endations (buy, sell, etc.) on stocks. If S E C ’s belief is true, how does the
analysts’ private inform ation affect the m arket through their recommendations?
T he literatu re has mainly focused on sh o rt periods around recomm endations. Stickel
(1985) and other work3 report evidence th a t T he Value Line Investm ent Survey and
l I am grateful to Craig M acK inlay and Frank D ieb o ld for their encouragem ent and guidance. I
ap preciate com m en ts and insights from Gary G orton, P etra T odd and participants at Penn E conom et­
rics W orkshop and W harton F inance Sem inar. I also appreciate su ggestion s from sem inar participants
a t U niversity o f O regon, U niversity o f S outh C arolina, F lorida S ta te U niversity and C harles River
A ssociates. I am in debted to Sean C am pbell and P hillip Stocken for their generous help and exten sive
com m ents. I th an k First Call for providing H istorical R ecom m en dation D atab ase and K enneth French
for access to h istorical factor returns. A ny errors are m y ow n responsibility.
'U n le ss otherw ise noted, the an alysts refered to in th is paper are sell-side equity a n alysts, i.e., those
work for brokerage firms and p ub licly issue earnings forecasts and sto ck ratings.
3For exam p le, Lloyd-D avies and Canes (1978), Liu, S m ith and S yed (1990) and Francis and Soffer
(1997).

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security analysts have private inform ation th a t, when revealed, results in small b u t
statistically reliable price adjustm ents in a short period after the recommendations,
e.g., three o r five days around the events. More recently, Womack (1996) examines
stock returns m onths after recommendations. He not only finds significant abnorm al
returns during the three-day event window, b u t also docum ents evidence of the con­
tinuation of abnorm al returns for up to six months following the recommendations.
This is the so-called “post-recom m endation drift” . T he existence of longer-term post­
recom m endation abnorm al returns is a puzzle. It supports the idea th a t the m arket
is slow to incorporate information tran sm itted by the recom m endations and thus, the

initial price reactions to these recommendations are incomplete. It is striking evidence
against th e sem i-strong form of market efficiency hypothesis4.
In this chapter, I investigate the informativeness of seil-side equity analysts’ recom­
m endations by exam ining both the short- and longer-term abnorm al stock returns after
changes in analysts’ ratings. I construct a unique d a ta set th a t consists of recommen­
dations hand-collected individually from First Call and th en combined with earnings
inform ation from I /B /E /S and COMPUSTAT Industrial Quarterly. T he paper makes
two m ajor contributions.
F irst, I show th a t the market possibly derives different inform ation from the same
or sim ilar recom m endations by top brokerage firms. To examine the impact of the
brokerage firms on m arket reactions, I control for m arket capitalization of the stock,
concurrent earnings news and other factors. T he results show th a t the brokerage firms
l Since the 1960s, there has been em pirical research in academ ia challenging the m arket efficiency hy­
p oth esis. T h e d eb a te has historically been concentrated on tests for th e weak form o f market efficiency,
or m ore generally, tests for return predictability (Fam a, 1991). For th e sem i-strong form, tests of how
quickly secu rity prices reflect public inform ation announcem ents, there has recently been substantial
work d ocu m en tin g overeaction/underreaction of stock prices to certain events. For the strong-form ,
tests o f w hether any investors have private inform ation th a t is not fully reflected in market prices, or
tests for private inform ation, there is work on inside trading (Jaffe,1974; Seyhun,19S6), professional
portfolio m anagem ent (J en sen ,1968; Ippolito,1989) and security an alysis (Stickel,1985).

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differ in term s o f th e im pact of their analysts’ recom m endations on subsequent stock
returns, although all th e brokerage firms in th e sam ple are ranked inside the top 20 by
Institutional In vesto r during the sam pling period. T he difference is most prom inent
in th e case of dow ngrading from “b u y ’. One explanation is th a t the brokerage firms

differ in term s of th e degree of interest conflicts between them and th e investors, yet
another explanation is th a t the market has unobservable belief ab o u t how accurate the
recom m endations are and the belief varies across different brokers.
D istinguishing recom m endations from individual brokers is new in the literature^.
Since an individual analyst usually needs to o b tain approval from a research oversight
com m ittee in his or her firm before issuing ratin g changes, especially in the case of
downgrading, it is worthwhile to analyze the recom m endations by different brokerage
firms.
Second, I docum ent th a t the m arket generally reacts quickly to the analysts’ rec­
om m endations, which contradicts the existence of longer-term post-recom m endation
abnorm al returns docum ented by Womack (1996). One possible explanation for the
drift is th a t the m arket does not fully incorporate the inform ation em bedded in ana­
lysts’ recom m endations, i.e., the m arket is inefficient. A nother possibility is th a t the
drift is due to bias in m easured abnorm al returns. Still another possible explanation is
th a t the post-recom m endation drift is sample-specific, e.g., it m ight be sensitive to the
tim e period or certain characteristics of the firms. I show th a t th e existence of “post­
recom m endation d rift” is not robust to the sam pling period. T h e d a ta I use is from
1995 to 1997 instead of 1989-1991 as in W omack (1996). T he results do not provide
evidence of significant post-event abnorm al returns, except when firms are downgraded
from “b u y ’. In th e case where it is supported, the post-recom m endation drift is fragile
in th a t it is sensitive to the m agnitude of ra tin g change, th e m arket capitalization of
°Stickel (1995) exam in es w hether the analysts, w ho are selected into A ll-S tar A m erican first and
second team s by In stitu tio n a l In vestor, have more influence on sto ck reactions than other an alysts.
However, he d id not consider different brokerage firms.

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th e subject company, and th e broker issuing the recomm endation. For example, sm all
stocks downgraded from “buy” have negative abnorm al returns until 60 trad in g days
after the rating change, while large stocks do not have significant post-event abnorm al
returns. Thus the results question th e existence of “post-recom m endation drift” and
show th a t in general th e m arket quickly incorporates information tran sm itted from the
recom m endations.
T h e above results are obtained using the size-adjusted model, which assumes the
expected return of a stock to be the retu rn of the m arket portfolio in the sam e firm-size
decile. To examine th e sensitivity of th e results to different models, two o th er models
axe adopted to m easure th e expected returns: th e restricted m arket model and the
nonparam etric local linear regression m ethod (LLR). Different from the size-adjusted
model, these two models derive each stock’s specific expected returns by using param e­
ters employing an estim ation period. T he two models are different from the widely used
m arket model. The nonparam etric local linear regression m ethod is adopted to control
for nonlinearity and reduce the bias caused by outliers since it imposes fewer function
form restrictions th a n the m arket model6. However, the existence of pre-event abnor­
m al returns makes it difficult to specify the estim ation period for the m arket model
or th e nonparam etric LLR. Thus, I propose a restricted m arket model as an alterna­
tive when significant abnorm al returns exist during the pre-event period. R esults from
these models are close to each other in the short-run. In the longer-run, th e restricted
m arket model and the nonparam etric LLR have results generally consistent w ith those
from the size-adjusted model. In some cases, they further reject the existence of the
post-recom m endation drift.
T he rem ainder of th e chapter is organized as follows. Section 1.2 outlines a frame­
work for interpreting th e empirical results in fight of information transm ission from
stock analysts to investors. Section 1.3 presents d a ta construction and sam ple descrip­
tion. Section 1.4 describes th e empirical methodology. Section 1.5 examines the im pact
fiT h e market m odel assum es a linear relationship b etw een the stock return and th e m arket return.

4

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of different brokerage firms on short-run stock returns. Section 1.6 studies the longerterm returns. Section 1.7 examines robustness of th e results to alternative models of
expected returns. Section 1.8 discusses the results and Section 1.9 summarizes the
paper.

1.2. Information. Transmission from Analysts to Investors
P rior to issuing recom m endations, the analyst gathers inform ation on the individual
stock from firm m anagers, custom ers and suppliers, and analyzes these d a ta to form
an opinion on w hether to buy or sell the stock. Then, he issues his recom mendation
usually after o b taining approval by a research oversight com m ittee in his firm.
It is reasonable to assum e th a t the analyst decides w hether a stock is overvalued
or undervalued by inputting his information, b o th public and private, into a valuation
model. Public inform ation includes earnings of the subject com pany and other types
of inform ation gathered, for example, from reports to the shareholders. T he analyst
may also have some private information th a t the investors do not have. T he private
inform ation m ay come from th e selective disclosure of inform ation from the subject
com pany to the analysts, as the SEC believes is the case. For example, the informa­
tion m ay come from private meetings with high-level m anagem ent or from information
conferences held by the company exclusively for analysts7. The stock price movements
around th e recom m endations can thus be rationalized as investors infer and react to
the potential private inform ation embedded in stock recom mendations.
However, would the recomm endation necessarily reflect the analyses true opinion?
The an aly st’s objective is not necessarily to make th e most accurate recommendation.
T here are m ultiple forces pulling him in other directions. For example, an analyst faces
pressure not only from the managem ent of the com pany he covers, but also from the
investm ent banking departm ent in his brokerage firm (Balog, 1991). T he analyst may
‘ T h is w ould change in the presence o f R egulation FD (Fair D isclosure). T h e sam ple in this study

does not go beyond D ecem ber 1997.

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have incentive to cultivate m anagem ent relations. Interest conflicts also arise when the
a n aly st’s firm provides investm ent banking services for the com pany he is following
(M ichaely Sz Womack, 1999).
Therefore, inferring inform ation from analysts’ recommendations is not an easy
task. A few theoretical papers, such as M organ and Stocken (2000) and Cheng (2000)
show how th e interest conflicts could reduce the information content of the recom­
m endations issued by equity analysts. Different brokerage firms m ight have different
degrees of interest conflicts and valuation models of differing accuracy. Thus investors
m ay respond differently to th e sam e recom m endation by different brokers. In Section
1.5, stock reactions to recom m endations are com pared across brokerage firms and the
results show th a t the brokerage firms indeed have different influence on stock reactions.
A nother question is: how quickly do th e investors react to the inform ation? If
a post-recom m endation drift exists, it would show th a t the m arket slowly takes the
inform ation into account. In Section 1.6, I show th a t in general, the m arket reacts
quickly to th e analysts’ recom m endations.

1.3. Data Construction and Sample Description
1.3.1. Data Construction
D ata from a variety of sources, including T h e Value Line Investm ent Survey, Zacks
Investm ent Research, Investext, and First Call, have been used in em pirical work inves­
tigating analysts’ recom m endations8. I use th e First Call Historical Recommendation
D atabase. It is a compilation of individual recom m endations from more th an 220 ana­
lysts a t leading Wall Street an d regional research firms. First Call distributes analysts’

recom m endations to its subscribers through an on-line system.

As brokerage firms

'’For exam p le, Francis and Soffer (1997) use In vestext; Francis and Philbrick (1993) and Stickel
(1985) use T h e Value Line Investm ent Survey; Stickel (1995) and Barber, Lehavy, M cN ichols & True­
m an (1999) use Zacks; W omack (1996) uses F irst Call.

6

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issue recom m endations from th eir “morning calls”3 electronically, F irst Call makes it
available alm ost im m ediately to its subscribers who are also custom ers of th e broker­
age firms. T he advantage o f th e F irst Cali Recom m endation D atabase to researchers
is th a t the brokerage firms have an incentive to verify th e accuracy of the inform ation
on F irst Call since F irst Call is sold to professional, in stitu tio n al investm ent managers.
T he sample analyzed in the current research comes from recom m endations issued
between O ctober 1, 1995 and December 31, 1997 by six top brokerage firms identified
by Institutional In vesto r (II). I I annually ranks security analysts and research d ep art­
m ents of m ajor brokerage firms, mainly according to polls of in stitutional investors,
on the basis of stock picking, earnings forecasts, w ritten reports and overall service. I
have access to six brokerage firms among th e top 20 ranked by II. Since some brokerage
firms enter agreem ents w ith F irst Call which preclude F irst C all from d istributing their
recom m endations to anyone other th a n the brokerage houses’ clients, the recom m enda­
tions of several brokerage houses, including M errill Lynch and G oldm an Sacks, are not
included in the d a ta s e t10. Table 1 presents th e list of brokerage firms in th e sample
and shows their ranking as reported in II.
D uring the period between O ctober 1995 and Decem ber 1997, I read the headlines

and docum ents individually to collect the following inform ation for every observation:
th e d ate th e recom m endation is dissem inated by F irst Call, th e d a te on the underlying
docum ent, the old rating, th e new rating, the brokerage firm issuing the recom m enda­
tion and th e stock’s ticker symbol.
Daily stock returns are obtained from CRSP. CO M PUSTA T Industrial Q uarterly
is used to search for dates of quarterly earnings announcem ents and values of quarterly
earnings. I use In stitu tio n al Broker E stim ation System (I /B /E /S ) Sum m ary History
to find consensus earnings forecasts and I /B /E /S Daily D etail H istory to find earnings
9T h e m orning research conference calls are held at m ost brokerage firms a b o u t two hours before the
sto ck m arket opens for trading in N ew York. A nalysts and portfolio stra teg ists speak ab ou t, interpret
an d p ossib ly change op in ion s on firms or sectors th ey follow.
l0Zacks d a ta has th e sa m e problem (Barber, Lehavy, M cN ichols & Truem an (1999)).

7

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estim ates by individual analysts11. Earnings surprise is th e difference between the
actu al q uarterly earning and the quarterly earnings forecast consensus. T he earnings
forecast revision is th e difference between th e newly issued earnings forecast and the
consensus. A nalysts usually forecast earnings for different tim e horizons, for example,
cu rren t year, next year, quarters of a year, etc. A m ajority (m ore th a n 53%) of them
are for th e current year and the next year. It is also common th a t multiple analysts
issue earnings forecasts on a certain stock. If there is only one earnings forecast revision
for th e current year, I use it as the earnings revision. If there are multiple revisions
occurring around th e sam e tim e12, I take the average. If there is no revision for the
cu rren t year, I consider the revision in th e order of next year an d then this quarter,
next q u arter, etc.

1.3.2. Sample Description
Table 2 describes th e rating systems used by th e brokerage firms in the sample. T he
sim plest rating system consists of three categories: “buy” , “hold” and “sell” . Five out
of th e six brokerage firms in the d a ta set have two additional interm ediate ratings:
one betw een “buy” and “hold” , and the oth er between “hold” an d “sell” . A lthough
th e nam es of the ratings sometimes differ across brokerage firms, I adopt a typical
set of recom m endations: “buy” , “a ttractiv e” , “n eu tral” , “u n a ttra ctiv e ” , and “sell” .
For Prudential, who has a three-rank system , I take its “hold” as “neutral” . P u ttin g
different ratings into one rating system requires some subjective judgem ent.
Excluding th e 21 U.S. companies whose stock prices are not available on CRSP
an d all non-U.S. companies, there are 1209 ratin g changes on 942 stocks. Only new
changes in ratings are included. R eiterations of rating changes are excluded, because
th ey are usually repeated several times and have been shown to have less im pact th an
11 I / B / E / S also has records o f earnings announcem ent d ates, but they are th e d ates w hen the earnings
are reported to I / B / E / S .
12Here it m eans th ey occur w ithin five days around th e rating changes.

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new changes of ratings (Francis and Soffer, 1997). There are 19 types of rating changes
w ith non-zero observations. The frequency of observations for different categories is
sum m arized in Panel A of Table 3.
It is well known th a t very few “sell” recommendations are made, and this d a ta set
is no exception: “sell” recommendations appear in only 16 out of 1209 observations
and new coverage was never initiated w ith a “sell” rating. T he managem ent has been
known to penalize analysts who issue “sell” recommendations and pessimistic reports
on a company. Some offended firms punish these analysts by excluding them from con­

ference calls, meetings and other forms of direct contact w ith management (Siconolfi,
1995)13. Several other explanations have also been proposed for this bias. McNichols
and O ’Brien (1997) find th a t analysts tend to cover companies about which they are
optim istic; D ugar and Nathan(1995) show th a t financial analysts of brokerage firms
th a t provide investment banking services to a company (investment banker analysts)
are optim istic relative to other (noninvestm ent banker) analysts when making earn­
ings forecasts and investment recommendations; Michaely & Womack (1999) provide
evidence of bias of analysts towards “buy” ratings on the stocks their brokerages un­
derw rite. In this d a ta set, the lack of “sell” recommendations is more prom inent than
all previous studies. Since there are so few “u nattractive” and “sell” ratings, I combine
them into one category in the analysis. In doing so, the two upgrades from “sell” to
“u n attractiv e” and one downgrade from “unattractive” to “sell” in the d a ta set are
discarded. Thus, th e sample has 1206 observations.
Term inations of coverage are not included in the sample because there are too few
of them for meaningful analysis. To com pare w ith the literature, I also construct four
additional types of recommendations: “added to buy” , “removed from buy” , “added
to u nattractive/sell” and “removed from unattractive/sell” . These recommendations
are the combinations of certain rating changes. For example, “added to buy” is the
com bination of “from neutral to buy” , “from attractive to buy” and “initiated as buy” .
l3T h is m ight change after R egulation F D is im plem ented.

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T he four cases have 369, 274, 38 an d 53 observations respectively.
Panel B of Table 3 shows th e frequency of recomm endations issued on individual
stocks in th e sample. M ore th a n 77% of the stocks only have one ratin g change and less
th an 4% of the stocks have th ree or m ore14. Thus, although m ultiple recom m endations

of the sam e stock occur in the sam ple, they are few an d should not m aterially change
the results.

Among th e stocks who have multiple rating changes, a b o u t 53% have

more th a n one brokerage firm am ong the six following them an d a b o u t 61% have
multiple recom m endations issued by the same firm15. For recom m endations by multiple
brokerage firms on th e sam e stocks, only 12% of them are less th a n one m onth apart
from the rating change issued by a different brokerage firm.
Table 4 summarizes the m arket capitalization of the stocks. T hey are predom inantly
large-capitalization com panies. M ore th a n 50% of th e sample are from the two largest
m arket capitalization deciles16. I call stocks in deciles 9-10 large, deciles 6-8 medium
and deciles 1-5 small.
Table 5 summarizes the frequency of recomm endations occurring around quarterly
earnings surprises and earnings forecast re-visions. A sm all percentage of stocks (about
5%) can not be found in CO M PUSTA T or I/B /E /S . A bout 10% (com bination of pos­
itive and negative surprises) of th e recommendations have some q u arterly earnings
surprises w ithin days -5 to + 5. For “added to buy” , “removed from buy” , “added to
u nattractive/sell” , and “removed from un attractiv e/sell” , there are ab o u t 9%, 13%,
11 T h e stock w ith seven rating ch an ges is D ell.
toT h e average duration o f th e recom m endations is about 11 m onths roughly calcu lated from the
su bset o f m ultiple recom m endations issued by the sam e brokerage firms.
t6C R SP ranks all N Y SE com p anies b y their market cap italization and d ivides them into 10 equally
p opu lated portfolios.

A M E X an d N A SD A Q stocks are then placed into d eciles according to their

respective capitalizations, d eterm in ed by N Y SE breakpoints. T h e portfolios are rebalanced each year,
using th e security market cap ita liza tio n a t the end o f the previous year to rank th e secu rities. The
largest secu rities are placed in p ortfolio 10 and sm allest in portfolio 1. If a secu rity sta rts trading in

the m idd le o f a year, its first cap ita liza tio n o f the year is used in th e ranking. I take the d ecile to which
each stock belongs at th e b egin ning o f th e pre-event period as its size.

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19% and 12% of recom m endation issued around quarterly earnings surprises respec­
tively. A pparently there is an asym m etry about what recom m endations are more likely
to be issued around quarterly earnings surprises, i.e., upgrades seem to be less driven
by earnings surprises than downgrades. M ore than 30% (com bination o f positive and
negative revisions) of recom m endations are around some earnings forecast revisions.
Upgrades are more likely to be associated w ith positive earnings surprises or positive
earnings forecast revisions th a n negative earnings surprises o r negative earnings forecast
revisions. Downgrades act similarly.

1.4. The Empirical Methodology
I use the size-adjusted model to derive the main results. In Section 1.7, I adopt a
restricted m arket model and nonparam etric local linear regression m eth o d to examine
the robustness of the results.
According to the size-adjusted model, th e abnormal retu rn of stock i at tim e t is:
A R it

—

R it

R s iz e ,t- >

where £ is the trading day relative to th e recommendation d ate (£ = 0), R it is the return
of stock

2

on day £ and R Size,t is th e retu rn of value-weighted C R SP portfolio for the

same firm-size decile on day £. T h e size decile index portfolios are form ed from stocks
listed on the NYSE, AMEX, and NASDAQ. Stocks w ith returns for any given day are
com pared to the decile portfolios based on th eir market capitalization a t th e beginning
of the pre-event period.
Using terminology from C am pbell, Lo and MacKinlay (1997), the Average Abnor­
mal R eturn across N stocks a t tim e £ is:
1 N
A A R t = — J 2 ARiti= i

T he Average Cum ulative A bnorm al R eturn from t\ to
^2
A C A R ( t \ , £2 ) =
^ AARt,.

£2

is:

t= ti

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T he f-statistic is:
t

—

V

n

■

ACAR
a (A C A R ) ’

w here a (A C A R ) is th e cross-sectional standard deviation of A C A R 1‘.
K othari and W arner (1997) and Lyon, B arber and Tsai (1999) po in t out th a t longhorizon param etric te st statistics do not satisfy the assum ed zero m ean and unit nor­
m ality assum ptions and over-reject the null hypothesis of no abnorm al performance.
T h eir sim ulation results show th a t the distribution of stan d ard te st statistic has a pos­
itive m ean and it is fat-tailed relative to a unit-norm al distribution. For this reason. I
use a b o o tstrapped t-statistic distribution in this p ap er18.
F irst Call provides th e specific date and tim e when it dissem inates information to
its clientele. This inform ation is recorded in the headline. We thus know the date
1‘ Fam a (1998) argues th a t theoretical and statistical considerations su ggest th a t formal inferences
ab o u t long-term returns sh ould be based on averages or sum s o f short-term abnorm al returns rather
than the b uy-and-hold abnorm al returns. Cum ulative A bnorm al R eturn (C A R ) is less likely to yield
spurious rejections o f m arket efficiency than m ethods th a t ca lcu late b u y-and -h old returns by com ­
p oun din g sin gle period returns. F irst, the buy-and-hold m eth od can m agnify underperform ance or
overperform ance, even if it occurs in on ly a single period, due to the nature o f com pounding single­

p eriod returns. Second, d istrib ution al properties and test sta tistics for C A R s are b etter understood.
Barber and Lyon (1997) and K othari k Warner (1997) d ocum ent th at test s ta tistic s of long-horizon
b uy-and-hold abnorm al returns are m ore significantly right-skewed than cu m u lative abnorm al returns.
L8T h e b ootstrap p in g proceeds as follows: Draw 1,000 b ootstrap p ed resam ples from the original
sam ple o f th e sam e size N . In each resam ple, calculate the statistic:
b

/77
~

AC ARb
a ( A C A R b) ’

where A C A R b and a { A C A R b) are the A C A R and its cross-sectional stand ard d eviation in the boot­
strap ped resam ple. I reject the null hypothesis that the m ean abnorm al return is zero if £<Xi or £ > x uFrom th e 1,000 resam ples, I calcu late th e two critical valu esfyj and x u ) f ° r th e t-sta tistic , £, to reject
th e null h ypothesis at the a significance level by solving:
P r(£6 < X I) = P r ( £ fc> x J = | -

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(and even tim e of th e day) th a t the inform ation was sim ultaneously made available
to th e entire clientele of F irst Call. I call it th e dissem ination d ate and use it as the
event date. For each event in the sample, th ere axe both benchm ark and individual
stock returns for th e period from 261 days before until 261 days after the event date,
generally a b o u t two years in calender time.

1.5. Brokerage Firms and Short-run Market Reactions

1.5.1. Three-day Event Window
Column 1 of Table 6 lists the ACARs of the three-day event window for all the types
of rating changes using th e size-adjusted model. Most of th e ACARs are statistically
significant at least a t a = 0.10.

Upgrades lead to positive abnorm al returns and

downgrades lead to negative abnorm al returns, which is consistent w ith the literature.
Downgrades lead to larger retu rn movements than corresponding upgrades. For
example, “from buy to n eu tral” leads to a three-day A CAR o f -2.64%, while “from
neutral to buy” only has a three-day ACAR of 4-1.08%. Downgrades of smaller mag­
nitude lead to sm aller retu rn movements, for example, “from buy to attractiv e” has
a three-day ACAR of -1.23%, while “from buy to neutral” has a three-day ACAR of
-2.64%. O n th e oth er hand, upgrades of sm aller m agnitude do not necessarily result in
smaller retu rn movements, for example, “from attractiv e to buy” and “from neutral to
buy” display sim ilar re tu rn movements.
Table 7 presents results for “added to buy” , “removed from buy” , “added to
unattractiv e/sell” , “removed from u n attractiv e/sell” and th eir subsets stratified by
size. Except for “rem oved from u n attractiv e/sell” , all other types of recommendations
result in significant three-day ACARs. “Added to u n attractiv e/sell” has an ACAR of
-3.76% during the three-day window. By contrast, “removed from u nattractive/sell”
results in much sm aller abnorm al returns, +0.52% .
The recom m endations seem to have bigger im pacts on sm aller stocks. Large stocks

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