Three Essays on Analyst Target Prices

159 Pages·2012·1.74 MB·English

Three Essays on Analyst Target Prices

A thesis submitted to The University of Manchester for the degree of

Doctoral of Philosophy

in the Faculty of Humanities

2012

Noor Hashim

Manchester Business School
CONTENTS
Abstract ....................................................................................................................... 5
Declaration .................................................................................................................. 6
Copyright Statement ................................................................................................... 7
Acknowledgements ..................................................................................................... 8
Chapter 1
Introduction
1.1. Motivation ............................................................................................................ 9
1.2. Thesis structure .................................................................................................. 15
References ................................................................................................................. 16
Chapter 2
Does a bull-bear valuation analysis increase the accuracy of
analysts’ target prices?
Abstract ................................................................................................................................ 18
2.1. Introduction ................................................................................................................. 19
2.2. Prior research and development of hypotheses ....................................................... 23
2.3. Data and sample .......................................................................................................... 26
2.4. Research design .......................................................................................................... 28
2.5. Results .......................................................................................................................... 32
2.5.1. Determinants of the choice to supplement target prices with a BBA ...................... 32
2.5.2. Univariate analysis ................................................................................................. 36
2.5.3. Testing for selection bias and hidden bias .............................................................. 37
2.5.4. Difference-in-differences estimation of the treatment effect ................................... 38
2.5.5. The propensity-score matching procedure .............................................................. 39
2.5.6. Covariate balance between treatment and control samples ................................... 40
2.5.7. Estimating the average treatment effect using PSM combined with DID ............... 41
2.5.8. Bad news and the effect of a BBA on target price accuracy ................................... 42
2.6. Sensitivity analysis ..................................................................................................... 43
2.7. Conclusion ................................................................................................................... 45
References ........................................................................................................................... 47
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Chapter 3
Does analyst ranking affect how informative target prices are to
institutional investors?
Abstract ................................................................................................................................ 70
3.1. Introduction ................................................................................................................. 71
3.2. Research hypotheses .................................................................................................. 76
3.3. Sample .......................................................................................................................... 79
3.4. Research design and model ........................................................................................ 80
3.5. Empirical results ......................................................................................................... 88
3.5.1. Univariate analysis ................................................................................................. 88
3.5.2. Star analysts and target price revisions .................................................................. 89
3.5.3. Target price revisions and institutional trading ..................................................... 90
3.5.4. Target price quality and analyst star ranking ......................................................... 92
3.5.5. Sensitivity tests ........................................................................................................ 93
3.6. Conclusion ................................................................................................................... 94
References ........................................................................................................................... 96
Chapter 4
Do analysts use their cash flow forecasts when setting target prices?
Abstract .............................................................................................................................. 110
4.1. Introduction ............................................................................................................... 111
4.2. Prior literature and research hypotheses ................................................................ 115
4.3. Data and sample ........................................................................................................ 120
4.4. Research design ........................................................................................................ 121
4.5. Empirical estimation and results ............................................................................. 127
4.5.1. Univariate analysis ............................................................................................... 127
4.5.2. Endogenous switching model estimation .............................................................. 129
4.5.3. Economic significance of the results ..................................................................... 132
4.5.4. Sensitivity analysis ................................................................................................ 134
4.6. Conclusion ................................................................................................................. 136
References ......................................................................................................................... 139

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Chapter 5
Conclusion
5.1. Summary of results .......................................................................................... 156
5.2. Implications and suggestions for future research ............................................. 158
References ............................................................................................................... 159

This thesis contains 51,380 words including title page, tables, and footnotes.
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Abstract
The University of Manchester
Noor Hashim
Doctor of Philosophy (PhD)
Three Essays on Analyst Target Prices
August 2012

This thesis presents three essays on analyst target prices. The essays contribute to the
major debate on the value of analyst target prices in the capital market by addressing
the following three questions: Does a bull-bear valuation analysis increase the
accuracy of analysts’ target prices? Does analyst ranking affect how informative
target prices are to institutional investors? And, do analysts use their cash flow
forecasts when setting target prices?

In the first essay, I explore whether conducting a bull–bear analysis (BBA) increases
target price accuracy. A bull–bear analysis is a risk assessment tool that analysts use
to enhance the credibility of their valuations and limit target price uncertainty. Using
propensity score matching to control for selection bias, combined with a difference-
in-differences estimation to allow for company- and analyst-specific effects, I
estimate the effect of supplementing target prices with a BBA on the target price
accuracy of US stocks during 2008–2009. The results suggest that target prices are
more accurate when analysts supplement them with a BBA. The findings contribute
to the literature exploring the determinants of analyst ability to produce accurate
target prices.
The second essay examines whether analyst ranking status affects institutional
investors’ decisions to incorporate target price information into their investment
strategies. Evidence shows that market participants value analyst target prices. There
is limited evidence, however, on how target price revisions influence the decisions of
sophisticated investors. The examination of this study is relevant for the economic
question: Does analyst reputation mitigate or exacerbate the conflicts of interest that
analysts face? Consistent with institutional investor trades being based on superior
information, I observe differences in the information content of target price revisions
by star and non-star analysts. Additionally, a duration analysis shows that the quality
of analyst target price revisions significantly increases the hazard of analysts losing
their star ranking.
In the final essay, I examine whether analysts’ decisions to issue cash flow forecasts
depend endogenously on their decision to use these forecasts to set target prices.
Using an endogenous switching regression model, with analyst report regimes of
disclosure and non-disclosure of cash flow forecasts, I find that cash flow revisions
are more important than earnings revisions in explaining the magnitude of target
price revisions in the cash flow disclosure regime. Cash flow forecasts influence and
are influenced by analyst valuation choices. Additional analysis shows that cash
flow-based pseudo-target prices play a greater role in explaining target price implied
returns than do earnings-based pseudo-target prices. These findings provide insights
into analysts’ valuation decision processes and their sophisticated valuation input
choices.
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Declaration
I, Noor Hashim, declare that no portion of the work referred to in the thesis has been
submitted in support of an application for another degree or qualification of this or
any other university or other institute of learning.

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Copyright Statement
i. The author of this thesis (including any appendices and/or schedules to this
thesis) owns any copyright in it (the “Copyright”) and s/he has given The
University of Manchester the right to use such Copyright for any
administrative, promotional, educational and/or teaching purposes.
ii. Copies of this thesis, either in full or in extracts, may be made only in
accordance with the regulations of the John Rylands University Library of
Manchester. Details of these regulations may be obtained from the
Librarian. This page must form part of any such copies made.
iii. The ownership of any patents, designs, trade marks and any and all other
intellectual property rights except for the Copyright (the “Intellectual
Property Rights”) and any reproductions of copyright works, for example
graphs and tables (“Reproductions”), which may be described in this thesis,
may not be owned by the author and may be owned by third parties. Such
Intellectual Property Rights and Reproductions cannot and must not be
made available for use without the prior written permission of the owner(s)
of the relevant Intellectual Property Rights and/or Reproductions.
iv. Further information on the conditions under which disclosure, publication
and exploitation of this thesis, the Copyright and any Intellectual Property
Rights and/or Reproductions described in it may take place is available
from the Head of School of Manchester Business School (or the Vice-
President) and the Dean of the Faculty of Humanities.

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Acknowledgements
Many people have been very helpful to me during the time it took me to write this
thesis, for which I would like to thank them wholeheartedly. Professor Norman
Strong, in February 2009 I approached you to ask if you would like to be my PhD
supervisor. I am very pleased that you agreed. I have been very fortunate to work
with you and I owe you too much. In you I found a rewarding cooperation, clearly
seen by this thesis. I would like to express my sincere appreciation to you for your
time, patience, guidance and encouragement throughout my studies at Manchester
Business School. You challenged me and pushed me to defend my reasoning and
helped me grow. I appreciate your approachability, generosity with your research
knowledge and your willingness to help me out with career development. I will
treasure all your valuable advices to pass them on when I become a supervisor
myself one day. I sincerely hope that we can continue working together after this
collaboration has ended.
My PhD committee chair, Prof. Martin Walker, thank you for your support, valuable
comments and suggestions. Prof. Stuart Hyde and Prof. Julie Froud, thank you for
always having open doors, for listening to me and helping me navigate life as a PhD
student. My thanks also go to the faculty who taught me the first year PhD training
courses. Dr. Marie Dutordoir, Prof. Martyn Andrews, Dr. Konstantinos
Stathopoulos, Dr. Arif Khurshed, Dr. Edward Lee, Dr. Roberto Mura, Dr. Ning
Gao, Dr. Asad Kausar, Dr. Maria Marchica, Prof. Ser-Huang Poon, Dr. Alex Taylor,
and Prof. Richard Stapleton, I thank you all. My senior, (doctor to be) Tuan Ho,
thank you for your helpful advice and fruitful discussions. I also greatly
acknowledge the help of the staff members of MBS who only knew me by name,
but did things above and beyond their duties to help me out. Daniel Wheatcroft and
Anusarin Lowe, you were always there helping me find last minute assistance. Mark
Greenwood and Xia Hong, you deserve a big thank you.
The people who did not directly contribute to this thesis but whose presence and
support made all the difference also deserve a thank you. My colleagues Dimitris
Kostas and Erwin Hansen and my neighbour Mrs. Marion Davis, I extend my
special thanks to you for your care and support. My friend, Maryam Bolos, I can
never say a thank you enough. My little sister Ayah, you are the only one who
makes me laugh when I do not even want to smile. Thank you for always being
there. My brother Ahmed, you are a very supportive companion. I thank you
especially for your support during a few computer crises. It is wholly attributable to
you that none of my computers have ever left the house via the window. Mom and
Dad, you deserve all of the credit, for your love and prayers, and for enduring and
understanding throughout everything.
Special thanks go to the University of Manchester for awarding me the MBS
Doctoral Bursary.

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Chapter 1
Introduction
1.1. Motivation
A critical challenge for any economy is the efficient allocation of resources to
investment opportunities. Equity analysts play an important role in this allocation
process as they act as intermediaries providing information to investors on the
quality of firms’ equities. To the extent that sell-side analysts perform their roles,
they add value in the capital market by analyzing information, facilitating its flow
through markets, helping investors make use of this information in investment
decisions and thereby improving market efficiency. As part of their equity reports,
analysts produce a target price for the firm’s stock. A target price is not only an
estimate of a firm’s value but also an outcome of a thorough analysis that requires
the ability to recognize whether a stock is misvalued and whether a price correction
is likely to take place in the future. Whether analyst target prices add value in the
capital market is a subject of debate in the literature. My thesis presents three essays
that address the debates concerned with whether equity analysts have the ability and
incentives to produce accurate target prices, whether target prices are informative to
sophisticated investors and whether analysts perform rigorous valuations when
setting target prices. I answer the following yet unexplored three questions: Does a
bull-bear valuation analysis increase the accuracy of analysts’ target prices? Does
analyst ranking affect how informative target prices are to institutional investors?
And do analysts use their cash flow forecasts when setting target prices? The three
essays answer calls by Schipper (1991) and Brown (1993) for research to improve
our understanding of analyst decision processes.
The first essay investigates the link between target price accuracy and supplementing
target prices with a bull-bear analysis (BBA). In the literature, there is an increasing
focus on target prices, yet there are concerns about their usefulness because of their
limited accuracy. Asquith et al. (2005) find that 54.3% of target prices are accurate in
a sample of 818 Institutional Investor’s All-American Research Team analyst reports
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issued during 1997–1999.1 Bonini et al. (2010) find a target price accuracy of 33.1%
for a sample of 10,939 reports covering companies listed on the Milan Stock
Exchange during 2000–2006. Based on a sample of 1,000 analyst reports on German
stocks during 2002–2004, Kerl (2011) finds a target price accuracy of 56.5% and an
average prediction error of 33%. Bradshaw et al. (2012) find an accuracy of 64% and
a prediction error of 45% for a sample of 492,647 target prices for US firms during
2000–2009. Researchers attribute the limited accuracy to analysts’ inability or lack
of incentive to produce accurate target prices. The supporting evidence, however, is
inconclusive. I argue that information uncertainty is one of the factors that influences
target price accuracy. The inputs to the valuation models that analysts use to generate
their target prices are subject to uncertainty and so are analyst target prices. I
therefore conjecture that a BBA enhances analysts’ assessments of investment risk
and consequently improves the quality of their valuations. A BBA is a risk
assessment technique whereby analysts assess the effect of changing valuation model
inputs in at least two scenarios, best and worst cases. Supplementing valuation with a
BBA involves weighing up both good and bad outcomes, thereby assessing the
uncertainty attached to the investment. By examining how target price accuracy
relates to analyst choice to conduct a BBA, I investigate whether conducting a BBA
improves future stock price predictability.
I use a propensity score matching (PSM) research design combined with a
difference-in-differences (DID) methodology to analyze the performance dynamics
of analyst target prices supported by a BBA. I conduct this analysis using a hand-
collected sample of analyst reports issued during 2008–2009. Combining DID with
PSM makes it possible to compare BBA report target price accuracy (the treatment
group) to the target price accuracy of non-BBA reports (the control group) while
controlling for unobserved analyst effects. Consistent with expectations, the analysis
shows that analysts are more likely to supplement target prices with a BBA when
they face higher information uncertainty in terms of company age, stock liquidity,
and company size and higher company risk indicated by negative return on assets
and leverage. They are also more likely to conduct a BBA when they have more
experience covering the company and when they are affiliated. The DID matching

1 The figures on accuracy in this paragraph refer to whether the stock price equals or exceeds the
target price at some time during the ensuing twelve months.
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