NBER WORKING PAPER SERIES
DIAGNOSTIC EXPECTATIONS AND STOCK RETURNS
Pedro Bordalo
Nicola Gennaioli
Rafael La Porta
Andrei Shleifer
Working Paper 23863
http://www.nber.org/papers/w23863
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
September 2017
Gennaioli thanks the European Research Council and Shleifer thanks the Pershing Square
Venture Fund for Research on the Foundations of Human Behavior for financial support of this
research. We are grateful to seminar participants at Brown University and Sloan School, and
especially to Josh Schwartzstein, Jesse Shapiro, Pietro Veronesi, and Yang You for helpful
comments. We also thank V. V. Chari, who encouraged us to confront our model of diagnostic
expectations with the Kalman filter. The views expressed herein are those of the authors and do
not necessarily reflect the views of the National Bureau of Economic Research.
NBER working papers are circulated for discussion and comment purposes. They have not been
peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies
official NBER publications.
© 2017 by Pedro Bordalo, Nicola Gennaioli, Rafael La Porta, and Andrei Shleifer. All rights
reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit
permission provided that full credit, including © notice, is given to the source.
Diagnostic Expectations and Stock Returns
Pedro Bordalo, Nicola Gennaioli, Rafael La Porta, and Andrei Shleifer
NBER Working Paper No. 23863
September 2017
JEL No. D03,D84,G02,G12
ABSTRACT
We revisit La Porta’s (1996) finding that returns on stocks with the most optimistic analyst long
term earnings growth forecasts are substantially lower than those for stocks with the most
pessimistic forecasts. We document that this finding still holds, and present several further facts
about the joint dynamics of fundamentals, expectations, and returns for these portfolios. We
explain these facts using a new model of belief formation based on a portable formalization of the
representativeness heuristic. In this model, analysts forecast future fundamentals from the history
of earnings growth, but they over-react to news by exaggerating the probability of states that have
become objectively more likely. Intuitively, fast earnings growth predicts future Googles but not
as many as analysts believe. We test predictions that distinguish this mechanism from both
Bayesian learning and adaptive expectations, and find supportive evidence. A calibration of the
model offers a satisfactory account of the key patterns in fundamentals, expectations, and returns.
Pedro Bordalo
Saïd Business School
University of Oxford
Park End Street
Oxford, OX1 1HP
United Kingdom
pedro.bordalo@sbs.ox.ac.uk
Nicola Gennaioli
Department of Finance
Università Bocconi
Via Roentgen 1
20136 Milan, Italy
nicola.gennaioli@unibocconi.it
Rafael La Porta
Brown University
70 Waterman Street
Room 101
Providence, RI 02912
and NBER
rafael.laporta@brown.edu
Andrei Shleifer
Department of Economics
Harvard University
Littauer Center M-9
Cambridge, MA 02138
and NBER
ashleifer@harvard.edu
2
I. Introduction
La Porta (1996) shows that expectations of stock market analysts about long-term earnings
growth of the companies they cover have strong predictive power for these companies’ future
stock returns. Companies whose earnings growth analysts are most optimistic about earn poor
returns relative to companies whose earnings growth analysts are most pessimistic about.
Figure 1 offers an update of this phenomenon. Stocks are sorted by analyst long-term
earnings per share growth forecasts (LTG). The LLTG portfolio is the 10% of stocks with most
pessimistic forecasts, the HLTG portfolio is the 10% of stocks with most optimistic forecasts. The
figure reports geometric averages of one-year returns on equally weighted portfolios.
Figure 1. Annual Returns for Portfolios Formed on LTG. In December of each year between 1981 and 2015, we form
decile portfolios based on ranked analysts' expected growth in earnings per share and report the geometric average
one-year return over the subsequent calendar year for equally-weighted portfolios with monthly rebalancing.
Consistent with La Porta (1996), the LLTG portfolio earns an average return of 15% in the
year after formation, while the HLTG portfolio earns only 3%.
2
Adjusting for systematic risk
2
The spread in Figure 1 is in line with, although smaller than, previous findings. LaPorta (1996) finds an average
yearly spread of 20% but employed a shorter sample (1982 to 1991). Dechow and Sloan (1997) use a similar sample
to La Porta (1996) and find a 15% spread. Appendix A shows that the spread also holds in sample subperiods.
3
deepens the puzzle: the HLTG portfolio has higher market beta than the LLTG portfolio, and
performs much worse in market downturns.
3
Over the past 35 years, betting against extreme
analyst optimism has been on average a good idea. La Porta (1996) interprets this finding as
evidence that analysts, as well as investors who follow them or think like them, are too optimistic
about stocks with rapidly growing earnings, and too pessimistic about stocks with deteriorating
earnings.
In this paper we analyze the dynamics of expectation formation and offer a
psychologically founded theory that jointly accounts for the behavior of fundamentals,
expectations, and returns. We propose a new learning model in which beliefs are forward looking
just as with rational expectations, but distorted by representativeness, which biases the
interpretation of the news. Specifically, analysts update excessively in the direction of states of the
world whose objective likelihood rises the most in light of the news. The model delivers over-
reaction to news and extrapolation. It also makes sharp predictions that distinguish it from both
Bayesian learning and mechanical adaptive expectations. We test, and confirm, several of these
new predictions.
After describing the data in Section II, in Section III we document three facts. First,
HLTG stocks exhibit fast past earnings growth, which slows down going forward. Second,
forecasts of future earnings growth of HLTG stocks are excessively optimistic, and are
systematically revised downward later. Third, HLTG stocks exhibit good past returns but their
average returns going forward are low. The opposite dynamics obtain for LLTG stocks, but in a
much less extreme form, an asymmetry we do not account for in our model.
3
We find
, and
(Appendix A). The HLTG-LLTG spread holds within size buckets and it
is strongest for intermediate B/M levels.
4
Our model of learning in Section IV is based on Gennaioli and Shleifer’s (GS, 2010)
formalization of Kahneman and Tversky’s (1972) representativeness heuristic. As in GS (2010)
and Bordalo, Coffman, Gennaioli and Shleifer (BCGS, 2016), a trait is representative of a group
when it occurs more frequently in that group than in a reference group . The representative
trait is quickly recalled and its frequency in group is exaggerated. To illustrate, consider a
doctor assessing the health status of a patient after a positive test. The representative patient is
“sick”, because sick people are more frequent among patients who tested positive than in the
overall population. The sick patient type quickly comes to mind and the doctor inflates its
probability, which in reality may be low if the disease is rare.
In the present setting, analysts learn about firms’ unobserved fundamentals on the basis of
a noisy signal (e.g., current earnings). The rational benchmark is the Kalman Filter. Relative to
this benchmark, representativeness causes analysts to inflate the probability of firm types whose
likelihood has increased the most in light of recent earning news. After exceptionally high
earnings growth, the representative firm is a “Google”, and analysts inflate its probability. There
is a kernel of truth: Googles are truly more likely among firms exhibiting exceptional growth.
Beliefs, however, go too far: Googles are quite rare in absolute terms. Following our work on
credit cycles (BGS 2017), we say that this distorted inference follows a “Diagnostic Kalman
Filter” to emphasize that it overweighs information diagnostic of certain firm types.
Section V maps the model to the data. It starts by considering a key implication of the
kernel of truth hypothesis: expectations exaggerate the incidence of Googles in the HLTG group
because these firms are relatively more likely there. The data confirms that the HLTG group has a
fatter right tail of strong future performers than all other firms. These exceptional performers are
thus representative of the HLTG group, even though they are unlikely in absolute terms. As the
