The Best of Times, the Worst of Times: Testing which Behavioral Biases Affect Analyst Forecasts

• Author: Yuk Ying Chang,Wei‐Huei Hsu
• DOI: http://doi.org/10.1111/irfi.12168
• Date: 01 December 2018
• Published date: 01 December 2018

The Best of Times, the Worst of

Times: Testing which Behavioral

Biases Affect Analyst Forecasts*

YUK YING CHANG AND WEI-HUEI HSU

School of Economics and Finance, Massey University, New Zealand

ABSTRACT

Mood-induced optimism, cognitive inaccuracy, and distraction can affect

analyst forecasts. This study compares and contrasts these influences. The

novelty of our approach is that we first show that these behavioral biases

have different implications for analysts’forecast errors conditioned on the

errors being positive and negative. We then use proxies for positive and neg-

ative moods to empirically test the support for each of these biases. Consis-

tent with cognitive precision, we find that analysts make less (more) accurate

forecasts when they are in positive (negative) moods. We further show that

these results are driven neither by sentiment associated with contemporane-

ous economic or market conditions nor by under- or overreaction to more

bad news released on days immediately before weekends or holidays.

JEL Codes: G24; G14; D03; G02

Accepted: 29 November 2017

I. INTRODUCTION

A fairly large body of literature suggests that the moods or psychological states

of people affect their judgment. People in a more positive mood are more opti-

mistic and assign higher probabilities to positive events and lower probabilities

to negative events (Wright and Bower 1992). For example, a happier investor is

more optimistic about the expected performance of stock markets (Kaplanski

et al. 2015). A college student is also more optimistic about his own perfor-

mance after watching his college sports team win compared to when he

watches them lose (Hirt et al. 1992).

However, positive bias is also consistent with distraction. Sport events may

draw attention away (Gantz and Wenner 1991; Schmidt 2013; Ehrmann and

Jansen 2017), thereby increasing the likelihood that people will make more or

* We thank Sudipto Dasgupta and Ling Cen for their helpful suggestions. We also thank Kee-Hong

Bae, Michael Brennan, Bei Cui, Jie Gan, Gilles Hilary, Peter Joos, Hongping Tan, Xiaoyun Yu, and

the participants in the 2016 FMA Asia/Pacific Conference, the 2016 Asian Finance Association Con-

ference and the Massey University seminar. We acknowledge Andrea Bennett for proofreading our

drafts and Massey Business School for providing financial support.

© 2018 International Review of Finance Ltd. 2018

International Review of Finance, 18:4, 2018: pp. 637–688

DOI: 10.1111/irfi.12168

larger errors. For example, Drake et al. (2016) find that market reactions to earn-

ings news released during the NCAA basketball tournament are muted. In addi-

tion, positive bias may be explained by cognitive inaccuracy. Happy investors

may be easily swayed by emotion, which may decrease both their logical consis-

tency and their attention to detail (Schwarz and Bless 1991). Hence, such inves-

tors may be less analytical and their predictions about market performance may

be more biased. Meloy (2000) reports that mood-elevated consumers display

severely distorted evaluations of new product information. Therefore, it is

unclear whether positive bias is completely driven by optimism.

In this paper, we differentiate among explanations based on optimism, dis-

traction, and cognitive accuracy using analyst forecasts. Analysts produce fore-

casts higher and lower than realized earnings, which allow us to distinguish

between optimism and cognitive accuracy explanations, as discussed further

below. Moreover, analyst forecasts are widely followed and watched. Analyst

accuracy is an important issue, and related studies have long existed in the

mainstream literature (e.g., Basi et al. 1976).

1

However, although optimism and

distraction are fairly well-known subjects and have been discussed in the litera-

ture, cognitive precision is less researched, except in the psychology field. This

study provides evidence consistent with cognitive accuracy.

Our arguments are as follows. When an analyst is in a more positive mood,

he is more optimistic, and his forecast errors are more positive on average. Con-

versely, when an analyst is in a more negative mood, he is more pessimistic,

and his forecast errors are more negative on average. Importantly, these mood-

induced biases are not ex post conditional on whether the errors are positive or

negative. For example, assume an unbiased analyst whose mean positive errors

average + 10¢ and whose mean negative errors average −10¢ (the basis of com-

parison). In this case, when the analyst is more optimistic, the corresponding

positive and negative errors will average, for example, +12¢ and −9¢, respec-

tively. In contrast, when an analyst is more pessimistic, the positive and nega-

tive errors will average, for example, +9¢ and −12¢, respectively. We call this

“the optimism explanation.”

2

When a happy mood negatively affects the accuracy with which an analyst

processes information, he will make larger errors on both sides, for example,

making average positive and negative errors of +12¢ and −12¢, respectively. In

contrast, when an analyst is in a negative mood, he will process information

more systematically and substantively (Martin and Clore 2013). Hence, he is

more accurate and his average positive and negative errors may be smaller, for

1 On September 1, 2017, there were 77,000 and 59,500 Google Scholar hits for “accuracy of

analyst forecasts”and “analyst forecast errors”, respectively.

2 This example compares a mood-affected analyst with an unbiased analyst. However, a similar

argument applies if we compare an analyst’s forecast errors when he is exposed to mood-

affecting events with his average positive and negative forecast errors. The latter comparison

is more consistent with our empirical methodology, which considers “within variation”and

subsumes time-invariant analyst effects with analyst fixed effects.

© 2018 International Review of Finance Ltd. 2018638

International Review of Finance

example, +9¢ and −9¢, respectively. We refer to this as “the cognition

explanation.”

However, when an event disturbs an analyst emotionally and distracts him

from his work, regardless of whether it is a happy or sad event, the analyst consis-

tently commits larger errors. In this case, regardless of whether the analyst is in a

positive or negative mood, the average positive and negative errors are larger, for

example, +12¢ and −12¢,respectively. We term this “thedistraction explanation.”

Studies find that people are happier when they anticipate holidays (Gilbert

and Abdullah 2002; Van Boven and Ashworth 2007; Nawijn et al. 2010) and on

holidays (Bollen et al. 2011; Sharpe 2014). Achor and Gielan (2016) suggest that

taking more vacations leads to greater happiness. Using various daily mood

measures, including the Gallup Mood Survey and Facebook Gross National

Happiness Index, Autore and Jiang (2017) provide evidence of uplifted mood

swings when holidays approach. Good mood has also been proposed as a possi-

ble explanation for significant preholiday effects in finance (Thaler 1987;

Fabozzi et al. 1994; Frieder and Subrahmanyam 2004; Bialkowski et al. 2012;

Bergsma and Jiang 2015; Hirshleifer et al. 2016; Autore and Jiang 2017). Hence,

we hypothesize that analysts are in good moods when holidays are upcoming

and that the analysts become happier as holidays get closer.

3

Conversely, studies suggest that disaster information causes a negative mood

or affects mood adversely (Johnson and Tversky 1983; Göritz and Moser 2006;

Västfjäll et al. 2008; Papousek et al. 2014). Kaplanski and Levy (2010a) find sig-

nificantly negative stock market returns following aviation disasters. Antoniou

et al. (2017) document more negative sentiment effects due to terrorist attacks

and mass shootings among managers of corporate policies, as well as an

increase in cash holdings and a decrease in R&D expenditure. We consider it

likely that disaster and holiday events will be associated with different moods

to distinguish between the distraction and cognitive accuracy explanations.

Studies also suggest that people tend to be in more negative moods in

autumn as a result of seasonal affective disorder (SAD) (Kamstra et al. 2003).

Evidence consistent with the SAD effect includes studies of seasonal anomalies

(Lakonishok and Smidt 1988), the Halloween indicator (Bouman and Jacobsen

2002), financial analysts and equity market returns (Lo and Wu 2010), mutual

fund flows (Kamstra et al. 2017), and the mood beta of stocks (Hirshleifer

et al. 2016). Therefore, we hypothesize that analysts will be in more negative

moods in October.

4

3 If people have a variety of moods when there is a forthcoming holiday, there will be a bias

against finding significant preholiday effects of positive moods.

4 For the United States, fall is defined as September 21 to December 20. October is the first full

month of fall. In addition, Hirshleifer et al. (2016) find that the value-weighted CRSP return

is lowest in October, at −0.37%, in their sample period (1963–2015). We thus choose October

to test the SAD effect. We do not consider the effects of “blue Monday,”because several stud-

ies (e.g., Stone et al. 2012) suggest that blue Monday does not exist. Moreover, Wang et al.

(1997) find that negative average stock returns on Mondays are neither prevalent nor robust.

© 2018 International Review of Finance Ltd. 2018 639

The Best of Times, the Worst of Times