The role of accounting fundamentals and other information in analyst forecast errors

• Date: 01 June 2018
• DOI: http://doi.org/10.1111/infi.12129
• Author: Danilo S. Monte‐Mor,Cristiano M. Costa,Fernando C. Galdi
• Published date: 01 June 2018

DOI: 10.1111/infi.12129

ORIGINAL ARTICLE

The role of accounting fundamentals and other

information in analyst forecast errors

Danilo S. Monte-Mor

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Fernando C. Galdi

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Cristiano M. Costa

2

1

Fucape Business School, Vitoria, ES,

Brazil

2

Universidade do Vale do Rio dos

Sinos −Campus de Porto Alegre, Porto

Alegre, RS, Brazil

Correspondence

Danilo S. Monte-Mor, Fucape Business

School, Vitoria Espirito Santo, Brazil.

Email: danilo@fucape.br

Funding information

Coordenação de Aperfeiçõamento de

Pessoal de Nível Superior (Coordination

for the Improvement of Higher Education

Personnel)

Abstract

In this paper, we study analyst forecast errors in the United

States, and decompose these errors into two different

sources: accounting fundamentals and other information.

Using data from 1983 to 2012, our results lead to two

conclusions. First, using the decomposition approach, we

show that on average, the component of analyst forecast

errors based on ‘accounting information’is optimistic;

however, the component of analyst forecast errors based on

‘other information’is pessimistic. Second, although

occasionally analysts make forecasts with small errors,

the decomposition of such errors provide, on average, larger

(positive) accounting errors, and larger (negative) other

information errors. In this case, our results suggest that

analysts' luck occasionally surpasses their skills.

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INTRODUCTION

The literature has long offered conflicting conclusions on how analysts provide biased information.

Although most of the prior literature has documented optimistic bias (see, e.g. Clayman & Schwartz,

1994; Francis & Philbrick, 1993), a few other studies have failed to reject efficiency and unbiasedness

in analyst forecasts after implementing approaches that minimize methodological flaws (Basu &

Markov, 2004; Abarbanell & Lehavy, 2003; among others).

Most of the studies suggesting that analysts tend to be optimistic in their forecasts show that the

mean of analyst forecasts is positive (see, e.g. reviews by Cowen, Groysberg, & Healy, 2006; Das,

Levine, & Sivaramakrishnan, 1998; Lim, 2001). However, although the mean forecast error is

optimistic, other moments of the distribution (such as the median) are not optimistic. For example, in

Abarbanell and Lehavy (2003), the only statistical indication that supports analyst optimism is the

mean of forecast error (consensus forecast of quarterly earnings issued prior to earnings announcement

International Finance. 2018;21:175–194. wileyonlinelibrary.com/journal/infi © 2018 John Wiley & Sons Ltd

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are, on average, greater than reported earnings). Conversely, the median of forecast error equals zero,

which suggests unbiased forecasts, whereas the percentage of forecasts below reported earnings is

significantly greater than the percentage of forecasts above reported earnings, suggesting analyst

pessimism.

Several studies have analy sed skewness in the distribution of forecast errors an d possible causes

of analysts' bias by discuss ing the information that is r eflected in forecast error s (Frankel & Lee,

1998; Gode & Mohanram, 2009; Hughes, Liu, & Su, 2008; Lo & E lgers, 1998; So, 2013). Most of

these studies have either focus ed on the interplay of consensus analyst forecasts , past forecast errors,

and firm characteristics or hav e taken approaches that shift th e focus towards the time-serie s

prediction of future earnin gs using historical infor mation contained in the fin ancial statements.

Although many of these studies recognize the relevance of analyzi ng past information in explaining

analyst forecast errors, th e role of ‘other informatio n’has not yet been a focus in asses sments of

analysts' accuracy.

In the earnings forecasting process, analysts consider not only historical accounting data reflecting

firms' prior performance but also other information not yet contained in the financial statements that

has yet to have an impact on future earnings. Examples of other information include, inter alia, the

granting of a new patent, the regulatory approval of a new drug, new long-term contracts and sudden

CEO death (Myers, 1999). The other information considered by analysts may affect future

performance forecasts and thus the asymmetries in forecast error distribution. In such cases, other

information that involves analysts' accuracy should be considered in the analysis.

In this paper, we examine the extent to which analyst forecast errors are related to accounting

fundamentals and other information. To identify and test analysts' accuracy in processing information

from these two sources, we first disaggregate total analyst forecast errors into one error related to past

accounting information and another error related to other information. We define forecast errors as the

mean of analyst forecasts minus actual earnings. We base our analyst error disaggregation approach on

Ohlson's (1995) linear information dynamic, which links accounting fundamentals and other

information with expectations of future earnings. We also consider the nonlinear relation between

accounting fundamentals and valuation functions (Burgstahler & Dichev, 1997; Collins, Pincus & Xie,

1999; Zhang, 2000) to estimate our models.

Our disaggregation tests verify whether analysts efficiently include in their forecast new

information (other information) and the persistence of past information (accounting fundamentals). In

the usual accounting setting, the Mishkin (1983) test is applied to assess whether the market rationally

prices the persistence of accounting components according to its association with the rational forecast

of future earnings (see, e.g., Sloan, 1996; Xie, 2001). With modifications in the regression system

commonly used in the Mishkin test, we obtain a similar test that allows us to verify whether analysts

rationally forecast future earnings.

Despite certain similarities between our descriptive statistics and the widely held belief among

accounting and finance academics that analysts generally produce optimistic forecasts, our analyses of

the distribution of forecast errors based on other information raises doubts about this conclusion. Our

results show that other information (accounting) forecast errors of much greater magnitude are

observed in the ‘pessimistic’(‘optimistic’) tail of the distribution rather than in the ‘optimistic’

(‘pessimistic’) tail.

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These characteristics of the distributions of accounting and other information

forecast errors suggest that analysts may have different behaviours in forecasting the persistence of

accounting data and the impact of new information on earnings. In such cases, we argue that the

controversial results in the literature may have been influenced by differences in sample selection

procedures that consider firms in which such information plays different roles. Such findings support

Abarbanell and Lehavy (2003) and Cohen and Lys's (2003) main conclusion that certain papers on

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