Conditional Persistence of Earnings Components and Accounting Anomalies

• Author: Shai Levi,Itay Kama,Eli Amir
• DOI: http://doi.org/10.1111/jbfa.12127
• Date: 01 September 2015
• Published date: 01 September 2015

Journal of Business Finance & Accounting

Journal of Business Finance & Accounting, 42(7) & (8), 801–825, September/October 2015, 0306-686X

doi: 10.1111/jbfa.12127

Conditional Persistence of Earnings

Components and Accounting Anomalies

ELI AMIR,ITAY KAMA AND SHAI LEVI∗

Abstract: We suggest that the failure of investors to distinguish between an earnings compo-

nent’s autocorrelation coefficient (unconditional persistence) and the marginal contribution of

that component’s persistence to the persistence of earnings (conditional persistence) provides

a partial explanation of post-earnings-announcement drift, post-revenue-announcement drift,

and the accrual anomaly. When the conditional persistence of revenue surprises is high (low)

relative to its unconditional persistence, both the post-earnings-announcement drift and the

post-revenue-announcement drift are high (low), because investors’ under-reaction to revenues

and earnings is stronger when the persistence of revenue surprises is more strongly associated

with the persistence of earnings surprises. Also, the mispricing of accruals decreases substantially

when the conditional persistence of accruals is high relative to its unconditional persistence,

because investors’ over-reaction to accruals is mitigated when the persistence of accruals is

indeed more strongly associated with the persistence of earnings. Our findings also suggest that

financial analysts’ failure to distinguish between unconditional and conditional persistence of

revenues and accruals results in more biased revenue and earnings predictions.

Keywords: earnings components, persistence, post-earnings-announcement drift, accrual

anomaly, forecast errors

1. INTRODUCTION

Investors’ failure to fully recognize that the various components of earnings differ

in their persistence and that each component contributes differently to the overall

persistence of earnings is a common driver behind the post-earnings-announcement

drift, the post-revenue-announcement drift, and the accrual anomaly. Richardson et al.

(2010) argue that post-announcement drifts are linked to investors’ misconception

of earnings persistence and to their inability to assign different persistence measures

to the various earnings components. Sloan (1996) and Richardson et al. (2005)

argue that the accrual anomaly occurs because investors fail to recognize that the

∗The first author is from the Tel Aviv University and City University of London. The second author is from

the Tel Aviv University and University of Michigan. The third author is from the Tel Aviv University. The

authors thank seminar participants at UC Berkeley, Copenhagen Business School (Denmark), University of

Michigan, University of Toronto, University of Oulu (Finland), Tel Aviv University and Temple University

for useful comments. Eli Amir and Itay Kama are grateful to the Henry Crown and Kassirer Institutes at Tel

Aviv University for research funding. (Paper received February 2015, revised version accepted July 2015).

Address for correspondence: Itay Kama, Tel Aviv University and University of Michigan, 701 Tappan Street,

Ann Arbor, MI 48109, USA.

e-mail: ikama@umich.edu

C

2015 John Wiley & Sons Ltd 801

802 AMIR, KAMA AND LEVI

accrual and cash flow components of earnings have different persistence, and that

a larger accrual component reduces the overall persistence of earnings.1The post-

earnings-announcement drift (Bernard and Thomas, 1989, 1990; and Chan et al.,

1996) occurs because investors incorrectly assess earnings persistence (Ball and Bartov,

1996; Rangan and Sloan, 1998; and Cao and Narayanamoorthy, 2012) and partially

ignore the differential contributions of the various earnings components to earnings

persistence (Ertimur et al., 2003; Jegadeesh and Livnat, 2006a; and Shivakumar, 2006).

Jegadeesh and Livnat (2006b) and Kama (2009) argue that the failure of investors to

recognize the contribution of revenue surprises to the persistence of earnings surprises

drives the post-revenue-announcement drift.

Amir et al. (2011) distinguish between conditional and unconditional persistence

measures. Unconditional persistence, traditionally used in the literature, is the

autocorrelation coefficient obtained from the time series of a component variable.

Conditional persistence of an earnings component (for instance, revenues or accruals)

is defined as the marginal contribution of the component’s persistence to the overall

persistence of earnings.2Hence, conditional persistence, as recently introduced by

Amir et al. (2011), recognizes that the persistence of earnings depends on the

persistence of the earnings components.

The persistence of an earnings component is important in security pricing because

it explains the overall persistence of earnings. The traditional unconditional persis-

tence of each component is measured independently from the persistence of the other

components and the overall persistence of earnings (Lipe, 1986), and hence it is less

useful than the conditional persistence in security pricing (Amir et al., 2011; Bauman,

2014; Esplin et al., 2014; and Lim, 2014).

Insofar as it is more difficult to measure the conditional persistence of earnings

components than the traditional unconditional persistence, investors may be partially

fixated on the traditional and relatively easy to measure unconditional persistence

of an earnings component in pricing securities. Given that the three accounting

anomalies that we study – the post-earnings-announcement drift, the post-revenue-

announcement drift and the accrual anomaly – are partly driven by incorrect estima-

tion of the persistence of earnings components and their contribution to the overall

persistence of earnings, we suggest that the fixation of investors on a component’s

unconditional persistence and their tendency to neglect its conditional persistence

provide another explanation for the three anomalies.

To examine our assertion, we use two decompositions of earnings. In the first

one, we decompose standardized unexpected earnings into standardized unexpected

revenue and standardized unexpected expenses. In the second one, we decompose

earnings into operating cash flows and accruals. We compute the unconditional and

conditional persistence of each component and construct a measure of the distance

between the conditional and unconditional persistence, which we label the adjusted

conditional persistence (ACP).

We focus our empirical analysis on standardized unexpected revenue growth

(SURG), and the accrual component of earnings (ACC). We focus on SURG because

1 Xie (2001) and Cheng et al. (2012) show that a greater mispricing exists with respect to discretionary

accruals, which are usually characterized by lower persistence relative to other accruals.

2 The slope coefficient obtained when the persistence of earnings is regressed on the persistence of

earnings components multiplied by the mean of the explanatory variable is used as a measure of the

component’s conditional persistence.

C

2015 John Wiley & Sons Ltd