BEHAVIORAL ECONOMIC & FINANCIAL MANAGEMENT-series 3 vol 1 : -collection of unpublished-HIGH QUALITY & ORIGINAL RESEARCH WITH EMPIRICAL EVIDENCE AND REALISTIC ANALYSIS by EMAMI ALIREZA & AMINI AMROLLAH & EMAMI MOSTAFA

Author:EMAMI, ALIREZA & AMINI, AMROLLAH & EMAMI, MOSTAFA [EMAMI, ALIREZA]
Language: eng
Format: epub
Publisher: 1497507944
Published: 2020-12-21T16:00:00+00:00

2001 from Ericksonet al.(2004) and for the period 1994-2003 from Lane and O’Connell

(2006). This results in 95 firms. I then use the online WRDS name search tool to identify

the GVKEY for each GAAP violator. Out of these 95 firms, 76 firms and 191 firm-years

have valid GVKEY values. Out of these 76 firms and 191 firm-years, 14 firms and 34

firm-years are in the validation sample. In Table 14, I report the distribution of these 34 GAAP violators in the DA deciles.

Contrary to my prior expectation, I find no evidence of a concentrated distribution of

GAAP violators in the top DA deciles. This result suggests that the DA measure

estimated from the performance-adjusted FLMJ model does not have enough power to

identify extreme earnings manipulators (GAAP violators).

To measure earnings management more accurately, in the next step I focus on a

particular type of earnings management for a particular purpose, which is accrual

manipulation for the purpose of avoiding negative earnings. This aligns my research on

earnings management with my research on expectations management, which also focuses

on benchmark-beating behaviour. Accordingly, I narrow the research question to

“examining the impact of loss-avoidance accrual management on firm valuation”.

Specifically, I examine how this type of earnings management impacts the abilities of

accounting valuation models to predict firms’ true intrinsic values.

In the next section, I use discretionary accrual estimates to identify firms that

manipulate accruals to meet the positive earnings benchmark, following the “distribution

of earnings after management approach”. In the subsequent section, I present a validation

test for the ability of the proposed measure to capture the notion of earnings management.

3.3.1.4. Definition of Earnings Manipulators and Non-manipulators Prior studies, such as Matsumoto (2002), define earnings manipulators to be the

firm-years with positive DA and non-manipulators to be those with negative DA.

However, since some firms may have positive DA by chance instead of by earnings manipulation, defining firm-years with positive DA as manipulators may misclassify

many firm-years.

To avoid this pitfall, I use DA together with the zero-earnings benchmark to identify

firms that are likely to have manipulated earnings for the purpose of avoiding negative

earnings. Firms are motivated to report positive earnings to avoid punishment by the

stock market (see Skinner and Sloan, 1999), to maximize management’s bonus

compensation (see Healy, 1995) and to enhance reputations with stakeholders (see

Bowenet al., 1995; Burgstahler and Dichev, 1997). One approach they take to achieve

the positive earnings benchmark is to create income-increasing discretionary accruals.

Therefore, I define earnings manipulators to be the firm-years whose earnings before

discretionary accruals are less than zero and whose earnings after discretionary accruals

are greater than zero. Since these firms are likely to have created income-increasing

discretionary accruals to avoid reporting negative earnings, I refer to them as loss

avoidance accrual manipulators. In this context, “earnings” are measured using earnings

before extraordinary items and “discretionary accrual” is the performance-adjusted

discretionary accrual estimated from the FLMJ model.

As a next step, I construct a matched non-manipulator control sample. To do so, I

first create a group of firms-years that have earnings before and after discretionary

accrual both greater than zero. Since these firms do not need to manipulate accruals in

order to produce positive earnings, I refer to them as non-manipulators.

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