The incidence of earnings management on information asymmetry in an uncertain environment some canadian evidence

Journal

of

International

Accounting,

Auditing

and

Taxation

22 (2013) 26–

38
Contents

lists

available

at

SciVerse

ScienceDirect
Journal

of

International

Accounting,
Auditing

and

Taxation
The

incidence

of

earnings

management

on

information
asymmetry

in

an

uncertain

environment:

Some

Canadian
evidence
Denis

Cormier
∗
, Sylvain

Houle, Marie-Josée

Ledoux
ESG

UQÀM,

School

of

Management,

University

of

Quebec

at

Montreal,

P.O.

Box

8888,

Montréal,

Québec,

Canada

H3C

3P8
a

r

t

i

c

l

e

i

n

f

o
Keywords:
Corporate

governance
Earnings

management
Environmental

uncertainty
Information

asymmetry
a

b

s

t

r

a

c

t
In

this

study,

we

investigate

the

association

between

earnings

management

and

informa-
tion

asymmetry

considering

environmental

uncertainty.

Results

show

that

a

complex

and
dynamic

environment

weakens

the

relationship

between

discretionary

accruals

and

infor-
mation

asymmetry

measured

as

share

price

volatility

and

bid-ask

spread.

More

specifically,
the

positive

relationship

between

earnings

management

and

information

asymmetry

is
weakened

for

diversified

firms,

those

intensively

investing

in

R&D,

and

those

facing

high
sales

volatility.

This

highlights

the

difficulty

for

investors

to

assess

earnings

management

in
an

uncertain

environment.

Finally,

in

such

a

context,

discretionary

accruals

are

more

likely
to

be

detected

by

investors

for

firms

cross-listed

on

a

U.S.

stock

exchange,

a

more

liquid
and

transparent

stock

market

compared

with

the

Canadian

stock

market.
© 2013 Published by Elsevier Inc.

1.

Introduction
In

this

paper,

we

investigate

the

association

between

earnings

management

and

information

asymmetry

considering

environmental

uncertainty.

The

theory

of

the

firm

(e.g.

Child,

1972;

Williamson,

1975)

recognizes

that

environmental

uncer-
tainty

places

significant

constraints

on

firms,

affecting

strategy

and

decision-making.

Although

firms

are

constrained

by

the
nature

of

their

environment,

managers

do

have

opportunities

to

respond

strategically

to

uncertainty

(Ghosh

&

Olsen,

2009).
One

of

these

opportunities

is

earnings

management.

The

extent

of

opportunistic

earnings

management

is

likely

to

be

higher
when

information

asymmetry

is

high

(Dye,

1988;

Trueman

&

Titman,

1988).

In

turn,

earnings

management

could

increase
the

uncertainty

for

investors

about

the

distribution

of

a

firm’s

future

cash

flows,

which

would

create

information

asymmetry
between

informed

and

less

informed

investors

(Bhattacharya,

Desai,

&

Venkataraman,

2012).
Two

dimensions

generally

characterize

environmental

uncertainty:

complexity

and

dynamism

(Child,

1972;

Thompson,
1967).

According

to

Thompson

(1967)

and

Terreberry

(1968),

a

complex

environment

is

one

in

which

interactive

relationships
relevant

for

decision

making

require

a

high

degree

of

abstraction

in

order

to

produce

manageable

mappings.

A

dynamic
environment

is

one

in

which

relevant

factors

for

decision

making

are

in

a

constant

state

of

change.
Prior

research

suggests

that

complexity

of

the

environment

increases

the

difficulty

for

investors

to

assess

earnings

man-
agement

(Lim,

Ding,

&

Thong,

2008).

Financial

reporting

is

expected

to

be

more

complex

for

firms

with

diversified

business
and

geographical

operations.

Hence,

we

expect

earnings

management

to

increase

with

the

level

of

diversification

and

to

be
more

difficult

to

detect

by

stock

market

participants.
∗
Corresponding

author.

Tel.:

+1

514

987

3000;

fax:

+1

514

987

6629.
E-mail

address:



(D.

Cormier).
1061-9518/$

–

see

front

matter ©

2013 Published by Elsevier Inc.
/>D.

Cormier

et

al.

/

Journal

of

International

Accounting,

Auditing

and

Taxation

22 (2013) 26–

38 27
Moreover,

dynamic

firms,

especially

those

investing

intensively

in

R&D,

contribute

to

information

asymmetry

(e.g.

Aboody
&

Lev,

2000).

In

such

an

asymmetrical

context,

we

can

argue

that

earnings

management

is

more

likely

to

occur

(e.g.

Francis,
LaFond,

Olsson,

&

Schipper,

2005)

and

less

likely

to

be

detected

by

market

participants.

Dynamic

firms

are

also

characterized
by

sales

volatility,

which

affects

managerial

decisions

(e.g.

Child,

1972;

Cyert

&

March,

1963;

Williamson,

1975).

It

is

assumed
that

it

is

in

managers’

interests

to

reduce

the

variability

of

reported

earnings

(Ghosh

&

Olsen,

2009;

Gul,

Chen,

&

Tsui,

2003)
but

in

a

volatile

environment,

earnings

management

is

expected

to

be

more

difficult

to

detect

because

of

a

lack

of

stability

in
accounting

figures.

Therefore,

we

can

expect

earnings

management

occurring

in

a

dynamic

environment

to

affect

information
asymmetry

to

a

lesser

extent

than

in

a

stable

environment.
The

Canadian

institutional

environment

may

provide

managers

unique

earnings

management

opportunities.

A

notable
difference

distinguishes

the

Canadian

from

the

U.S.

equity

market.

In

the

U.S.,

ownership

in

publicly

traded

firms

is

highly
dispersed

while

in

Canada,

ownership

is

highly

concentrated.

In

firms

with

concentrated

ownership,

there

is

a

real

possi-
bility

that

dominant

shareholders

may

mistreat

or

expropriate

outside

shareholders

by

earnings

manipulation.

Therefore,
differences

in

earnings

management

opportunities

may

exist

between

Canada

and

the

U.S.

However,

by

listing

their

shares
in

the

U.S.,

Canadian

firms

bond

themselves

to

better

disclosure

and

governance

practices

(Stulz,

1999).

We

expect

earnings
management

practices

to

differ

between

Canadian

firms

and

U.S.

firms

as

well

as

their

impact

on

information

asymmetry.

We
consider

that

Canada

provides

a

unique

context

to

assess

the

incidence

of

earnings

management

on

information

asymmetry,
especially

in

an

uncertain

environment.
Our

main

findings

are

the

following.

First,

we

observe

that

environmental

complexity

and

dynamism

lead

to

earnings

man-
agement,

especially

for

firms

not

cross-listed

on

a

U.S.

stock

market.

Second,

in

such

contexts,

earnings

management

affects
information

asymmetry

to

a

lesser

extent.

Third,

our

findings

suggest

that

in

the

presence

of

complexity

and

dynamism,
discretionary

accruals

are

more

likely

to

be

detected

by

investors

for

firms

listed

on

a

U.S.

stock

exchange.

This

result

is

consis-
tent

with

the

view

that

the

U.S.

stock

market

is

more

liquid

and

transparent

than

the

Canadian

market

in

the

way

information
is

collected

and

analyzed,

and

thus

is

in

a

better

position

to

detect

earnings

management

in

a

context

of

uncertainty.

Finally,
the

quality

of

corporate

governance

is

associated

with

less

earnings

management.

Overall,

our

findings

support

the

view

that

accrual

anomaly

is

partly

driven

by

investors’

failure

to

correctly

assess

future

earnings

implications

of

accruals

(Gong,
Li,

&

Xie,

2009).
Compared

with

prior

research,

the

present

study

innovates

by

investigating

how

the

level

of

complexity

and

dynamism
affects

the

stock

market’s

assessment

of

earnings

management.

By

combining

an

analysis

of

specific

environmental

incentives
to

engage

in

earnings

management

with

an

assessment

of

the

incidence

on

information

asymmetry,

this

study

contributes
to

the

understanding

of

earnings

management

implications.

Results

also

suggest

the

importance

of

controlling

for

the
endogenous

nature

of

earnings

management,

namely

corporate

governance

mechanisms.
The

remainder

of

the

paper

is

organized

as

follows.

Section

2

presents

the

theoretical

background

and

develops

hypothe-
ses.

The

study’s

methodology

is

described

in

Section

3.

Results

are

presented

in

Section

4.

Finally,

Section

5

provides

a
discussion

of

the

potential

implication

of

the

results.
2.

Background

and

hypotheses
2.1.

Stock

market

assessment

of

earnings

management
Prior

research

shows

a

relationship

between

earnings

management

and

information

asymmetry

in

the

market

place.
Measuring

accruals

management

as

the

standard

deviation

of

residuals

from

regressions

relating

current

accruals

to

cash
flows,

Francis,

LaFond,

Olsson,

and

Schipper

(2004)

and

Francis

et

al.

(2005)

find

that

earnings

management

is

associated
with

higher

information

asymmetry,

leading

to

higher

costs

of

debt

and

equity.

They

also

show

that

investors

put

more
importance

(in

the

determination

of

the

cost

of

capital)

on

accruals

that

reflect

intrinsic

features

of

the

firm’s

business
model,

relative

to

accruals

that

reflect

a

combination

of

pure

noise

and

opportunistic

choices

and

management’s

attempts

to
make

earnings

more

informative.

In

the

same

vein,

Liu

and

Wysocki

(2007)

argue

that

the

documented

relationship

between
accrual

management

and

the

cost

of

capital

is

driven

primarily

by

the

volatility

of

firms’

operating

activities

that

are

not
related

to

accounting

choices

and

less

subject

to

managerial

manipulation.
However,

prior

evidence

also

suggests

that

investors’

ability

to

assess

earnings

management

may

be

imperfect.

The
market’s

inability

to

fully

detect

earnings

management

is

associated

with

an

increase

in

the

heterogeneity

of

market

beliefs
(Ronen

&

Yaari,

2008).

The

accrual

anomaly

is

characterized

by

stock

markets

overweighting

the

accrual

persistence.

Pincus,
Rajgopal,

and

Venkatachalam

(2007)

find

that

stock

prices

tend

to

overweight

the

role

of

accruals

persistence,

especially
discretionary

accruals.

The

authors

observe

negative

abnormal

returns

in

year

t

+

1

for

firms

with

positive

discretionary
accruals

in

year

t.

This

is

particularly

the

case

for

countries

having

common

law

traditions

such

as

Australia,

Canada,

the

UK
and

the

U.S.

Soares

and

Stark

(2009)

reach

the

same

conclusion

for

a

British

sample

(1989–2004)

since

they

find

that

average
annual

abnormal

returns

generally

decline

as

prior

period

accruals

move

from

low

to

high.

This

outcome

is

consistent

with
the

accruals

anomaly

since

investors

overweight

the

persistence

of

accruals

and

underweight

the

persistence

of

cash

flows
in

predicting

the

next

period’s

earnings.

An

explanation

of

the

accrual

anomaly

is

that,

as

documented

by

Lev

and

Nissim
(2006),

investors

avoid

extreme-accruals

firms

because

of

their

attributes

such

as

small

size,

low

profitability,

and

high
risk.

Lev

and

Nissim

also

observe

that

the

high

information

and

transaction

costs

associated

with

the

implementation

of

a
28 D.

Cormier

et

al.

/

Journal

of

International

Accounting,

Auditing

and

Taxation

22 (2013) 26–

38
profitable

accrual

strategy

reduce

profits

for

investors

who

trade

on

accruals

information.

Therefore,

we

expect

earnings
management

to

increase

information

asymmetry

in

the

stock

market.
2.2.

Environmental

uncertainty

and

stock

market

assessment

of

earnings

management
The

various

terms

used

to

depict

environmental

uncertainty

fall

generally

into

two

categories

(Child,

1972;

Terreberry,
1968;

Thompson,

1967):

complexity

and

dynamism

(or

instability).

Mintzberg

(1979)

describes

three

dimensions

of

the
environment

similar

to

those

proposed

by

Child

(1972)

but

adds

new

features

for

each.

He

introduces

the

term

market
diversification

to

reflect

what

Thompson

(1967)

means

by

heterogeneity

and

Child

(1972)

by

complexity.

Mintzberg

(1979)
reserves

the

term

complexity

for

the

degree

of

sophisticated

knowledge

necessary

to

operate

in

a

given

environment

of

a
technical

or

scientific

nature.
Environmental

dynamism

refers

to

the

rate

of

change

and

instability

of

the

environment

(Dess

&

Beard,

1984).

Dynamic
environments

are

characterized

by

changes

in

various

market

elements,

such

as

customer

preferences,

technology,

and
competitor

structure.

According

to

Dess

and

Beard

(1984),

uncertainty

is

the

outcome

of

complexity

and

dynamism.

Earnings
management

in

a

context

of

uncertainty

is

likely

to

reinforce

the

accrual

anomaly

given

the

difficulty

for

investors

to

detect
earnings

management

in

such

a

context.

If

such

is

the

case,

the

impact

of

earnings

management

on

information

asymmetry
will

be

lower

in

an

uncertain

environment

than

in

a

stable

environment.
In

this

study,

we

assess

environmental

uncertainty

by

referring

to

complexity

and

dynamism.

We

assert

that

a

complex
and

dynamic

environment

weakens

the

relationship

between

earnings

management

and

information

asymmetry.
2.2.1.

Complexity
There

is

mixed

evidence

concerning

the

relationship

between

complexity

and

information

asymmetry.

Accounting

is
more

complex

for

firms

with

diversified

business

and

geographical

operations.

Agrawal,

Jaffe,

and

Mandelker

(1992)

find
that

acquisitions

that

are

focus

decreasing

(negative

focus),

such

as

those

motivated

by

diversification

or

financial

motives
like

asset-stripping,

experience

superior

long-term

performance

than

acquisitions

which

are

focus

preserving

or

increasing
(positive

focus).

In

contrast,

Megginson,

Morgan,

and

Nail

(2004)

find

evidence

of

positive

focus

outperformance,

and

hence
a

positive

relationship

between

corporate

focus

and

long-term

acquisition

performance.

Erwin

and

Perry

(2000)

examine
the

effect

of

foreign

acquisitions

by

U.S.

firms

on

analyst

forecast

errors.

For

a

sample

of

focus-preserving

foreign

acquisitions
and

focus-decreasing

acquisitions,

they

find

that

post-merger

analyst

forecast

errors

are

significantly

higher

for

U.S.

firms
that

choose

to

expand

internationally

outside

their

core

business

segment,

relative

to

those

that

undertake

global

expansion
within

their

core

business.

However,

using

earnings

forecast

errors

and

earnings

forecast

dispersion

to

measure

information
asymmetry,

Jiraporn,

Miller,

Yoon,

and

Young

Sang

(2008)

show

that

diversified

firms

do

not

suffer

more

severe

information
asymmetry

compared

with

non-diversified

firms.
Thomas

(2002)

reaches

the

same

conclusion

as

Jiraporn

et

al.

(2008)

examining

the

relationship

between

corporate
diversification

and

asymmetric

information

proxies

derived

from

analyst

forecast

errors

and

forecast

dispersion,

as

well

as
abnormal

returns

associated

with

earnings

announcements.

Thomas

(2002)

argues

that,

even

if

the

errors

outsiders

make
in

forecasting

segment

cash

flows

are

larger

than

the

errors

they

make

in

forecasting

focused

firm

cash

flows,

if

these

errors
are

not

perfectly

positively

correlated,

the

consolidated

forecast

may

be

more

accurate

than

a

forecast

for

a

focused

firm.
Concerning

the

relationship

between

diversification

and

earnings

management,

Lim

et

al.

(2008),

based

on

a

sample

of
seasoned

equity

offering,

find

that

current

discretionary

accruals

are

higher

among

diversified

firms

than

for

non-diversified
ones.

Their

evidence

is

consistent

with

the

view

that

earnings

management

is

related

to

the

extent

of

a

firm’s

diversification.
Hence,

given

the

increase

in

the

difficulty

for

investors

to

assess

earnings

management

for

diversified

firms,

we

expect

the
positive

relationship

between

earnings

management

and

information

asymmetry

to

be

weakened

for

firms

with

diversified
operations,

i.e.,

business

operations

and

geographical

operations.
H1.

A

complex

environment

weakens

the

relationship

between

earnings

management

and

information

asymmetry.
2.2.2.

Dynamism
Dynamic

firms,

especially

those

highly

involved

in

intangible

assets,

attract

market

participants

and

especially

financial

analysts

(Barth,

Kasznik,

&

McNichols,

2001).

Those

firms

offer

a

growth

potential

and

are

then

more

scrutinized

by

investors.
According

to

Milliken

(1987),

a

firm’s

top

management

is

most

likely

to

experience

the

so-called

“response

uncertainty”
either

in

the

course

of

choosing

from

a

number

of

possible

strategies

or

in

the

course

of

formulating

a

response

to

an
immediate

threat

in

the

environment.

Investing

intensively

in

R&D

may

be

a

response

to

such

uncertainty.

Aboody

and

Lev

(2000)

argue

that

the

relative

uniqueness

of

R&D

investments

makes

it

difficult

for

outsiders

to

learn

about

the

productivity
and

value

of

a

given

firm’s

R&D

from

the

performance

and

products

of

other

firms

in

the

industry.

Furthermore,

while
most

physical

and

financial

assets

are

traded

in

organized

markets,

R&D

is

not.

No

asset

prices

derive

directly

from

R&D
information.

Thus,

the

level

of

R&D

contributes

to

information

asymmetry.

Given

the

scarcity

of

public

information

about
R&D

activities,

Aboody

and

Lev

(2000)

hypothesize

and

document

that

R&D

contributes

to

information

asymmetry

between
firms

and

investors.

In

such

a

context,

we

can

argue

that

earnings

management

is

more

likely

to

occur

(e.g.

Francis

et

al.,
2005)

and

less

likely

to

be

detected

by

market

participants.
D.

Cormier

et

al.

/

Journal

of

International

Accounting,

Auditing

and

Taxation

22 (2013) 26–

38 29
Milliken

(1987)

defines

the

so-called

“effect

uncertainty”

as

an

inability

to

predict

what

the

nature

of

the

impact

of

a

future
state

of

the

environment

or

environmental

change

will

be

on

the

organization.

In

a

context

of

volatility,

firms

are

facing

the
effect

uncertainty.

Volatility

is

a

powerful

contextual

factor

affecting

managerial

decisions

for

dynamic

firms

(Child,

1972;
Cyert

&

March,

1963;

Williamson,

1975).

Prior

research

suggests

that

managers

have

incentives

to

reduce

the

variability

of
reported

earnings

(e.g.

Gul

et

al.,

2003;

Wang

&

Williams,

1994).

Leblebici

and

Salancik

(1981)

find

that

bank

loan

officers
search

for

more

information

when

making

loan

decisions

in

a

volatile

environment.

Accounting

standards

provides

a

degree
of

flexibility,

giving

opportunistic

discretion

to

managers

in

reporting

earnings

in

an

attempt

to

reduce

the

variability

in
reported

earnings

via

accrual

management

(e.g.

Bannister

&

Newman,

1996).
Sales

or

earnings

variability

increases

the

level

of

difficulty

for

investors

to

assess

earnings

management.

In

the

context
of

sales

or

earnings

volatility,

managers

have

incentives

to

reduce

the

variability

of

reported

earnings

(Gul

et

al.,

2003).
For

firms

with

unstable

sales

or

earnings,

earnings

management

might

be

difficult

to

detect

by

market

participants.

Prior
research

(e.g.

Ghosh

&

Olsen,

2009)

shows

that

managers

use

discretionary

accruals

to

reduce

the

variability

in

reported
earnings

when

facing

high

sales

volatility.

In

our

view,

in

a

context

of

high

sales

variation,

it

is

more

difficult

for

investors

to
assess

earnings

management.

Therefore,

we

expect

the

positive

relationship

between

discretionary

accruals

and

information
asymmetry

to

be

weakened

for

firms

involved

in

research

and

development

activities

and

those

facing

unstable

sales.
This

gives

rise

to

the

second

hypothesis:
H2.

A

dynamic

environment

weakens

the

relationship

between

earnings

management

and

information

asymmetry.
3.

Method
3.1.

Sample
We

fixed

the

sample

based

on

an

initial

data

set

in

which

we

collected

data

about

corporate

governance.

Our

starting
point

is

189

non-financial

firms

representing

the

Toronto

Stock

Exchange

S&P/TSX

Index

identified

in

2002.

Mergers

and
acquisitions,

bankruptcies

and

delistings

reduced

our

sample

from

189

to

155

in

2005.

For

an

initial

sample

of

155

firms,
there

are

missing

data

for

governance

variables

and

share

price

volatility

(18

firms).

The

sample

comprises

137

firms

listed
on

the

Toronto

stock

exchange

(TSE)

in

2005

(133

firms

for

the

bid-ask

spread

estimation

model).

The

resulting

137

firms

represent

80%

of

the

market

capitalization

of

non-financial

firms

listed

on

the

TSE.
Governance

variables

are

collected

from

proxy

statements.

Share

price

volatility

and

bid-ask

spread

are

measured

for
the

year

2005

while

financial

and

governance

variables

were

collected

based

on

the

information

available

in

Stock

Guide

(a
Canadian

database)

in

the

spring

of

2005,

i.e.

2004

financial

statements.

Bid-ask

spread

is

collected

from

Canadian

Market
Research

Center

database

(CFRM

TSX).

Sample

firms

operate

in

the

following

industries

(one-digit

SIC

codes):

materials;
energy;

consumer

products

&

services;

industrials;

Information

technology.
3.2.

Empirical

model
This

study

attempts

to

provide

an

integrated

analysis

of

earnings

management

and

information

asymmetry.

We

refer

to
the

following

simultaneous

equations:
ABSDACC
i,t
=

ˇ
0
+

ˇ
1
SIZE
i,t
+

ˇ
2
INDDIR
i,t
+

ˇ

3
CEOCHAIR
i,t
+

ˇ
4
BODSIZE
i,t
+

ˇ
5
BODSIZESQR
i,t
+

ˇ
6
ACSIZE
i,t
+

ˇ
6
ACSIZE
i,t
(1)
SPV/BAS
i,t

=

ˇ
0
+

ˇ
1
SYSRISK
i,t
+

ˇ
2
FFLO
i,t
+

ˇ
3
ANFOLL
i,t
+

ˇ
4
ABSDACC
i,t
+

ˇ
5
ABSDACC

∗

US
i,t
+

ˇ
6
ABSDACC

∗

ENVUNC
i,t
+

ˇ
7
ABSDACC

∗

ENVUNC

∗

US
i,t
+

ˇ
8
ENVUNC
i,t
+

ˇ
9
ENVUNC

∗

US
i,t
+

ˇ
10
US
i,t
(2)
where

ABSDACC

is

the

absolute

value

of

estimated

discretionary

accruals

scaled

by

lagged

total

assets;

SPV

is

the

share

price
volatility;

BAS

is

the

bid-ask

spread;

SIZE

is

the

natural

logarithm

of

total

assets;

INDDIR

is

the

percentage

of

independent
directors

on

the

board;

CEOCHAIR

is

the

indicator

variable

taking

the

value

of

1

if

the

CEO

also

is

the

Chairman

of

the

board,

0

otherwise;

BODSIZE

is

the

number

of

directors

on

the

board;

BODSIZESQR

is

the

square

of

the

number

of

directors

on

the
board;

ACSIZE

is

the

number

of

members

on

the

audit

committee;

SYSRISK

is

the

systematic

risk

(beta);

FFLO

is

the

percentage
of

free

float;

ANFOLL

is

the

number

of

analysts

following

a

firm;

ENVUNC

is

the

environmental

uncertainty

(SEGMENTS,

RD,
SCV);

US

is

the

U.S.

listing.
3.2.1.

Estimation

of

earnings

management
According

to

Schipper’s

(1989)

definition,

earnings

management

is

“a

purposeful

intervention

in

the

external

financial
reporting

process,

with

the

intent

of

obtaining

some

private

gain

(as

opposed

to,

say,

merely

facilitating

the

neutral

operation
of

the

process)”.

Consistent

with

Han

and

Wang

(1998)

and

Erickson

and

Wang

(1999),

earnings

management

is

estimated
by

a

cross-sectional

regression

for

the

2001

to

2003

period

(155

firms

× 3

years

=

465

firm-year

observations).

The

estima-
tion

of

normal

accruals

is

cross-sectional

based

on

industry

specific

observations

(5

industries

for

3

years).

Industry-level
30 D.

Cormier

et

al.

/

Journal

of

International

Accounting,

Auditing

and

Taxation

22 (2013) 26–

38
estimations

remove

the

variation

in

the

normal

accruals

that

is

common

across

firms

in

the

same

industry

(Burgstahler,
Hail,

&

Leuz,

2006;

Dechow,

Sloan,

&

Sweeney,

1995;

Dechow,

Sloan,

&

Sweeney,

1996;

Francis

et

al.,

2005;

Leuz,

Nanda,

&
Wysocki,

2003).

1
Observations

vary

among

industries:

from

43

for

industrials

to

174

for

consumer

products

and

services.

We

introduce

discretionary

accruals

in

absolute

value

to

capture

earnings

management.
Hribar

and

Collins

(2002)

argue

that

the

difference

between

net

income

and

cash

flow

from

operations

is

the

correct
measure

of

total

accruals

and

that

the

use

of

a

balance

sheet

approach

may

lead

to

a

systematic

bias

in

discretionary

accruals
estimation.

They

show

that

balance

sheet

accruals

estimates

are

predictably

biased

in

studies

where

the

partitioning

event
is

correlated

with

either

mergers

or

acquisitions,

or

discontinued

operations.

The

authors

demonstrate

that

tests

of

market
mispricing

of

accruals

are

understated

due

to

erroneous

classification

of

“extreme”

accruals

firms.
2
While

a

firm’s

total
accruals

are

easily

accessible

from

its

financial

statements,

normal

and

discretionary

accruals

are

not

directly

observable
and

must

be

inferred

through

an

estimation

model.
Jones’s

(1991)

model

performs

poorly

for

firms

with

“extreme”

performance

(Dechow

et

al.,

1995;

Kothari,

Leone,

&
Wasley,

2005).

Since

we

focus

on

environmental

uncertainty

that

is

likely

to

involve

firms

with

extreme

performance

(e.g.
high

sales

volatility),

we

will

rely

on

Dechow

and

Dichev

(2002)

(DD)

who

model

accruals

as

a

function

of

current,

past,
and

future

cash

flows.

Accruals

anticipate

future

cash

collections/payments

and

reverse

when

cash

previously

recognized
in

accruals

is

collected/paid.

DD

focus

on

short-term

working

capital

accruals.

However,

according

to

McNichols

(2002),

DD
model

does

not

control

for

fundamental

factors

influencing

accruals.

We

base

accruals

estimation

on

DD

model

as

modified
by

McNichols

combining

DD

and

Jones

models.

Based

on

Jones

model,

fixed

assets

and

year-to-year

change

in

sales

(a
firm’s

underlying

performance)

are

added

to

the

regression

estimations.

We

focus

on

working

capital

accruals,

which

are
accruals

net

of

depreciation,

amortization

and

unusual

items.

Unusual

items

(field

70

in

Stock

Guide

database)

include

such
elements

as

non-recurring

gains

and

losses,

restructuring

provisions,

write-offs,

and

other

non-operating

gains

or

losses.
This

is

consistent

with

the

view

that

short-term

accruals

are

harder

to

detect

by

market

participants

than

are

long-term
accruals

(e.g.

impairment

of

assets).
For

a

given

industry,

working

capital

accruals

are

modeled

in

the

following

manner:
WC

accruals

it
=

˛
1
+

˛
2
Operating

cash

flow
it−1
+

˛
3
Operating

cash

flow
it
+

˛
4
Operating

cash

flow
it+1
+

˛
4
Change

in

sales
it
+

˛
4
Fixed

assets
it
+

ε
it
(3)
The

coefficients

from

the

above

regressions

(variables

scaled

by

lagged

total

assets)

are

then

used

to

compute

normal
accruals.

Mean

industry

coefficients

are

as

follows

(average

adjusted

R-square

of

51.3%)
3
:
−0.065

Operating

cash

flow
it−1
−

0.568

Operating

cash

flow
it
+

0.271

Operating

cash

flow
it+1
+

0.092

Change

in

sales
it
−

0.029

Fixed

assets
it
3.2.2.

Determinants

of

earnings

management
Firm

size

(SIZE).

Large

firms

take

into

account

the

reputation

costs

when

engaging

in

earnings

management.

These

firms
may

have

a

better

knowledge

of

the

market

environment,

better

control

over

their

operations

and

better

understanding

of
their

businesses

relative

to

small

firms

(Kim

et

al.,

2003).

Large

firms

may

have

more

sophisticated

internal

control

systems
and

have

more

qualified

internal

auditors

as

compared

to

small

firms.

Moreover,

prior

research

documents

a

negative
association

between

firm

size

and

extreme

accruals

(Lev

&

Nissim,

2006).

Hence,

we

expect

firm

size

to

be

negatively

related
to

earnings

management.
Corporate

governance.

We

expect

the

quality

of

corporate

governance

to

be

negatively

related

to

earnings

management.
Corporate

governance

mitigates

the

degree

of

earnings

management

and

improves

the

quality

of

financial

reporting

(Beasley,
Carcello,

Hermanson,

&

Lapides,

2000;

Warfield,

Wild,

&

Wild,

1995).

Governance

variables

are

introduced

to

capture

how
corporate

governance

acting

as

a

monitoring

factor

affects

earnings

management.

The

board’s

monitoring

influences

man-
agerial

discretion

and

induces

firms

to

more

transparency

in

organizational

performance

measurement

and

reporting

(Eng

&

Mak,

2003;

Fama,

1980).

Five

variables

are

used

to

capture

the

board

effectiveness:

Independent

directors

(INDDIR);

board
1
Dechow

et

al.

(2010)

emphasize

that

accruals

models

can

be

estimated

at

the

firm

level,

which

allows

variation

across

firms

in

the

determinants
of

normal

accruals.

However,

firm-level

estimation

assumes

time-invariant

parameter

estimates

and

imposes

sample

survivorship

biases.

Therefore,

the
models

are

most

frequently

estimated

at

the

industry

level.

This

specification

assumes

constant

coefficient

estimates

within

the

industry.

Hence,

the

authors
argue

that

some

firms

may

have

large

residuals

because

of

variation

induced

by

industry

membership

rather

than

because

of

earnings

management.
2
The

balance

sheet

method

of

accruals

may

create

bias

in

the

estimation

of

normal

accruals

when

the

firm

is

involved

in

mergers

and

acquisitions.
Hence,

the

change

in

working

capital

accounts

can

be

affected

by

the

operation

of

mergers

and

acquisitions

without

any

earnings

management

intention.
The

Canadian

stock

market

is

very

active

in

mergers

and

acquisitions.
3
Coefficients

do

not

vary

substantially

when

we

estimate

accruals

based

on

a

cross-sectional

regression

for

the

whole

sample

(Adjusted

R-square;

51.7%).
WC

accruals
it
=

−0.125

Operating

cash

flow
it−1
(0.000)
−

0.465

Operating

cash

flow
it
(0.000)
+

0.260

Operating

cash

flow
it+1
(0.000)
+

0.072

Change

in

sales
it
(0.000)
−

0.014

Fixed

assets
it
(0.257)
+
ε
it
.
D.

Cormier

et

al.

/

Journal

of

International

Accounting,

Auditing

and

Taxation

22 (2013) 26–

38 31
chair

duality

(CEOCHAIR);

board

size

(BODSIZE);

board

size

squared

(BODSIZESQR);

and

audit

committee

size

(ACSIZE).
4
Ghosh,
Marra,

and

Doocheol

(2010)

find

that

earnings

management

does

not

vary

with

board

composition

and

structure,

or

with
the

audit

committee

composition,

expertise,

and

ownership.

However,

they

find

that

firms

with

smaller

boards

and

audit
committees

have

larger

discretionary

accruals.

Some

prior

studies

assume

the

relationship

between

board

size

and

board
performance

to

be

curvilinear

(e.g.

Eisenberg,

Sundgren,

&

Wells,

1998;

Golden

&

Zajac,

2001;

Vafeas,

1999;

Yermack,

1996).
To

control

for

the

possible

curvilinearity

in

the

relationship

between

board

size

and

earnings

management,

we

include

the
variable

board

size

squared

as

an

instrument.

We

treat

ABSDACC

as

an

endogenous

variable

and

governance

variables

as
instruments.
5
3.2.3.

Determinants

of

information

asymmetry
Several

proxies

exist

to

capture

information

asymmetry.

Welker

(1995),

Healy,

Hutton,

and

Palepu

(1999),

Leuz

and
Verrecchia

(2000),

and

Francis

et

al.

(2005)

show

that

the

extent

of

information

asymmetry

– proxied

by

bid-ask

spread,

share

price

volatility

or

stock

liquidity

(trading

volume)

–

is

negatively

associated

with

disclosure

quality.

In

the

current
study,

we

use

share

price

volatility

and

bid-ask

spread

to

assess

information

asymmetry.

Share

price

volatility

is

measured
as

the

standard

deviation

of

percentage

changes

in

daily

stock

prices

for

the

year

2005.
Prior

studies

suggest

that

various

firm

attributes

are

related

to

information

asymmetry

(Leuz

&

Verrecchia,

2000).

Based
on

that

literature,

we

use

analyst

following,

systematic

risk,

and

free

float

as

key

determinants

of

share

price

volatility

and
bid-ask

spread.

These

four

variables

are

introduced

as

control

variables

for

information

asymmetry.
Systematic

risk

(SYSRISK).

A

positive

relationship

is

expected

between

systematic

risk

and

information

asymmetry.

The
higher

a

firm’s

systematic

risk

(beta),

the

more

difficult

it

is

for

investors

to

accurately

assess

a

firm’s

value

and

the

more
likely

they

are

expected

to

incur

information

costs

to

assess

its

risk

drivers.

Prior

research

documents

an

association

between
systematic

risk

and

the

cost

of

capital

(e.g.

Botosan

&

Plumlee,

2005;

Botosan,

1997;

Hail

&

Leuz,

2006;

Leuz

&

Verrecchia,
2000;

Mikhail,

Walther,

&

Willis,

2004).

Beta

is

extracted

from

Stock

Guide

database

and

is

computed

based

on

percentage
stock

price

change

week

over

week

for

a

period

of

260

weeks

ending

at

the

end

of

2005

fiscal

year.
Free

float

(FFLO).

We

expect

a

negative

association

between

free

float

and

information

asymmetry.

Control

blocks

usually
have

greater

access

to

private

information

than

a

diffuse

ownership

(Leuz

&

Verrecchia,

2000).

Free

float

is

used

as

an
inverse

proxy

for

insider

control.

The

variable

is

measured

as

one

minus

the

percentage

of

voting

shares

that

are

closely

held
(percentage

of

votes

attached

to

the

shares

of

a

firm

held

by

directors,

and

individuals

or

companies

that

own

more

than
10%

of

shares

outstanding).
Analyst

following

(ANFOLL).

We

expect

a

negative

relationship

between

the

number

of

financial

analysts

that

follow

a

firm
on

an

annual

basis

and

information

asymmetry.

A

firm’s

analyst

following

may

proxy

for

the

extent

of

its

communication
with

financial

analysts

(Leuz,

2003).

Prior

research

suggests

that

analyst

coverage

reduces

asymmetry

in

the

stock

market
(Alford

&

Berger,

1999).
Environmental

uncertainty

(ENVUNC).

Environmental

uncertainty

refers

to

firm

complexity

and

dynamism

and

is

captured
by

the

number

of

business

and

geographical

segments

(SEGMENTS),

R&D

intensity

(RD),

and

sales

coefficient

of

variation
(SCV).

We

estimate

three

separate

regressions

adding

in

turn

interaction

terms

ABSDACC*SEGMENTS,

ABSDACC*RD,

and
ABSDACC*SCV

to

the

regressions.

We

also

measure

overall

uncertainty

by

the

combination

of

the

three

variables.
We

expect

coefficients

on

interaction

terms

ABSDACC*SEGMENTS,

ABSDACC*RD,

and

ABSDACC*SCV

to

be

negatively

associ-

ated

with

information

asymmetry.

The

variable

SEGMENTS

proxies

for

complexity

and

is

measured

as

one

if

the

total

number
of

segments

is

greater

than

the

sample

median.

Dynamism

is

captured

by

the

variables

RD

and

SCV.

RD

is

a

binary

variable
measured

as

R&D

scaled

by

sales

greater

than

the

sample

median.

SCV

is

a

binary

variable

that

takes

the

value

of

one

if

the
sales

coefficient

of

variation

is

greater

than

the

sample

median.

SCV

is

calculated

for

each

firm

based

on

annual

observations
from

2000

to

2004.

Prior

research

(e.g.

Ghosh

&

Olsen,

2009)

shows

that

managers

use

discretionary

accruals

to

reduce
variability

in

reported

earnings

for

firms

with

high

sales

volatility.

In

a

context

of

high

sales

volatility,

it

is

more

difficult

for
investors

to

assess

earnings

management.
U.S.

listing

(US).

Managerial

incentives

for

earnings

management

are

affected

by

the

intensity

with

which

a

firm

is

mon-
itored

by

investors

or

their

agents

such

as

securities

regulators

and

financial

analysts

(Healy

et

al.,

1999).

The

SEC

has
traditionally

been

diligent

in

its

pursuit

of

firms

with

inappropriate

disclosure

and

reporting

practices

(e.g.,

Feroz,

Park,
&

Pastena,

1991).

Moreover,

market

efficiency

is

enhanced

if

the

number

of

analysts

following

the

firm

increases

(Lang

&
Lundholm,

1996).

The

numbers

involved,

as

well

as

the

geographical

dispersion

of

analysts

in

the

United

States,

ensure

that
firms

face

a

critical

audience

and

high

scrutiny

when

reporting

to

investors.

In

this

vein,

based

on

a

sample

of

Canadian

oil
and

gas

firms,

Cormier

and

Magnan

(2002)

show

that

managerial

incentives

for

earnings

management,

as

well

as

investors’
appreciation

of

various

performance

metrics,

can

be

affected

by

the

magnitude

of

external

monitoring

that

accompanies

a
U.S.

stock

exchange

listing.
4
In

Canada,

audit

committees

must

comprise

at

least

three

independent

members.

We

consider

that

adding

a

few

more

members

could

enhance

the

monitoring

role

of

the

audit

committee.
5
Prior

studies

document

that

the

presence

of

an

independent

and

competent

board

of

directors

should

limit

managers’

ability

to

manage

earnings

(Klein,
2002;

Peasnell

et

al.,

2003).

More

recently,

Chang

and

Sun

(2009)

show

that

the

passage

of

the

Sarbanes-Oxley

Act

(SOX)

improves

the

effectiveness

of

an
independent

audit

committee

and

other

corporate

governance

functions

in

monitoring

the

earnings

quality

of

cross-listed

foreign

firms.
32 D.

Cormier

et

al.

/

Journal

of

International

Accounting,

Auditing

and

Taxation

22 (2013) 26–

38
Table

1
Descriptive

statistics.
N

=

137 Minimum

Maximum

Mean

Standard

deviation

Mean

Non-U.S.

listing

(N

=

69)

Mean

U.S.

listing

(N

=

68)
NACC

−0.108

0.094

−0.029

0.018

−0.030

−0.028
DACC

−0.834

0.251

0.013

0.098

0.006

0.020
ABSDACC

0.000

0.834

0.052

0.085

0.056

0.048
BAS 0.001

0.101 0.008 0.012 0.009 0.006
SPV 0.818

10.385 2.168 1.494

2.140

2.196
ANFOLL 0

35

7.107

5.888

6.513

7.746
SYSRISK

0.02

2.71

0.682

0.489

0.529

0.825
FFLO

0.098

0.999

0.776

0.225

0.749

0.828
RD

0

0.826

0.040

0.114

0.016

0.065
SEGMENTS

1

31

4.964

3.717

4.227

5.712
SCV 0.023

1.677

0.293

0.259

0.283

0.322

INDDIR

0

0.860

0.360

0.178

0.334

0.383
COCHAIR

0

1

0.200

0.401

0.211

0.191
BODSIZE

4

18

9.987

2.755

10.211

9.897
ACSIZE 2 9 3.980

1.103

3.986

4.000
Total

Assets

(in

million

$

Can)

26

40,076

4844

7226

3521

6093
US

0

1

0.486

0.501

–

–
NACC

is

estimated

normal

accruals

scaled

by

lagged

total

assets;

DACC

is

estimated

discretionary

accruals

scaled

by

lagged

total

assets;

ABSDACC

is

the
absolute

value

of

estimated

discretionary

accruals

scaled

by

lagged

total

assets;

BAS

is

the

bid-ask

spread;

SPV

is

share

price

volatility;

ANFOLL

is

the

number
of

analysts

following

a

firm;

SYSRISK

is

Systematic

risk

(beta);

FFLO

is

the

percentage

free

float;

RD

is

the

amount

of

R&D

in

%

of

sales;

SEGMENTS

is

the
number

of

business

and

geographical

segments;

SCV

is

the

sales

coefficient

of

variation

calculated

for

each

firm

based

on

annual

observations

from

2000
to

2004;

INDDIR

is

the

percentage

of

independent

directors

on

the

board;

CEOCHAIR

is

an

indicator

variable

taking

on

the

value

of

1

if

the

CEO

also

is

the
Chairman

of

the

board;

0

otherwise;

BODSIZE

is

the

number

of

directors

on

the

board;

ACSIZE

is

the

number

of

members

on

the

audit

committee;

US

is

an

indicator

variable

for

U.S.

listing.
Prior

research

document

that,

compared

to

Canadian

and

most

other

national

stock

markets,

the

U.S.

stock

market

is
the

most

efficient

in

the

way

information

is

collected

and

analyzed,

the

most

liquid

and

the

most

transparent

in

matters

of
corporate

disclosure

(e.g.

Saudaragan

&

Biddle,

1992;

Saudaragan

&

Meek,

1997).

We

add

interaction

terms

ABSDACC*US

and
ABSDACC*ENVUNC*US

to

assess

the

effect

of

U.S.

cross-listing

on

information

asymmetry

and

expect

U.S.

markets

to

be

in

a
better

position

to

detect

earnings

management

in

contexts

of

complexity

and

dynamism.

We

expect

a

negative

relationship
between

the

interaction

term

ABSDACC*ENVUNC*US

and

information

asymmetry.
4.

Results
4.1.

Descriptive

statistics
Table

1

provides

some

descriptive

statistics.

Sample

firms

are

quite

large

(total

assets

averaging

$5

billion).

About

78%
of

sample

firms

are

free

float.

Firms

are

followed

on

average

by

seven

financial

analysts.

Discretionary

accruals

are

on
average

1.3%

of

total

assets

while

the

absolute

value

of

total

discretionary

accruals

averages

5.2%

of

total

assets.
6
On

average,
firms

invest

in

R&D

in

a

proportion

of

4.0%

of

total

assets,

operate

in

close

to

five

business

divisions

and

geographical
segments,

and

exhibit

high

sales

volatility

(mean

coefficient

of

sales

variation

of

29%).

Moreover,

36%

of

sample

firms

have
independent

directors

while

20%

are

presenting

CEO

and

board

chair

duality.

Half

of

sample

firms

are

cross-listed

on

a

U.S.
stock

exchange.

We

also

observe

more

environmental

uncertainty

(segments,

R&D

intensity,

and

sales

volatility,)

for

cross-
listed

firms.

Finally,

the

level

of

discretionary

accruals

(in

absolute

value)

is

much

less

important

for

cross-listed

firms

(0.048
versus

0.056)

compared

with

non-cross-listed

firms.

There

are

no

significant

differences

in

governance

variables

among
cross-listed

and

non-cross-listed

firms.
In

Table

2,

we

present

discretionary

accruals

in

absolute

value

(ABSDACC)

for

high

and

low

levels

of

environmental
uncertainty,

namely,

sales

coefficient

of

variation,

number

of

segments,

and

research

and

development

intensity.

Results
confirm

our

expectations.

We

also

present

differences

for

non-U.S.

versus

U.S.

listing.

We

observe

more

ABSDACC

for

non-U.S.
listing

for

firms

that

are

facing

high

uncertainty.

U.S.

listing

firms

do

not

show

significant

differences

in

the

level

of

ABSDACC
between

high

and

low

uncertainty.
Table

3

presents

correlations.

Consistent

with

our

expectation,

ABSDACC

is

positively

associated

with

share

price

volatility
(SPV)

(0.24).

We

observe

a

positive

association

between

SPV

and

R&D

intensity

(RD)

(0.17),

which

is

in

line

with

prior

evidence
(e.g.

Aboody

&

Lev,

2000).

We

also

observe

an

association

between

ABSDACC

and

board

size

(BODSIZE)

(−0.12)

and

audit
committee

size

(ACSIZE)

(−0.17).

This

result

shows

the

importance

to

treat

ABSDACC

endogenously.

Moreover,

we

observe

a
positive

relationship

between

ABSDACC

and

sales

volatility

(SCV)

(0.22).

Finally,

U.S.

listing

(US)

is

associated

with

systematic
6
We

split

the

sample

based

on

the

median

of

a

measure

of

an

overall

uncertainty

(combining

SEGMENTS,

RD

and

SCV:

1

if

two

or

three

uncertainty
measures

are

larger

than

the

sample

median,

0

otherwise).

Results

(not

tabulated)

show

that

normal

accruals

are

equals

among

the

two

groups

(2.9%
versus

3.0%)

while

the

absolute

value

of

discretionary

accruals

is

almost

the

double

for

the

environmental

uncertainty

group

(7.0%

versus

3.6%).
D.

Cormier

et

al.

/

Journal

of

International

Accounting,

Auditing

and

Taxation

22 (2013) 26–

38 33
Table

2
Descriptive

statistics.

Mean

discretionary

accruals

in

absolute

value

based

on

dynamism,

diversity

and

volatility.
Low High

t

difference
p

value
SEGMENTS

0.065

0.078

0.338
-

Non-U.S.

listing

0.051

0.122

0.062
-

U.S.

listing

0.091

0.052

0.173
RD 0.041

0.055

0.064
-

Non-U.S.

listing 0.042

0.052

0.114
-

U.S.

listing 0.038

0.056

0.123
SCV 0.044

0.050

0.288
-

Non-U.S.

listing

0.037

0.058

0.022
-

U.S.

listing 0.055

0.043

0.225
SEGMENTS

is

the

number

of

business

and

geographical

segments;

RD

is

the

amount

of

R&D

in

%

of

sales;

SCV

is

the

sales

coefficient

of

variation

calculated

for

each

firm

based

on

annual

observations

from

2000

to

2004.
Table

3
Correlations.
Variable
a
2

3

4

5

6

7

8

9

10

11

12

13

14

15
1 SPV
*
0.48
*
0.24
*

0.26

0.13
*
−0.44
*
−0.14

0.04

0.05
*
−0.38
*
−0.33
*
0.17

−0.04
*
0.13

0.02
2

BAS

1
*
0.28

−0.03

0.01
*
−0.31
*
−0.19

−0.02

−0.02
*
−0.14

−0.10

−0.06

−0.08

0.02

−0.13
3 ABSDACC

1
*
0.13

0.06
*
−0.26
*
−0.18

0.08

−0.05
*
−0.12
*
−0.17
*
0.10

−0.03
*
0.22

−0.08
4

SYSRISK

1
*
0.17

−0.11

*
0.20
*
0.15

−0.02

−0.12

−0.13

0.04

−0.01

0.09
*
0.29
5

FFLO

1
*
−0.14

0.12
*
0.24

−0.01

−0.12

0.05

0.05

0.05
*
−0.22
*
0.17
6 SIZE 1
*
0.26

−0.01

−0.01
*
0.53
*
0.42
*
−0.10

−0.01
*
0.40

*
0.14
7

ANFOLL

1

−0.05

−0.03

0.03

0.06
*
0.18
*
0.13
*
0.31
*
0.14
8

INDDIR

1

−0.04

0.03

0.10

−0.04

−0.05

0.04

0.08
9

CEOCHAIR

1
*
−0.16
*
−0.20

0.04
*
0.16

−0.06

0.01
10 BODSIZE

1
*
0.55

−0.28

−0.06

0.13

−0.04
11

ACSIZE

1

−0.10

0.01

0.07

−0.01
12

RD

1

*
0.16
*
0.14
*
0.20
13 SEGMENTS 1
*
0.16
*
0.16
14

SCV

1

0.08
15 US

1
a
SPV

is

share

price

volatility;

BAS

is

the

bid-ask

spread;

ABSDACC

is

the

absolute

value

of

estimated

discretionary

accruals

scaled

by

lagged

total

assets;
SYSRISK

is

Systematic

risk

(beta);

FFLO

is

the

percentage

of

free

float;

SIZE

is

the

natural

logarithm

of

total

assets;

ANFOLL

is

the

number

of

analysts

following
a

firm;

INDDIR

is

the

percentage

of

independent

directors

on

the

board;

CEOCHAIR

is

an

indicator

variable

taking

on

the

value

of

1

if

the

CEO

also

is

the
Chairman

of

the

board;

0

otherwise;

BODSIZE

is

the

number

of

directors

on

the

board;

ACSIZE

is

the

number

of

members

on

the

audit

committee;

RD

is

the
amount

of

R&D

in

%

of

sales;

SEGMENTS

is

the

number

of

business

and

geographical

segments;

SCV

is

the

sales

coefficient

of

variation

calculated

for

each
firm

based

on

annual

observations

from

2000

to

2004;

US

is

an

indicator

variable

for

U.S.

listing.
*
Significant

at

0.10.
risk

(SYSRISK)

(0.29),

free

float

(FFLO)

(0.17),

firm

size

(SIZE)

(0.14),

analyst

following

(ANFOLL)

(0.14),

RD

(0.20)

and

complexity
(SEGMENTS)

(0.16).

Bid-ask

spread

(BAS)

is

positively

associated

with

ABSDACC

(0.28)

and

negatively

associated

with

SIZE
(−0.31),

ANFOLL

(−0.19),

and

BODSIZE

(−0.14).
4.2.

Multivariate

analyses
Since

we

posit

that

corporate

governance

affects

earnings

management

and

information

asymmetry

simultaneously,
we

first

determine

whether

an

interaction

exists

between

these

variables

using

Hausman

test.

The

Student

t-test

of
the

coefficient

for

the

variable

residuals

constitutes

the

Hausman

test.

Using

this

procedure,

for

the

overall

uncertainty
regression,

we

reject

the

null

hypothesis

of

no

endogeneity

with

respect

to

SPV

and

ABSDACC

(t-test

=

−4.57;

p

<

0.00)

and

with

respect

to

BAS

and

ABSDACC

(t-test

=

−3.65;

p

<

0.00).

Therefore,

we

treat

ABSDACC

as

an

endogenous

variable.
In

light

of

this

diagnostic,

we

rely

on

a

three-stage

estimation

procedure

for

a

system

of

simultaneous

equations.

3SLS
(which

combines

2SLS

and

Seemingly

Unrelated

Least

Square

–

SURE)

may

improve

the

efficiency

of

parameter

estimates
when

there

is

contemporaneous

correlation

of

errors

across

equations.

Moreover,

the

greater

the

intra-equation

mul-
ticollinearity,

the

more

likely

3SLS

is

to

have

a

considerable

gain

in

efficiency

for

the

entire

system

of

SURE

(Binkley,
1982).

In

practice,

the

contemporaneous

correlation

matrix

is

estimated

using

OLS

residuals.

For

overall

uncertainty
regressions,

we

observe

a

significant

correlation

of

errors

across

equations

(−0.14

between

ABSDACC

and

SPV

equations
and

−0.10

between

ABSDACC

and

BAS

equations).

Concerning

intra-equation

multicollinearity,

we

observe

that

some
interaction

terms

are

highly

correlated.

ABSDACC

is

correlated

at

0.86

with

ABSDACC*SCV

and

0.87

with

ABSDACC*RD.
Since

multicollinearity

could

be

an

issue,

SURE

is

likely

to

improve

the

efficiency

of

the

entire

system

(Binkley,

1982).
STATA

software

is

being

used.

Finally,

we

exclude

from

regressions

observations

with

standardized

residuals

exceeding
two.
34 D.

Cormier

et

al.

/

Journal

of

International

Accounting,

Auditing

and

Taxation

22 (2013) 26–

38
Table

4
3SLS

estimation

of

the

relationship

between

discretionary

accruals

and

share

price

volatility

in

interaction

with

environmental

uncertainty.
Environmental

uncertainty
Complexity

segments Dynamism

R&D

Dynamism

sales

volatility

Overall

uncertainty
ABSDACC

regression
SIZE

−
***
−0.025
***
−0.037
***
−0.039
***
−0.036
INDDIR

−

0.009

0.001

0.001

0.001
CEOCHAIR

+

−0.021

−0.022

−0.029

−0.027
BODSIZE −−0.031

−0.032
*
−0.044 −0.021
BODSIZESQR +
**
0.002
*
0.002
**
0.002

0.001
ACSIZE −
**
−0.025
**
−0.025
**
−0.027
**
−0.025
Adjusted

R-square

10.4%

12.9%

12.8%

10.2%
Chi
2
/p

value

16.4

(0.000)

20.67

(0.027)

24.76

(0.003)

17.05

(0.009)
SPV

regression
SYSRISK

+
***
0.544
***
0.526
*
0.444
***
0.669
FFLO

−

0.252
*
0.965

0.051

0.329
ANFOLL

−

0.005

−0.023

−0.013

0.002
ABSDACC

+
***
44.001
***
20.263
***
51.822
***
37.970
ABSDACC*US +/−
***
−43.020
***
−16.646
**
−44.621
***
−33.7001
ABSDACC*ENVUNC

−
**
−37.937
***
−13.774
**

−46.773
***
−11.537
ABSDACC*ENVUNC*US

+/−
**
33.451
***
13.590
**
40.155
***
8.119
ENVUNC

+/−
***
1.028
*
0.651
**
1.504
**
0.241
ENVUNC*US

+/−
**
−0.634

−0.310

−0.244

0.053
US +/−
***
1.147

0.446
*
0.908
***
0.763
Coefficient

difference

ABSDACC

and
ABSDACC*ENVUNC
37.78

(0.000) 24.21

(0.000) 25.11

(0.000)

18.50

(0.000)
Coefficient

difference

ABSDACC

and
ABSDACC*US
2.86

(0.091)

7.28

(0.007)

3.27

(0.070)

3.06

(0.080)
Coefficient

difference

ABSDACC*ENVUNC

and
ABSDACC*ENVUNC*US
2.22

(0.136)

0.00

(0.964)

2.40

(0.122)

2.15

(0.143)
Adjusted

R-square

49.9%

26.6%

23.8%

52.8%

Chi
2
/(p

value)

126.09

(0.012)

52.56

(0.000)

46.08

(0.000)

143.46

(0.000)
N/Outliers

128/9

137/0

137/0

131/6

ABSDACC

is

the

absolute

value

of

estimated

discretionary

accruals

scaled

by

lagged

total

assets;

SPV

is

share

price

volatility;

SIZE

is

the

natural

logarithm
of

total

assets;

INDDIR

is

the

percentage

of

independent

directors

on

the

board;

CEOCHAIR

is

an

indicator

variable

taking

on

the

value

of

1

if

the

CEO

also
is

the

Chairman

of

the

board,

0

otherwise;

BODSIZE

is

the

number

of

directors

on

the

board;

ACSIZE

is

the

number

of

members

on

the

audit

committee;
SYSRISK

is

the

systematic

risk

(beta);

FFLO

is

the

percentage

of

free

float;

ANFOLL

is

the

number

of

analysts

following

a

firm;

ENVUNC

is

the

environmental
uncertainty

variable;

US

is

an

indicator

variable

for

U.S.

listing.
*
p

<

0.10.
**
p

<

0.05.
***
p

<

0.01.
One-tailed

if

directional

prediction,

two-tailed

otherwise.
Endogenous

variables:

ABSDACC,

SPV.
In

Table

4,

we

present

results

of

the

3SLS

regression

on

the

determinants

of

earnings

management

(ABSDACC)

and

informa-
tion

asymmetry

(SPV,

BAS).

Concerning

the

impact

of

corporate

governance

on

earnings

management,

results

first

show

that
the

number

of

members

of

the

audit

committee

(ACSIZE)

is

negatively

related

to

ABSDACC

in

all

four

regressions.

Coefficients
on

the

variable

BODSIZESQR

are

positive

and

significant,

which

is

consistent

with

a

non-linear

relationship

between

board
size

and

earnings

management.

After

a

certain

breakpoint,

the

size

of

the

board

is

associated

with

more

earnings

manage-
ment.

Our

results

are

somewhat

consistent

with

Ghosh

et

al.

(2010)

who

find

that

earnings

management

varies

with

board
size

and

audit

committee

size.

Finally,

consistent

with

prior

research,

firm

size

(SIZE)

is

negatively

related

to

ABSDACC.

Concerning

the

determinants

of

share

price

volatility

(SPV),

results

are

consistent

with

our

hypotheses.

As

expected,
systematic

risk

(SYSRISK)

is

positively

related

to

SPV.

Coefficients

on

the

main

effect

for

environmental

uncertainty,

i.e.
SEGMENTS,

RD,

SCV

and

overall

uncertainty

are

significant

and

positively

related

to

SPV.

Concerning

the

effect

of

U.S.

listing,
coefficients

on

the

interaction

term

ABSDACC*US

are

negative

and

significant

in

all

four

regressions,

which

suggests

that

in
the

presence

of

low

uncertainty,

earnings

management

creates

less

asymmetry

for

cross-listed

firms.

For

all

regressions,
The

Student

t-tests

show

that

the

sum

of

coefficients

on

ABSDACC

and

ABSDACC*US

is

statistically

different

from

zero.

In

the
presence

of

low

environmental

uncertainty,

there

is

still

asymmetry

created

by

earnings

management

for

U.S.

listed

firms.
The

low

level

of

earnings

management

under

low

uncertainty

for

cross-listed

firms

could

also

explain

its

small

impact

on
information

asymmetry

(see

Table

2).
Complexity.

Consistent

with

H1,

the

relationship

between

ABSDACC

and

SPV

is

smaller

for

firms

with

operations

and
geographical

diversification

since

the

coefficient

on

the

interaction

term

ABSDACC*SEGMENTS

is

negative

and

significant
(−37.937;

p

<

0.05)

while

the

coefficient

on

ABSDACC

is

positive

and

significant

(44.001;

p

<

0.01).

The

Student

t-test

for
D.

Cormier

et

al.

/

Journal

of

International

Accounting,

Auditing

and

Taxation

22 (2013) 26–

38 35
Table

5
3SLS

estimation

of

the

relationship

between

discretionary

accruals

and

bid-ask

spread

in

interaction

with

environmental

uncertainty.
Environmental

uncertainty

Complexity

segments Dynamism

R&D

Dynamism

sales

volatility

Overall

uncertainty
ABSDACC

regression
SIZE

−
***
−0.040
***
−0.039
***
−0.039
***
−0.039
INDDIR

−

0.001

0.001

0.001

0.001
CEOCHAIR

+

−0.041

−0.033

−0.038

−0.038
BODSIZE −−0.039

−0.027

−0.042

−0.034
BODSIZESQR +
*

0.002

0.002
*
0.002
*
0.002
ACSIZE −
**
−0.025
**
−0.024
**
−0.023
**
−0.023
Adjusted

R-square

12.9%

12.7%

12.7%

12.9%
Chi
2
/p

value

23.36

(0.000)

19.49

(0.003)

22.71

(0.051)

20.88

(0.000)
BAS

regression
SYSRISK

+

−0.001

0.001

−0.001

0.001
FFLO

−

−0.002

−0.001

0.001

−0.003
ANFOLL

−
*
−0.001

−0.001

−0.001
**
−0.001
ABSDACC

+
***
0.228
***

0.129
***
0.274
***
0.308
ABSDACC*US +/−
***
−0.215
***
−0.109
***
−0.245
***
−0.241
ABSDACC*ENVUNC

−
**
−0.189
***
−0.089
***
−0.236
***
−0.095
ABSDACC*ENVUNC*US

+/−
**
0.139

*
0.079
***
0.213

0.040
ENVUNC

+/−
***
0.009

0.002
*
0.005
*
0.002
ENVUNC*US

+/−
*
−0.005

0.003

−0.001

−0.001
US +/−
***

0.007
*
0.004
*
0.004
**
0.009
Coefficient

difference

ABSDACC

and
ABSDACC*ENVUNC
52.43

(0.000) 29.40

(0.000) 66.41

(0.000)

11.64

(0.000)
Coefficient

difference

ABSDACC

and
ABSDACC*US
3.82

(0.051)

7.10

(0.007)

2.68

(0.101)

2.80

(0.092)
Coefficient

difference
ABSDACC*ENVUNC

and
ABSDACC*ENVUNC*US
2.28

(0.132)

0.21

(0.650)

1.53

(0.217)

2.04

(0.153)
Adjusted

R-square

37.4%

30.4%

35.7%

27.2%
Chi
2
/(p

value)

79.92

(0.000)

60.57

(0.000)

74.25

(0.000)

52.47

(0.000)
N/Outliers

132/1

133/0

131/2

133/0
ABSDACC

is

the

absolute

value

of

estimated

discretionary

accruals

scaled

by

lagged

total

assets;

BAS

is

the

bid-ask

spread;

SIZE

is

the

natural

logarithm

of
total

assets;

INDDIR

is

the

percentage

of

independent

directors

on

the

board;

CEOCHAIR

is

an

indicator

variable

taking

on

the

value

of

1

if

the

CEO

also
is

the

Chairman

of

the

board,

0

otherwise;

BODSIZE

is

the

number

of

directors

on

the

board;

ACSIZE

is

the

number

of

members

on

the

audit

committee;
SYSRISK

is

the

systematic

risk

(beta);

FFLO

is

the

percentage

of

free

float;

ANFOLL

is

the

number

of

analysts

following

a

firm;

ENVUNC

is

the

environmental
uncertainty

variable;

US

is

an

indicator

variable

for

U.S.

listing.
*
p

<

0.10.
**
p

<

0.05.
***
p

<

0.01.
One-tailed

if

directional

prediction,

two-tailed

otherwise.
Endogenous

variables:

ABSDACC,

BAS.
coefficient

difference

on

ABSDACC

and

ABSDACC*SEGMENTS

is

statistically

different

from

zero

(t

=

37.78;

p

<

0.000).

This
result

suggests

that

uncertainty

does

not

eliminate

the

effect

of

earnings

management

on

share

price

volatility.
The

coefficient

on

the

interaction

term

ABSDACC*SEGMENTS*US

is

positive

and

significant

(33.451;

p

<

0.05).

The

Student
t-test

shows

that

the

sum

of

coefficients

on

ABSDACC*SEGMENTS

and

ABSDACC*SEGMENTS*US

is

statistically

close

to

zero
(t

=

2.22;

p

<

0.136).

For

U.S.

listed

firms,

the

impact

of

earnings

management

on

asymmetry

would

not

differ

in

the

presence
or

absence

of

complexity.

As

a

sensibility

analysis,

we

use

the

logarithm

of

SEGMENTS

instead

of

a

binary

variable.

Essentially,
results

(not

tabulated)

remain

similar

to

those

presented

in

Table

4.

Our

findings

are

consistent

with

the

argument

that

the
U.S.

stock

market

is

more

liquid

and

transparent

than

the

Canadian

market

in

the

way

information

is

collected

and

analyzed,
and

thus,

is

in

a

better

position

to

detect

earnings

management

in

a

context

of

complexity.
Dynamism.

Consistent

with

H2,

the

relationship

between

ABSDACC

and

SPV

is

smaller

for

firms

involved

in

R&D

activities
since

the

coefficient

on

the

interaction

term

ABSDACC*RD

is

negative

and

significant

(−13.774;

p

<

0.01).

The

Student

t-test
for

coefficient

difference

on

ABSDACC

and

ABSDACC*RD

is

statistically

different

from

zero

(t

=

24.21;

p

<

0.000).

This

result
suggests

that

uncertainty

does

not

eliminate

the

effect

of

earnings

management

on

share

price

volatility.

The

coefficient

on

the

interaction

term

ABSDACC*RD*US

is

positive

and

significant

(13.590;

p

<

0.01).

The

Student

t-test

shows

that

the

sum
of

coefficients

on

ABSDACC*RD

and

ABSDACC*RD*US

is

statistically

close

to

zero

(t

=

0.00;

p

<

0.964).

For

U.S.

listed

firms,

the
impact

of

earnings

management

on

asymmetry

would

not

differ

in

the

presence

or

absence

of

dynamism

as

expressed

by
R&D

intensity.
Also

consistent

with

H2,

the

relationship

between

ABSDACC

and

SPV

is

smaller

for

firms

with

high

sales

volatility

since
the

coefficient

on

the

interaction

term

ABSDACC*SCV

is

negative

and

significant

(−46.773;

p

<

0.05).

The

Student

t-test

for
36 D.

Cormier

et

al.

/

Journal

of

International

Accounting,

Auditing

and

Taxation

22 (2013) 26–

38
coefficient

difference

on

ABSDACC

and

ABSDACC*SCV

is

statistically

different

from

zero

(t

=

25.11;

p

<

0.000).

This

result

sug-
gests

that

uncertainty

does

not

eliminate

the

effect

of

earnings

management

on

share

price

volatility.

The

coefficient

on
the

interaction

term

ABSDACC*SCV*US

is

positive

and

significant

(40.155;

p

<

0.05).

The

Student

t-test

shows

that

the

sum

of
coefficients

on

ABSDACC*SCV

and

ABSDACC*SCV*US

is

statistically

close

to

zero

(t

=

2.40;

p

<

0.122).

For

U.S.

listed

firms,

the
impact

of

earnings

management

on

asymmetry

would

not

differ

in

the

presence

or

absence

of

dynamism

as

expressed

by
sales

volatility.

Regressions

for

overall

uncertainty,

combining

SEGMENTS,

RD

and

SCV

provide

quite

similar

results.

The

only
difference

is

that

the

coefficient

on

BODSIZESQR

is

not

associated

with

ABSDACC.
In

Table

5,

we

present

results

for

bid-ask

spread

(BAS)

regressions.

Except

for

the

coefficient

on

SYSRISK,

and

main

effects
on

RD

and

overall

uncertainty

that

are

not

significant,

results

are

consistent

with

those

presented

in

Table

4

for

share

price
volatility.

Finally,

since

17%

of

sample

firms

exhibit

negative

earnings,

as

a

sensitivity

analysis,

we

include

a

binary

variable
as

a

control

variable

in

share

price

volatility

and

bid-ask

spread

models.

The

coefficient

is

not

significant.

Finally,

we

observe
a

certain

level

of

substitution

between

ABSDACC

and

US

on

their

effect

on

information

asymmetry

(SPV

or

DAS).

U.S.

listing
in

itself

is

associated

with

more

asymmetry.
Kothari

et

al.

(2005)

suggest

that

adding

ROA

to

the

Jones

model

yields

an

improvement

in

normal

accruals

estimation.
As

a

sensitivity

analysis,

we

estimate

a

total

accrual

model

based

on

firm-specific

time

series

regression

(data

from

1990

to
2004).

Due

to

missing

data,

the

estimation

is

based

on

115

firms.

For

a

given

firm

(i),

current

period

(t)

total

normal

accruals
are

modeled

in

the

following

manner

(variables

scaled

by

lagged

total

assets

except

for

ROA):
Total

accruals
it
=

˛
1
+

˛
2

Change

in

Sales
it
+

˛
3
PPE
it
+

˛
4
ROA
it
Focusing

on

the

regression

for

overall

uncertainty,

results

(not

tabulated)

based

on

total

accruals

do

not

differ

from

those
based

on

working

capital

accruals

presented

in

Tables

4

and

5.

As

expected,

total

accruals

and

normal

total

accruals

are

larger
than

WC

accruals

(mean

total

accruals

of

−0.051

versus

−0.012

and

mean

normal

total

accruals

of

−0.057

versus

−0.025).
Kanagaretnam,

Lobo,

and

Whalen

(2007)

findings

are

consistent

with

the

view

that

firms

with

higher

levels

of

corporate
governance

have

lower

information

asymmetry

around

quarterly

earnings

announcements

as

measured

by

changes

in

bid-
ask

spreads.

As

a

last

sensitivity

analysis,

we

add

governance

variables

to

SPV

and

BAS

regressions.

No

governance

variables
are

significant

and

results

remain

similar

to

those

presented

in

Tables

4

and

5.
5.

Conclusion
In

this

paper,

we

examine

the

association

between

earnings

management

and

information

asymmetry

considering

envi-
ronmental

complexity

and

dynamism.

Our

findings

suggest

that

investors

have

difficulty

to

detect

earnings

management

in
such

contexts.

As

expected,

findings

show

that

the

association

between

earnings

management

and

information

asymmetry
is

reduced

for

firms

facing

complexity

and

dynamism.

More

specifically,

the

positive

effect

of

earnings

management

on
information

asymmetry

is

smaller

for

diversified

firms,

those

intensively

investing

in

R&D

and

facing

high

sales

volatility.
Our

findings

also

suggest

that

in

the

presence

of

complexity

and

dynamism,

discretionary

accruals

are

more

likely

to

be
detected

by

investors

for

firms

listed

on

a

U.S.

stock

exchange.

Our

results

also

show

that

corporate

governance

is

associated
with

less

earnings

management,

suggesting

that

the

board

of

directors

plays

a

monitoring

role.

Corporate

governance

may
restrain

earnings

management

in

a

country

like

Canada

where

the

ownership

is

highly

concentrated.
The

results

of

this

study

should

be

interpreted

with

caution

for

at

least

the

following

reason.

Like

all

earnings

management
studies,

the

present

study

relies

on

a

specific

measure

of

discretionary

accruals

that

may

not

entirely

capture

the

underlying
phenomenon.

More

specifically,

the

relationship

between

discretionary

accruals

and

information

asymmetry

is

likely

to

be

driven

by

the

variation

in

the

underlying

economic

of

the

firm.

In

a

context

of

environmental

uncertainty

where

variations

in
firms’

specific

economic

factors

are

likely

to

be

high,

results

can

reflect

the

difficulty

to

measure

discretionary

accruals

rather
than

the

market’s

inability

to

assess

earnings

management.

However,

we

feel

that

relying

on

industry-specific

estimations
allows

for

more

confidence

in

the

results.

Moreover,

using

an

alternative

estimation

model

of

discretionary

accruals

does
not

alter

our

results.
Concerning

future

research,

with

a

larger

sample,

we

might

attempt

to

distinguish

earnings

increasing

versus

earnings
decreasing

accruals

in

the

way

they

affect

asymmetry

in

the

stock

market.
Acknowledgements
We

thank

the

fund

for

education

and

good

governance

of

the

Autorité

des

marchés

financiers

(Québec)

and

PWC

for
financial

support.

Information,

opinions

and

views

expressed

in

this

article

are

the

sole

responsibility

of

the

authors.

The
content

of

this

article

does

not

necessarily

reflect

the

opinion

of

the

Authority

and

PWC;

any

errors

are

the

responsibility

of
the

authors.
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