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Accounting Research JournalInstitutional Ownership and Income Smoothing: Australian EvidencePing-Sheng Koh

Ar tic le info rmation:To cite this document:Ping-Sheng Koh, (2005),"Institutional Ownership and Income Smoothing: Australian Evidence ", Accounting ResearchJournal, Vol. 18 Iss 2 pp. 93 - 110Permanent link to this document:http://dx.doi.org/10.1108/10309610580000678

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http://dx.doi.org/10.1108/10309610580000678



Institutional Ownership and Income Smoothing: Australian Evidence

93

Institutional Ownership and IncomeSmoothing: Australian Evidence

Ping-Sheng KohUQ Business School

University of Queensland

Abstract

This study examines the rarely investigatedassociation between institutional ownershipand income smoothing. The results supportthe predicted positive association betweeninstitutional ownership and the likelihood offirms smoothing earnings towards theirearnings trend in general. However, thisassociation is not systematic across all firms.The positive association is most evident among

profit firms with pre-managed earnings abovetheir earnings trend. No significant associationis found for profit firms with pre-managedearnings below their earnings trend and lossfirms in general. This study also finds that, inAustralia, while institutional ownership has anon-linear association with income increasingearnings management (Koh, 2003), suchassociation manifests itself within the incomesmoothing framework. The results of this studyhighlight the complexities in the association

between institutional ownership and earningsmanagement strategies, and future research can

benefit by explicitly examining the trade-offs between alternative earnings managementincentives and the factors that affect the relativestrength of these incentive trade-offs.

Keywords : Institutional Investors; Discretionary Accruals;Earnings Management; Income Smoothing.

JEL classification : G32, M41

Acknowledgements : The author was a doctoral candidate atthe University of Tasmania when the background research tothis paper and initial data collection commenced. Comments

by Peter Clarkson, Phil Gray, Joseph Fan, Jayne Godfrey,Terry O’Keefe, Norman Wong, and seminar participants at

the University of Tasmania, and participants at the 2002AAANZ Conference and the 2002 EAA Annual Congressare greatly appreciated. I acknowledge the financial supportfrom the UQ Business School.

1. IntroductionDespite growing numbers of studies on theassociation between institutional ownership andearnings management (e.g. Bushee, 1998;Cheng and Reitenga, 2000; Koh, 2003), theassociation between institutional ownership andincome smoothing is rarely investigated(Carlson and Bathala, 1997). 1 This studyexamines the association between institutionalownership and the likelihood of a portfolio firmsmoothing reported earnings through accrualsmanagement. Income smoothing in this studyrefers to earnings management toward anearnings trend to reduce earnings variability(e.g., Beidleman, 1973). 2 The practice ofincome smoothing is well established (e.g.,Beidleman, 1973; Godfrey and Jones, 1999;Ronen and Sadan, 1981; Subramanyam, 1996)and firms are facing increasing pressure toreport smooth earnings. For example, a

prominent FORTUNE 500 firm’s CEO claimsthat, “[t]he No. 1 job of management is tosmooth out earnings” (Loomis, 1999).Managers have incentives to smooth reportedearnings as it can affect earnings predictability,

perceived risk of the firm, managers’ personalwealth and job security, among others. That is,

1 Bushee (1998) examines earnings management via cutting research and development expenditure; Chengand Reitenga (2000) examine accruals management;whilst Koh (2003) examines income increasing accrualsmanagement. Carlson and Bathala (1997) is the onlyknown study directly investigate the association

between institutional ownership and income smoothing.There are no known studies on the association betweeninstitutional ownership and income smoothing inAustralia.

2 For this study, a firm is smoothing income if

Abs(Earnings-Trend)<Abs(NDE-Trend), where NDE isnon-discretionary earnings or pre-managed earnings(measured as reported earnings less estimateddiscretionary accruals).

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ACCOUNTING RESEARCH JOURNAL VOLUME 18 N O 2 (2005)

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smoothed reported earnings can be used as amean to increase shareholder value, firm valueand/or managers’ personal wealth.

This study focuses on institutional investorsas they are more likely to be the price-setting

marginal investors (Bushee, 2001; Hand,1990). Hence, institutional investors, relative toindividual investors, are the more likelycandidates to create capital market incentives,among others, for managers to manageearnings, including income smoothing. Theissue of how institutional investors affectfinancial reporting behaviour have attractedmuch attention in Australia. The extent of suchattention is evident by the Parliamentary JointCommittee being called upon to examine, inter

alia , what corporate governance rolesinstitutional investors play in their portfoliofirms and how their involvement, or lackof, affect firms’ financial reporting behaviour(see Parliamentary Joint Committee onCorporations and Securities, 1994).

The results support the prediction that, ingeneral, portfolio firms with higher institutionalownership are more likely to smooth reportedearnings. More importantly, the positiveassociation between institutional ownership andincome smoothing is not uniform across allfirms. Specifically, the positive association

between institutional ownership and incomesmoothing is most evident among profit firmswith pre-managed earnings above theirearnings trend. 3 No significant association isfound among (a) profit firms with pre-managedearnings below their earnings trend; and (b)loss firms in general. The results of this studyare robust to a wide range of specifications andare unlikely to be a product of measurementerrors arising from the discretionary accrualsestimation process. Further analysis reveals thatwhile Koh (2003) finds institutional ownershipfollows a concave association with incomeincreasing discretionary accruals, this concaveassociation appears to operate within theincome smoothing framework. This suggestsincome smoothing and income increasingearnings management strategies are notmutually exclusive.

3 Pre-managed earnings (or non-discretionary earnings) isreported earnings less estimated discretionary accruals.

This study’s findings highlight thecomplexities in examining managers’incentives to manage earnings. Managers facedifferent trade-offs between the capital market(e.g. Dechow and Skinner, 2000), contracting

and other incentives in arriving at their earningsmanagement strategies. The resultant earningsmanagement strategies are likely to bedependent on, inter alia , (a) whether firms arereporting profit; and (b) whether firms arelikely to be above their earnings trend prior toaccruals management, as these appear to be twoof the discriminating factors on the relativestrength of the incentives trade-offs. As such,future research can benefit by explicitlyinvestigating the trade-offs between earnings

management incentives and the conditions thataffect the relative strength of these trade-offs.

This paper contributes to the literature in thefollowing ways. First, it represents the firstknown empirical study on the association

between institutional ownership and incomesmoothing in Australia, and therefore extendsthe limited empirical evidence on (a) theassociation between institutional ownership andincome smoothing beyond the US environment(e.g. Carlson and Bathala, 1997) (b) theassociation between institutional ownership andearnings management in general in Australia(e.g., Koh 2003). Second, it provides evidencethat the association between institutionalownership and income smoothing is notsystematic across all firms, a phenomenon thathas not been documented by extant literature.These findings suggest that future earningsmanagement studies can benefit by examiningthe trade-offs between alternative earningsmanagement incentives and the conditionsaffecting the relative strength of these trade-offs. Third, this paper provides evidenceindicating income smoothing and incomeincreasing earnings management strategies arenot mutually exclusive. This represents anotheraspect not previously examined or documentedin the earnings management literature. Finally,it suggests that income smoothing behaviour(as associated with institutional ownership)may explain the lack of evidence supportingthe view that institutional activism leads to animproved performance in target firms whenaccounting-based performance measures areused as performance benchmarks (Karpoff,1998).





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The paper is structured as follows: section 2develops the hypothesis on the association

between institutional ownership and incomesmoothing; section 3 describes the researchdesign; and section 4 reports the analysis of

results. Section 5 concludes the paper.

2. Hypothesis Development:Institutional Ownership and IncomeSmoothing IncentivesThe practice of income smoothing has beenwidely publicised in the media, evidenced byacademic research, and has attracted muchregulatory attention (e.g. Fox, 1997; Loomis,1999; Godfrey and Jones, 1999; Carlson andBathala, 1997; and Levitt, 1998). Empiricalevidence suggests that the capital market exerts

pressure on managers to manage reportedearnings, including smoothing reportedearnings (e.g. Barth et al., 1999; Bushee, 1998;Burgstahler and Dichev, 1997; and Myers andSkinner, 1999), in addition to the incentivescreated by various agency relationships (e.g.Lambert, 1984; Moses, 1987). Compared toindividual investors, institutional investors arethe more likely candidates to exert these capital

market incentives, as they are more likely to bethe marginal investors who are the price-settersin the capital markets (Bushee, 2001; Hand,1990). Further, empirical studies have shownthat institutional investors are associated withearnings management, incremental to otherinvestors (e.g., Bushee, 1998; Carlson andBathala, 1997; Koh, 2003).

2.1 Institutional Investors and IncomeSmoothing IncentivesEmpirical studies on the association betweeninstitutional ownership and income smoothingare rare. 4 Carlson and Bathala (1997) argue that

by smoothing income, portfolio firms are ableto maintain institutional investor interest intheir stocks. Their argument is based on theview that (a) institutional investors are likely todivest when firms under-perform or haveunpredictable performance (Graves andWaddock, 1990; Porter, 1992); and (b) prudentstandards, as accepted by courts, are based

4 There is one known study directly examining theassociation between institutional ownership and incomesmoothing (Carlson and Bathala, 1997), while noknown studies exist in Australia.

upon the characteristics of isolated assets (suchas portfolio firms’ earnings) rather than uponthe asset’s marginal effect on a portfolio(Badrinath et al., 1989; Del Guercio, 1996).That is, institutional investors prefer a

smoothed reported earnings stream as it justifies their investment in the portfolio firms both in terms of (a) the predictable andsatisfactory performance of the portfolio firms;and (b) the portfolio firms being of high“quality”, thus satisfying the prudencestandards applied by the courts. In addition,firms with a smooth earnings stream are likelyto provide more sustainable capital gains andmore predictable dividend payout over time(e.g., Ronen and Sadan, 1981).

Therefore, investing in firms with smoothreported earnings streams not only satisfies the

prudence standards, the consistent and predictable performances, realised andunrealised capital gains and more predictabledividend income streams will in turn reflectfavourably on institutional investors’ own

performance. Since institutional investors arelikely to communicate with the portfolio firmmanagers (Ramsay et al., 2000), 5 any

preference for smooth reported earningsstreams can be communicated to portfolio firmmanagers. As such, it is expected thatinstitutional investors prefer smooth and

predictable earnings trend for their portfoliofirms, regardless of their investment horizons.

In turn, incentives for portfolio firmmanagers to smooth reported earnings cancome from, inter alia , the desire to maintain asmooth and predictable earnings trend that thecapital market values, and to reduce perceivedfirm risk and cost of capital (e.g. Barth et al.,1999; Levitt, 1998; Loomis, 1999; Ronen andSadan, 1981; Fox, 1997). Smoothing incomealso enables managers to create accounting

5 A recent survey of twelve major Australian institutionalinvestors reveals that nine of these investors maintainedfrequent communication with their portfolio firms’management. Of the three institutional investors that donot have regular communication with their portfoliofirms, only one indicates a preference to assess its

portfolio firms “on how the figures stack up”. For the

remaining two institutions, one employs external fundmanagers and is kept informed through regular meetingswith its external fund managers; while the other holdsmeetings with the senior management of the portfoliofirms as and when issues arise (Ramsay et al., 2000).





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slack during good times for future periods (e.g.Fudenberg and Tirole, 1995; DeFond and Park,1997). Ceteris paribus , this increases thelikelihood of firms maintaining a predictableand steady earnings stream over multiple

periods. Managers also have incentives tosmooth reported earnings for reasons such as

job security, compensation and maintaining orimproving their human capital (e.g. DeFondand Park, 1997; Matsunaga and Park, 2000;Fama, 1980; Murphy, 1999; Lambert, 1984;Moses, 1987).

Given the above arguments, it is expectedthat as institutional ownership increases,incentives for portfolio firm managers tosmooth earnings also increase. Formally:

H1: Ceteris paribus , the higher the institutionalownership, the more likely it is for portfoliofirm managers to smooth their reportedearnings.

2.2 Are Income Smoothing IncentivesUniform Across All Circumstances?Managers face various incentive trade-offswhen exercising their accruals discretion.Hence, income smoothing incentives areunlikely to dominate the final outcome at alltime. One circumstance where income

smoothing incentives may dominate is whenfirms are already above their earnings trend prior to accruals management . For these firms,following an income smoothing strategy islikely to allow managers to continue theirearnings trend and concurrently achieve otherearnings objectives in the short-term. Sinceother earnings objectives are likely to bedependent upon existing earnings trend tocertain extent, 6 smoothing earnings towardsearnings trend can concurrently allow

managers to continue the smoothed earningstrend in the current period, create accountingslack for future periods and meet other short-term earnings objectives. Therefore, for firmswith pre-managed earnings above theirearnings trend, incentives to smooth earningsmanagers are likely to be stronger.

6 One potential exception is when there is a structuralchange to a firm’s underlying earnings generatingcapacities which lead to a structural change to the

underlying earnings trend. However, this is unlikely to be a regular, systematic behaviour for a firm. To theextent structural change to underlying earnings trend isregular, this will bias against finding evidenceconsistent with the arguments posited here.

In contrast, choices between accrualsmanagement strategies available to firms with

pre-managed earnings below their earningstrend are less straightforward. If there aresufficient discretionary accruals, these firms

can choose to concurrently smooth earningstowards their earnings trend and meet otherobjectives. On the other hand, if there areinsufficient discretionary accruals, managers ofthese firms can choose to manage earningstowards their earnings trend to achieve a“smooth” earnings trend but may not be able toachieve other earnings objectives (e.g., Gaveret al., 1995). This strategy can potentiallyreduce the firms’ ability to smooth earnings inthe future periods. Alternatively, they can

choose to deviate from their earnings trend bytaking an earnings bath to create accountingslack for future periods and take the capitalmarket punishment on their share prices (e.g.,Healy, 1985; Barth et al., 1999; Myers andSkinner, 1999; Levitt, 1998). Consequently, theeventual accruals management strategies forindividual firms with pre-managed earningsbelow their earnings trend are likely to bedependent upon the relative strength of thevarious incentives faced by these firms. Thissuggests accruals management strategiesamong firms with pre-managed earnings below their earnings trend are likely to be morediverse and less systematic than firms with pre-managed earnings above their earnings trend.

Given the above, it is expected that the predicted positive association betweeninstitutional ownership and income smoothingis more prevalent among portfolio firms thathave pre-managed earnings above theirearnings trend than portfolio firms that have

pre-managed earnings below their earningstrend.

3. Research Design3.1 Measuring Institutional OwnershipConsistent with Koh (2003), this study definesinstitutional investors as large investors, otherthan natural persons, who exercise discretionover the investments of others, and include thefollowing organisations: insurance companies(life and non-life), superannuation and pensionfunds, investment trusts (including unit trusts),financial institutions (including banks and banknominee companies, finance companies,





97

building societies, credit cooperatives),investment companies, and other nomineecompanies associated with the above categoriesof institutions. The proxy for institutionalownership (PIO) is calculated as the total

shares held by institutional investors (asdefined above) divided by total sharesoutstanding.

Institutional ownership information is handcollected from the Connect 4 Annual Reportand Top Shareholders databases. Measuringinstitutional ownership in Australia can be

problematic as shareholders can hide theiridentity via nominee companies (Stapledon,1996b). Nominee companies typically reportshareholding information under their company

names rather than their clients’ names. As such, publicly available shareholding information(e.g., Top 20 shareholders disclosures), withthe exception of substantial shareholdingdisclosures, is likely to capture institutionalownership with noise. To reduce the noise inthis variable, this study uses the TopShareholders database as the primary datasource in constructing the institutionalownership variable. The Top Shareholdersdatabase contains top 40 shareholders’shareholdings sourced directly from companyshare registries. Sourcing shareholdinginformation directly from company shareregistries breaks down much of theshareholdings by nominee companies. To theextent both Connect 4 and Top Shareholdersdatabases capture institutional ownership withnoise, this will bias against finding thehypothesised positive association (Johnston,1991; Kennedy, 1992).

3.2 The Income Smoothing MeasureThis study uses discretionary accruals asthe income smoothing instrument, as itencompasses a wide range of earningsmanagement techniques available to managerswhen preparing financial statements (Fields etal., 2001; Francis, 2001). This is also consistentwith the idea that managers can use multipleearnings management techniques to achievetheir earnings objectives (e.g. Ronen andSadan, 1981; Zmijewski and Hagerman, 1981).

In particular, the cross-sectional variation of themodified Jones model (Dechow et al., 1995) isused to obtain a proxy for discretionaryaccruals, consistent with recent studies (e.g.

Kasznik, 1999; Subramanyam, 1996; Teoh etal., 1998a, 1998b). 7 The modified Jones modelhas been identified as the most powerful modelfor estimating discretionary accruals among theexisting models, although this is not without

imprecision or flaws (Dechow et al., 1995;Guay et al., 1996; Hansen, 1998). Recentevidence indicates that the cross-sectionalmodel outperforms its time-series counterpartin detecting accruals management (Bartov etal., 2000).

Equation (1) below is estimated for eachindustry (defined by two-digit ASX industrycode) and fiscal year combination (Teoh et al.,1998a, 1998b; Bartov et al., 2000).TACC i,t/A i,t-1 = α 1(1/A i,t-1)

+ α2(∆REV i,t/A i,t-1)+ α3(PPE i,t/A i,t-1) + εi,t (1)

The discretionary accruals proxy (DACC)for this study is obtained by fitting the modifiedJones accruals model as follows:

DACC i,t = TACC i,t/A i,t-1 – [ α ̂ 1(1/A i,t-1)

+ α ̂ 2({∆REV i,t - ∆REC i,t}/A i,t-1)

+ α ̂ 3(PPE i,t/Ai,t-1)] (2)

where TACC is total accruals, A is total assets,∆REV is the change in operating revenue,∆REC is the change in net receivables, PPE isgross property, plant and equipment, t and t-1are time subscripts, and i is the firm subscript.Changes in revenues are included to control forthe economic circumstances of a firm;whilst gross property, plant and equipmentare included to control for the portion oftotal accruals related to non-discretionarydepreciation expenses (Jones, 1991). Dechowet al. (1995) modify the Jones (1991) model byremoving the discretionary components ofrevenues through changes in accountsreceivable.

7 Compared to the time-series accruals model, the cross-sectional version has several advantages: a) it generatesa larger sample size to facilitate hypothesis testing; b)the number of observations per model is greater for thecross-sectional model, which enhances the efficiencyand precision of the estimates; c) the time-series modelsuffers potential survivorship bias as it generally

requires a minimum of 10 years of observations toachieve a reasonable level of estimation efficiency(Dechow et al., 1995); d) given the lengthy time periodrequired by the time-series model, it is possible for themodel to be misspecified due to non-stationarity.





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Portfolio firms are classified as incomesmoothers if their reported earnings arecloser to their earnings trend than arenon-discretionary earnings (NDE). Thatis, a portfolio firm is an income

smoother (SMOOTH=1) if Abs(Earnings– Trend)<Abs(NDE–Trend). Earnings is thereported earnings before interest and tax and

before extraordinary items (EBIT t). Earningstrend reference point (Trend) is the prior yearearnings level, EBIT t-1,

8 while non-discretionaryearnings (NDE) is earnings (EBIT t) lessdiscretionary accruals (DACC t). Earnings(EBIT t), earnings trend and NDE are all scaled

by prior year total assets, as the discretionaryaccruals proxy (DACC t) is scaled by prior year

total assets.3.3 Control Variables and ModelSpecificationsGiven that portfolio firms’ income smoothingdecisions are likely to be influenced by factorsin addition to institutional ownership, severalcontrol variables are introduced to capture thecontracting incentives that have been found toinfluence managers’ discretionary accountingchoices. The control variables included in thisstudy are size, leverage, managerial ownership,cash flows from operations, industrymembership and year.

The size hypothesis (Watts and Zimmerman,1978; 1986; 1990) posits that large firms aremore politically visible. In addition, largeearnings fluctuations attract public scrutiny(e.g. Ronen and Sadan, 1981; Moses, 1987). Assuch, managers of large firms are more likelyto exploit latitude in accounting discretion toreduce political attention, by reporting a

smooth earnings trend. Similarly, larger firms

8 The income smoothing literature has used prior yearearnings level as the trend reference point since the1960s (e.g., Archibald, 1967; Copeland, 1968; White,1970). Relative to using an earnings volatility measure,this study’s approach is not subject to problemsassociated with structural breaks in historical earningstrend and does not require long historical time seriesdata which would significantly reduce the sample sizeof this study, and thus the power of subsequent tests.Further, this study’s approach seeks to capture the

attempt to smooth earnings consistent with the researchobjective, whilst an earnings volatility measure is likelyto capture the achievement of earnings smoothing(Moses 1979 as cited in Moses 1987), which is not thefocus of this study.

are likely to smooth earnings, as they are morelikely to be mature firms and have more

predictable and synchronised revenues andearnings (Carlson and Bathala, 1997). Givenfirm size can proxy for other aspects of the firm

in addition to political exposure, there is nospecific prediction on the association betweenfirm size and income smoothing. This studyuses total assets as a proxy for firm size (SIZE).

Managers are more likely to exercise theiraccounting discretion when they are closer todefault on debt covenants (Press and Weintrop,1990). A leverage ratio is used to proxy for afirm’s proximity to debt covenant violation.Consistent with the debt hypothesis, this studyexpects that as the firms approach their

accounting-based debt covenants, managers ofthese firms are more likely to adopt aggressiveearnings management techniques to preventviolation of these debt covenants (Watts andZimmerman, 1986), rather than smooth theirearnings. Leverage (LEV) is measured by theratio of total liabilities to total tangible assets(Cotter, 1998; Ramsay and Sidhu, 1998;Whittred and Zimmer, 1986).

Warfield et al. (1995) contend that as theseparation between ownership and controlwidens, managers capitalise on the latitude inreporting numbers. As managerial ownershipincreases, there is a corresponding increase inalignment of manager and shareholder interests(Jensen and Meckling, 1976). As shareholdersare likely to benefit from smooth and

predictable reported earnings levels (e.g.through the increase in firm value and dividendincome), managerial ownership is expected to

be positively associated with incomesmoothing. Managerial ownership, MGRSH,is measured as the ratio of directors’shareholdings (direct, indirect and beneficialholdings) to total ordinary shares outstanding.

Given the negative association between cashflows and accruals (e.g. Dechow, 1994) and theuse of accruals management as the incomesmoothing instrument in this study, a controlvariable, cash flows from operations (CFO), isintroduced to control for this association. It isexpected that firms with high (low) cash flowsfrom operations would have low (high) levelsof accruals. It is also expected that firms withlow levels of accruals will have lessdiscretionary accruals than firms with highlevels of accruals. Accordingly, firms with high





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levels of cash flows from operations are lesslikely to be able to manage accruals, includingincome smoothing.

By adopting the cross-sectional accrualsmodel in the Australian environment, the

resultant sample firms are likely to beconcentrated in a small number of industries.An indicator variable, MINE, is introduced tocontrol for potential industry clustering effects.MINE takes a value of 1 for mining firms (i.e.firms with a two-digit ASX industryclassification code between 01 and 05,

inclusive) and 0 otherwise. Finally, a series ofdummy variables for fiscal year (YEAR) arecreated to control for potential time specificeffects on earnings management strategies.Table 1 summarises all the variable definitions

of this paper.To test the hypothesis, the following logit

model is employed to regress SMOOTH onfirm’s institutional ownership and a set ofcontrol variables as discussed above. A

positively significant β1 is consistent withHypothesis 1.

(+) (?) (–) (+) (–)Prob(SMOOTH i = 1) = α + β1PIO i + β2SIZE i + β3LEV i + β4MGRSH i + β5CFO i

+ β6MINE i + !=

1997

1994k

βk YEAR k + εi (3)

where:SMOOTH i = 1 if firm i is an income smoother, i.e., Abs(Earnings – Trend) < Abs(NDE –

Trend); 0 otherwisePIO i = Percentage institutional ownership for firm i SIZE i = Natural logarithm of total assets for firm i LEV i = Ratio of total liabilities to total tangible assets for firm i MGRSH i = Ratio of directors’ shareholdings (direct, indirect and beneficial holdings) to total

ordinary shares outstandingCFO i = Cash flows from operations/total assets for firm i MINE i = Mining company dummy variable (1 if firm i is a mining firm - two-digit ASX

industry classification code between 01 and 05; 0 otherwise)YEAR k = Dummy variables for years within the test periodEarnings = Current year earnings before interests and tax and before extraordinary items

divided by prior year total assetsTrend = Prior year earnings before interests and tax and before extraordinary items divided

by prior year total assets NDE = Non-discretionary earnings as measured by earnings less discretionary accruals

(DACC) as proxied by modified Jones accruals model

To examine whether income smoothingincentives are uniform across firms with pre-managed earnings higher than earnings trendand firms with pre-managed earnings lowerthan earnings trend, the above logit regressionmodel is run within each of the sub-sample. Asdiscussed in Section 2.4, this study expectsincome smoothing incentives to be strongeramong portfolio firms that have pre-managedearnings higher than their earnings trend thanthose with pre-managed earnings lower thantheir earnings trend. As such, it is expected β1 for the sub-sample with firms with pre-managed earnings higher than their earnings

trend to be more significant than sub-samplewith firms with pre-managed earnings lower than their earnings trend.

Prior research shows that the price-earningsrelation for profit and loss firms areasymmetrical suggesting that profit firms havegreater incentives to manage earnings,including income smoothing, than loss firms(e.g., Basu 1997; Hayn 1995). Further, to theextent that there are fewer growth optionsembedded in the stock price and/or valuationare based more on book value than on earningsfor loss firms, loss firms have less incentivesthan profit firms to manage earnings (Basu





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Table 1Variable Definitions

Variable Definition Expected Sign

SMOOTH Income smoother 1 if Abs(Earnings-Trend) <Abs(NDE-Trend)

Dependent variable

PIO Institutionalownership

Ratio of institutional ownership tototal ordinary shares outstanding

+

SIZE Firm size Natural logarithm of total assets No prediction

LEV Leverage Ratio of total liabilities to totaltangible assets

-

MGRSH Managerialownership

Ratio of directors’ shareholdings(direct, indirect and beneficialholdings) to total ordinary shares

outstanding

+

CFO Cash flows fromoperations

Cash flows from operationsdivided by total assets

-

MINE Mining company 1 if a firm is a mining firm - two-digit ASX industry codes between01 and 05; 0 otherwise

No prediction

YEAR k Year variable A series of dummy variableswhere 1 if the firm is drawn fromyear k; 0 otherwise.

No prediction

Earnings = Current year earnings before interests and tax and before extraordinary items divided by prior year total assetsTrend = Prior year earnings before interests and tax and before extraordinary items divided by prior year total assets

NDE = Non-discretionary earnings as measured by earnings less discretionary accruals (DACC)TACC = Total accruals as measured by: (Income before extraordinary items – cash flows from operations) divided by

prior year total assets NDACC = Non-discretionary accruals calculated as: α ̂ 1(1/A i,t-1) + α ̂ 2({∆REV i,t - ∆REC i,t}/A i,t-1) + α ̂ 3(PPE i,t/Ai,t-1)DACC = Discretionary accruals as measured by total accruals (TACC) less non-discretionary accruals (NDACC)

1997; Hayn 1995; Ohlson 1995). Given thisstudy’s sample (see below) includes both profitand loss making firms, additional tests areconducted to ensure any findings are not

affected by the inclusion of loss firms. Toachieve this, the logit regression is re-fitted to profit and loss firms separately.

3.4 Sample SelectionFollowing Koh (2003), the sample includesnon-finance related firms between 1993 and1997 on the Compustat Global Vantage(n=836). 9 Firms classified in diversifiedresources, diversified industrial, miscellaneousindustrial and miscellaneous services industries(ASX two-digit industry classification codes

9 Firms with ASX two-digit classification codes of 16 to20 are regarded as finance related firms.

of: 05, 23, 21 and 22 respectively; n=213) 10 andfirms with insufficient data to construct all thevariables (n=77) are excluded. Further,industries with less than ten observations in a

year are excluded to ensure efficiency inaccruals model estimation (Dechow et al.,1995; Jones, 1991; n=344). The final sampleconsists of 202 firm-year observations (72distinct firms) for accruals estimation andempirical analysis.

10 The cross-sectional accruals model implicitly assumesfirms, within the same industry-year combination, are

reasonably homogeneous. These industries include firmfrom diverse backgrounds and the accruals model fittedto these industries is unlikely to provide meaningfulestimated coefficients. For more details, see Koh(2003).





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4. Results4.1 Descriptive StatisticsTable 2 reports the descriptive statistics forthe dependent and explanatory variables for

all sample firms, non-income smoothers(SMOOTH=0) and income smoothers(SMOOTH=1) in columns 1, 2 and 3respectively. Column 4 of Table 2 presents thet-tests on the differences in means betweenincome smoothers and non-income smoothers.There are 104 income smoothers with 50 (54)manage accruals upwards (downwards) tosmooth towards their earnings trend. Forincome smoothers, 53 (51) of them reportedcurrent year earnings higher (lower) than their

earnings trend. Of the 98 non-incomesmoothers, 43 (55) manage accrualsdownwards (upwards) away from theirearnings trend.

Institutional investors, on average, holdaround 48.04% of total shares outstanding ofthe sample firms. This is comparable to theaverage institutional ownership level reported

by Koh (2003) of 47% between 1993 and 1997inclusive, and Stapledon (1998) of around 49%as at 31 March 1997. 11 There is weak evidence

that firms that do smooth earnings have higheroverall institutional ownership, PIO, than firmsthat do not (50.55% and 45.38% respectively,

p<0.10). On the other hand, directors haveaverage equity interests of approximately11.81% of total outstanding shares with morethan half of the sample firms having less than0.6% of director relevant interests(median=0.56%, not reported). Managerialownership between income smoothers and non-income smoothers is not statistically different

( p>0.10).The average total liabilities to total tangibleassets ratio, LEV, for the sample firms is53.87%. This average leverage is within theleverage covenant constraints in the Australian

private debt market (Cotter, 1998). Cotter

11 The 49% is calculated based on the informationdisclosed by Stapledon (1998, pp. 244-245) and thedefinition of institutional investors used in this study. Itis made up of life insurance companies andsuperannuation funds (25.2%); unit trusts, investmenttrusts and other financial institutions (9.4%); banks(3.8%) and overseas institutional investors (10.6%).

(1998, p.191) finds that the median range of theleverage covenant constraint is between48.75% and 70% (and between 52.5% to67.5% when only the 4 major banks areconsidered). In addition, the mean leverage for

income smoothers (48.53%) is below the lowerend of the covenant constraint rangesdocumented by Cotter (1998), while the meanleverage for non-income smoothers (59.54%)falls around the mid-point of the covenantconstraint ranges. This suggests that non-income smoothers, on average, are closer totheir leverage constraints than incomesmoothers. Consistent with the expectation thatfirms closer to their leverage constraints areless likely to smooth their earnings, the average

leverage for a non-income smoother issignificantly higher than the average forincome smoothers ( p<0.05).

On average, the sample firms’ cash flowsfrom operations, CFO, are around 11% of totalassets. There is no significant difference

between the cash flows from operations ofincome smoothers and non-income smoothers( p>0.10). Further, firm size is not different

between income smoothers and non-incomesmoothers ( p>0.10), thus reducing thelikelihood that subsequent results might be theartefact of size effects. Sample firms are mainlymining firms, MINE, but they are evenlydistributed between income smoothers andnon-income smoothers ( p>0.10).

Spearman correlations between theexplanatory variables are documented inTable 3. Total institutional ownership (PIO) is

positively associated with firm size (SIZE)consistent with the popularity of indexingstrategies among institutional investors inAustralia (Stapledon, 1996a; 1996b). The

positive association between institutionalownership and firm size is also consistentwith the U.S. evidence (e.g., Carlson andBathala, 1997). Some weak evidence thatinstitutional ownership is negatively associatedwith leverage also exists ( p<0.10). Theseresults suggest that institutional investors aremore likely to invest in larger firms andfirms with lower leverage.

Firm size (SIZE) is positively associatedwith leverage (LEV), consistent with Cotter’s(1998) finding that larger firms have higher





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Table 2 Descriptive Statistics for Complete (All), Income Smoothing (SMOOTH=1)

and Non-Income Smoothing (SMOOTH=0) Samples

Sample

SMOOTH

Differences in Means( t-Tests; 2-tail p-value)

Variable All 0 1 (2) – (3)

(1) (2) (3) (4)

PIO Mean 0.4804 0.4538 0.5055 -0.0517Std. Dev. 0.2210 0.2246 0.2156 (0.0968)

SIZE Mean 5.6849 5.8370 5.5416 0.2955Std. Dev. 1.6439 1.6598 1.6237 (0.2025)

LEV Mean 0.5387 0.5954 0.4853 0.1101Std. Dev. 0.3641 0.4427 0.2611 (0.0342)

MRGSH Mean 0.1181 0.1119 0.1239 -0.0121Std. Dev. 0.1864 0.1867 0.1869 (0.6465)

CFO Mean 0.1097 0.1206 0.0993 0.0213Std. Dev. 0.1049 0.1139 0.0951 (0.1523)

Dichotomous Variables: Frequency Frequency FrequencySMOOTH = 1 104 N/A N/A

= 0 98 N/A N/A

MINE = 1 141 67 74= 0 61 31 30

N 202 98 104

PIO = Percentage institutional ownershipSIZE = Natural logarithm of total assetsLEV = Ratio of total liabilities to total tangible assetsMGRSH = Ratio of directors’ shareholdings (direct, indirect and beneficial holdings) to total ordinary shares outstandingCFO = Cash flows from operations/total assetsMINE = Mining company dummy variables (1 if a firm is a mining firm; 0 otherwise)

SMOOTH = 1 if a firm an income smoother, i.e., Abs(Earnings – Trend) < Abs(NDE – Trend); 0 otherwiseEarnings = Current year earnings before interests and tax and before extraordinary items divided by prior year total assetsTrend = Prior year earnings before interests and tax and before extraordinary items divided by prior year total assets

NDE = Non-discretionary earnings as measured by earnings less discretionary accruals (DACC) as proxied bymodified Jones accruals model

leverage constraint levels. SIZE is alsonegatively correlated with directors’ equityinterests (MGRSH), suggesting that directors’equity interest in the firm is declining as firmsize increases. Mining firms are likely to besmaller (SIZE), have lower leverage ratios

(LEV), and lower managerial ownership(MGRSH) than industrial firms.

4.2 Regression ResultsTable 4 presents results for the logit regressionfor income smoothers (SMOOTH) oninstitutional ownership and a set of controlvariables. Column 1 reports the logit regressionresults for the full sample. The estimated

coefficient for institutional ownership (PIO) issignificantly positive (p<0.05), supportingHypothesis 1 that the higher a firm’s





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Table 3Spearman Correlation Matrix for Explanatory Variables

PIO SIZE LEV MGRSH CFO MINE

PIO 1.0000 .3327 -.1299 -.0513 .0647 .0615 p-value . .0000 .0654 .4680 .3604 .3847

SIZE 1.0000 .3591 -.2259 .0527 -.2429 p-value . .0000 .0012 .4565 .0005

LEV 1.0000 .1427 -.1107 -.5149 p-value . .0428 .1167 .0000

MGRSH 1.0000 -.2273 -.4023 p-value . .0011 .0000

CFO 1.0000 .1687 p-value . .0164

*All p-values are 2-tailed.

PIO = Percentage institutional ownershipSIZE = Natural logarithm of total assetsLEV = Ratio of total liabilities to total tangible assetsMGRSH = Ratio of directors’ shareholdings (direct, indirect and beneficial holdings) to total ordinary shares outstandingCFO = Cash flows from operations/total assetsMINE = Mining company dummy variables (1 if a firm is a mining firm; 0 otherwise)

institutional ownership, the more likely the firm isto smooth reported earnings. 12 Two other control

variables, leverage (LEV) and cash flows fromoperations (CFO), are marginally significant atthe 10% and with the expected signs.

Income Smoothing Incentives of Profitversus Loss FirmsAs discussed in Section 3.3, incentives tomanage earnings are likely to be different

between loss-making and profit-making firms(e.g., Basu, 1997; Hayn, 1995; Ohlson, 1995).Given the sample includes both profit and lossmaking firms, earnings management incentives

12 A piecewise regression, similar to Morck et al. (1988),is used to examine whether the association betweeninstitutional ownership and income smoothing is non-linear. Koh (2003) finds a concave association betweeninstitutional ownership and income increasingdiscretionary accruals with the turning point taking

place at around 55% of institutional ownership. Usingthis turning point as the cut-off point, the unreportedresults indicate that institutional ownership is positivelyassociated with income smoothing in both institutional

ownership regions above and below 55% of institutionalownership ( p<0.05) and there is no difference betweenthe two estimated slope coefficients ( p>0.10). Theseresults indicate that the association between institutionalownership and income smoothing is linear.

may differ between these firms. To examine the potential differential earnings management

incentives, additional tests are performed by re-fitting the logit regression to sub-samples of profit and loss making firms separately. 13

Column 2 of Table 4 reports the results ofre-fitting the logit regression to profit firms.The estimated coefficient for institutionalownership (PIO) is positively significant, andmore importantly, the significance level of theestimated coefficient has improved from theinitial 5% level to the 1% level. This findingfurther supports the predicted positive

association between institutional ownership andincome smoothing. Compare to all samplefirms, the leverage (LEV) of profit firms arenot associated with income smoothing ( p>0.10)suggesting that these firms are in betterfinancial position such that their leverage levelshave no effect on their accruals managementdecisions. For loss firms, the estimatedcoefficient for institutional ownership (PIO) isnegative but insignificantly associated withincome smoothing ( p>0.10; Column 3 of

13 Incidentally, all profit (loss) firms have positive(negative) non-discretionary earnings.





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Table 4Logit Regression of Income Smoother on Institutional Ownership Variables

and Control Variables

Prob(SMOOTH = 1) = α + β1PIO + β2SIZE + β3LEV + β4MGRSH + β5CFO + β6MINE + !=

1997

1994k

βk YEAR k

Estimated Coefficients ( p-value )*Variable Expected SignFull Sample Profit Firms # Loss Firms ##

(1) (2) (3)

Constant ? 2.1074(0.0641)

2.1109(0.1101)

2.1490(0.5556)

PIO + 1.4505(0.0283)

2.2064(0.0068)

-2.1141(0.1687)

SIZE +/- -0.1579(0.1514)

-0.2445(0.0638)

0.2513(0.4838)

LEV - -0.8587(0.0579)

-0.3280(0.3409)

-1.2319(0.1390)

MGRSH + 0.6729(0.2290)

1.0316(0.1620)

-3.3449(0.1068)

CFO - -2.0895(0.0795)

-1.0267(0.3018)

-6.0148(0.0620)

MINE +/- -0.2946(0.4824)

-0.0504(0.9227)

-0.7425(0.6515)

Nagelkerke R 2 0.0980 0.1329 0.4734Sample size (N) 202 156 46

* 1-tail p-value is reported if direction of the coefficient is predicted, otherwise 2-tail p-value is reported# Profit firms all have NDE > 0## Loss firms all have NDE < 0

PIO = Percentage institutional ownershipSIZE = Natural logarithm of total assetsLEV = Ratio of total liabilities to total tangible assetsMGRSH = Ratio of directors’ shareholdings (direct, indirect and beneficial holdings) to total ordinary shares outstandingCFO = Cash flows from operations/total assetsMINE = Mining company dummy variables (1 if a firm is a mining firm; 0 otherwise)SMOOTH = 1 if a firm an income smoother, i.e., Abs(Earnings – Trend) < Abs(NDE – Trend); 0 otherwiseEarnings = Current year earnings before interests and tax and before extraordinary items divided by prior year total assetsTrend = Prior year earnings before interests and tax and before extraordinary items divided by prior year total assets


Table 4). These findings are consistent withexisting argument that earnings managementincentives, including income smoothingincentives, are likely to be stronger for profitthan loss firms, and hence their earningsmanagement strategies are likely to differ.

4.3 Are Income Smoothing IncentivesUniform Across All Circumstances?As posited in Section 2.2, it is also expectedthat the predicted positive association betweeninstitutional ownership and income smoothingis more prevalent among firms with pre-





105

Table 5Logit Regression of Income Smoother on Institutional Ownership Variables

and Control Variables (Additional Tests)

Prob(SMOOTH = 1) = α + β1PIO + β2SIZE + β3LEV + β4MGRSH + β5CFO + β6MINE + !=1997

1994k βk YEAR k

Estimated Coefficients ( p-value )*

Constant PIO SIZE LEV MGRSH CFO MINE Nagelkerke

? + +/- - + - +/- R 2

Panel A: Firms with NDE above their earnings trend (n=110)

1.2486(0.4211)

3.0255(0.0034)

-0.1732(0.2902)

-0.0769(0.4647)

2.0358(0.0620)

0.7385(0.3750)

-0.6730(0.2460)

0.1810

Panel B: Firms with NDE below their earnings trend (n=92)2.0665

(0.3091) 0.3867

(0.3850) -0.0185(0.9203)

-1.9516(0.0426)

-1.2823(0.2153)

-5.0078(0.0251)

0.3016(0.6977)

0.2384

Panel C: Profit firms with NDE above their earnings trend (n=102)

0.8587(0.5935)

3.1538(0.0036)

-0.1848(0.2844)

0.2459(0.4019)

2.4865(0.0457)

1.2061(0.3130)

-0.4577(0.4973)

0.2170

Panel D: Profit firms with NDE below their earnings trend (n=54)

9.3702(0.7124)

1.2587(0.2585)

-0.3200(0.2438)

-1.7281(0.1763)

-1.6635(0.1901)

-8.4417(0.0386)

0.9215(0.4035)

0.4053

Panel E: Firms with positive discretionary accruals (n=105)

0.7780(0.6274)

2.0976(0.0397)

-0.2741(0.1223)

0.2021(0.4161)

-0.5918(0.3439)

-5.3352(0.0092)

0.7224(0.2477)

0.1900

*1-tail p-value is reported if direction of the coefficient is predicted, otherwise 2-tail p-value is reported

PIO = Percentage institutional ownershipSIZE = Natural logarithm of total assetsLEV = Ratio of total liabilities to total tangible assetsMGRSH = Ratio of directors’ shareholdings (direct, indirect and beneficial holdings) to total ordinary shares outstandingCFO = Cash flows from operations/total assetsMINE = Mining company dummy variables (1 if a firm is a mining firm; 0 otherwise)SMOOTH = 1 if a firm an income smoother, i.e., Abs(Earnings – Trend) < Abs(NDE – Trend); 0 otherwiseEarnings = Current year earnings before interests and tax and before extraordinary items divided by prior year total assetsTrend = Prior year earnings before interests and tax and before extraordinary items divided by prior year total assets


managed earnings above their earnings trend(i.e., NDE > Trend). 14 To investigate this, thelogit regression is regressed on two sub-samples, viz., firms with pre-managed earnings(NDE) above versus below their earnings trend

14 To the extent that firms with NDE greater than theirearnings trend do not manage earnings downwards tosmooth earnings, it would bias against finding resultsconsistent with this expectation.

(results are reported in Panels A and B of Table5 respectively). 15 For firms with NDE above their earnings trend, the estimated coefficient ofthe institutional ownership is positive andsignificant ( p=0.0034), while firms with NDE

15 Income smoothers among firms with pre-managedearnings above (below) their earnings trend correspondto firms that smooth their earnings down (up) towardstheir earnings trend.





106

below their earnings trend have insignificantcoefficient ( p=0.3850). These results suggestthat firms with high institutional ownership aremore likely to smooth income if theyare above their earnings trend before

accruals management. In contrast, institutionalownership is not associated with incomesmoothing when firms’ NDE is below theirearnings trend. Therefore, the initial findingson Column 1 of Table 4 can be attributedmainly to the association between institutionalownership and income smoothing among firmswith pre-managed earnings above theirearnings trend.

The above findings also provide preliminaryempirical evidence indicating that the choices

between income smoothing and other earningsmanagement strategies that a manager faceswill depend upon whether a firm’s pre-managed earnings is higher or lower than itsearnings trend. The results suggest that suchincentive trade-offs are less ambiguous amongfirms with pre-managed earnings that exceedtheir earnings trend than those below the trend.

Income Smoothing Incentives of Profitversus Loss FirmsFurther tests are conducted to examine whetherthe differences between firms with NDE aboveand below their earnings trend persist whenonly profit firms are considered. Consistentwith the preceding findings, for profit firmswith NDE that exceed their earnings trend,institutional ownership (PIO) is significantly

positively associated with income smoothing atthe 1% level (Table 5, Panel C). However, for

profit firms with NDE below their earningstrend, their institutional ownership is not

associated with income smoothing (Table 5,Panel D).Combining the results from Panels B and D

of Table 5 indicates that for firms below theirearnings trend prior to accruals management,institutional ownership is not associated withincome smoothing regardless of whether theyare profit or loss making firms. 16

16 For brevity, the results for loss firms with NDE belowor above their earnings trend are not reported. Theseresults reveal that institutional ownership is notassociated with income smoothing ( p-values>0.10),consistent with earlier findings.

Among the control variables, cash flowsfrom operations (CFO) are negativelyassociated with income smoothing only amongfirms with NDE below their earnings trend ( p-values <0.05; Table 5 Panels B and D). This

indicates the negative association between cashflows and accruals (thus discretionary accruals)is more likely to affect accruals managementdecisions among firms that are not performingwell (as measured by whether pre-managedearnings greater or less than earnings trend).Leverage of firms with NDE below theirearnings trend is negatively associated withincome smoothing ( p<0.05; Table 5 Panel B)while institutional ownership is not associatedwith income smoothing. This suggests that

these firms’ accruals management strategy ismore likely to respond to incentives created bytheir leverage ratio rather than incentivescreated by institutional investors. Managerialownership (MGRSH) appears to associate withincome smoothing only when firms’ NDE isabove their earnings trend (Table 5 Panels Aand C).

4.4 Reconciling to Evidence in Koh (2003)Further analysis examines the association

between institutional ownership and incomesmoothing within a sub-sample of firms with

positive discretionary accruals. This analysisenables an examination of whether aggressiveearnings management behaviour, as associatedwith institutional ownership, forms part of agreater income smoothing strategy. 17 Further, it

provides a link between this study and the onlyother known study on the association betweeninstitutional ownership and accrualsmanagement in Australia (Koh, 2003). 18 Table5 Panel E reports the results of re-fitting thelogit regression of this study using only firmswith positive discretionary accruals. The

17 Income smoothing via accruals and income increasingaccruals management are not mutually exclusive – tosmooth earnings via accruals, firms will have to eitherengage in income increasing or decreasing discretionaryaccruals, depending on the firms’ non-discretionaryearnings (NDE) relative to their earnings trend.Therefore, an income increasing (or decreasing)accruals management can form part of an incomesmoothing strategy.

18 Koh (2003) finds that the association betweeninstitutional ownership and income increasingdiscretionary accruals follows a concave relation.





107

estimated coefficient for institutionalownership measure continues to be positivelysignificant at the 5% level. These results andthose of Koh (2003) in combination suggestthat, in Australia, while institutional ownership

and income increasing discretionary accrualsfollow a concave association, such associationmanifests itself within the income smoothingframework.

4.5 Sensitivity AnalysisGiven the income smoothing construct of thisstudy, the robustness of the results dependsupon the direction of the estimateddiscretionary accruals. To ensure that thereported results are not a product ofmeasurement errors arising out of thediscretionary accruals estimation process,observations with estimated discretionaryaccruals around zero are excluded. Specifically,observations with estimated discretionaryaccruals between -1% and 1% of prior yeartotal assets are excluded (see Balsam et al.,2002).

Trimming observations with estimateddiscretionary accruals around zero excludesobservations that are most susceptible tomisclassification (based on estimateddiscretionary accruals), thus enhancing thevalidity of the reported results. The re-fittedlogit regressions yield qualitatively similarresults as reported earlier. 19 Thus, the earlierfindings are unlikely to be a product ofmeasurement error in estimated discretionaryaccruals.

Other robustness tests include (a) using threealternative institutional ownership measures,specifically institutional ownership by top 1, 3and 5 institutional investor(s); (b) usingdifferent cash flows from operations measures;(c) using lagged total accruals as a control foraccounting flexibility and accruals reversion(Koh, 2003); (d) controlling for the potentialnon-linear relation between firm size andincome smoothing; (e) using market value ofequity as an alternative size measure; and (f)controlling for firms’ growth options.Controlling for all these issues does not changethe results reported earlier. Therefore, the

19 In fact, the significance levels of the estimatedcoefficients improved as compared to the initial resultsreported earlier.

findings of this study are robust to differentmodel specifications.

5. ConclusionsThis study examines the rarely studied

associations between institutional ownershipand income smoothing. It extends theempirical evidence on the associations

between institutional ownership and earningsmanagement to the domain of incomesmoothing as well as beyond the USinstitutional environment. It also providesempirical evidence suggesting that adjustedaccounting based performance measures may

be more appropriate when investigating theeffects of institutional monitoring on portfolio

firm performance when accounting based performance measures are used (e.g. Karpoff,1998).

The results indicate that while institutionalownership is positively associated with thelikelihood of portfolio firms smoothingearnings in general, it is not uniform across allfirms. Specifically, this positive association ismost evident among profit firms with pre-managed earnings above their earnings trend.However, no association is found among (a)

profit firms with pre-managed earnings belowearnings trend; and (b) loss firms in general.The findings of this study are robust to a widerange of specifications and are unlikely to be anartefact of measurement errors arising from thediscretionary accruals estimation process.Additional tests also reveal that the concaveassociation between institutional ownership andincome increasing discretionary accruals (Koh,2003) seems to manifests within theframework of smoothing income. Overall, theresults highlight the complex relations

between institutional ownership and earningsmanagement strategies, and future research can

benefit by explicitly examining the trade-offs between alternative earnings managementincentives and the factors that affect the relativestrength of these incentive trade-offs.

Although this study shows consistentevidence supporting the positive association

between institutional ownership and thelikelihood of firms smoothing income,important caveats must be mentioned. Whileincome smoothing is a multiple period earningsmanagement strategy, the tests conducted areessentially a single period test. This study can





108

be extended to incorporate a multiple perioddesign. 20 Another limitation is the use ofdiscretionary accruals as the smoothinginstrument. As with all studies that employ themodified Jones model to proxy discretionary

accruals, this study inherits all the limitations ofthis estimation technique. Use of the cross-sectional version of the modified Jones model(which has been shown to be more powerfuland better specified than its time-seriescounterpart) mitigates some of the criticisms ofthe model (Bartov et al., 2000; Peasnell et al.,2000), while tests excluding observations thatare most susceptible to discretionary accrualmeasurement errors yield qualitativelyconsistent results.

Another limitation rests on the adoption of prior year earnings level as the earnings trendreference point that firms are smoothing theirearnings towards. However, to the extent thatfirms smooth their earnings towards otherearnings levels (such as analysts’ forecasts) andin the absence of a structural change in theunderlying earnings trend, other earnings levelsto which firms might smooth their earningstowards are unlikely to be significantlydifferent from prior year earnings levels.Finally, measuring institutional ownership inAustralia can be problematic (Stapledon,1996b). However, to the extent this studycaptures institutional ownership with noise, itwill bias against finding results consistent withthe hypothesised positive association(Johnston, 1991; Kennedy, 1992).

Given these caveats, this paper provides thefirst known large sample, cross-sectionalevidence of institutional ownership and incomesmoothing in Australia. It also indicates thatincome smoothing behaviour, as associatedwith institutional ownership, may explain thelack of evidence in the existing literature toindicate that institutional activism leads to an

20 Moses (1979, as cited in Moses 1987) suggests that asingle period income smoothing studies reflect theattempt to smooth earnings whilst multiple periodstudies capture how successful firms smoothed theirearnings. The former is the focus of this study. Further,a multiple period design to test income smoothing inthis study’s context would significantly reduce thenumber of observations and thus the power ofsubsequent tests. As such, a single period design ofsmoothing towards a trend is adopted.

improved performance in target firms whenaccounting based performance measures areused as performance benchmarks (Karpoff,1998).

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