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Doktorandenseminar des Competence Centers Corporate Financeder Universität Hohenheim, 27. Januar 2006
Good versus Bad Earnings Management:
Is Income Smoothing a Deliverance?
Dipl.-Pol. Matthias Johannsen, MSc.
2
agenda
theoretical considerations conceptual definitions theoretical motivation research question existing literature
set up of empirical investigation research design variable computation hypotheses to be tested
results descriptive statistics hypothesis tests fixed effects panel regressions
references
3
conceptual definitions
earningsmanagement
income smoothing
“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)” (Schipper 1989: 92)
“actions [by the management of the firm] to dampen fluctuations of the firms’ publicly reported net income”(Trueman/Titman 1988: 127)
note: income smoothing can be achieved by earnings management activities
4
theoretical motivation I opportunistic e.m. and information uncertainty
example
effects increase in current reported earnings increase in net assets reduces future reported residual earnings increase in net assets requires adjusted future depreciation and
hence reduction of future earnings
upward management of reported earnings in order to avoid reporting of loss by reducing depreciation
in general opportunistic earnings management has only a transitory effect
current earning are of little use for predicting future earnings opportunistic earnings management increases information uncertainty
5
theoretical motivation II income smoothing and information uncertainty
example
effects reported earnings follow more closely the general trend reported earnings have lower fluctuations
upward management of reported earnings in case of temporary reduction of earnings
downward management of reported earnings in case of temporary increase of earnings
current earning are very useful for predicting future earnings earnings management to smooth income increases information uncertainty
6
effect of earnings management on the information uncertainty of earnings
research question
opportunistic earnings
management
earnings management to smooth income
no significant
effect
decrease in information uncertainty
increase in information uncertainty
scheme by Guay/Kothari/Watts (1996)
does the market react differently to earnings management depending on the degree of income smoothing present?
7
existing literature I earnings management
verification of opportunistic earnings management Teoh/Welch/Wong (1998), before IPO Burgstahler/Eames (1998), to meet analysts’ forecasts Detzler/Machuga (2002), in cases of non-routine change of CEO
decreasing effect on information uncertainty Francis et al. (2003), earnings management leads to larger cumulative
abnormal returns Marquardt/Wiedmann (2004), earnings management reduces the
explanatory power of in a price-earnings regression Francis et al. (2004), earnings management increases the cost of equity
increasing effect on information uncertainty Subramanyam (1996), discretionary accruals which are often taken as a
measure of earnings management have significant information content in price-earnings regressions
8
existing literature II income smoothing
verification of income smoothing Schmidt (1979), in an older sample for the German Market Kasanen/Kinnunen/Niskanen (1996), in order to sustain a smooth
dividend stream Lim/Lustgarten (2002), for the US
decreasing effect on information uncertainty Bitner/Dolan (1996) show that equity markets pay a premium for shares
of income smoothing firms Zarowin (2002), income smoothing increases the value relevance of
earnings Tucker/Zarowin (2005), income smoothing increases the information
content of reported earnings
9
agenda
theoretical considerations conceptual definitions theoretical motivation research question existing literature
set up of empirical investigation research design variable computation hypotheses to be tested
results descriptive statistics hypothesis tests fixed effects panel regressions
references
10
timepost event
period
t2 = end of last fiscal year
t1 = beginof estimation period
the event
t3 = z trading days after end of fiscal year
t0 = x trading days after end of fiscal year
estimation period
research design I event study methodology and its timing
11
to, the event time
research design II structure of the analysis
compare average monthly absolute abnormal returns
separation of sample firm years
earnings management high
earnings management low income smoothing
low
income smoothing high
income smoothing low
income smoothing high
12
according to the model of Dechow/Sloan/Sweeney (1995):
total accruals are given by
these are regressed on
the first earnings management measure
= absolute value of the one period ahead forecast error
1
t
tttttt AT
DEPDSTCEQLCTACTTAC
tittiiti
ti
ti
titi
ti
DaDaAT
PPEa
AT
RECSALESa
ATactiTAC ,
1,
,3
1,
,,2
1,1
1,
tiDACEMGMT ,_
variable computation I earnings management variable alternative 1
13
according to the model of Dechow/Dichev (2002):
regress changes in working capital and changes on cash
the residuals are changes in working capital unrelated to past, current and future cash realizations
the second earnings management measure is the standard deviation of all current and past firm specific forecast errors:
tittiiti
ti
ti
ti
ti
ti
ti
ti DaDaAT
CFOa
AT
CFOa
AT
CFOac
AT
WCP,
1,
1,3
1,
,2
1,
1,1
1,
,
titiSDEMGMT
,,_
variable computation II earnings management variable alternative 2
ti,
14
according to the model of Lang/Ready/Yetman (2003): measurement of dampening of fluctuations in performance
In order to keep a large sample modifications are made
(as robustness check, cash from operations and operating income were used without changing the results)
ti
ti
INCOME
SALEStiINCSM
,
,
,
2
2
,
,
,
ti
ti
incomeoperating
operationsfromcash
tiINCSM
variable computation III income smoothing variable
15
unexpected earnings (Ball/Brown (1968) and subsequent)
Momentum effect (Jegadeesh/Titman (1993))
extreme financial performance (own computation)
ii XEX
tititi
XEXSUE
,,
,
2
8,
t
ttii rMOMENTUM
4
,,,,
,
titititi
ti
LEVGZABSCSEGZABSCEQGZABSATGLNZABS
EXTRZ
unexpected earnings X-E[X] are the residuals from an AR(1) specification of earnings
variable computation IV control variables
16
according to the Fama/French (1992, 1996) Three Factor Model
the risk premium is estimated by
abnormal returns, denoted , are the one period ahead forecast errors of the above equation
absolute cumulative abnormal returns are given by
titititftmitfti HMLaSMBaRRaRR ,,3,2,,,1,,
t
ttii AABStomonthsCARABSaverage
0,1
10
variable computation V absolute cumulative abnormal returns
absolute cumulative abnormal returns serve as the proxy variable for information uncertainty
tiA ,
17
hypothesis 1
hypothesis 2
firm years with high (low) degrees of earnings management show high (low) absolute cumulative abnormal returns
firms years with high (low) degrees of income smoothing show low (high) absolute cumulative abnormal returns
hypothesis 3 assuming that hypotheses 1 and 2 cannot be rejected assuming that income smoothing is a special form of
earnings management It is expected that the differences in absolute cumulative
abnormal returns between high and low earnings management firm years disappear for income smoothing firm years
hypotheses to be tested
18
agenda
theoretical considerations conceptual definitions theoretical motivation research question existing literature
set up of empirical investigation research design variable computation hypotheses to be tested
results descriptive statistics hypothesis tests fixed effects panel regressions
references
19
Sample Statistic EMGMT_DAC EMGMT_SD INCSM SUE MOMENTUM EXTRZ
EMGMT_DACtotal
below the median
above the median
EMGMT_SDtotal
below the median
above the median
INCSMtotal
below the median
above the median
meanSTDEVmean
STDEVmean
STDEV
meanSTDEVmean
STDEVmeanSTDEV
meanSTDEVmean
STDEVmeanSTDEV
0.14630.22590.03310.02070.25950.2758
0.14690.21480.0780.10330.22250.2722
0.14590.22510.17360.24740.1201
0.85961.9390.38220.87451.32392.4979
0.93262.01670.09570.05111.76952.5949
0.93262.01671.42382.62430.4468
17.039333.884818.954735.30515.123132.2972
14.170626.999517.929732.491610.411619.3583
20.397449.10062.95932.086337.8403
0.11431.09940.19991.04070.02791.1494
0.17041.27170.39611.2561-0.0641.246
0.07351.1073-0.05881.16970.1951
0.03440.43120.04140.34430.02730.5047
0.04650.44190.0390.31850.0540.5388
0.04680.44530.04330.49820.0504
0.30250.52610.20910.30140.39850.6709
0.27890.45010.19070.22850.37660.592
0.3840.71340.4470.7960.3233
0.1988 0.8985 64.8854 1.0319 0.3858 0.6176
descriptive statistics moments of key variables
20
hypothesis test I hypotheses 1 and 2: separation of sample
all observations for EMGMT_DAC
average monthly ABS(CAR)
T for equality to average
T for equality to averageabove the median
below the median
all observations for EMGMT_SD
average monthly ABS(CAR)
T for equality to average
T for equality to averageabove the median
below the median
all observations for INCSM
average monthly ABS(CAR)
T for equality to average
T for equality to averageabove the median
below the median
21
Panel A: EMGMT_DAC
Sample Statistic τ_0 τ_0_4 τ_0_6
Panel B: EMGMT_SD
Sample Statistic τ_0 τ_0_4 τ_0_6
Panel C: INCSM
Sample Statistic τ_0 τ_0_4 τ_0_6
totalbelow the
medianabove the
median
totalbelow the
medianabove the
median
totalbelow the
medianabove the
median
avg. monthly ABS(CAR)avg. monthly ABS(CAR)
T of H0 ABS(CAR) = total avg.avg. monthly ABS(CAR)
T of H0 ABS(CAR) = total avg.
avg. monthly ABS(CAR)avg. monthly ABS(CAR)
T of H0 ABS(CAR) = total avg.avg. monthly ABS(CAR)
T of H0 ABS(CAR) = total avg.
avg. monthly ABS(CAR)avg. monthly ABS(CAR)
T of H0 ABS(CAR) = total avg.avg. monthly ABS(CAR)
T of H0 ABS(CAR) = total avg.
0.08880.0787
-4.5167***0.1001
3.7467***
0.10250.0847
-4.9234***0.1253
4.0965***
0.08850.1007
4.8748***0.077
-5.803***
τ_0_2
0.06330.0519
-7.721***0.0761
5.6939***
τ_0_2
0.0790.0552
-10.5592***0.1104
6.7129***
τ_0_2
0.06270.0762
6.9412***0.0506
-10.326***
0.05510.0438
-8.9091***0.0678
6.192***
0.07380.0479
-14.5964***0.107
7.5834***
0.05350.066
7.5434***0.0421
-11.9926***
0.04580.0377
-7.6582***0.0548
5.6561***
0.05970.0405
-12.3685***0.0842
7.1775***
0.04470.0539
6.9882***0.0363
-10.1589***
hypothesis test II hypotheses 1 and 2: results
22
hypothesis test III hypothesis 3: separation of sample
observations for EMGMT_DAC if INCSM
is available
Z for: above the median minus below the median
Z for EMGMT_DAC : above the median minus below the median
Z for EMGMT_DAC : above the median minus below the median
INCSM = below the median
INCSM = above the median
observations for EMGMT_SD if INCSM is
available
Z for: above the median minus below the median
Z for EMGMT_SD : above the median minus below the median
Z for EMGMT_SD : above the median minus below the median
INCSM = below the median
INCSM = above the median
23
hypothesis test IV hypothesis 3: results for EMGMT_DAC
Panel A: EMGMT_DAC
Sample Statistic τ_0 τ_0_2 τ_0_4 τ_0_6
total ifINCSM is available
total and INCSM =below the median
total and INCSM =above the
median
0.0215 0.0242 0.0239 0.0171
5.7174*** 9.0167*** 9.9537*** 8.9518***
1.2731 1.4323 1.5199 1.4301
0.0226 0.0289 0.0308 0.022
3.7993*** 6.2943*** 7.4374*** 7372***
1.2729 1.2956 1.4203 1.3951
16.6552***
0.0155 0.0136 0.0115 0.0081
3.3272*** 4.898*** 4.9488*** 4.2926***
1.1866 1.4347 1.3455 1.1731
12.2478***
avg. monthly difference ABS(CAR):above – below EMGMT_DAC
STDEV [ABS(CAR) above] / STDEV [ ABS(CAR) below]
avg. monthly difference ABS(CAR):above – below EMGMT_DAC
STDEV [ABS(CAR) above] / STDEV [ ABS(CAR) below]
Z statistic of average Z-ratio over all event windows
avg. monthly difference ABS(CAR):above – below EMGMT_DAC
STDEV [ABS(CAR) above] / STDEV [ ABS(CAR) below]
Z-ratio of H0: difference = 0
Z-ratio of H0: difference = 0
Z-ratio of H0: difference = 0
Z statistic of average Z-ratio over all event windows
24
hypothesis test V hypothesis 3: results for EMGMT_SD
Panel B: EMGMT_SD
Sample Statistic τ_0 τ_0_2 τ_0_4 τ_0_6
total ifINCSM is available
total and INCSM =below the median
total and INCSM =above the
median
avg. monthly difference ABS(CAR):above – below EMGMT_SD
STDEV ABS(CAR) above] / STDEV [ ABS(CAR) below]
avg. monthly difference ABS(CAR):above – below EMGMT_SD
STDEV ABS(CAR) above] / STDEV [ ABS(CAR) below]
Z statistic of average Z-ratio over all event windows
avg. monthly difference ABS(CAR):above – below EMGMT_SD
STDEV ABS(CAR) above] / STDEV [ ABS(CAR) below]
Z-ratio of H0: difference = 0
Z-ratio of H0: difference = 0
Z-ratio of H0: difference = 0
Z statistic of average Z-ratio over all event windows
0.0406 0.0552 0.0591 0.0436
6.1202*** 10.7159*** 12.5258*** 11.6522***
1.358 1.7773 2.1748 1.9471
0.0455 0.0647 0.0694 0.0506
4.604*** 7.7967*** 9.422*** 8.7567***
1.5433 1.5335 1.8971 1.7487
20.7142***
0.0281 0.0309 0.0339 0.0257
3.3628*** 5.5974*** 6.7725*** 5.9831***
1.0603 1.6231 1.933 1.7487
14.4842***
25
H1: coefficient on > 0 coefficient on < 0
tittii
i
DaDaEXTRZaMOMENTUMABSa
SUEABSaINCSMaEMGMTacCARABS
,54
321 50_50__0
second estimation: hypothesis 3 (only last three rows of next table)
first estimation: hypotheses 1, 2
tittii
i
DaDaEXTRZaMOMENTUMABSaSUEABSa
INCSMEMGMTaINCSMaEMGMTacCARABS
,654
321 50_50_50_50__0
H1: coefficient on < 050_50_ INCSMEMGMT
50_EMGMT50_INCSM
fixed effects panel regressions I hypotheses 1- 3: model specification
26
fixed effects panel regressions II hypotheses 1- 3: estimation results EMGMT_DAC
IndependentVariable
Dependent Variable
Panel A: EMGMT_DAC
ABS(CAR_0)
ABS(CAR_0_2)
ABS(CAR_0_4)
ABS(CAR_0_6)
intercept
EMGMT_DAC_50
INCSM_50
ABS(SUE)
ABS(MOMENTUM)
EXTRZ
adjusted R2
multiplicative effect: high – low earnings management (low income smoothing)
multiplicative effect: high – low earnings management (high income smoothing)
0.0706*** 0.0568*** 0.0531*** 0.0468***
-0.0008 0.0039 0.004 0.0023
-0.0043 -0.0109** -0.0123*** -0.0085*
0.0129*** 0.0041*** 0.0008 -0.0012
0.0375* 0.022*** 0.0186*** 0.0069
0.0071 0.0049 0.0047** 0.0051**
0.2417 0.4218 0.5027 0.4712
0.0042 -0.0016 -0.0076 -0.0046
-0.0031 0.0048 0.0083 0.0049
0.0011 0.0032 0.0006 0.0003
EMGMT_DAC_50 * INCSM_50
27
fixed effects panel regressions III hypotheses 1- 3: estimation results EMGMT_SD
IndependentVariable
Dependent Variable
Panel B: EMGMT_SD
ABS(CAR_0)
ABS(CAR_0_2)
ABS(CAR_0_4)
ABS(CAR_0_6)
intercept
EMGMT_SD_50
INCSM_50
ABS(SUE)
ABS(MOMENTUM)
EXTRZ
adjusted R2
multiplicative effect: high – low earnings management (low income smoothing)
multiplicative effect: high – low earnings management (high income smoothing)
EMGMT_SD_50 * INCSM_50
0.0725*** 0.0867*** 0.0829*** 0.0745***
0.007 -0.01 -0.0006 -0.0045
0.009 -0.0174 -0.0186*** -0.0149***
0.0098*** -0.0016 -0.0035 -0.0047
0.0303 0.0251*** 0.011* -0.0074
0.0028 -0.0054* -0.001 -0.0007
0.1594 0.3646 0.4564 0.4009
-0.0016 0.003 -0.0086 -0.0128
0.0077 -0.0114 0.0034 0.0016
0.0061 -0.0084 -0.0051 -0.0112
28
agenda
theoretical considerations conceptual definitions theoretical motivation research question existing literature
set up of empirical investigation research design variable computation hypotheses to be tested
results descriptive statistics hypothesis tests fixed effects panel regressions
references
29
Ball, Ray, and Philip Brown, 1968, An Empirical Evaluation of Accounting Income Numbers, Journal of Accounting Research, vol. 16, p. 159 – 177.
Bitner, Larry N., and Robert C. Dolan, 1996, Assessing the Relationship between Income Smoothing and the Value of the Firm, Quarterly Journal of Business & Economics, vol. 35, no.1, p. 16 – 35.
Burgstahler, David C., and Michael J. Eames, 1998, Management of earnings and analysts forecasts, University of Washington working paper.
Dechow, Patricia M., and Ilia D. Dichev, 2002, The Quality of Accruals and Earnings: The Role of Accrual Estimation Errors, The Accounting Review, vol. 77, supplement, p. 35 – 59.
Dechow, Patricia M., Richard G. Sloan, and Amy P. Sweeney, 1995, Detecting Earnings Management,, The Accounting Review, vol. 70, no. 2, p. 193 – 225.
references I
30
Detzler, Miranda Lam, and Susan M. Machuga, 2002, Earnings Management Surrounding Top Executive Turnover in Japanese Firms, Review of Pacific Basin Financial Markets and Policies, vol. 5, no. 3, p. 343 – 371.
Fama, Eugene F., and Kenneth R. French, 1992, The Cross-Section of Expected Stock Returns, The Journal of Finance, vol. 47, no. 2, p. 427 – 465.
Fama, E.; French, K.; 1996; Multifactor Explanations of Asset Pricing Anomalies, Journal of Finance, vol. 51, no.1, p. 55.
Francis, Jennifer, Ryan LaFond, Per Olsson, and Katherine Schipper, 2003, Accounting Anomalies and Information Uncertainty, Duke University Fuqua School of Business working paper.
Francis, Jennifer, Ryan LaFond, Per Olsson, and Katherine Schipper, 2004, The Market Pricing of Accruals Quality, Stockholm Institute of Financial Research working paper.
references II
31
Guay, Wayne R., S. P. Kothari, and Ross L. Watts, 1996, A Market-Based Evaluation of Discretionary Accrual Models, Journal of Accounting Research, vol. 34, supplement, p. 83 – 105.
Jegadeesh, Narasimhan, and Sheridan Titman, 1993, Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency, Journal of Finance, vol. 48, no. 1, p. 65 – 91.
Kasanen, Eero, Juha Kinnunen, and Jyrki Niskanen, 1996, Dividend-based earnings management: Empirical evidence from Finland, Journal of Accounting and Economics, vol. 22, p. 283 – 312.
Lang, Mark, Jana Smith Raedy, and Michelle Higgins Yetman, 2003, How Representative Are Firms That Are Cross-Listed in the United States? An Analysis of Accounting Quality, Journal of Accounting Research, vol. 41, no. 2, p. 363 – 386.
references III
32
Lim, Steve C., and Steven Lustgarten, 2002, Testing for Income Smoothing Using the Backing Out Method: A Review of Specification Issues, Review of Quantitative Finance and Accounting, vol. 19, p. 273 – 290.
Marquardt, Carol A., and Christine I. Wiedman, 2004, The Effect of Earnings Management on the Value Relevance of Accounting Information, Journal of Business Finance & Accounting, vol. 31 (3) & (4), p. 297 – 332.
Schipper, Katherine, 1989, Commentary on Earnings Management, Accounting Horizons, vol. 3, issue 4, p. 91 – 102.
Schmidt, Franz, Bilanzpolitik deutscher Aktiengesellschaften, Empirische Analyse des Gewinnglättungsverhaltens, (Gabler, Wiesbaden, 1979).
Subramanyam, K. R., 1996, The pricing of discretionary accruals, Journal of Accounting and Economics, vol. 22, p. 249 – 281.
references IV
33
Teoh, Siew Hong, Ivo Welch, and T. J. Wong, 1998, Earnings Management and the Long-Run Market Performance of Initial Public Offerings, The Journal of Finance, vol. 53, no. 6, p. 1935 – 1974.
Trueman, Brett, and Sheridan Titman, 1988, An Explanation for Accounting Income Smoothing, Journal of Accounting Research, vol. 26, supplement, p. 127 – 143.
Tucker X. Jenny, and Paul Zarowin, 2005, Dose Income Smoothing Improve Earnings Informativeness?, University of Florida, Warrington College of Business and New York University, Stern School of Business Working Paper.
Zarowin, Paul, 2002, Does Income Smoothing Make Stock Prices More Informative?, New York University, Stern School of Business working Paper.
references V
34
Thank you very much for your attention.