Post on 28-Feb-2023
ORI GINAL RESEARCH
Earnings management and long-run stock performancefollowing private equity placements
De-Wai Chou Æ Michael Gombola Æ Feng-Ying Liu
� Springer Science+Business Media, LLC 2009
Abstract We investigate whether the documented earnings management preceding
public equity offerings applies to private placements of equity. We also investigate whe-
ther earnings management can help explain long-run stock performance following private
placements. Our main findings are: (1) little evidence of upward earnings management
around private equity placements, and (2) little predictive power of abnormal accruals for
long-run stock performance following private equity placements. These results suggest that
earnings management is not responsible for post-offering underperformance, if any, for
firms issuing equity privately. Our results are robust to two alternative measures of
earnings management and three measures of abnormal returns estimated over two sample
periods.
Keywords Earnings management � Private equity issues � Long-run performance
JEL Classification G32
1 Introduction
Evidence of earnings management has been documented for a variety of public equity
offerings. It has been shown around IPOs by Teoh et al. (1998a) and DuCharme et al.
(2004), around SEOs by Teoh et al. (1998b), Rangan (1998), and Jo and Kim (2007) and
around reverse LBOs (i.e., second IPOs) by Chou et al. (2006). Furthermore, these studies
D.-W. ChouYuan-Ze University, Taoyuan, Taiwan, ROCe-mail: dwchou@saturn.yzu.edu.tw
M. Gombola (&)Drexel University, Philadelphia, PA, USAe-mail: gombola@drexel.edu
F.-Y. LiuRider University, Lawrenceville, NJ, USAe-mail: liuf@rider.edu
123
Rev Quant Finan AccDOI 10.1007/s11156-009-0129-8
provide evidence of a negative relation between earnings management and post-issue stock
return performance. The evidence leads to a conclusion that earnings management can
explain, at least in part, stock price performance following public equity issuance.
Unlike IPOs and SEOs, earnings management around private placements of equity
might be more limited. Managers of firms issuing private equity might have limited
opportunity to manage earnings due to limited information asymmetry between informed
private placement investors and managers of firms issuing equity privately. Managers
might also have limited time to manage earnings prior to a private placement when the
private placement is arranged quickly in order to raise unforeseen funds.
Managers of firms issuing equity privately might still practice earnings management,
just like IPOs and SEOs, and choose to compensate informed private placement investors,
who can see through earnings management, with substantial discounts of the market price.1
Outside investors, who cannot detect earnings management, could be deceived by the
private placement announcement. When earnings management is later reversed, these
outside investors could be disappointed by the lower-than-expected earnings, leading to
poor long-run stock price performance. This view is consistent with the investor overop-
timism hypothesis presented by Teoh et al. (1998a) for public offerings and applied to
private placements by Hertzel et al. (2002).
Teoh et al. (1998a, b) argue that earnings management could be used as a device to
heighten investor optimism about the performance and future prospects of issuing firms. If
investor optimism is heightened in the presence of earnings management, then we should
expect a positive relation between the extent of earnings management at the private
placement and the magnitude of post-offering underperformance following private equity
placements. Hertzel et al. (2002) report underperformance for their sample of private
equity placements during the period between1980 and 1996.
The objective of this study is to investigate whether managers of firms issuing private
equity manage earnings upward and whether the earnings management explanation for
long-run stock performance of public issues also holds for private issues. We measure
earnings management by two proxies, discretional current accruals (DCA) estimated by the
modified Jones (1991) model and DCA estimated based on the performance-matched
approach espoused by Kothari et al. (2005). We do not find significant evidence of earnings
management for our overall sample of equity private placements from either measure of
DCA.
We find that median and mean DCA of the issue year are positive, but not statistically
significant for either earnings management proxy. The median (mean) estimate of DCA
from the modified Jones (1991) model is 0.05% (2.94%) of total assets, which is smaller
than the median (mean) DCA of 4.01% (9.95%) reported by Teoh et al. (1998a) for IPOs.
DCA estimates from the performance-matched approach are larger, but are still not sta-
tistically significant. Our findings are consistent with the view that managers of firms
issuing equity privately generally have limited opportunity or incentive to manage earnings
upwards around the time of the private issue.
Although the overall sample does not provide evidence of significant earnings man-
agement, it remains possible that long-run stock performance following private placements
is cross-sectionally related to earnings management. To examine the cross-sectional
relation between earnings management and post-issue stock returns, we first stratify our
sample into quartiles based on the magnitude of discretionary current accruals (DCA) in
1 The average private discounts reported by Hertzel and Smith (1993) and Hertzel et al. (2002) are 20.1%and 16.5%, respectively. This compares to 3.0% for SEO discounts reported in Mola and Loughran (2004).
D.-W. Chou et al.
123
the issue year, ranging from the most ‘‘aggressive’’ quartile (i.e., with the largest discre-
tionary current accruals) to the most ‘‘conservative’’ quartile (i.e., with the smallest DCA).
To control for size effects, we construct DCA quartiles that are composed of firms with
similar firm size.
Our preliminary results show that firms in the more aggressive earnings management
quartiles (with higher DCA) experience greater post-offering underperformance. Our
further results show that after controlling for firm size across DCA quartiles, evidence of
greater underperformance for more aggressive earnings management quartiles disappears.
Such evidence points to the importance of controlling for firm size when examining stock
price effect of private placements.
Further examination of the relation between earnings management and long-term stock
performance following private placements is performed by cross-sectional regressions of
3-year stock returns using DCA as an independent variable while controlling for firm size
and book-to-market. To avoid potential problems from overlapping multi-year stock
returns, we also conduct rolling regressions of monthly stock returns following a procedure
developed by Fama and MacBeth (1973). Neither of these regression models provides
evidence that DCA is a significant factor explaining post-issue stock returns for the overall
sample. The coefficient for the DCA variable is negative, but never statistically significant.
We also compare stock performance following private placements of equity for our
sample period between 1980 and 2000 with that during the shorter 1980–1996 period used
by Hertzel et al. (2002). We find that the two sample periods differ considerably in the
degree of underperformance. Consistent with Hertzel et al. (2002), our results for the
1980–1996 period show considerable evidence of underperformance following private
placements, according to any of the three abnormal return measures employed, buy-and-
hold abnormal returns, abnormal returns relative to the Fama and French (1993) three-
factor model and the four-factor model.
However, over the longer period from 1980 to 2000, evidence of underperformance is
limited to buy-and-hold abnormal returns. Abnormal returns estimated from the three-
factor and four-factor models do not provide significant evidence of underperformance.
Although stock performance following private placements differs across the two time
periods, our results provide no evidence of a cross-sectional relation between stock price
performance and discretionary accruals, regardless of the time period.
Our results showing that underperformance following private placements is not related
to earnings managements offer another example of the uniqueness of private equity
placements. Earlier, Hertzel and Smith (1993) and Wruck (1989) demonstrate a positive
announcement effect that is opposite of the negative announcement effect shown for public
equity offerings. Similarly, Goh et al. (1999) show upward earnings forecast revisions for
private placements that are opposite to those for public equity offerings. The uniqueness of
private placements might imply that explanations for post-offering underperformance
following public equity offering might not transfer to private equity placements. Instead,
researchers might need to look at the unique characteristics of companies that offer equity
privately in order to explain their post-offering stock performance.
2 Earnings management and private placements of equity
In contrast to public equity issues, which are underwritten, registered with the Securities
Exchange Commission (SEC) and sold to a large number of investors, private placements of
equity are not necessarily registered with the SEC and typically negotiated individually with
Earnings management and long-run stock performance
123
a limited number of prospective investors. These investors can be sophisticated institutional
investors or accredited individual investors, who expend considerable effort in researching
the earnings power and prospects of firms. Their research could include private information
not available to outside investors. Therefore, information asymmetry between investors and
managers of firms issuing equity privately is more limited than for public offerings.
Private placements also differ from public offerings in their speed and flexibility. A
private placement can be arranged quickly in order to raise unforeseen required funds and
might not allow managers sufficient time to manage earnings prior to the offering. Personal
negotiations between buyer and seller also allow tailoring the placement to meet the
specific needs of the buyer and seller. Brophy et al. (2004) point out that a substantial
proportion of equity private placements in their sample are structured so that the ultimate
offer price is determined after the offering is announced. For structured offerings, pre-
offering earnings management could result in a lower offering price.
In the information-signaling model presented by Hertzel and Smith (1993), a signal of
undervaluation is conveyed by the purchase and commitment by private placement
investors together with the managerial choice of issuing equity privately. If private
placement of equity reflects managerial choice of issuing equity rather than forgoing a
profitable investment, it rules out the possibility that managers take advantage of investor
optimism at the time of the issue. Even if accredited individual investors participate in the
placement, the presence of sophisticated institutional investors will mitigate the problem of
information asymmetry.
Furthermore, one class of accredited individual investors includes officers and directors
of the issuing firm, for whom there exists virtually no information asymmetry. Therefore, a
managerial attempt to mislead the sophisticated investors participating in the private
placement through earnings management is unlikely in the Hertzel and Smith (1993)
information signaling model.
Wruck (1989) argues that more concentrated ownership resulting from private place-
ments of equity would lead to monitoring and incentive alignment effects due to the
involvement of private placement investors, who are expected to provide monitoring and
expert advice in exchange for substantial discounts from current market value. The
monitoring and alignment effects could increase efficiency and future performance. Since
the purchasers are quasi-management insiders, they would have no incentive to mislead
themselves with earnings management.
A view opposite to the implication of the monitoring hypothesis presented by Wruck is
expressed by Barclay, Holderness and Sheehan (2004). They posit that private placements
are often arranged between managers and passive or friendly investors who do not generate
conflicts with managers. The investors are compensated for their passivity through the
discounts offered in the private placement. Rather than offering monitoring and discipline,
the passive investors help to entrench managers. Therefore, earnings management could be
known to managers and passive investors, but not to uninformed external investors.
Investor optimism prior to an offering followed by post-offering disappointment is
described by Teoh et al. (1998a) in their discussion of the effects of earnings management.
Earnings management involves ‘‘borrowing’’ earnings from future periods to show better
current earnings. With higher current earnings shown due to earnings management,
investors become more optimistic about the future prospects of the firm. However, when
the borrowed earnings result in lower future earnings, investors become disappointed.
Teoh and Wong (2002) find that, following IPOs and SEOs, not only can investors be
misled by earnings management but so can analysts. Analyst forecasts of earnings are
based on extrapolations of reported earnings that are managed upwards by high levels of
D.-W. Chou et al.
123
accruals. When these accruals are reversed after IPOs and SEOs, analysts are forced to
revise downward their earnings forecasts. Teoh and Wong find that accounting accruals
can predict analyst forecast errors and that the forecast errors predicted from accruals are
significantly associated with post-issue stock performance for IPOs and SEOs. Their
findings of analyst credulity for earnings management indicates that trained and well-
informed analysts can be misled by earnings management practices. If analysts could be
misled by earnings management then perhaps other well-informed investors, such as
investors in private placements, could also be misled by earnings management practices.
Jo and Kim (2007) show that earnings management is inversely related to disclosure
frequency. Firms with greater disclosure frequency underperform to a lesser extent after an
SEO. Firms that disclose infrequently, but then increase disclosure frequency immediately
before an offering also manage earnings aggressively before the offering. Such behavior is
consistent with an attempt to publicize the stock in order to generate investor optimism and
a higher stock price.
The operating performance and firm size information presented by Hertzel et al. (2002)
suggests that firms in their sample are in desperate need of operating funds and are
dangerously close to NASDAQ NMS de-listing standards. Under these circumstances
issuing firms could be desperate to obtain external capital, and are willing to accept ‘‘last
resort’’ financing in a private placement. Since numerous examples of accounting fraud
have been shown by firms desperate to maintain their current stock prices, the managers of
firms issuing private placements also have ample motivation (or desperation) to engage in
earnings management to maintain the stock price and obtain badly-needed external capital.
3 Sample and methodology
3.1 The sample
Our initial sample of private placements of equity was identified by a keyword search of
Dow Jones News Service for articles published in Dow Jones Newswires from 1980 to
2000. This keyword search identified more than 1,200 articles that were individually read
in order to determine the initial sample. Our sample includes only private placements of
common stock or common stock with warrants. We exclude private placements of common
stock that contain other types of securities such as debt securities or convertible preferred
stock since the focus of our study is on private placements of equity. The initial sample
includes 782 private placements of equity.
We keep only the first of multiple private placements by the same firm within a 3-year
period to avoid overlapping return calculations for the same firm. This exclusion reduces
the sample by exactly 100 private placements. The further exclusion of firms without
available data in the CRSP monthly returns file reduces the sample size to 562 placements.
The sample is further reduced to 289 private placements of equity issued by firms with
available Compustat data for computation of discretionary current accruals, our proxy for
earnings management. We use the sample of 289 private placements for earnings man-
agement analyses.2
2 In our final sample of 289 firms, 286 firms have complete return data for the 36 months following theplacement. For the three exceptions, one firm has available data for 35 months with the last month missing,one firm has available data for 30 months with the last 6 months missing, and one firm has available9 months of data with the remaining 27 months missing.
Earnings management and long-run stock performance
123
In Table 1, Panel A reports the time distribution of the sample with available CRSP data
and the sample with available CRSP and Compustat data. The distribution of the two
samples is very similar across the sample period. Private placements are clustered some-
what in time with a large number of placements in 1993 and again in 2000. The largest
number of private placements in a single year is observed for 2000, when stock prices were
at high levels. The high level of private placement activity during 2000 provides moti-
vation to extend the sample period beyond the 1980–1996 sample period used by Hertzel
et al. in order to consider the offerings after that period.
Consistent with the tendency of firms issuing private placements to be small in size,
firms traded on the NYSE or AMEX comprise 15% of either the 1980–1996 or 1980–2000
sample, and 85% of either sample is made up of NASDAQ-traded firms. The proportion of
Table 1 Time distribution and firm characteristics of private placements of equity sample from 1980 to2000
Panel A: Time distribution of private equity placements sample
Fiscal year The sample with available CRSP data The sample with available CRSP and compustat data
Frequency % Frequency %
1980 8 1.42 4 1.38
1981 7 1.25 1 0.35
1982 11 1.96 4 1.38
1983 19 3.38 7 2.42
1984 12 2.14 4 1.38
1985 33 5.87 9 3.11
1986 27 4.80 12 4.15
1987 28 4.98 14 4.84
1988 29 5.16 14 4.84
1989 29 5.16 20 6.92
1990 20 3.56 8 2.77
1991 28 4.98 9 3.11
1992 38 6.76 15 5.19
1993 54 9.61 30 10.38
1994 29 5.16 15 5.19
1995 33 5.87 19 6.57
1996 26 4.63 17 5.88
1997 28 4.98 18 6.23
1998 25 4.45 16 5.54
1999 21 3.74 15 5.19
2000 57 10.14 38 13.15
Total 562 100.00 289 100.00
Panel B: Summary statistics of firm characteristics for the sample of private placements of equity
Market value of equity ($million) Book-to-market Total assets ($million)
Mean 156.57 0.31 77.77
Median 41.71 0.24 18.30
D.-W. Chou et al.
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NASDAQ companies (85%) in both samples is slightly higher than the proportion (79%) in
the sample used by Hertzel et al. (2002).
Panel B of Table 1 reports firm characteristics of our sample. Both our sample and the
sample used by Hertzel et al. are skewed toward small growth firms with low book-to-
market ratios.3 The mean (median) book-to-market ratio is 0.31 (0.24). To the extent that a
book-to-market ratio less than one indicate growth opportunities, the book-to-market
values indicate that our sample is tilted toward growth firms. The mean market value of
equity for firms in our sample is $156.5 million. The median market value of equity for our
sample is $41.7 million, which is below the current minimum of $50 million necessary to
maintain listing in the NASDAQ National Market System.4 Given the extreme small size
of firms offering private placements, control for firm size is essential in analyzing
performance.
3.2 Measuring abnormal accruals
If earnings management is employed to increase earnings, the increase can be accom-
plished through accelerating recognition of revenues or delaying recognition of expenses.5
Differences between revenues recognized and cash received or between expenses recog-
nized and cash expenditures create accruals or deferrals. Since the basis of earnings
management lies in the difference between cash flows and earnings, analyzing accruals,
which is the difference between cash flows and earnings, provides insight into earnings
management practices. Because short-term accruals are more easily subject to manage-
ment, the focus of our study, like that of studies such as Teoh et al. (1998a, b), is on short-
term accruals.
Computation of accruals in our study is based on definitions of accruals by Perry and
Williams (1994) that are also used by Teoh et al. (1998a, b).6 Perry and Williams (1994)
compute total accruals as the change in non-cash working capital (excluding current
maturities of long-term debt less total depreciation expense for the current period). Their
definition is similar to Jones (1991); differing by the exclusion of adjustment for income
taxes. Perry and Williams (1994) include income tax in their model because the income tax
accrual could be an important component of an earnings management strategy.
Earnings management is revealed in an abnormal level of accruals relative to the firm’s
business activity. A regression model is used to estimate the expected accruals. Deviations
from expected accruals could be attributed to earning management. Teoh et al. (1998a, b)
call these deviations DCA.
Expected accruals, which can also be called nondiscretionary current accruals, are
estimated from a cross-sectional regression of current accruals in a given year on the
change in sales. This regression uses an estimation sample that includes all firms with
the same two-digit SIC code as the private equity issuer, but exclude the issuer and
3 The industry distribution of our sample, not reported, is very similar to that reported by Hertzel et al.(2002), with the top nine SIC/industry codes for our sample firms the same as theirs.4 In reviewing the FACTIVA issuance reports, the statement that the offering allowed the firm to maintainNMS listing was observed frequently.5 Earnings management can also be accomplished through changes in accounting methods, and changes incapital structure such as debt defeasance and debt-equity swaps.6 Teoh et al. (1998a) provide a detailed description of the definition and construction of accrual measures intheir study. The description includes the specific Compustat items used to construct accrual measures. Wefollow their description and definition in the construction of our accrual estimates.
Earnings management and long-run stock performance
123
other private equity issuers. In addition to the SIC filter, non-ordinary common stocks,
such as ADRs, closed-end funds and REITs, are removed from the estimation sample.
To reduce heteroskedasticity in the data, we scale all variables in the regression by
total assets.
We also use an alternate DCA measure proposed by Kothari et al. (2005). Kothari et al.
argue that the traditional discretionary accrual models, such as the modified Jones (1991)
model, might be mis-specified when applied to samples of firms with extreme perfor-
mance. The traditional models over-estimate accruals for firms with extreme good per-
formance and under-estimate accruals for firms with poor performance. To mitigate the
problem of estimating accruals for firms in performance extremes, Kothari et al. propose a
performance-matched approach to estimating abnormal accruals.7
Following Kothari et al. (2005), for each sample firm we identify a matched firm with
the closest ROA of the issue year and the same two-digit SIC. Then, we estimate DCA
following the method of Teoh et al. (1998a) described above for each of the sample firms
and matched firms. Abnormal earnings management is defined as the difference between
the DCA of the sample firm and the DCA of its matched firm.
3.3 Measuring long-run stock price performance
Measuring long-run stock returns remains a controversial topic. Fama (1998) indicates that
long-term returns are sensitive to the expected return model used to measure the abnormal
returns and the statistical tests conducted. We use three methods: (1) buy-and-hold
abnormal return (BHAR) method, (2) the three-factor model of Fama and French (1993),
and (3) a four-factor model, which includes the Fama–French three factors and a
momentum factor used by Carhart (1997).
Ritter (1991) points out that the BHAR method provides an appropriate description of
the return experience for investors, since investors do not rebalance their portfolios on a
monthly basis, as implied by the Fama and French (1993) approach. Instead, they hold on
to their shares for a longer time period. Longer holding periods are particularly appropriate
for investors in private placements since these investors may not be able to re-sell their
shares quickly after the offering, but can only sell to other qualified investors or wait until
the shares become registered with SEC for public trading.
Among others, Fama (1998) points out that the BHAR method can be problematic
because the long-term buy-and-hold returns distribution is skewed. To address the skew-
ness problem, we test statistical significance of the buy-and-hold abnormal return by using
a skewness-adjusted t-statistic, which is derived by Hall (1992) and similar to the proce-
dure described in Lyon et al. (1999), in addition to the conventional cross-sectional
t-statistic. Mitchell and Stafford (2000) show that the cross-dependence problem of
overlapping event-firm stock returns can inflate t-statistics of BHAR. To address the cross-
sectional dependence problem, we use the monthly calendar-time portfolio approach,
recommended by Fama (1998) and Mitchell and Stafford (2000), to estimate both the
three-factor model and the four-factor model.
7 They find that matching on the firm’s return on assets (ROA) tends to be better than matching on othervariables.
D.-W. Chou et al.
123
3.3.1 Buy-and-hold return method
We estimate buy-and-hold abnormal returns relative to a benchmark as follows:
BHARi ¼YT
t¼1
ð1þ Ri;tÞ �YT
t¼1
ð1þ Rbenchmark;tÞ ð1Þ
where Ri,t is the monthly return for firm i in month t, and Rbenchmark,t is the monthly return
for the benchmark (i.e., the control firm) in month t. We calculate buy-and-hold returns for
the 3-year period beginning the month following the issue month, or until either the sample
or control firm delists, whichever is sooner.
In calculating buy-and-hold abnormal returns, we use a size-and-book-to-market-mat-
ched sample as the benchmark. The size and book-to-market control sample approach
provides an appropriate benchmark for two reasons. First, our sample consists of firms that
are relatively smaller in size, with 85% of the sample firms traded on the NASDAQ, as
would be expected in a private placement sample. Secondly, small firms and firms with low
book-to-market ratios tend to produce lower stock returns (e.g., Fama and French 1992,
Barber and Lyon 1997).
We follow the procedure suggested by Lyon et al. (1999) in constructing the size and
book-to-market matched sample. We first identify all firms in the CRSP database with a
market value of equity between 70% and 130% of the market value of equity of a sample
firm. Then, from this set of firms, we choose the firm with the book-to-market closest to
that of the sample firm. Firm size is defined as the total market value of equity of the firm
(i.e., closing stock price multiplied by the number of shares outstanding) measured on the
first day of the issue month. Book-to-market is defined as the firm’s book value of equity
divided by its market value of equity, measured at the fiscal year end prior to the equity
issue. To avoid a small matched sample, if the book-to-market ratio is not available for the
issuing firm in Compustat, we match only by size using CRSP data.
3.3.2 The calendar time three-factor model and four-factor model
We estimate abnormal stock returns based on the three-factor model of Fama and French
(1993) using calendar-time portfolio approach. Portfolios of private placements are formed
monthly, in calendar time. The regression model is:
Rp;t � Rft ¼ ap þ bp Rmt � Rftð Þ þ sp SMBt þ hp HMLt þ ep;t; ð2Þ
where Rp,t is the return on portfolio p in month t, Rft is the return on one-month treasury
bills in month t, Rmt is the return on a market index in month t, SMBt is the difference in the
returns of a portfolio of small and big stocks in month t, and HMLt is the difference in the
returns of a portfolio of high book-to-market stocks and low book-to-market stocks in
month t, and ep,t is the error term for portfolio p in month t. The estimate of the intercept
coefficient (ap) provides a test of the null hypothesis of zero average abnormal return.
We also estimate abnormal stock returns based on a four-factor model, which includes
the three factors of the Fama and French (1993) model and a momentum factor used by
Carhart (1997), using calendar-time portfolio approach. Portfolios of private placements
are formed monthly, in calendar time. The regression model is:
Rp;t � Rft ¼ ap þ bpðRmt � RftÞ þ sp SMBt þ hp HMLt þ pp PR1YRt þ ep;t ð3Þ
Earnings management and long-run stock performance
123
where Rp,t, Rft, Rmt, SMBt, and HMLt are defined as in Eq. 2, PR1YRt is the difference in
the returns of a portfolio of prior-year high return stocks and prior-year low return stocks in
month t, and ep,t is the error term for portfolio p in month t. The intercept coefficient (ap)
provides a test of the null hypothesis of no abnormal performance.
4 Results
4.1 Discretionary current accruals around private placements of equity
Table 2 reports DCAs estimated by the modified Jones (1991) model and by the Kothari
et al. (2005) performance-matched method for the 5 years surrounding private placements
of equity in our sample in Panel A. Summary statistics of firm characteristics for DCA
quartiles and size-equivalent DCA quartiles are presented in Panels B and C, respectively.
As shown in Panel A, the mean DCA for the issue year is 2.94% of total assets, which is not
statistically significant (p-value = 0.14). The mean DCA for our sample is about one-fourth
of the mean DCA of 9.95% for IPOs reported by Teoh et al. (1998a). The median DCA for the
placement year is 0.05% of total assets, which is positive but not statistically significant
(p-value = 0.51). In contrast, the median DCA for the issue year of IPOs reported by Teoh
et al. (1998a) is 4.01% of total assets. None of the other years surrounding the placement
shows a significant, positive mean or median value for DCA. The only statistically significant
DCA value is negative, and occurs for the second year after the offering.
Performance-matched estimates of discretionary current accruals are larger than mod-
ified Jones (1991) model estimates, but are still not statistically significant. The median
performance-matched DCA are several times larger than the modified Jones model DCA,
but are not significant. The mean performance-matched DCA are approximately the same
size as the modified Jones DCA and do not approach statistical significance. None of the
years before or after the offering shows a significant mean or median performance-matched
DCA, either positive or negative.8
Although Panel A of Table 2 shows no statistically significant evidence of earnings
management for our full sample, the positive sign suggests some further investigation
whether a few firms might still practice earnings management. We follow Teoh et al.
(1998a) in classifying firms in quartiles according to issue-year DCA and then examine
whether the subset of firms practicing more aggressive earnings management suffers
greater post-offering underperformance.
As shown in Panel B, the distribution of DCA across quartiles is relatively symmetrical
around zero, with two quartiles displaying positive mean and median DCA and two
quartiles displaying negative mean and median DCA. The most aggressive quartile has
mean DCA of 35.03% of total assets, median DCA of 18.24% and a minimum DCA of
8.04%. Although median and mean DCA are large for the most aggressive quartile, they
are still much smaller than corresponding DCA values for the most aggressive quartile of
IPO firms. Teoh et al. (1998a) show the most aggressive DCA quartile of IPO firms has a
mean DCA of 53.92%, a median of 39.76% and a minimum DCA of 18.5%.
Results in Panel B show that there is no specific pattern for either firm size or book-to-
market across DCA quartiles. Median and mean values of firm size are smallest for
Quartile 1 and largest for Quartile 3. Median and mean book-to-market are largest for
8 Remaining analysis employs the DCA estimates from the modified Jones (1991) model. Analysis usingthe performance-matched DCA estimates provides similar results.
D.-W. Chou et al.
123
Quartile 2, and smallest for Quartile 4. Although there is no specific pattern in firm size
across DCA quartiles, the results indicate that firms in the most aggressive earnings
management quartile (i.e., Quartile 1) are smallest in firm size. Small firms might afford a
higher level of information asymmetry, enabling managers the opportunity to practice
earnings management.9
Table 2 Discretionary current accruals (DCA) for private placements of equity and quartiles from 1980–2000
Panel A: Time-series distribution of discretionary current accruals
Method DCA Year
-2 -1 0 1 2
Modified Jones Median 0.0038 0.0010 0.0005 0.0024 -0.0026
p-value (Wilcoxon) (0.53) (0.78) (0.51) (0.92) (0.03)
Mean 0.0382 -0.0336 0.0294 0.0060 -0.0064
p-value (t-test) (0.33) (0.29) (0.14) (0.66) (0.74)
Performance-matched Median 0.0128 -0.0009 0.0217 0.0198 -0.0075
p-value (Wilcoxon) (0.59) (0.85) (0.15) (0.14) (0.34)
Mean 0.0004 -0.0085 0.0268 0.0044 -0.0299
p-value (t-test) (0.98) (0.75) (0.20) (0.83) (0.19)
Panel B: Summary statistics of firm characteristics in issue year for DCA quartiles
Discretionary currentaccruals (DCA)
Market value Book-to-market
Mean Median Mean Median Mean Median
Most aggressive quartile (DCA C 0.0804) 0.3503 0.1824 87.5 24.5 0.32 0.18
Quartile 2 (0.0005 B DCA \ 0.0804) 0.0374 0.0372 89.9 32.9 0.39 0.30
Quartile 3 (-0.0709 B DCA \ 0.0005) -0.0300 -0.0270 157.6 56.2 0.33 0.24
Most conservative quartile (DCA \ -0.0709) -0.2400 -0.1671 121.1 28.6 0.20 0.14
Panel C: Summary statistics of firm characteristics in issue year for size-equivalent DCA quartiles
Discretionary current accruals (DCA) Market value Book-to-market
Mean Median Mean Median Mean Median
Most aggressive quartile 0.3054 0.1459 246.2 41.7 0.31 0.20
Quartile 2 0.0458 0.0304 117.4 41.8 0.40 0.26
Quartile 3 -0.0310 -0.0182 123.6 41.6 0.29 0.21
Most conservative quartile -0.2064 -0.1251 132.2 41.2 0.24 0.15
Note: Panel A reports the time series distribution of discretionary current accruals (DCA) from year -2 to year2 relative to the issue year (year 0). We follow the methodology in Teoh et al. (1998a) to estimate discretionarycurrent accruals. The t-test is used for testing the mean of DCA and the Wilcoxon signed rank test is used fortesting the median DCA. The benchmark firms used to estimate expected DCA are matched to sample firms by2-digit SIC code. p-values appear in parentheses. Panel B reports summary statistics for DCA of the issue year(year 0), market value and book-to-market for DCA quartiles. Panel C reports summary statistics for DCA ofthe issue year (year 0), market value and book-to-market for size-equivalent DCA quartiles
9 For their IPO sample, Teoh et al. (1998a) also find that firms in the most aggressive DCA quartile aresmallest in firm size.
Earnings management and long-run stock performance
123
Our results shown in Panel B indicate a potential size effect for earnings management in
firms offering private placements, with higher DCA for smaller firms. To minimize the
effect of firm size on the relation between DCA and stock performance, we construct
portfolios of firms similar in size but differing in DCA following the portfolio construction
procedure described by Teoh et al. (1998a). First, we rank our sample firms by market
capitalization on the issue day. Then, taking each contiguous set of four firms, we place the
firm with the highest DCA into the first (most aggressive) portfolio, the firm with the next
highest DCA in the second portfolio, the third highest DCA in the third portfolio and the
firm with the lowest DCA into the fourth (most conservative) portfolio. This procedure
ensures that the size effect is minimized and only differences in DCA remain across the
four portfolios.
As shown in Panel C, the most aggressive DCA quartile has a mean DCA of 0.3054 and
median DCA of 0.1459, which amount to 30% and 15%, respectively, of the offering firm’s
total assets and much more than the offering firm’s earnings. By construction, the median
market value of equity is very similar across all four DCA quartiles, ranging from a
minimum median value of $41.2 million to a maximum of $41.7 million. The book-to-
market is also fairly consistent across these DCA quartiles with the mean ranging from
0.24 for the most conservative quartile to 0.40 for the second most aggressive quartile and
the median ranging from 0.15 for the most conservative quartile to 0.26 for the second
most aggressive quartile. The most aggressive quartile and quartile 3 are almost equal in
the mean and median of book-to-market.
4.2 Long-term stock performance following private placements of equity
Table 3 contains long-term returns following private placements of equity, with 3-year
buy-and-hold abnormal returns (BHAR) presented in Panel A, monthly abnormal returns
estimated from the three-factor model of Fama and French (1993) in Panel B, and monthly
abnormal returns estimated from the four-factor model in Panel C. Each panel contains
estimates of abnormal returns for the sample of private equity placements in the 1980–
1996 period, and estimates for the sample in the longer 1980–2000 period.
As shown in Panel A, BHARs relative to the book-to-market matched sample provide
evidence of underperformance following a private placement for both the 1980–2000 and
1980–1996 periods. Three-year mean and median BHAR are negative and significant,
regardless of the time periods tested and the test statistics used. Also, the degree of
underperformance shown for the 1980–1996 period is comparable to that shown by Hertzel
et al. (2002) for the same sample period.
Panel B reports the alpha coefficient estimated from the three-factor model of Fama and
French (1993). For the 1980–1996 period, the alpha coefficient is negative and statistically
significant, which is consistent with the finding by Hertzel et al. (2002). The alpha coef-
ficients of -0.0085 for value-weighted portfolios and -0.0084 for equally weighted
portfolios are both significant at the 0.01 level (t = -3.14 and -2.76, respectively). The
alpha coefficients, which measure monthly abnormal returns, are equivalent of an implied
3-year abnormal returns of approximately -26% (=((1 - 0.0085)36 - 1) 9 100).10
10 The implied three-year abnormal return is calculated as (1 ? a)36 - 1.0, where alpha measures themonthly abnormal return.
D.-W. Chou et al.
123
The evidence of underperformance shown for the 1980–1996 period, however, does not
carry over to the longer 1980–2000 period. The alpha coefficients remain negative, but do
not approach statistical significance (t = -0.79 and -1.38). This finding might be due to
the superior stock performance of small growth firms during the last few years of the
1990s, when small firms outperformed large growth firms. During most of the 1990s, large
growth firms outperformed small firms.
As show in Panel C, the alpha coefficient estimated from the four-factor model also
produces similar evidence of underperformance for the 1980–1996 period, but not for the
1980–2000 period. The alpha coefficient is negative and significant at the 0.01 level
(t = -2.67 and -2.21). For the 1980–2000 period, the alpha coefficient remains negative,
but not statistically significant (t = -0.88 and -0.33). Similar to the evidence reported in
Panel B from the three-factor model, these results indicate that underperformance of
private equity placements might not be robust to different sample periods.
Table 3 Post-issue 3-year buy-and-hold abnormal returns and average monthly abnormal returns relative tocalendar-time three-factor model and four-factor model for the sample of 562 private placements of equity
Panel A: 3-year buy-and-hold abnormal returns (BHAR)
1980–2000 period 1980–1996 period
Mean -20.88% -16.21%
Median -41.22% -37.77%
Cross-sectional t-stat. (-5.05)*** (-3.31)***
Skewness-adjusted t-stat. [-3.53]*** [-2.47]**
Panel B: Calendar time three-factor model
1980–2000 period 1980–1996 period
Value-weighted Equally weighted Value-weighted Equally weighted
Alpha coefficient -0.0026 (-0.79) -0.0047 (-1.38) -0.0085 (-3.14)*** -0.0084 (-2.76)***
Panel C: Calendar time four-factor model
1980–2000 period 1980–1996 period
Value-weighted Equally weighted Value-weighted Equally weighted
Alpha coefficient -0.0029 (-0.88) -0.0011 (-0.33) -0.0074 (-2.67)*** -0.0066 (-2.21)**
Note: The buy-and-hold abnormal return (BHAR) is calculated relative to the book-to-market matchedsample. To test the significance of the mean value of buy-and-hold abnormal returns, we employ both thecross-sectional t-statistic, and the skewness-adjusted t-statistics. The three-factor regression model of Famaand French (1993) is: Rpt - Rft = ap ? bp (Rmt - Rft) ? sp SMBt ? hp HMLt ? ept. The four-factorregression model is: Rpt - Rft = ap ? bp (Rmt - Rft) ? sp SMBt ? hp HMLt ? pp PR1YRt ? ept. Rpt isthe simple return on portfolio p, Rft is the return on one-month Treasury bills, Rmt is the return on a value-weighted market index, SMBt is the difference in the returns of a portfolio of small and big stocks, HMLt isthe difference in the returns of a portfolio of high book-to-market stocks and low book-to-market stocks, andPR1YRt is the difference in the returns of a portfolio of high and low prior year return stocks, and ep,t is theerror term for portfolio i in month t. The statistics are reported in parentheses
*** Statistical significance at the 0.01 level; ** statistical significance at the 0.05 level
Earnings management and long-run stock performance
123
4.3 Post-issue stock performance for DCA quartiles
Table 4 contains post-issue average abnormal returns for DCA quartiles, without con-
trolling for firm size across quartiles. Buy-and-hold abnormal returns (BHARs) relative to
size and book-to-market matched firms are presented in Panel A, monthly average
abnormal returns relative to the three-factor model are presented in Panel B, and monthly
abnormal returns relative to the four-factor model are presented in Panel C.
Panel A shows that firms in the most aggressive DCA quartile (i.e., Quartile 1) have the
worst mean 3-year BHARs, with -24.50% and -21.95% for the 1980–2000 period and the
1980–1996 period, respectively. These BHARs are statistically significant, regardless of
the test statistics used. The negative BHARs for Quartile 1 are followed closely by the
average 3-year BHARs of -21.59% and -20.23% for Quartile 2. These BHARs
are statistically significant, except when the skewness-adjusted t-statistic is used for the
1980–1996 period (t = -1.61). Results for the two more aggressive DCA quartiles
provide evidence of underperformance following private placements of equity.
In contrast, firms in the more conservative DCA quartiles (i.e., Quartile 3 and Quartile
4) do not show significant experience of underperformance. Mean BHAR for these two
quartiles are slightly negative with one case of being positive, but insignificant. Since firms
in the two more aggressive quartiles exhibit BHAR that are negative and significant, but
firms in the two more conservative quartile do not, there is some indication that firms
practicing aggressive earnings management around private placements experience worse
post-offering stock performance than firms that do not.
As shown in Panel B, average abnormal returns relative to the Fama–French three-
factor model show significant underperformance only for firms in the most aggressive
quartile (i.e., the quartile with the highest DCA). For the 1980–2000 period, the estimated
alpha coefficient (i.e., average monthly abnormal return) is -0.0214, which is significant at
the 0.01 level (t = -3.43). This monthly abnormal return implies a 3-year abnormal return
of -54.1%. For the 1980–1996 period, the alpha coefficient of -0.0174 is significant at the
0.05 level (t = -2.53), implying a 3-year abnormal return of -46.8%.
The estimated alpha coefficients shown for Quartiles 2, 3, and 4 are negative, but
not significant for all cases. A monotonic relation between DCA quartile and the
estimated alpha coefficient is not evident in the results shown in Panel B. Conse-
quently, there is not much evidence of a negative relation between DCA and post-issue
stock returns.
Panel C contains estimates of monthly abnormal returns measured relative to the four-
factor model for DCA quartiles. Again, evidence of underperformance is exhibited for
firms in the most aggressive DCA quartile during both periods, but not for the other three
quartiles in either period. Monthly abnormal returns, measured by the alpha coefficient, are
negative and statistically significant for firms in the most aggressive DCA quartile during
both sample periods, but not for the other three quartiles. Again, there is not much of a
pattern of abnormal returns across DCA quartiles, except for significant underperformance
shown by the most aggressive DCA quartile.
Overall, results in Table 4 show some evidence of underperformance for firms that
practice aggressive earnings management around an equity private placement. In the next
section, we will examine whether these results are robust to controlling for firm size in
size-equivalent DCA portfolios.
D.-W. Chou et al.
123
4.4 Post-issue stock performance for size-equivalent DCA quartiles
Table 5 contains post-issue average abnormal returns for size-equivalent DCA quartiles of
289 private placement firms. Buy-and-hold abnormal returns (BHAR) relative to size and
Table 4 Post-issue 3-year buy-and-hold abnormal returns and calendar-time average monthly abnormalreturns relative to the three-factor and four-factor models for the DCA quartiles of 289 private placements ofequity from 1980 to 2000
Sample period Quartile 1 2 3 4
Panel A: 3-year BHAR relative to size and book-to-market matched firms for DCA quartiles
1980–2000 Mean BHAR -24.50% -21.59% -7.70% –8.42%
Cross-sectional t-stat. (-2.92)*** (-2.68)*** (-0.41) (-0.67)
Skewness-adjusted t-stat. [-2.37]** [-2.11]** [-0.39] [-0.64]
N 72 73 72 72
1980–1996 Mean BHAR -21.95% -20.23% 12.27% -6.57%
Cross-sectional t-stat. (-2.08)** (-2.25)** (0.47) (-0.49)
Skewness-adjusted t-stat. [-1.73]* [-1.61] [0.53] [-0.47]
N 52 51 48 51
Panel B: Monthly abnormal returns and 3-year abnormal returns relative to three-factor model for DCA quartiles
1980–2000 Alpha coefficient -0.0214 -0.0024 -0.0081 -0.0019
WLS t-stat. (-3.43)*** (-0.38) (-1.23) (-0.25)
Implied 3-year abnormal returns -54.1% -8.3% -25.4% -6.6%
N 72 73 71 71
1980–1996 Alpha coefficient -0.0174 -0.0052 -0.0067 -0.0088
WLS t-stat. (-2.53)** (-0.89) (-0.85) (-1.29)
Implied 3-year abnormal returns -46.8% -17.1% -21.5% -27.3%
N 52 51 48 50
Panel C: Monthly abnormal returns and implied 3-year abnormal returns relative to the four-factor model for DCAquartiles
1980–2000 Alpha coefficient -0.0152 0.0037 -0.0057 0.0025
WLS t-stat. (-2.44)** (0.57) (-0.85) (0.32)
Implied 3-year abnormal return -42.4% 14.2% -18.6% 9.4%
N 72 73 71 71
1980–1996 Alpha coefficient -0.0136 -0.0069 0.0005 -0.0075
WLS t-stat. (-1.92)* (-1.15) (0.06) (-1.07)
Implied 3-year abnormal return -38.9% -22.1% 1.8% -23.7%
N 52 51 48 50
Note: The buy-and-hold abnormal return (BHAR) is calculated relative to the book-to-market matched sample. Totest the significance of the mean value of buy-and-hold abnormal returns, we employ both the cross-sectionalt-statistic, and the skewness-adjusted t-statistics. The three-factor regression model of Fama and French (1993) is:Rpt - Rft = ap ? bp (Rmt - Rft) ? sp SMBt ? hp HMLt ? ept. The four-factor regression model is:Rpt - Rft = ap ? bp (Rmt - Rft) ? sp SMBt ? hp HMLt ? pp PR1YRt ? ept. Rpt is the simple return on portfoliop, Rft is the return on 1-month treasury bills, Rmt is the return on a value-weighted market index, SMBt is thedifference in the returns of a portfolio of small and big stocks, HMLt is the difference in the returns of a portfolio ofhigh book-to-market stocks and low book-to-market stocks, and PR1YRt is the difference in the returns of aportfolio of high and low prior year return stocks, and ep,t is the error term for portfolio i in month t. The regressioncoefficients reported in the table are estimated using weighted least squares for value-weighted portfolios. Theimplied 3-year abnormal returns are estimated as: (1 ? alpha coefficient)36 - 1
*** Statistical significance at the 0.01 level; ** statistical significance at the 0.05 level; * statistical significance atthe 0.10 level
Earnings management and long-run stock performance
123
book-to-market matched firms are presented in Panel A, monthly average abnormal returns
relative to the three-factor model of Fama and French (1993) are presented in Panel B, and
monthly abnormal returns relative to the four-factor model are presented in Panel C.
Results in Panel A of Table 5 show that, although a consistent pattern of abnormal
returns is shown in Table 4, such a pattern is not evident after controlling for firm size. For
the 1980–2000 period, the most aggressive DCA quartile is the only quartile that exhibits
abnormal negative BHARs significant at the .05 level (t = -1.98). Although less signif-
icant (t = -1.76), the mean abnormal return for the most conservative quartile is slightly
worse, as is the mean abnormal return for the second most conservative quartile. For the
1980–1996 period, the greatest underperformance is not shown by the most aggressive
size-equivalent DCA quartile. Furthermore, the most aggressive size-equivalent portfolio
does not even exhibit underperformance that is significant at any conventional level
(t = -1.59). The second most conservative quartile is the only quartile with abnormal
BHARs that are significant at the .05 level (t = -2.55).
Panel B of Table 5 contains monthly abnormal returns relative to the Fama and French
(1993) three-factor model for size-equivalent DCA quartiles. Again, there is not much
evidence of a monotonic relation between size-adjusted DCA quartile and post-offering
performance. The only quartile exhibiting significant negative abnormal performance over
the 1980–2000 period is the most conservative quartile. This finding is opposite to the
hypothesis that more aggressive DCA quartiles would exhibit greater underperformance
following the offering. For the 1980–1996 period, the second most aggressive
size-equivalent DCA portfolio exhibits significant underperformance at the .10 level
(t = -1.82). None of the other quartiles show significant abnormal returns.
Panel C of Table 5 contains monthly abnormal returns relative to the four-factor
model. Results are very similar to those reported for the three-factor model in Panel B.
Again, the only quartile exhibiting negative and significant abnormal performance over
the 1980–2000 period is the most conservative quartile. The only quartile exhibiting
negative and significant abnormal performance over the 1980–1996 period is the second
most aggressive quartile. If anything, the four-factor model shows a lesser degree of
underperformance for any of the quartiles. In particular, over the 1980–2000 period,
abnormal returns for the most aggressive quartile and the second most conservative
quartile are positive, although not statistically significant. The finding of positive
abnormal returns for two out of four quartiles during the 1980–2000 period raises the
question as to whether stock returns exhibit underperformance at all following private
placements over this sample period.
4.5 Regressions of post-issue 3-year stock performance
To test whether long-term post-issue stock performance can be explained by DCA, we
estimate a cross-sectional regression of the 3-year market-adjusted BHARs with DCA as
an independent variable, and firm size and book-to-market as control variables. The results
of the regression model are presented in Table 6. The coefficient for DCA is negative but
not statistically significant, regardless whether the value-weighted (t = -1.35) or equally
weighted market index (t = -1.18) is used as the benchmark. We do not find evidence of a
negative relation between post-offering long-term stock performance and the magnitude of
DCA, unlike results of previous research for IPOs and SEOs [e.g., Teoh et al. (1998a, b)].
Our results shown in Table 6 do not provide significant evidence in support of a neg-
ative relation between DCA and post-issue long-term stock returns following private
placements of equity after controlling for firm size and book-to-market. These results are
D.-W. Chou et al.
123
similar to those shown in Table 5 where firm size is controlled by using size-equivalent
DCA quartiles.
4.6 Monthly calendar-time regressions: the Fama–Macbeth procedure
To address the issue of potential problems relating to overlapping multi-year returns
employed in the regressions shown in Table 6, we estimate monthly regressions of stock
Table 5 Post-issue 3-year buy-and-hold abnormal returns and calendar-time average monthly abnormalreturns relative to the three-factor and four-factor models for the size-equivalent DCA quartiles of 289private equity placements
Sampleperiod
Quartile 1(most aggressive)
2 3 4
Panel A: 3-year BHAR relative to size and book-to-market matched firms for size-equivalent DCA quartiles
1980–2000 Mean BHAR -19.73% 1.85% -22.61% -21.75%
Skewness-adjustedt-stat.
(-1.98)** (0.10) (-1.76)* (-1.76)*
N 73 72 71 72
1980–1996 Mean BHAR -19.65% 9.8% -28.75% 0.40%
Skewness-adjustedt-stat.
(-1.59) (0.44) (-2.55)** (0.03)
N 51 51 50 50
Panel B: Monthly abnormal returns relative to three-factor model for size-equivalent DCA quartiles
1980–2000 Alpha coefficient -0.0035 -0.0026 -0.0054 -0.0156
WLS t-stat. (-0.45) (-0.46) (-0.78) (-2.52)**
N 73 72 70 72
1980–1996 Alpha coefficient -0.0089 -0.0105 -0.0065 -0.0050
WLS t-stat. (-1.36) (-1.82)* (-0.94) (-0.71)
N 51 51 50 49
Panel C: Monthly abnormal returns relative to the four-factor model for size-equivalent DCA quartiles
1980–2000 Alpha coefficient 0.0040 -0.0015 0.0001 -0.0122
WLS t-stat. (0.05) (-0.26) (0.01) (-1.94)*
N 73 72 70 72
1980–1996 Alpha coefficient -0.0085 -0.0119 -0.0007 -0.0001
WLS t-stat. (-1.25) (-1.98)** (-0.10) (-0.01)
N 51 51 50 49
Note: The buy-and-hold abnormal return (BHAR) is calculated relative to the book-to-market matchedsample. To test the significance of the mean value of buy-and-hold abnormal returns, we employ both thecross-sectional t-statistic, and the skewness-adjusted t-statistics. The three-factor regression model of Famaand French (1993) is: Rpt - Rft = ap ? bp (Rmt - Rft) ? sp SMBt ? hp HMLt ? ept. The four-factorregression model is: Rpt - Rft = ap ? bp (Rmt - Rft) ? sp SMBt ? hp HMLt ? pp PR1YRt ? ept. Rpt isthe simple return on portfolio p, Rft is the return on 1-month treasury bills, Rmt is the return on a value-weighted market index, SMBt is the difference in the returns of a portfolio of small and big stocks, HMLt isthe difference in the returns of a portfolio of high book-to-market stocks and low book-to-market stocks, andPR1YRt is the difference in the returns of a portfolio of high and low prior year return stocks, and ep,t is theerror term for portfolio i in month t. The regression coefficients reported in the table are estimated usingweighted least squares for value-weighted portfolios. The implied 3-year abnormal returns are estimated as:(1 ? alpha coefficient)36 - 1
*** Statistical significance at the 0.01 level; ** statistical significance at the 0.05 level; * statistical sig-nificance at the 0.10 level
Earnings management and long-run stock performance
123
returns following a procedure developed by Fama and MacBeth (1973) and also used by
Teoh et al. (1998a, b). Similar to Teoh et al., an interaction variable is used to capture the
incremental explanatory power for post-issue returns by immediate pre-issue DCA, relative
to DCA effects for other periods. The interaction variable (DCA*Dummy) is constructed
so that the dummy takes a value of one for DCA immediately preceding an issue and zero
otherwise. Since we find no significant evidence of positive DCA around the issue year and
no significant evidence of a negative relation between DCA and 3-year stock returns in an
OLS regression, we expect that significance for this variable is unlikely. Other independent
variables in the regression model are the three independent variables used in the cross-
sectional regression reported in Table 6. These are DCA, the book-to-market ratio, and
firm size, with natural logarithms of the last two variables used in the model.
We estimate three sets of monthly regressions of stock returns for the 3 years following
private placements of equity; the first set for year ?1, the second set for year ?2, and the
third set for year ?3, relative to year 0 (the issue year). Each set of regressions includes
about 240 monthly cross-sectional regressions of stock returns. The first set of regressions
begins in 1982 and ends in 2001; the second set of regressions begins in 1983 and ends in
2002; the third set of regressions begins in 1984 and ends in 2003. For year ?1 monthly
regression, the dummy is set to one for DCA from the preceding year and zero otherwise,
for year ?2 monthly regressions, the dummy is set to one for DCA from 2 years prior and
zero otherwise, and for year ?3 regressions, the dummy is set to one for DCA from 3 years
prior and zero otherwise. We aggregate the coefficients of monthly regressions to calculate
average coefficients for each of the 3 years. The standard t-statistic is used to test sig-
nificance of the average coefficient.
As shown in Table 7, the average coefficient for the DCA variable is negative for all
three sets of regressions, but not statistically significant at the conventional level. The
average coefficient for the DCA variable for the third year following the issue year is
negative and marginally significant (t = -1.59). This result indicates a negative but not
particularly strong relation between DCA and return in non-offering periods.
The average coefficient for the DCA*Dummy variable for the second year following the
issue year is negative and marginally significant (t = -1.60). The coefficient for the
Table 6 Cross-sectional regressions of 3-year post-issue buy-and-hold abnormal returns for the sample of289 private placements of equity from 1980 to 2000
Market index Intercept DCA BK/MK Size F-Value R2
Equally-weighted
0.1960 (0.59) -0.5322 (-1.18) 0.1572 (1.44) -0.0489 (-0.59) 1.33 0.015
Value-weighted
-0.0005 (-0.00) -0.6138 (-1.35) 0.1548 (1.41) -0.0020 (-0.02) 1.16 0.014
Note: The dependent variable is the 3-year buy-and-hold abnormal stock return, adjusted for benchmarkstock returns. Two benchmarks are used, the CRSP equally-weighted and value-weighted indexes. Post-issue stock returns are calculated for the 3-year period, beginning the month following of the issue month.The independent variables include DCA, BK/MK and SIZE. DCA is the discretionary current accruals in theissue year. BK/MK is the natural logarithm of the ratio of book value over market value of equity at thefiscal year of prior to the issue. SIZE is the natural logarithm of the closing price of common stockmultiplied by the number of shares outstanding at the fiscal year end prior to the issue. The t-statistics appearin parentheses
*** Statistical significance at the 0.01 level; ** statistical significance at the 0.05 level; * statistical sig-nificance at the 0.10 level
D.-W. Chou et al.
123
DCA*Dummy variable for the other 2 years, however, is positive and insignificant. A
negative value for the coefficient is consistent with an increase in the relation between
explanatory DCA and return during private placement periods, but a positive value is not.
Overall, the results of the monthly regressions of stock returns shown in Table 7 are
consistent with the results of the regressions of 3-year stock returns shown in Table 6. We
conclude that our results are robust to whether multi-year regressions or calendar time
monthly regressions are used. Unlike IPOs and SEOs, there is no significant evidence of a
general negative relation between DCA and post-issue stock returns for private placements
of equity.
4.7 Summary of results
Our examination of earnings management and its relation to long-term returns utilizes two
alternative measures of DCA and three alternative measures of post-offering performance.
Neither of the alternative estimates of DCA provides significant evidence of earnings
management and neither shows a significant relation to any of three alternative measures of
long-term returns after controlling for firm size. We control for firm size by using size-
matched DCA quartiles and by incorporating firm size as a control variable in an OLS
regression model and as a control variable in a Fama–Macbeth calendar time regression
model. Each of the three approaches to controlling for firm size removes any significant
relation between DCA and long-term returns. Therefore, if investor over-optimism is
responsible for post-offering underperformance following private placements, we are
comfortable in the belief that any investor over-optimism is not due to earnings
management.
The alternative estimates of post-offering returns uniformly show evidence of under-
performance during the 1980–1996 sample period but not during the 1980–2000 sample
period. In the later sample period there is no evidence of underperformance from abnormal
returns estimated from the three factor model or the four-factor model.
Table 7 Time-series averages of monthly cross-sectional regressions of stock returns for the sample of 289private placements of equity
DCA DCA*Dummy Size BK/MK
Year ?1 Average coefficient -0.0180 5.5530 0.0020 0.0018
t-statistic (-0.26) (0.97) (0.47) (0.77)
Year ?2 Average coefficient -0.0627 -5.5702 0.0027 0.0017
t-statistic (-1.02) (-1.60) (1.01) (0.70)
Year ?3 Average coefficient -6.5585 4.1799 0.0030 0.0042
t-statistic (-1.59) (0.64) (0.89) (1.91)*
Note: The dependent variable is monthly stock returns. DCA is the discretionary current accruals in the issueyear. DCA*Dummy is an interaction variable of DCA and a dummy variable that is one when immediatepre-issue DCA is used and zero otherwise. BK/MK is the natural logarithm of the ratio of book value overmarket value of equity at the fiscal year end prior to the issue. SIZE is the natural logarithm of the closingprice of common stock multiplied by the number of shares outstanding at the fiscal year end prior to theissue. The average coefficient is the time-series averages of each month’s cross-sectional coefficient. Thet-statistics are reported in parentheses
*** Statistical significance at the 0.01 level; ** statistical significance at the 0.05 level; * statistical sig-nificance at the 0.10 level
Earnings management and long-run stock performance
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5 Conclusions
We investigate whether, like public equity issues (IPOs and SEOs), earnings management
is prevalent for firms issuing equity privately and whether the earnings management
explanation for poor long-run stock performance of public issues can also hold for private
issues of equity. Unlike public equity issues, we find little evidence of earnings manage-
ment for firms issuing equity privately. This evidence is consistent with the view that the
opportunity for earnings management in private issues might be more limited than for
public issues due to the lesser degree of information asymmetry between managers and
purchasers of private equity issues.
Also unlike public issues of equity, we do not find an overall significant negative
relation between the proxies for earnings management and stock performance following
private placements of equity. After controlling for firm size across earnings management
quartiles, in a regression model, or a Fama–Macbeth calendar time regression model, the
evidence of worse stock price performance for quartiles with greater magnitude of earnings
management disappears. We show that controlling for firm size is a critical issue in
studying the effects of private placements.
Our finding of lesser evidence of underperformance during the 1980–2000 period, as
compared to the earlier 1980–1996 period, could be due to structural change in the market
for private placements, with the greater participation by hedge funds in this market or the
development of more exotic instruments for the private placement market. It could also be
due to changing market leadership after the Internet bubble period of the late 1990s.
During much of the 1990s large growth firms outperformed small firms and value firms.
After the bubble, small firms experienced better performance. In any event, our findings
suggest further investigation of structural changes in the market for private equity place-
ments and effects of those structural changes on post-offering performance.
Our overall findings provide another example of the uniqueness of private equity
placements. Not only does their announcement effect differ from public offerings, but the
absence of earnings management also differs from the documented presence of earnings
management prior to public offerings. Since earnings management does not explain the
post-offering underperformance following private placements, as it does for public
offerings, researchers must look to other characteristics of firms issuing equity privately in
order to provide an explanation.
Acknowledgments The authors wish to thank Mike Hertzel for valuable comments on a previous versionof this manuscript. Any errors remain ours. Feng-Ying Liu acknowledges support of a Davis fellowship fromRider University.
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