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O R I G I N A L R E S E A R C H

Earnings management and long-run stock performance

following private equity placements

De-Wai Chou Æ Michael Gombola Æ Feng-Ying Liu

Published online: 31 May 2009Ó 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, forfirms 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 Á Private equity issues Á 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: [email protected]

M. Gombola (&)Drexel University, Philadelphia, PA, USAe-mail: [email protected]

F.-Y. LiuRider University, Lawrenceville, NJ, USAe-mail: [email protected]

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Rev Quant Finan Acc (2010) 34:225–245DOI 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 limitedopportunity 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 topoor 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 privateequity 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 statisticallysignificant 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 placementsis 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).

226 D.-W. Chou et al.

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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 managementquartiles (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 proceduredeveloped 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 equityplacements. 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 227

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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 placementinvestors 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 privateplacement 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 isexpressed 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 bettercurrent 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


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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 preferredstock 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.


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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 wereat 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 to

2000

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 2.421984 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


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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 abook-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.


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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 betweenthe 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 problematicbecause 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.


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3.3.1 Buy-and-hold return method

We estimate buy-and-hold abnormal returns relative to a benchmark as follows:

BHARi ¼YT

t ¼1ð1 þ Ri;tÞ À

YT

t ¼1ð1 þ 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, aswould 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 À Rft ¼ ap þ bp Rmt À 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 HML t 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 À Rft ¼ ap þ bpð Rmt À RftÞ þ sp SMBt þ hp HMLt þ pp PR1YRt þ ep;t ð3Þ


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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 medianperformance-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 suffersgreater 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.


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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 earningsmanagement 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 0.24Most 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 of the 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.


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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 marketcapitalization 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 aminimum 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.


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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


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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 averageabnormal 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 aggressivequartile (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.


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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 of equity 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 quartiles1980–2000 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 DCA

quartiles

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, SMB t is thedifference in the returns of a portfolio of small and big stocks, HMLt is the difference in the returns of a portfolio of high 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 at

the 0.10 level


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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. Forthe 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-factormodel. 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 privateplacements 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 equallyweighted 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


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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

Sample

period

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 50Panel B: Monthly abnormal returns relative to three-factor model for size-equivalent DCA quartiles


WLS t -stat. (-0.45) (-0.46) (-0.78) (-2.52)**

N 73 72 70 72


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


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-factor

regression 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


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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 zerootherwise. 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 regressionsbegins 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 of 289 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 benchmark stock 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 stock multiplied by the number of shares outstanding at the fiscal year end prior to the issue. The t -statistics appear

in parentheses*** Statistical significance at the 0.01 level; ** statistical significance at the 0.05 level; * statistical sig-nificance at the 0.10 level


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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. Weconclude 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 earningsmanagement 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 issue

year. 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


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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 privateissues 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 earningsmanagement 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 findingssuggest 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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