2002. 18

Earnings Forecast and Earnings Management ofJapanese Initial Public Offerings Firms

Kazuhisa Otogawa

Earnings Forecast and Earnings Management ofJapanese Initial Public Offerings Firms
Kazuhisa Otogawa*August 1, 2002

*Graduate School of Business Administration, Kobe University. I wish to greatly
acknowledge the financial support of Grant-in-Aid for Encouragement of Young
Scientists 13730110 of the Ministry of Education, Culture, Sports, Science, andTechnology in Japan. Correspondence: 2-1, Rokko, Nada, Kobe, 657-8501, JAPAN.
Tel/Fax: +81-78-803-6911. E-mail: otogawa@rose.rokkodai.kobe-u.ac.jp

Earnings Forecast and Earnings Management ofJapanese Initial Public Offerings Firms

Abstracts

I investigated earnings management surrounding initial public offerings (IPOs) using
828 Japanese firms that went public in the over-the-counter market between 1990 and
1999. As the proxy of earnings management, I measure discretionary accruals following
the methodology employed by Kasznik (1999). First, I found evidence that firms whose
managers have overestimated earnings at IPO time have significantly positive
discretionary accruals to mitigate forecast error, but firms whose managers haveunderestimated earnings do not have negative discretionary accruals. Second, I foundthat managers, on average, use significantly positive discretionary accruals to increase
reported earnings in years just after the IPO but not in years before the IPO.

Keywords:

initialpublicofferings;

management

earnings

forecast;

earnings

management; discretionary accruals.
Data Availability: The data used in this study are publicly available from the sourcesindicated in the text.

1

Earnings Forecast and Earnings Management ofJapanese Initial Public Offerings Firms

1. Introduction
The Japan Securities Dealers Association Automated Quotation (JASDAQ) market,
which is the Japanese over-the-counter market, requires a preliminary inquiry of theunderwriter of any initial public offerings (IPOs) firm as well as the filing of many
documents,1 including an application for a new listing, which requires documented profit
planning for the next two fiscal years. The Japan Securities Dealers Association (JSDA),
which is the supervisor of JASDAQ, provides the investors with the Summary of New
Listing Firms, which includes excerpts of the documents that IPO firms submitted.
Consequently, although they are prohibited by security law from including
forward-looking information in the prospectus, all firms that go public in the JASDAQ
market disclose their management’s earnings forecast for the next year.
The first purpose of this study was to investigate whether a manager who issues aninaccurate earnings forecast at the time of IPO reduces the forecast error by managing
reported earnings so that they more closely match the forecasted number. Kasznik (1999)
analyzed discretionary accruals for each of 499 firm-years with management earnings
forecasts issued between 1987 and 1991. Using 828 Japanese firms that went public
between 1990 and 1999, I find the same evidence as that of Kasznik (1999), in that firms
whose managers have overestimated earnings have a significant level of positive
discretionary accruals to mitigate their forecast error, but firms whose managers haveunderestimated earnings do not have negative discretionary accruals to do so.
Teoh, Welch, and Wong (1998b) and Teoh, Wong, and Rao (1998) investigated therelationship between earnings management during the year of IPO and subsequent2

underperformance, while Aharony, Lin, and Loeb (1993), Friedlan (1994), andDuCharme, Malatesta, and Sefcik (2001) investigated earnings management in the
period prior to IPO. The second purpose of the present study was to examine earnings
management during both the pre- and post-IPO periods. I found that managers use
significantly positive discretionary accruals to increase reported earnings in years justafter the IPO but not in the years before the IPO.
The literature has mainly documented earnings management by U.S. IPO firms.
Aharony, Lee, and Wong (2000) are exceptional in that they examined earnings
management in the financial packaging of Chinese state-owned enterprises for public
listing. I examined whether Japanese IPO firms engage in earnings management, in thehopes of enhancing international understanding of earning management around the
IPO.
The remainder of the paper is organized as follows: Section 2 briefly surveys earlier
studies and describes my hypotheses. Section 3 explains sample selection and data.
Section 4 details the proxy of earnings management. Section 5 presents the empiricalresults, and Section 6 concludes the paper.

2. Review of Prior Research and Research Hypotheses
In the United States, many managers do not issue quantitative earnings forecasts or
do so only sporadically, because they fear legal actions by investors and loss of reputation
for accuracy if they issue forecasts that later turn out to be less than perfectly accurate.
The costs associated with legal exposure and loss of reputation due to inaccurate
forecasts are higher for overestimates than for underestimates. Consistent with theasymmetric loss function, Kasznik (1999) shows that managers who have overestimatedearnings use positive discretionary accruals to manage reported earnings upward, but
3managers who have underestimated earnings do not use negative discretionary accruals
to manage reported earnings downward.
In a less litigious environment such as Japan, a wish to avoid potential legal liability
might not provide the incentive for managers who issue inaccurate earnings forecasts tomanage reported earnings so that they more closely match the forecasts. However,according to the Fair Practice Rule set forth by the JSDA, when issuing new stocks tointerested parties such as officers, employees, traders, and financial institutions during
the two years up to the fiscal year end just before the IPO, parties who have subscribedfor stocks are prohibited from selling them for six months after the IPO. If management
forecasts that later turn out to be inaccurate trigger a decline in stock prices, managers
might have an incentive to manage reported earnings so that they more closely match
the forecasts, for the people that underwrote stocks in the pre-IPO period. So I tested thesame hypothesis as Kasznik (1999) using a sample of Japanese IPOs.

H1: A manager who issues an inaccurate earnings forecast at the IPO time
reduces the forecast error by managing reported earnings so that they moreclosely match the forecasted number.

Some researches have analyzed earnings management of IPO firms.2 For example,
Teoh, Welch, and Wong (1998b) investigated earnings management during the year of
IPO and subsequent stock return. They find a significant negative relationship between
abnormal accruals during the IPO year and stock return in the three years after the IPO
fiscal year. Teoh, Wong, and Rao (1998) examined the association between IPO year
abnormal accruals and long-term earning performance in the years after IPO. They
provide evidence that high IPO-year abnormal current accruals predict low future4

earnings performance. On the other hand, there are several studies that investigate
earnings management in the period prior to IPO. Aharony, Lin, and Loeb (1993) find very
weak evidence that managers use discretionary accruals to increase reported earnings in
the period preceding IPO. Friedlan (1994) shows that IPO firms make positive
discretionary accruals in the most current financial statements included in theprospectuses. DuCharme, Malatesta, and Sefcik (2001) report evidence that aggressive
pre-IPO earnings management both increases IPO proceeds and decreases subsequent
stock returns. I tested the same hypothesis as those of prior researchers, but I used
Japanese IPO firms and examined discretionary accruals during both the pre- andpost-IPO periods to better understand earnings management of IPO firms.

H2: Firms engage in earnings management to boost reported earnings aroundthe IPO so that offering prices and post-IPO market prices are maintained at
a high level.

3. Sample Selection
The initial sample of this study consists of 848 firms that went public at the JASDAQ
between 1990 and 1999 (there are no banks, stock brokerage firms, or insurancecompanies included in the sample). I manually collected the earnings forecasts of these
IPO firms from the Summary of New Listing Firms furnished by the JSDA. From Nikkei
Needs, which is the database provided by Nihon Keizai Shimbunsya (similar to the Dow
Jones in the United States), I got financial statement data necessary to compute the test
variables explained in the next section. Because the necessary data were not available for
all IPO firms, I eliminated 20 firms for which some data were missing. The final sample
was 828 firms.
5--------------------------Table 1--------------------------Panel A of table 1 presents a sample distribution by the year of listing. Except for 1992,
in which IPOs were postponed by the slump in the stock market, the sample is almost
equally distributed among the sample years. Panel B indicates the sample distribution
based on Nikkei two-digit industrial codes. The presence of twenty-six separate two-digit
industrial codes, with sixteen of these representing at least one percent of the sample,
indicates a wide selection of industries, although more than half of my sample iscomprised of non-manufacturing firms, such as service, trading, and retail businesses.
--------------------------Table 2--------------------------Table 2 reports some characteristics of Japanese IPO firms. The mean total assets is19.47 billion yen, but the median is only 9.96 billion yen, suggesting that there are a few
outliers concerning firm size. The mean (median) financial leverage, total liabilities
divided by total assets, is 63.66 (65.39)%. IPO firms have better performance, as
indicated by a mean (median) return on assets of 10.32 (8.41)%. Japanese IPO firms are
considerably mature, having existed for a mean of 29.10 years and a median of 27.58
years from the date of incorporation to the date of going public. The mean (median) time
horizon of forecasts, measured as the number of days between the listing date and thefiscal year end when the IPO firm forecasts its earnings, is 140.31 (152) days. 3
Considering simultaneously that it takes a few months to go public after the application
is made and that the application must be accompanied by the management earnings
forecast, earnings forecasts issued by IPO firms are not designed to be preliminary
6earnings releases. About 80% of IPO firms select prestigious underwriters and auditors.

4. Measurement of Discretionary Accruals Proxy
Following methods similar to those of Kasznik (1999), I used discretionary accruals as ameasure of earnings management. Discretionary accruals were calculated by subtracting
non-discretionary accruals from total accruals. Because the statement of cash flows was
not available, I defined total accruals, TAC, as follows:
TAC = ∆CA – ∆CL – ∆CASH + ∆STD – DEP(1)∆CA is the change in current assets. ∆CL is the change in current liabilities. ∆CASH
is the change in cash. ∆STD is the change in debt included in current liabilities. DEP
is the depreciation expense.
To measure non-discretionary accruals, I estimated the following cross-sectionalmodel for each year and each industry:
TAC = β0 + β1 ∆ADJREV + β2 PPE + β3 ∆CFO + ε(2)∆ADJREV is the change in revenues adjusted for the change in account receivables. PPE
is the gross property, plant, and equipment. ∆CFO is the proxy of the change in cash flowfrom operations. I define operating cash flow as the net earnings before extraordinary
items minus total accruals. All variables are deflated by total assets at the beginning of
the year. ε is the error term.
Equation (2) is a modified and extended version of Jones’s (1991) model, which
describes total accruals as a function of the change in revenue and the level of property,
plant, and equipment. Because discretion could be exercised over revenues by changing
the timing of sales shipments, Dechow, Sloan, and Sweeney (1995) advocate that one
should adjust the sales revenue variable for the change in account receivables. Following
Dechow’s (1994) findings that there is a negative correlation between the change in7

operating cash flow and total accruals, Kasznik (1999) includes ∆CFO as an explanatory
variable.
As in DeFond and Jiambalvo (1994), I estimate the model using a cross-section
approach, because IPO firms usually do not have the long-term data necessary for
adopting a time-series approach. To obtain the estimated coefficients in equation (2), I
formed estimation portfolios by fiscal year and two-digit Nikkei industrial code, based on
the following selection criteria: firms had to (a) be listed with at least one of the Japanesestock exchanges (Tokyo, Osaka, Nagoya, JASDAQ, etc.) as of December 2000,4 (b) be notincluded in the initial IPO sample, (c) be not statistically extreme, defined as falling morethan three standard deviations from the mean in each estimation portfolio, (d) not havemissing values of items necessary to calculate total accruals, and (e) operate on a
12-month fiscal year. Each estimation portfolio must have more than ten observations. I
was able to make up a total of 330 estimation portfolios consisting of 23,713 firm-years.
--------------------------Table 3--------------------------Table 3 provides descriptive statistics for the cross-sectional OLS estimation results of
the accruals model. The mean (median) number of observations in the estimation
portfolios is 71.86 (43). As expected, the coefficient on change in revenue is generally
positive, but not statistically significant. The coefficients on property, plant, andequipment and change in operating cash flow are generally negative and statisticallysignificant. The model explains a significant portion of the variation in total accruals; themean (median) adjusted R2 is 0.517 (0.534).5
For each IPO firm, I computed the non-discretionary component of total accruals,
NDAC, using the estimated coefficients of the estimation portfolio matched by year and8industry as follows:
NDAC = b0 + b1 ∆ADJREV + b2 PPE + b3 ∆CFO

(3)

where b0, b1, b2, and b3 denote estimated coefficients β0, β1, β2, and β3, respectively.
The prediction error of the accruals model (the difference between total accruals andestimated non-discretionary accruals) is a proxy for discretionary accruals, DAC:DAC = TAC – NDAC

(4)

5. Empirical Results
5.1.

Earnings Forecast and Earnings management of IPO firms

In this section, I examine the hypothesis that a manager who issues an inaccurate
earnings forecast at IPO time reduces the forecast error by using discretionary accruals.
To do so, I had to identify proxies for the sample firms’ forecast error without including
earning management by managers. One way to estimate earnings absent earnings
management is to subtract the discretionary accruals proxy from reported earnings. As
indicated by Kasznik (1999), however, this could induce a mechanical correlation
between the earnings forecast error proxy and estimated discretionary accruals.
Therefore, I also used the reported forecast error, defined as the difference between
actual earnings before extraordinary items and taxes and its forecasted number, divided
by total assets at the beginning of the year.6 Assuming that firms with reported earnings
more (less) than the forecast also had non-discretionary earnings more (less) than theforecast, I expected that firms with reported earnings more (less) than the forecast would
have positive (negative) discretionary accruals to reduce forecast error.
To test this prediction, I assigned each IPO firm to one of ten portfolios, based on its
reported forecast error. Portfolio 1 consisted of firms having the largest negative forecast
error, that is, those firms whose management earning forecast was the most optimistic.
9

Portfolio 10 consisted of firms having the largest positive forecast error, that is, thosefirms whose management earning forecast was the most pessimistic.
--------------------------Table 4--------------------------Panel A of table 4 reports the descriptive statistics of management forecast error. The
mean reported forecast error deflated by lagged total assets is -0.0006, not significantly
different from zero (the one-tail p-value is 0.3298 using a t-test). While 359 IPO firms
have negative forecast errors suggesting that managers overestimate earnings, 469 firms
have positive forecast errors indicating that managers underestimate earnings. The
median of 0.0011 is significantly positive (the one-tail p-value is 0.0001 using a binominal
sign test). These results indicate that earnings forecasts of Japanese IPO firms on
average are pessimistically underestimated.7
Panel B of table 4 presents the mean and median discretionary accruals for decile
portfolios. For portfolios 1 to 4, in which reported earnings are below the management
forecasts, the mean and median discretionary accruals are positive at the statisticallysignificant level. Furthermore, firms in portfolio 1, that is, those have the largest
negative forecast errors, have more positive discretionary accruals than do the firms in
portfolios 2 to 4 at the significant level of 0.0389 or better, using a non-parametric
Wilcoxon signed rank test (not reported in the table).8 These results are consistent withthe prediction that managers who overestimate earnings at IPO time use
income-increasing discretionary accruals to mitigate their forecast errors. However, forportfolios 5 to 10, in which reported earnings are above the management forecasts, themean and median discretionary accruals are also significantly positive, except for the
median in portfolio 6. These findings generally provide no support for the prediction that10

managers who underestimate earnings manage reported earnings downward by using
negative discretionary accruals.

5.2.

Earnings Management around the IPO

As indicated in panel B of table 4, IPO firms have, on average, significantly positive
discretionary accruals during the fiscal year, of which the manager forecasts earnings at
IPO time (mean = 3.87%, median = 2.34% of the lagged total assets). Because there isrelatively little information available to investors from public sources about private firms,
they have to rely primarily on information provided by managers, such as the financial
statements of prior years and the like. If investors are unable to understand fully theextent to which IPO firms engaged in earnings management to increase reported
earnings by adopting positive discretionary accruals, high reported earnings could resultin a higher offering price. In addition, IPO firms have an incentive to boost income in the
period immediately after the IPO. According to the Fair Practice Rule, when issuing new
stocks to interested parties such as officers, employees, traders, and financial institutions
during the two years up to the fiscal year end just before the IPO, which is often observed,
parties who have subscribed for stocks are prohibited from selling them during the fixed
time period after IPO. Thus, managers might have an incentive to maintain highearnings until after the prohibitive period, as a reward for the people who underwrote
stocks in the pre-IPO period. I examined discretionary accruals during both the pre- andpost-IPO periods to gain a better understanding of earnings management in IPO firms. 9--------------------------Table 5--------------------------Table 5 presents the time-series distribution for earnings performance measure, return11

on assets (ROA), and proxy of earnings management, discretionary accruals (DAC),
during the seven years centered on year 0, defined as the fiscal year when the IPO firm
forecasts earnings.10 Panel A reports the return on assets, earnings before extraordinary
items, and taxes divided by total assets at the beginning of the year. From year -3 to year
-1, return on assets is at a high level and has a tendency to rise, reaching a peak in year
-1, that is, the year just before IPO. After IPO, superior earnings performances are notsustained, declining to less than half the level of the highest return on assets in year 3.
Panel B reports discretionary accruals, as defined previously. In years -3 and -2,
discretionary accruals were negative, having the effect of decreasing reported earnings.
In year -1, just before IPO, discretionary accruals changed to positive and were
statistically significant according to the binomial sign test (although they were notstatistically significant in a t-test). There is only weak evidence that high-level earnings
performances during the pre-IPO period were inflated by earnings management. In
contrast, in year 0 immediately after IPO and year 1, positive discretionary accruals
were statistically significant, keeping reported earnings from dramatically dropping
otherwise. Because a high level of discretionary accruals cannot be sustained over a long
period, discretionary accruals showed negative levels in year 3, though they were notstatistically significant.

5.3.

Robustness Check

At present because there is no perfect model separating discretionary accruals from
non-discretionary accruals, any proxies for discretionary accruals are noisy measures of
earnings management. Testing for earnings management using a proxy for discretionary
accruals could yield biased results if measurement error in the proxy is correlated withomitted variables associated with managers’ decision to go public.
12

Dechow, Sloan, and Sweeney (1995) find that measurement error in the estimation of
discretionary accruals is correlated with firm performance, in that firms with low (high)earnings tend to have negative (positive) prediction errors of the accruals model. As
indicated in tables 2 and 4, firms at IPO time are performing better than average.11 To
sum up, these findings raise concern that the positive accruals prediction errors
identified for IPO firms just reflect measurement errors associated with better
performances. Following the example of Kasznik (1999), I adjusted discretionary
accruals for potential measurement errors of the accruals model to check the robustnessof the above empirical results.
--------------------------Figure 1--------------------------I first calculated discretionary accruals for each of the 23,713 firm-years included in
the sample to estimate equation (2). I then ranked these observations on the level of
earnings divided by lagged total assets and assigned them to a percentile within theordered ranks. Percentile 1 (100) contains the 238 (237) observations with the lowest(highest) levels of earnings performance among the 23,713 observations. For eachpercentile, I computed a 95% confidence interval around the mean discretionary accruals.
Figure 1 plots the 100 confidence intervals. It reveals that the null hypothesis of zero
discretionary accruals is rejected at the five percent level for percentiles standing forlower and higher levels of earnings. The mean discretionary accruals are significantly
negative (positive) at the 0.05 levels for 12 (14) out of the lowest (highest) 20 percentiles,
using a parametric t-test. The null hypothesis that mean discretionary accruals are zero
was rejected for eleven out of the remaining 60 percentiles. The finding that firms withgood (bad) earnings performance have, on average, positive (negative) discretionary
13

accruals is consistent with the finding of Dechow, Sloan, and Sweeney (1995) and
Kasznik (1999).
--------------------------Figure 2--------------------------To assess the potential effects of this relationship on the earnings management
measures, I assigned each of 828 IPO firms to one of the percentile groups based on
earnings levels. As Figure 2 shows, more than 90% of the sample observations lie above
the 50th percentile, with 11.71% in the 100th percentile, which is consistent with thefinding that IPO firms perform better. This indicates that the positive discretionary
accruals of IPO firms noted in previous sections could be due to measurement errors inestimating the accruals model, and that IPO firms in Japan might have a more serious
problem with this type of error than listed firms that issue management earnings
forecasts in the United States, such as those examined by Kasznik (1999). Because of thispotential problem, I adjusted the discretionary accruals to control for this potential bias,
following the method employed by Kasznik (1999). Assuming that the mediandiscretionary accruals for each percentile group reflect measurement error correlatedwith that level of performance, I computed an adjusted proxy of discretionary accruals,
ADJDAC, as the difference between discretionary accruals based on equation (4), DAC,
and the median discretionary accruals, MED(DAC)g, for a percentile group g, matchedwith the IPO firm on earnings deflated by the lagged total assets:
ADJDAC = DAC – MED(DAC)g(5)--------------------------Table 6--------------------------14

Table 6 presents the mean and median adjusted discretionary accruals for decile
portfolios based on management forecast error, measured as the difference between
reported earnings and management earnings forecast deflated by the lagged total assets.
Removing the component of discretionary accruals that potentially reflects measurement
errors associated with firm performances reduces magnitudes of discretionary accruals,
from a mean (median) of 0.0387 (0.0234) to 0.0338 (0.0190), but the accruals are still
significantly positive. For portfolios 1 to 4, in which reported earnings are below the
management forecasts, the mean and median adjusted discretionary accruals are
statistically positive at the significant level. However, for portfolios 5 to 10, in which
reported earnings are above the management forecasts, the mean and median adjusted
discretionary accruals are also positive and statistically significant, except for the
median in portfolio 6. These results basically confirm previous ones, that is, they are
consistent with the prediction that managers who overestimate earnings use
income-increasing accruals to mitigate their forecast errors, but the results are notconsistent with the prediction that managers who underestimate earnings use
income-decreasing accruals to manage reported earnings downward.
--------------------------Table 7--------------------------Table 7 presents the time-series distribution for adjusted discretionary accruals during
the seven years centered on year 0, defined as the fiscal year during which the manager
forecasts the earnings at IPO time. In the pre-IPO period, the mean and median adjusted
discretionary accruals in not only year -3 and -2 but also year -1 are negative, and themeans are statistically significant except for the mean in year -3. These results suggestthat high-level earnings performances in years before IPO are not attained by earnings
15

management. In contrast, in the post-IPO period, adjusted discretionary accruals are
significantly positive for years 0 and 1 and then decline, turning to negative in year 3.
These results are fundamentally identical to previous findings that earnings
performances for the few years immediately following IPO are inflated by earnings
management.

6. Concluding Remarks
In this study I investigated earnings management of Japanese IPO firms from two
viewpoints. First I examined whether managers who issue an inaccurate earnings
forecast at IPO time engage in earnings management to reduce forecast error. When
overestimating earnings, managers use positive accruals to mitigate their forecast errors.
However, when underestimating earnings, managers do not use negative accruals tomanage reported earnings downward. These results are similar to those of Kasznik(1999), who investigated listed firms that issue management forecasts in the UnitedStates, and may show that the effects on manager’s legal exposure, reputation, and stock
price in the case of inaccurate forecasting are asymmetric between overestimations andunderestimations.
Second, I examined whether Japanese IPO firms engage in earnings management toboost earnings around the IPO so that offering prices and post-IPO market prices aremaintained at a high level. In the pre-IPO period, managers do not use discretionary
accruals to attain high-level earnings performances. In contrast, in the period
immediately after IPO, significantly positive discretionary accruals keep reported
earnings from declining dramatically, but such accruals cannot sustain earnings over along period. These results are not necessarily consistent with those of Aharony, Lin, andLoeb (1993), Friedlan (1994), and DuCharme, Malatesta, and Sefcik (2001), who16investigate pre-IPO earnings management of U.S. IPO firms, but they are almost
identical to the findings of Teoh, Welch, Wong (1988b) and Teoh, Wong, and Rao (1998),
which focus on post-IPO earnings management.
Among the literature that has mainly documented earnings management by U.S. IPO
firms, Aharony, Lee, and Wong (2000) is exceptional in that they examined earnings
management in the financial packaging of Chinese state-owned enterprises for public
listing. By making comparisons with these earlier researches, the present study, whichinvestigates whether Japanese IPO firms engage in earnings management, makes some
contribution to enhancing international understanding of earnings management aroundthe IPO.
Teoh, Welch, and Wong (1998b) and Teoh, Wong, and Rao (1998) also study the
relationship between earnings management during the year of IPO and subsequent
stock market performance. DuCharme, Malatesta, and Sefcik (2001) examine theassociation between earnings management just before IPO and initial firm value andsubsequent stock return. How and Yeo (2001) explore the impact of forecast disclosure
and its accuracy on the valuation of IPO firms. There remains ample room forinvestigating the stock market evaluation of earnings forecast disclosure and earnings
management by Japanese IPO firms.
[2002.8.2 622]

17

References
Aharony, J., C. J. Lee, and T. J. Wong 2000. Financial Packaging of IPO Firms in China.

Journal of Accounting Research 38 (Spring): 103-126.
Aharony, J., C. J. Lin, and M. P. Loeb. 1993. Initial Public Offerings, Accounting Choices
and Earnings Management. Contemporary Accounting Research 10 (Fall): 61-81.
Dechow, P. M. 1994. Accounting Earnings and Cash Flows as Measures of Firm
Performance: The Role of Accounting Accruals. Journal of Accounting andEconomics 18 (July): 3-42.
Dechow, P. M., R. G. Sloan, and A. P. Sweeney. 1995. Detecting Earnings Management.

The Accounting Review 70 (April): 193-225.
DeFond, M. L., and J. Jiambalvo. 1994. Debt Covenant Violation and Manipulation of
Accruals. Journal of Accounting and Economics 17 (January): 145-176.
DuCharme, L. L., P. H. Malatesta, and S. E. Sefcik. 2001. Earnings Management: IPO
Valuation and Subsequent Performance. Journal of Accounting, Auditing and

Finance 16 (Fall): 369-396.
Friedlan, J. M. 1994. Accounting Choices of Issuers of Initial Public Offerings.

Contemporary Accounting Research 11 (Summer): 1-31.
How, J. C. Y., and J. J. L. Yeo. 2001. The Impact of Forecast Disclosure and Accuracy on
Equity Pricing: The IPO Perspective. Journal of Accounting, Auditing and Finance
16 (Fall): 401-425.
Jones, J. J. 1991. Earnings Management during Import Relief Investigations. Journal of

Accounting Research 29 (Autumn): 193-228.
Kasznik, R. 1999. On the Association between Voluntary Disclosure and Earnings
Management. Journal of Accounting Research 37 (Spring): 57-81.
18

Otogawa, K. 2001. Initial Public Offerings and Management Earnings Forecasts inJapan. Sangyo-Keiri 61 (October): 50-58 (in Japanese).
Rangan, S. 1998. Earnings Management and the Performance of Seasoned Equity
Offerings. Journal of Financial Economics 50 (October): 101-122.
Shivakumar, L. 2000. Do Firms Mislead Investors by Overstating Earnings before
Seasoned Equity Offerings? Journal of Accounting and Economics 29 (June):339-371.
Suda, K., and A. Syutou. 2001. Manager’s Earnings Forecast and Discretion over
Reported Earnings. Sangyo-Keiri 61 (July): 46-56 (in Japanese).
Teoh, S. H., I. Welch, and T. J. Wong. 1998a. Earnings Management and theUnderperformance of Seasoned Equity Offerings. Journal of Financial Economics
50 (October): 63-99.
Teoh, S. H., I. Welch, and T. J. Wong. 1998b. Earnings Management and the Long-Run
Market Performance of Initial Public Offerings. Journal of Finance 53 (December):
1935-1974.
Teoh, S. H., T. J. Wong, and G. R. Rao. 1998. Are Accruals during Initial Public Offerings
Opportunistic? Review of Accounting Studies 3: 175-208.

19

Table 1
Sample Distribution
Freq. %
Panel A: By Year of IPO
1990
1991
1992
1993
1994
Panel B: By Industry
Foods
TextilePaper
ChemicalPharmacy
RubberCeramics
Steel
Nonferrous Metals
Machinery
Electrical Equipment
Automobile
Other Transport

84
95
15
53
104

27
7
4
33
8
5
16
7
26
39
62
18
3

10.14
11.47
1.81
6.40
12.56

3.26
0.85
0.48
3.99
0.97
0.60
1.93
0.85
3.14
4.71
7.49
2.17
0.36

Freq.

%

Total

136
111
100
62
68
828

16.43
13.41
12.08
7.49
8.21
100.00Precision Machinery
Other Manufacturing
Construction
Trading
RetailOther FinanceReal EstateLand Transportation
Marine Transportation
Warehouse
Communications
Gas
Service
Total

10
36
50
141
91
19
12
14
3
3
5
1
188
828

1.21
4.35
6.04
17.03
10.99
2.29
1.45
1.69
0.36
0.36
0.60
0.12
22.71
100.00

1995
1996
1997
1998
1999

20

Table 2
Characteristics of Japanese IPO firms
Mean
Std. Dev.
Q1 a

MedianQ3 aTotal assets b

19.4705

64.49936.08359.962017.5248

Financial leverage c

0.63660.16990.52580.65390.7653Return on assets d0.10320.10340.05100.08410.1296

Firm age e

29.0975

13.5700

18.332227.5795

38.7945

Forecast horizon f

140.3104

70.8856

106.0000

152.0000

191.0000

Lead underwriter g

661 (79.8% of 828 IPO firms)

Auditor h597 (84.3% of 708 IPO firms)
Sample consists of 828 IPO firms that went public in the JASDAQ during 1990-99, except forsummary statistics of auditor by the following reason.
a Q1 and Q3 are the first and third quartiles of the distribution, respectively.
b Total assets is measured as the assets at the beginning of the fiscal year for which the IPO
firm forecasts earnings (in billion yen).
c Financial leverage is measured as total liabilities divided by total assets, at the beginning of
the fiscal year for which the IPO firm forecasts earnings.
d Return on assets is measured as earnings before extraordinary items and taxes deflated bylagged total assets, at the beginning of the fiscal year for which the IPO firm forecasts earnings.
e Firm age is measured as the number of years from the incorporation to the listing.
f Forecast horizon is measured as the number of days from the listing date to the fiscal year end
when the IPO firm forecasts earnings, because I could not obtain the date in which managers
forecast earnings or apply for listing.
g Lead underwriter is the number of firms that select a prestigious underwriter for a public
offering. I consider the four largest Japanese brokerage firms to be prestigious.
h Auditor is the number of firms that select to be audited by a prestigious accounting firm. I
consider the six largest Japanese accounting firms, which are affiliated with the international
accounting firms called the Big 6, to be prestigious. I collected auditor data from the Summary of
New Listing Firms only issued after August 1991, because such data was not provided before that
year.

21

Table 3
Descriptive Statistics for Ordinary Least-Squares Estimation of the Accruals Model a
TAC = β0 + β1 ∆ADJREV + β2 PPE + β3 ∆CFO + ε
Nb

Mean

Std. Dev.

Q1 c

MedianQ3 c

Observations

330

71.8576

66.4368

19.25

43

114

b0

330

0.00560.0351-0.0076

0.00680.0217t-statistic

330

0.57861.7738-0.5239

0.5261

1.4957b1

330

0.01380.2802-0.0594

0.01730.0841t-statistic330

0.2802

1.5281-0.66980.23011.1800b2

330-0.06360.0441-0.0829-0.0591-0.0415

t-statistic

330-2.8835

1.9528-4.0872-2.7821-1.5060b3

330-0.48270.1671-0.5603-0.4818-0.4050t-statistic

330-7.1840

4.1344-10.3915-6.5594-3.8074

adj. R2330

0.5166

0.19460.42950.53400.6152F-statistic

330

29.0148

25.5875

8.518820.8580

44.1730

Variable definitions: TAC is total accruals, defined using a balance sheet approach(see equation 1); ∆ADJREV is change in revenues (adjusted for change inreceivables); PPE is gross property, plant, and equipment; ∆CFO is the change incash flow from operations. I define operating cash flow as net earnings before
extraordinary items minus total accruals. All variables are deflated by total assets atthe beginning of the year. b0, b1, b2, and b3 denote estimated coefficients β0, β1, β2, andβ3, respectively.
a Each of 23,713 sample firm-years is assigned to an estimation portfolio thatconsists of all non-initial IPO sample firms matched by year (judged from the
beginning of the fiscal year) and two-digit Nikkei industrial code.
b Nihon Keizai Shimbunsya classifies 36 two-digit industrial codes. Of them, I do
not estimate the accrual model for 6 industries, namely, banks, stock brokeragefirms, and insurance companies in which data are not available, and 3 industries
that have fewer than 10 observations. IPO firms in the final sample forecast theirearnings for 11 years between 1989 and 1999 (judged from the beginning of the fiscal
year).
c Q1 and Q3 are the first and third quartiles of the distribution, respectively.

22

Table 4
Management Forecast Errors and Discretionary Accruals
N
Mean Median Std. Dev.
Panel A: Management Forecast Errors a
All Samples 828 -0.0006
0.0011
Portfolio 1 83 -0.0700 -0.0425
2 83 -0.0162 -0.0165
3 83 -0.0078 -0.0078
4 83 -0.0029 -0.0028
5 83 0.0002 0.0003
6 83 0.0023 0.0022
7 83 0.0058 0.0057
8 83 0.0101 0.0099
9 82 0.0178 0.0166
10 82 0.0552 0.0420
N

Mean

0.04140.08390.00370.00160.00100.00070.00080.00100.00160.00350.0381p-value c Median p-value c Std. Dev. Positive Negative

Panel B: Discretionary Accruals (DAC) b
All Samples 828 0.0387 0.0000
0.0234 0.0000
0.1085581
247
Portfolio 1 83 0.0544 0.0009
0.0413 0.0000
0.153963
20
2 83 0.0342 0.0077
0.0163 0.0005
0.126257
26
3 83 0.0330 0.0002
0.0199 0.0002
0.080458
25
4 83 0.0217 0.0001
0.0298 0.0005
0.049557
26
5 83 0.0236 0.0004
0.0182 0.0011
0.061256
27
6 83 0.0159 0.0108
0.0096 0.1899
0.061846
37
7 83 0.0122 0.0130
0.0113 0.0042
0.049154
29
8 83 0.0352 0.0000
0.0227 0.0000
0.062763
20
9 82 0.0402 0.0000
0.0320 0.0000
0.088562
20
10 82
0.1171 0.0000
0.0654 0.0000
0.201965
17“All Samples” refers to 828 IPO firms that went public in the JASDAQ during 1990-99.
Each firm is assigned to one of the decile portfolios, based on management forecast error.
Portfolio 1 consists of firms having the largest negative forecast error, while Portfolio 10
consists of firms having the largest positive forecast error.
a Management forecast error is measured as the difference between reported earnings
before extraordinary items and taxes and its forecasted number, deflated by lagged total
assets.
b Discretionary accruals (DAC) is estimated as the accruals prediction error, i.e., thedifference between total accruals and estimated non-discretionary accruals.
c One-tail p-value of a parametric t-test (non-parametric binominal sign test) for the null
hypothesis that the mean (median) DAC is zero.

23

Table 5
Earnings Performance and Discretionary Accruals around the IPO
Year a
-3
-2
-1
0
1
2
3
Panel A: Return on Assets (ROA) b
N
585
694
803
828
750
695
603
Mean
0.10370.1020 0.1129 0.1032 0.0769 0.0632
0.0541
Median0.06670.0775 0.0882 0.0841 0.0633 0.0525
0.0434
Std. Dev. 0.2779
0.0920 0.0978 0.1034 0.0754 0.0738
0.0629Positive
579
692
803
820
704
636
546
Negative
6
2
0
8
46
59
57
Panel B: Discretionary Accruals (DAC) c
N
585
694
803
828
750
695
603
Mean
-0.0473 -0.0045 0.0005 0.0387 0.0318 0.0083 -0.0012
p-value d 0.1815
0.0832 0.4147 0.0000 0.0000 0.0026
0.2920Median -0.0032 -0.0009 0.0017 0.0234 0.0251 0.0039 -0.0003
p-value d 0.1235
0.2594 0.0790 0.0000 0.0000 0.0040
0.4353
Std. Dev. 1.2578
0.0863 0.0685 0.1085 0.0915 0.0785
0.0551
Positive
278
338
422
581
511
383
299
Negative
307
356
381
247
239
312
304
Sample consists of IPO firms that went public in the JASDAQ during
1990-99.
a Year 0 is relatively defined as the fiscal year for which the IPO firm
forecasts earnings.
b Return on assets (ROA) is measured as earnings before extraordinary
items and taxes, deflated by lagged total assets.
c Discretionary accruals (DAC) is estimated as the accruals prediction error,i.e., the difference between total accruals and estimated non-discretionary
accruals.
d One-tail p-value of a parametric t-test (non-parametric binominal signtest) for the null hypothesis that the mean (median) DAC is zero.

24

0.08

Accruals Prediction Error

0.06
0.04
0.02
0.00
-0.02
-0.04
-0.06
-0.08
1

11

21

31

41

51

61

71

81

91

Percentile

Figure 1. Ninety-five percent confidence intervals around mean accruals prediction
errors for percentiles of 23,713 observations in the estimation sample from 1989-99.
The accruals prediction error is measured for each observation as the difference
between total accruals and the predicted value from the accruals model (see table 3 formodel description and estimation specification). Percentile observations are ranked on
earnings before extraordinary items and taxes deflated by lagged total assets.

25

15%

100%

12%

80%
70%

9%

60%
50%
40%

6%

30%
3%

20%

Cumulative Frequency of
IPO Firms

Frequency of IPO Firms

90%

10%
0%

0%
1

11

21

31

41

51

61

71

81

91

Percentile

Figure 2. Frequencies and Cumulative Frequencies of 828 IPO firms matched withpercentiles of 23,713 observations in the estimation sample from 1989-99 based on
earnings before extraordinary items and taxes deflated by lagged total assets.

26

Table 6
Discretionary Accruals for Each of the Decile Portfolios Based on Management Forecast Error
When Adjusted for Potential Measurement Errors of the Accruals ModelN
Mean p-value b Median p-value b Std. Dev. Positive Negative
Adjusted Discretionary Accruals (ADJDAC) a
All Samples 828 0.0338
0.0000 0.0190
Portfolio 1
83 0.0532
0.0011 0.0356
2
83 0.0308
0.0135 0.0161
3
83 0.0285
0.0007 0.0163
4
83 0.0188
0.0005 0.0268
5
83 0.0201
0.0015 0.0147
6
83 0.0118
0.0408 0.0084
7
83 0.0079
0.0719 0.0089
8
83 0.0301
0.0000 0.0178
982 0.0332
0.0005 0.0262
10
82 0.1039
0.0000 0.0532

0.00000.00000.00110.00050.00050.00420.18990.02410.00020.00030.00010.10710.15370.12470.07870.04970.06010.06090.04880.06150.08750.2010

557
62
56
57
57
54
46
51
58
57
59

271
21
27
26
26
29
37
32
25
25
23

“All Samples” refers to 828 IPO firms that went public in the JASDAQ during 1990-99.
Each firm is assigned to one of the decile portfolios based on management forecast error,
measured as the difference between reported earnings before extraordinary items and taxes
and its forecasted number, deflated by lagged total assets. Portfolio 1 consists of firms having
the largest negative forecast error, while Portfolio 10 consists of firms having the largest
positive forecast error.
a Adjusted discretionary accruals (ADJDAC) is measured as the difference between theaccruals prediction error (see tables 3 and 4) and the median accruals prediction error for apercentile group of 23,713 non-IPO firms matched with the sample observation on earnings
before extraordinary items and taxes, deflated by lagged total assets.
b One-tail p-value of a parametric t-test (non-parametric binominal sign test) for the null
hypothesis that the mean (median) ADJDAC is zero.

27

Table 7
Discretionary Accruals around the IPO
When Adjusted for Potential Measurement Errors of the Accruals Model
Year a
-3
-2
-1
0
1
2
3
Adjusted Discretionary Accruals (ADJDAC) b
N
585
694
803
828
750
695
603
Mean
-0.0516 -0.0094 -0.0051 0.0338 0.0294 0.0074 -0.0017
p-value c 0.1610
0.00220.0177 0.0000 0.0000 0.0058
0.2198Median -0.0077 -0.0047 -0.0018 0.0190 0.0218 0.0033 -0.0010
p-value c 0.0015
0.01520.0900 0.0000 0.0000 0.0476
0.2843
Std. Dev. 1.2582
0.08650.0681 0.1071 0.0894 0.0768
0.0535
Positive
256
318
382
557
503
370
294
Negative
329
376
421
271
247
325
309
Sample consists of IPO firms that went public in the JASDAQ during
1990-99.
a Year 0 is relatively defined as the fiscal year for which the IPO firm
forecasts earnings.
b Adjusted discretionary accruals (ADJDAC) are estimated as the difference
between the accruals prediction error (see tables 3 and 5) and the medianaccruals prediction error for a percentile group of 23,713 non-IPO firms
matched with the sample observation on earnings before extraordinary items
and taxes, deflated by lagged total assets.
c One-tail p-value of a parametric t-test (non-parametric binominal sign test)
for the null hypothesis that the mean (median) ADJDAC is zero.

28

1 Japanese IPO firms tend to select the JASDAQ market more frequently than the Tokyo

Stock Exchange (TSE), which is the largest stock market in Japan, probably because
the former has less strict requirements for listing than does the latter. The TSE
organized a new section named Mothers on November 1999, and a new market,
NASDAQ Japan, was founded in June 2000. It is as easy to list in these new markets
as in the JASDAQ, so IPO firms now select from among these three stock markets forlisting.
2 Teoh, Welch, and Wong (1998a), Rangan (1998), and Shivakumar (2000) examinedearnings management around seasoned equity offerings (SEOs).
3 I used the listing date to calculate the time horizon of the forecast because I could not
obtain the date on which managers forecasted earnings or applied for listing.
4 The requirement was checked using Kabuka CD-ROM (Japanese firms’ stock pricedatabase) provided by Toyo Keizai Shimpousya.
5 The explanatory power of this accruals model is similar to ones reported in Suda andSyutou (2001). The Jones model and modified Jones model have poor explanatory
power in Japan, perhaps due to insignificant variable of change in revenues. According
to Suda and Syutou, the mean (median) adjusted R2 is 0.123 (0.061) in the Jones modeland 0.122 (0.054) in the modified Jones model.
6 Japanese IPO firms generally issue forecasts of sales, earnings before extraordinary
items and taxes, net earnings, dividends, net earnings per share, and dividends per
share. In this analysis, I focus on managers’ forecasts of earnings before extraordinary
items and taxes, which people generally regard as the most important measure of firm
performance in Japan.
7 Otogawa (2001) investigated bias and accuracy of management earnings forecasts
issued by Japanese IPO firms and existing listed firms. While earnings forecasts of
IPO firms are relatively pessimistic, listed firms are relatively optimistic. Moreover,
IPO firms have more accurate earnings forecasts than do listed firms.
8 In a two-sample t-test, the differences of discretionary accruals between portfolio 1 andportfolios 2 to 4 are significant at the level of 0.1783, 0.1317, and 0.0336, respectively.
9 Because I did not have the data I needed for year 1 for 1999 IPOs, for year 2 for 1998
and 1999 IPOs, nor for year 3 for 1997 to 1999 IPOs, sample sizes are smaller in the
post-IPO period. Some firms had incomplete data, especially for pre-listing years, orchanged fiscal year end. Exclusion of these firms led to even smaller sample sizes inthose cases.
10 Year 0 as used here corresponds to year 0 as defined by Teoh, Welch, and Wong (1998b)
and Teoh, Wong, and Rao (1998) in 780 of 828 IPO firms, and to year -1 in the othercases.
11 For example, the mean (median) ROA, earnings before extraordinary items and taxes
scaled by the lagged total assets, of the estimation sample is 0.0401 (0.0332).

29

Discussion Paper
No.
2001･1

AuthorTakashi Hatakeda
Nobuyuki Isagawa

2001･2Michiyasu Nakajima
Takeshi Mizuguchi
Katsuhiko Kokubu
Yasushi Onishi
Koji Okubayashi

2001･3
2001･4

2001･5
2001･6
2001･7

2001･8

2001･9Katsuhiko Kokubu
Akihiro Noda
Yasushi Onishi
Tomomi Shinabe
Kunio Miyashita
Akinobu Sakashita
KOKUBU,
Katsuhiko
NASHIOKA, ErikoDAIKUHARA, Rie
KOKUBU,
Katsuhiko
KURASAKA,
TomokoShuzo Murata

2001･11

Makoto Yano
Fumio Dei
Mahito Okura

2001･122001･13

Mahito Okura
Shuzo Murata

2001･14

Koji Okubayashi
Toshinori Takashina

2001･15
2001･16

Takuji Hara
Shuzo Murata

2001･17

Norio Kambayashi

2001･18
2001･19
2001･20

Yuichi Fukuta
Kazuhiro Tanaka
Kentaro Nobeoka
Kazuhiro Tanaka

2001･21

Mahito Okura

2001･22
2001･23

Kazuhiro Tanaka
Mahito Okura

2001･24

Katsuhiko Kokubu
Akihiro Noda
Yasushi Onishi
Tomomi Shinabe
Akira Higashida

2001･10

Title
Stock Price Behavior Surrounding Repurchase
Announcements:
Evidence from Japan
Material Flow Cost Accounting of IMU (October 2000)

Date
1／2001(in Japanese)

1／2001

Japanese Manufacturers Without Factories:
Cases of Sony, Matsushita, Misumi, People
Determinants of Environmental Report Publicationin Japanese Companies

1／2001

Logistics Strategy of Japanese Port Management
Issues and Method of Organizational Culture Studies:
Functionalism Approach
Environmental Accounting in Japanese Companies as ofNovember 2000

(in Japanese)

2／20013／2001(in Japanese)

3／20012／2001

Corporate Environmental Accounting: A Japanese
Perspective

The Comparative Study of Japanese & U.S. Companies(6) The Health & Long Term Care-The Management
Education
A Trade Model with Vertical Production Chain andCompetition Policy in the Downstream Sector
Risk Classification: A Study of the Tradeoff between
Quantity and Quality of Insured
Is the Separating Equilibrium Really Desirable?
The Comparative Study of Japanese & U.S. Companies(5) The Conflict Among Companies
Research Report on the relations between there-employment process and former job career at thecompulsory retirement of workers of a firm
The Social Shaping of Medicines
The Comparative Study of Japanese & U.S. Companies(7) Toward Restructuring Japanese Corporations
Cultural influences on IT usage among workers:a UK-Japanese comparison
A Test for Rational Bubbles in Stock Prices
Toward the Effective Reform of Japanese Corporate
Governance
Structural Changes in Top Management―The Effect ofCorporate Governance Reform in Japan
In the Separating Equilibrium Really Desirable ?
―revised version of No.2001･12―
Introduction to Corporate Governance
The Role of Some Insurance Contracts in a Subjective
Equilibrium Model with Observable Effort
Environmental Disclosure of Japanese companies

3／2001(in Japanese)

4／2001

12／2000(in Japanese)

5／2001(in Japanese)(in Japanese)

5／20016／2001(in Japanese)

7／2001(in Japanese)(in Japanese)

7／20017／20017／2001(in Japanese)

7／20017／2001(in Japanese)

7／2001(in Japanese)

8／2001(in Japanese)(in Japanese)

8／20018／2001(in Japanese)

8／2001

Discussion Paper
No.
2001･25

2001･26
2001･27

AuthorKatsuhiko Kokubu
Tomomi Shinabe
Akira Higashida
Yasushi Onishi
Akihiro Noda
Katsuhiko Kokubu
Eriko Nashioka
Masakazu Takagi

2001・28 Mahito Okura
2001・29 Atsushi Takao

2001・30 Mahito Okura
2001・31 Masakazu Takagi
2001・32 Norio Kambayashi
Harry Scarbrough2001・33 Fumitoshi Mizutani
Shuji Uranishi
2001・34 Mahito Okura

2001・35 Tsutomu Harada
2001・36 Nobuyuki Isagawa

2001・37 Nobuyuki Isagawa
2002・1

Nobuyuki Isagawa

2002・2

Nobuyuki Isagawa

2002・3Mahito Okura

2002・4

Elmer Sterken
Ichiro Tokutsu
Mahito Okura
Mahito Okura
Nobuyuki Isagawa
Koji Okubayashi
Toshinori Takashina

2002・5
2002・6
2002・7
2002・8

2002・9Hajime Shimizu

2002・10 Nobuyuki Isagawa
2002･11

Nobuyuki Isagawa

Title
Environmental Reporting of Japanese companies(in Japanese)

Date
8／2001

Environmental Accounting of Japanese companies(in Japanese)

8／2001Elementary Study of East Asian Corporate andManagement System
Vertical Differentiation in the Insurance Market
A Study on the Penetration Process of Modern Insurance
by Evolutionary Economics―Why do the Modern
Insurance and the Premodern Mutual Society coexist?―
An Essay in the Economics of Post-loss Minimisation:
An Analysis of the Effectiveness of the Insurance Law
and Clauses
Co-operation between Hanshin(Osaka-Kobe) Area andSouth-east Asia-Finding business opportunities of mutual
benefits
Cultural influences on IT use amongst factory managers：
A UK-Japanese comparison
The Post Office vs. Parcel Delivery Companies：
Competition Effects on Costs and Productivity
An Essay in the Economics of Post-loss Minimisation:
An Analysis of the Effectiveness of the Insurance Law
and Clauses―revised version of No.2001・30―
An Examination of IT Paradox in Japan
Open-Market Repurchase Announcements, Actual
Repurchases, and Stock Price Behavior in Inefficient
markets
Corporate Financial Strategy and Stock Price Behavior ina Noise Trader Model with Limited Arbitrage
Cross Holding of Shares, Unwinding Cross Holding of
Shares, and Managerial
Entrenchment
A Theory of Stock Price Behavior following Repurchase
Announcements
An Equilibrium Analysis of the Insurance Market
with Vertical Differentiation
What are the determinants of the number of bankrelations of Japanese firms?
Review Article：Adverse Selection in Insurance Market
Welfare Effect of Firm Size in Insurance Market
Does Investment Horison Affect Investment Strategy ?
Research Report of Careers and Employment Process ofWorkers over 60 years old :a Case of a Japnaese
Manufacturing Company
On the Existence of Tax Equilibrium : Incomplete Market
with Capital Income Tax
Cost of Financial Distress and Debt Restructuring :Debt Forgiveness and Equity-for-Debt Swap
Open-Market Repurchase Announcements, Actual
Repurchases, and Stock Price Behavior in Inefficient
Markets―revised version of No.2001･36―

9／2001(in Japanese)(in Japanese)

9／20019／20019／2001(in Japanese)

9／2001

10／2001
10／2001
11／2001(in Japanese)

11／2001
12／2001
12／2001(in Japanese)

1／ 2002(in Japanese)

1／ 20022／20023／2002(in Japanese)(in Japanese)(in Japnaese)

3／20023／20023／20024／2002(in Japnaese)

4／2002(in Japnaese)

4／20025／2002

Discussion Paper
No.
2002･12

AuthorKenji Kutsuna
Richard Smith

2002・13 Kunio Miyashita
2002・14 Hajime Shimizu
2002・15 Hajime Shimizu
2002・16 Koji Okubayashi
2002・17 Fumitoshi Mizutani
Shuji Uranishi
2002・18 Kazuhisa Otogawa

Title
Why Does Book Building Drive Out Auction Methodsof IPO Issuance?
Evidence and Implications from Japan
International Logistics and Modal Choice
Incomplete markets with capital income tax and public
goods
On the possibility of Pareto improving of tax-equilibrium
China-Japan Comparison of Work Organization
The Post Office vs. Parcel Delivery Companies:
Competition Effects on Costs and Productivity―revised version of No.2001･33―
Earnings Forecast and Earnings Management of JapaneseInitial Public Offerings Firms

Date
5／2002(in Japnaese)(in Japnaese)

6／20026／20026／20027／20027／20028／2002