Journal of International Financial Management and Accounting 10:3 1999
The Accuracy of Profit Forecasts and
their Roles and Associations with
IPO Firm Valuations
Gongmeng Chen
The Hong Kong Polytechnic University
Michael Firth*
The Hong Kong Polytechnic University
Abstract
China’s recent economic reforms include the corporatization and listing of formerly stateowned enterprises. In order to sell shares to both domestic and foreign investors, IPOs have
to overcome significant asymmetric information problems. Prospectuses for new issues
in China contain forecasts of corporate profits for the next year and these forecasts can be
used by investors to value companies and to make investment decisions. The study sets out
to assess the accuracy of these forecasts and hence the credibility that can be attached to
them. In addition to calculating various measures of accuracy and forecast superiority, we
also examine the bias and rationality of the forecasts. The results show that profit forecasts
are moderately accurate and they are better than time series extrapolations of historical
profits. Explaining cross-sectional differences in accuracy measures proves to be difficult.
Finally, the results indicate that profit forecasts are related to company valuations and that
investors predict the sign, and to some extent the magnitude, of forecast errors.
1. Introduction
Lack of information and information asymmetry between management
and potential investors are major uncertainties facing investors when
deciding whether to subscribe to new issues. These uncertainties are especially manifest in transitional economies such as the People’s Republic
of China (PRC). The very recency of the economic reforms coupled
with uncertainties of how state intervention may impinge on companies’
activities, makes investment in China stocks a risky task. Domestic
*Correspondence concerning this paper should be sent to: Michael Firth, Department of Accountancy,
The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. Tel: 852 2766 7062,
Email: acmaf@inet.polyu.edu.hk
The authors thank the referees and Richard Levich (the editor) for helpful comments on the paper.
The authors acknowledge the very capable research assistance of Lu Jian and Gao Ning. Chen also
acknowledges financial support for this project from The Hong Kong Polytechnic University Central
Research Grant.
© Blackwell Publishers Ltd. 1999, 108 Cowley Road, Oxford OX4 1JF, UK and 350 Main Street, Malden, MA 02148, USA.
Profit Forecasts and IPO Firm Valuations
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investors (A-shares) in China face considerable uncertainty as they have
little experience of equity shares; nevertheless, the lack of alternative
investment opportunities have led Chinese investors to embrace equity
investment. Foreign investors (B-shares) in China also face substantial
uncertainty as the level of disclosure is less than they are accustomed to
and because legal rights and remedies are not well established.
In order to alleviate some of the uncertainties in investing in China
stocks, IPO firms publish prospectuses which inform the public about the
operations, plans, and prospects of the companies. The disclosure levels
and quality of many B-share prospectuses are similar to prospectuses from
Hong Kong and other countries. In addition to various mandated disclosures that are required to be made in a China IPO prospectus, many
companies make further disclosures1 as they seek to convey their private
information about firm value to potential investors. One feature of both
A-share and B-share prospectuses is the provision of forecasts of corporate
profits for the current year. These forecasts feature prominently with many
prospectuses highlighting the prospective price earnings ratios. Investment
advisers and the financial news media use profit forecasts in their share
recommendations. A crucial factor for investors is the accuracy or credibility of these profit forecasts.
The purpose of this paper is to study the accuracy of profit forecasts
made in conjunction with IPOs in China. Although there are a number of
studies in this general area, none have examined data from China. The
China market is important as the scale of initial public offerings has been
very large and it will continue to grow in the future as China seeks to
restructure and modernize its industry. Information asymmetry problems
typically associated with IPOs are exacerbated in the case of China as its
free enterprise system is very new and because the companies are usually
‘equity carve-outs’ from state-owned enterprises (SOEs). Analyzing a
company that was formerly a part of an SOE is difficult because of the
lack of prior history, the subjective nature of what assets and liabilities
are carved-out, uncertainty over the abilities of managers, and the role of
government as a major shareholder. Our measures of forecast performance
include examining the bias and rationality of the prospectus forecasts. In
addition, we examine the relationship between initial stock returns and
forecast accuracy.
The paper proceeds by giving a brief history and overview of the IPO
process in China. Then, the relevant literature is summarized. This is followed by a description of the research design, a discussion of the empirical
results, and the conclusions.
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2. IPOs in China
About twenty years ago, the People’s Republic of China (PRC) set in
motion a string of economic reforms that were intended to transform a
centrally-planned socialist system into a socialist market system. The
socialist market economic system is one in which private enterprise is
encouraged and ‘free’ markets operate, but where the government keeps
some control and influence, especially in certain core sectors of industry.
Reasons for the change in philosophy included concerns that economic
growth and technological development were too slow and a belief that
market based systems would help stimulate the economy and enhance
social welfare of the people. One of the major steps taken to implement
economic reforms was the corporatization of state-owned enterprises
(SOEs). This involved setting up limited liability companies with ownership represented by share capital. The markets in which the corporatized
SOEs operate were opened up for competition, government and stateplanning involvement was significantly reduced, and company objectives
included the maximization of profits and shareholder wealth. Corporatized
SOEs had to modernize and expand and funding for these endeavours was
to come from private investors.
In order to sell shares to the public, companies issue prospectuses. The
form of the prospectus is stipulated by the China Securities Regulatory
Commission (CSRC). There are two distinct classes of investors in Chinese
SOEs; one class is domestic investors who are issued A-shares and the
other class consists of foreign investors who are issued B-, H-, or N-shares
depending on which exchange the shares are traded.2 Since 1991 there
have been more than 500 A-share listings and about 90 B-share listings
(by the end of 1996). In most cases the B-share companies have also
issued A-shares. A prospectus issued to foreign investors follows international practice with quite extensive disclosures relating to the company’s
history, operations, markets, management, financial history, plans for
the issue proceeds, and details of the issue. These prospectuses are quite
lengthy and the issues are underwritten, sponsored, and reported on by
well-known international underwriters, merchant and investment banks,
and Big Six auditing firms. The B-shares rank parri passu with A-shares in
terms of rights and dividends; the differences relate to who is allowed to
own the shares, where they are traded, and the currency in which dividends
are paid and prices are quoted. B-shares traded in Shenzhen pay dividends
in Hong Kong dollars while B-shares traded in Shanghai pay dividends in
U.S. dollars. The segmentation of the A-and B-share markets is quite
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effective as witnessed by the very large price differences between them
(Poon et al., 1998a).3 A-shares often cost twice as much as B-shares which
implies arbitrage opportunities are limited. A-share issues also require
prospectuses. These prospectuses contain less information than the B-share
prospectuses and they (the A-share prospectuses) are published in designated newspapers rather than given to prospective investors. Although the
A-share prospectuses are shorter than the B-share prospectuses, the most
important information is still contained in them; the extra information in
the B-share prospectus is mainly a background of the company, its markets,
and the plans and rules and regulations of the PRC. The CSRC requires
IPO prospectuses to include a forecast of the current year’s profit.4
A China IPO is normally issued at a fixed price and that price is detailed
in the prospectus. The number of shares to be issued is also given in the
prospectus. The period between the issue of the prospectus and the listing
date now averages about one month.5 Using the profit forecast and the issue
price, a prospective price earnings ratio is established and this is often
highlighted in the offer document. The financial news media, investment
analysts, and investment advisers use price earnings relatives in deciding
whether to invest and whether to recommend an investment. The price earnings relative is the prospective price earnings ratio of the IPO benchmarked
against similar, but listed, stocks. IPOs hire accounting firms to report on
the prospectus information (termed the reporting accountants). The company’s auditor is usually the reporting accountant. The reporting accountant, among other things, certifies calculations and accounting methods
used in the profit forecast. They do not, however, certify the assumptions
used by company directors in constructing the forecast.
The practices adopted in making B-share IPOs and the contents of
B-share prospectuses are similar to those in Hong Kong, Singapore, and
many British Commonwealth countries. In particular, the practices of Hong
Kong have strongly influenced the procedures used in China. Accounting,
legal, and banking advice for China IPOs has invariably come from Hong
Kong and the sponsors of the issues are usually financial institutions based
in Hong Kong (e.g., the Hong Kong regional offices of international banks).
There are, however, differences between China and other countries when
it comes to selling shares to the public. Central and regional government
still wield significant power in the economy and they usually remain
the majority shareholders in IPOs. This, together with the lack of comprehensive corporate and commercial laws, adds greater uncertainties to
the sale of new shares. In the early 1990s, the length of time between the
sale of shares and shares being listed was often quite lengthy (e.g., three
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Gongmeng Chen and Michael Firth
months or more) and this contrasts with lags of about three weeks in Hong
Kong, Malaysia, and Singapore. B-share issues are generally bought by
financial institutions, investment funds, and wealthy private investors.
Small investors do not generally subscribe to B-share issues and this contrasts with IPO markets in Hong Kong, Malaysia, and Singapore, where
small private investors are active subscribers to new issues. Because of
this feature, B-share issues are heavily marketed to professional investors.
Companies that issue A-shares but not B-shares adopt different practices. There is less disclosure by, and much less scrutiny of A-share IPOs.
Accountants, lawyers, and stockbrokers who advise on the new issue are
from the PRC and they don’t have strong reputations unlike the advisers
for B-share issues. Although A-share prospectuses contain a lot of information, investors do not make much use of them. IPOs need the approval of
central and regional government (and associated ministries and quasigovernment organizations) and only a minority of A-share companies are
approved to sell B-shares (about 20 per cent). China is concerned with its
reputation in the international arena and so B-share companies go through
a careful screening process. B-share prospectuses and annual reports use
international accounting standards whereas A-shares use PRC accounting
standards.
China IPOs are difficult to value. Firstly, the companies have very short
histories. Although SOEs are quite old, the equity carve-out that constitutes the IPO is usually quite recent. The decision on what assets and
liabilities to transfer depends on political as well as commercial considerations. While companies that list are normally required to have a record of
profitability in the three prior years, the methods of calculating profits
for segments of the business that form the IPO involve some subjective
allocations and decisions. Secondly, the product markets that the IPO
operates in may have only recently become open and competitive and in
some cases significant government control and regulation may still exist.
This makes it difficult to judge historical profitability and predict future
conditions. Thirdly, the state, government ministries, and local and regional
cities and provinces, between them, usually have a controlling stake in the
ownership of the IPO and so the company is subject to political and social
pressures that may impinge on financial objectives and performance.
3. Prior Research
The accuracy of profit forecasts appearing in IPO prospectuses has been
studied in a number of countries. In particular, new issues in Commonwealth
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countries have a long history of providing forecasts and in Malaysia, New
Zealand, and Singapore stock exchange listing requirements and company
law mandate the inclusion of profit forecasts in prospectuses. A characteristic of many countries where forecasts are provided is that IPO shares
are sold to the general public6 and the prospectus is the main source
of information for investors. This situation contrasts with many IPOs
made in the United States where the new issue is sold mainly or exclusively to institutions and preferred clients of the issuing house. Institutional
investors and large preferred clients of stockbrokers have many ways to
learn about the IPO and they do not need a prospectus profit forecast to
help them predict earnings and hence share price. For example, IPO ‘road
shows’, company meetings, and stockbroker reports, are available to the
large size investors who are typically approached to take up new issue shares.
New issue prospectuses in the United States typically do not publish profit
forecasts. This is due to concerns about lawsuits if the forecasts are proved
inaccurate and because investors have other means to predict earnings.
Research in Australia (Lee et al., 1993), Canada (Pedwell et al., 1994)
and New Zealand (Firth and Smith, 1992; Mak, 1989) revealed that
forecast errors were very large. The Australian and New Zealand studies
showed that the mean forecast errors were negative thus implying actual
profits were below forecast; these mean results were driven by a few very
large errors and the median results showed positive errors. Research from
Britain also shows significant absolute forecast errors and that the forecast
profits were lower than actual (Dev and Webb, 1972; Ferris and Hayes,
1977; Keasey and McGuinness, 1991). Keasey and McGuinness (1991)
concluded that new issue profit forecasts were more accurate than those
generated from time series extrapolations of historical earnings. Dev and
Webb (1972) found a positive relationship between forecast errors and the
forecast horizon while Ferris and Hayes (1977) found the opposite.
Studies on Asian IPO markets have found relatively small forecast
errors. Chan et al. (1996) and Jaggi (1997) reported mean absolute forecast errors for listings in Hong Kong to be 18 per cent and 12.9 per cent,
respectively. Both studies attempted to model cross-sectional variability in
forecast errors but found low levels of explanatory power (14.8 per cent
and 5 per cent, respectively). The length of the forecast horizon, company
size, auditor, debt to equity, industry, and year of issue were not statistically significant. Firth et al. (1995) found comparatively high forecast
accuracy for listings in Singapore although cross-sectional models seeking to explain forecast errors had disappointing results. Mixed results
are reported in Malaysia. Mohamad et al. (1994) found low to moderate
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forecast errors while a later study by Jelic et al. (1998) found somewhat
higher errors. Jelic et al., reported that young companies and companies
in the construction and service industries had the largest forecast errors;
they also showed that forecasts were much less accurate when the actual
profit fell from the pre-IPO year.
4. Research Design
4.1 Forecast error metrics
A number of error metrics are employed in this study. The basic forecast
accuracy measure used is the forecast error. It is calculated as:
FE = (AP – FP)/|FP|
(1)
where
FE = forecast error
AP = actual profit
FP = forecast profit
The difference between the actual profit and the forecast profit is scaled
by the forecast profit (FP). One alternative is to scale by actual profit. A
problem in using actual profit in our study is that a few companies have
profits very close to zero and so the FEs are extremely large and the mean
AFE likewise becomes very high. Utilizing FP as the divisor avoids very
high measures of forecast error. The results reported here use FP as the
denominator in equation 1. Replications of the study’s tests but scaling
forecast errors by actual profits, and where extreme outliers are omitted,
give qualitatively similar results to those reported here. Previous research
has mostly scaled forecast errors by forecast profit (Keasey and McGuinness,
1991; Mak, 1989; Firth and Smith, 1992; Mohamad et al., 1994; Jelic
et al., 1998; Chan et al., 1996; Firth et al., 1995; Clarkson et al., 1989;
Pedwell et al., 1994). Jaggi (1997) is an exception; he used the actual
profit as the denominator.
The absolute forecast error is given by
AFE = |FE|
(2)
The mean of FEs across all IPOs gives an indication of the biasedness of
forecast errors while the mean of AFEs indicates the overall level of
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accuracy. A positive mean value for FE implies a pessimistic bias where
forecasts are less than actuals. Conversely, a negative mean value for FE
indicates an optimistic bias as forecasts are higher than actual profits.
In order to better gauge the accuracy of the IPO profit forecast we
compare it against the forecasts from two simple time series models. The
comparison uses a superiority measure of forecast accuracy developed
by Brown et al. (1987) for examining the ability of investment analysts
to predict corporate earnings. Brown et al.’s measure is adapted for
assessing the superiority of IPO profit forecasts. The first superiority
model is:
SUP = ln (((APt – APt–1)/(APt – FPt))2)
(3)
where ln is the natural log operator. The numerator measures the actual
change in profit from year t-1 (the year prior to listing) to year t (the year
end after listing) and the denominator is the unscaled accuracy of the
profit forecast. The term (APt – APt–1) is the error from a simple random
walk time series prediction of profits; APt–1 is a random walk model
prediction of profits in time period t. If SUP . 0 then the IPO profit
forecast is more accurate than the random walk model and, conversely, if
SUP , 0 then the random walk forecast is superior. The change in actual
profits, the numerator in equation 3, may be regarded as a proxy for the
inherent difficulty in forecasting for a particular company; the greater the
change in earnings, the more difficult it is to forecast accurately.
The random walk times series prediction used in equation 3 is a very
simple model. An alternative is to construct a growth model for predicting
profits. Here the historical profit, APt–1, is multiplied by one plus the
growth rate of profits measured over the period t–3 to t–1. The growth
model, GRO, is:
1
GROt = APt–1 (APt–1/APt–3) ⁄
(4)
2
The refined superiority metric is:
RSUP = ln (((APt – GROt)/(APt – FPt))2)
(5)
If RSUP . 0 then the IPO forecast is more accurate than the growth
model forecast. APt–1, APt–2, and APt–3 are disclosed in prospectuses and so
investors can use these numbers to develop forecasts. On average we
expect positive signs on both SUP and RSUP as the company ought to be
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able to make more accurate forecasts than the mechanical time series
models.
4.2 Cross-sectional explanatory models of profit forecast accuracy
Accuracy measures vary across IPOs and, similar to studies in other
countries, we develop models to explain the magnitudes of AFE, SUP, and
RSUP. The selection of independent variables is based on the results from
other studies and from a priori reasoning. The model for explaining
A-share AFEs is as follows:
AFE = β0 + β1LSIZE + β2HOR + β3LEV + β4ROA + β5AGE +
β6OWN + β7FOR + β8BSIX + β9B8 + β10UND + β11EX +
15
20
i=12
i=16
Σ βiINDi + Σ βiYEARi
(6)
where
LSIZE = logarithm of total assets
HOR = length of the forecast period measured from the prospectus date
to the year end to which the forecast pertains
LEV = total debt/total assets
ROA = average return on assets in the previous three years
AGE = age of the company from incorporation to prospectus date
OWN = percentage of shares owned by the public
FOR = dummy variable taking the value one (1) if the IPO had previously
or concurrently issued B-shares, otherwise FOR is coded zero (0)
BSIX = dummy variable taking the value one (1) if the IPO engaged
the services of an international Big Six auditor, otherwise BSIX
is coded zero (0)
B8
= dummy variable coded one (1) if the IPO engaged the services
of one of the Big Eight auditors in China, otherwise B8 is coded
zero (0). The Big Eight auditors are Beijing Certified Public
Accountants, Beijing Zhonghua Certified Public Accountants,
Shanghai Certified Public Accountants, Shanghai Dahua Certified
Public Accountants, Lixin Certified Public Accountants, Sheko
Zhonghua Certified Public Accountants, Shenzhen Zhonghua
Certified Public Accountants, and Sheko Xinde Certified Public
Accountants. The Big Eight auditors are those with the largest
revenues. The ninth biggest auditor is much smaller than the
eighth.
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UND
= dummy variable coded one (1) if the IPO is underwritten by one
of the six largest underwriters in China, otherwise UND is
coded zero (0). The largest underwriters are Guotai Securities,
Nanfang Securities, Junan Securities, Shenyin-wanguo Securities,
Haitong Securities, and Huaxia Securities. These underwriters
are the largest in terms of the volume and value of IPOs underwritten. These six firms are generally acknowledged to be the
most prestigious underwriters.
EX
= dummy variable coded one (1) if the IPO is listed on the Shanghai
Securities Exchange, otherwise EX is coded zero (0)
IND = industry dummy variables representing utilities (β12), real estate/
property (β13), conglomerate (β14), and industrial/manufacturing
(β15)
YEAR = year dummy variables representing IPOs made in 1992 (β16),
1993 (β17), 1994 (β18), 1995 (β19), and 1996 (β20)
Similar models are used to explain SUPs and RSUPs (the independent
variables are as set out above). The AFE model for B-share returns is as
follows:
AFE = β0 + β1LSIZE + β2HOR + β3LEV + β4ROA + β5AGE +
11
15
i=8
i=12
β6OWN + β7EX + Σ βiINDi + Σ βiYEARi
(7)
The variables are defined above. The auditor and underwriter variables are
dropped as the foreign share IPOs invariably use internationally renowned
advisers and so there is little variation in their reputational standings.
There were only three observations in 1991 and they have been grouped
with 1992 observations in estimating β12. Similar models are used to
explain B-share SUP and RSUP measures.
Directional signs are hypothesized for explaining AFEs (opposite signs
apply to SUP and RSUP). A negative sign is hypothesized for LSIZE.
Larger companies may have more control of their market settings and
so they may find it easier to forecast. A positive sign is hypothesized for
HOR as the longer the period to be forecast, the more uncertainty there is.
Previous empirical research, however, has found mixed results (Dev and
Webb, 1972; Ferris and Hayes, 1977; Firth and Smith, 1992; Jelic et al.,
1998; Lee et al., 1993; Pedwell et al., 1994). Leverage (LEV) can be expected to have a positive sign as the more debt a company has, the more
risky its profits and cash flows (Eddy and Seifert, 1992). ROA is a control
variable for profitability. AGE is another risk variable and a negative
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coefficient is usually hypothesized (Jelic et al., 1998). In the case of China
IPOs, however, there is not a lot of variability in AGE as most companies
have been incorporated for less than three years (although the SOE may
be quite old, the separation and incorporation of the to-be-listed equity
carve-out is generally very recent). A negative coefficient is hypothesized
for OWN. IPOs may be extra vigilant in making profit forecasts when they
have a lot of shares held by the public. Firstly, the public investors are
‘outsiders’ and they have no other means to predict earnings (unlike insiders who have access to management and who may be involved in managing the operations of the business). Secondly, companies want to earn a
good reputation with external investors because they are likely to seek
additional financing from them in the future.
FOR is hypothesized to have a negative coefficient. Companies that
have issued, or are concurrently issuing, foreign shares will take extra care
in ensuring their forecasts are accurate because of the need to develop their
reputations. While domestic investors have limited investment opportunity
sets, foreign investors have many choices and they will demand high standards from management. Companies that have been selected for issuing
B-shares are likely to have more sophisticated corporate governance structures and management teams in place and so the importance of accurate
shareholder (and prospectus) information is emphasized. BSIX, B8, and
UND, are proxies for auditor and underwriter reputation variables and
negative coefficients are hypothesized. Some of the A-share IPOs had engaged the services of an international Big Six auditing firm although their
auditor (and reporting accountant) is a Chinese CPA firm. The Big Six firm
provided consultancy advice on accounting, financial, and management
matters. BSIX, B8, and UND are hypothesized to have negative coefficients
when explaining AFEs because they endeavour to maintain their reputations
by being associated with more accurate information disclosures (such
as profit forecasts). Research from other countries has produced mixed
results for the auditor variable (Firth and Smith, 1992; Firth et al.,
1995; Jelic et al., 1998). EX, IND, and YEAR are included as control
variables.
4.3 Bias and rationality tests
The bias and rationality of profit forecasts appearing in IPO prospectuses
can be evaluated by cross-sectional regressions of changes in actual earnings on forecast changes in earnings. The procedures of De Bondt and
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Thaler (1990) and Capstaff et al. (1995) are adapted to IPO forecasts. The
model is:
(APt – APt–1)/(APt–1) = α + β ((FPt – APt–1)/(APt–1))
(8)
AP and FP are as defined previously and α and β are estimated via the
regression.
Following the arguments of De Bondt and Thaler (1990) unbiased
forecasts imply α = 0; if α . 0 then the forecasts have a pessimistic bias
(forecasts less than actuals) and if α , 0 then the forecasts have an
optimistic bias. Rationality of forecasts implies β = 1. β . 1 is interpreted
as underreaction to available information (e.g., APt–1) because the forecast
is too low, and β , 1 is interpreted as overreaction to available information (Capstaff et al., 1995).
Equation 8 is modified to substitute GRO (as defined in equation 4) for
APt–1. Thus:
(APt – GROt)/(GROt) = α + β ((FPt – GROt)/(GROt))
(9)
The null hypotheses are α = 0 and β = 1.
The rationality of forecasts implies a one to one correspondence between
the actual change in profit and the forecast change in profit (equation 8)
and a one to one correspondence between the difference in actual profit and
growth forecast of profit and the difference in forecast profit and growth
forecast of profit (equation 9).
4.4 Profit forecasts, market valuation, and initial stock returns
IPO profit forecasts are used by investors in their decision making on
whether to subscribe for the new issue and whether to buy further shares
after listing. Investors apply an ‘appropriate’ price earnings multiple to the
earnings forecast to derive a likely market price; this estimate of market
price is then compared to the issue price and investment decisions are
made accordingly. The ‘appropriate’ price earnings multiples that are applied to profit forecasts may be based on those of similar but already listed
companies and/or based on those of recently listed IPOs (Firth, 1998). The
market value of a company on its first day of listing will be a function of
the profit forecast. Investors will, however, make adjustments if they can
predict errors in the forecasts. In order to examine whether investors
do correctly anticipate forecast errors, regression models based on Firth
(1997, 1998) are employed. The first model (equation 10) is:
MV/BV = β0 + β1PF/BV + β2FER/BV + β3EX + β4FOR
(10)
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Gongmeng Chen and Michael Firth
where
MV/BV = market value of the shares at the end of the first day of listing
scaled by book value of net assets
PF/BV = profit forecast published in the prospectus divided by book
value of net assets
FER/BV = forecast error, given by actual profit minus forecast profit,
divided by the book value of net assets
EX
= a dummy variable where listings on the Shanghai Securities
Exchange are coded one (1) and those listed on the Shenzhen
Stock Exchange are coded zero (0)
FOR
= a dummy variable coded one (1) if the A-share company has
B-shares either already issued or about to be issued, otherwise
FOR is coded zero (0)
Positive signs are expected on the PF/BV and FER/BV coefficients.
Market value is a function of the forecast profit. If investors can at least
directionally predict whether forecasts are optimistic or pessimistic then a
positive sign on FER/BV is expected. EX is added as a control variable for
the specific stock exchange. To some extent the stock exchange variable
is a proxy for the regional base of a company with Shenzhen listing many
businesses from Southern China while Shanghai listings are dominated by
companies from the East and North of China.7 FOR is a dummy variable
representing whether A-share IPOs also have existing or newly issued
B-shares. Companies with B-shares may expect to be valued more highly
because the state has specially selected these firms for foreign investment
and the state does not want these IPOs to be viewed as poor investments;
investors recognize this and price A-shares (that have B-shares) at a
premium. The FOR variable is dropped when examining B-share IPOs;
most B-share issues have A-shares.
A second model relates the initial stock return on IPOs to the forecast
error. If we assume the issue price is a function of the profit forecast and
‘underpricing’ is a fixed percentage, then any observed variation in the
initial returns may be a function of investors’ perceptions of the profit forecast accuracy (the ex ante perceptions being proxied by ex post realizations
of forecast errors). The model is:
IR = β0 + β1FE + β2LSIZE + β3EX + β4FOR
(11)
where
IR
= the percentage return on the IPO stock on the first day of trading
FE
= the percentage forecast error given by equation 1
LSIZE = log of the total assets of the company
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EX and FOR are as defined previously. The FOR variable is dropped
for the B-share sample analysis. A positive sign is hypothesized on the
FE variable.
4.5 Data
Our sample data consist of all IPOs made on the Shanghai Securities Exchange (SH) and the Shenzhen Stock Exchange (SZ) from 1991 to 1996
and which have profit forecasts published in their prospectuses. Table 1
presents a breakdown of the sample by year, by stock exchange, by
A-shares and B-shares, and by industry. There are about five times as
many A-share issues as there are B-share issues and the majority of companies are classified as being in the industrial and manufacturing sector.
Industry membership is as defined by the Shanghai Securities Exchange
and the Shenzhen Stock Exchange. Descriptive statistics relating to the
data are given in Table 2.
Table 1. Distribution of IPOs By Year and By Industry
Panel 1—By Year
Year
SHA
SZA
TOTAL A
SHB
SZB
TOTAL B
TOTAL
1991
1992
1993
1994
1995
1996
TOTAL
4
74
64
19
7
82
250
14
21
59
19
5
79
197
18
95
123
38
12
161
447
0
10
13
11
2
6
42
3
6
11
4
10
9
43
3
16
24
15
12
15
85
21
111
147
53
24
176
532
Panel 2—By Industry
Industry
SHA
SZA
TOTAL A
SHB
SZB
TOTAL B
TOTAL
Finance
Utilities
Property
Conglomerate
Industrial
TOTAL
40
21
9
42
138
250
21
12
18
17
129
197
61
33
27
59
267
447
3
3
3
4
29
42
2
7
3
3
28
43
5
10
6
7
57
85
66
43
33
66
324
532
SHA = A-share listings on the Shanghai Securities Exchange; SHB = B-share listings on the Shanghai
Securities Exchange; SZA = A-share listings on the Shenzhen Stock Exchange; SZB = B-share
listings on the Shenzhen Stock Exchange.
© Blackwell Publishers Ltd. 1999.
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Gongmeng Chen and Michael Firth
Table 2. Descriptive Statistics of Sample Data
Variable
Mean
Median
Standard deviation
LSIZE
HOR (months)
LEV %
ROA %
AGE (years)
OWN %
FOR %
BSIX %
B8 %
UND %
EX %
19.67
5.00
51.42
8.17
1.73
22.95
13.00
4.92
32.00
45.00
56.00
19.61
4.80
53.69
7.00
0.42
25.00
0.94
3.13
17.93
6.09
2.84
10.20
LSIZE = logarithm of total assets; HOR = length of the forecast period measured from the prospectus
date to the year end to which the forecast pertains; LEV = total debt/total assets; ROA = average return
on assets in the previous three years; AGE = age of the company from incorporation to prospectus
date; OWN = percentage of shares owned by the public; FOR = dummy variable taking the value one
(1) if the IPO had previously or concurrently issued B-shares, otherwise FOR is coded zero (0); BSIX
= dummy variable taking the value one (1) if the IPO engaged the services of an international Big Six
auditor, otherwise BSIX is coded zero (0); B8 = dummy variable coded one (1) if the IPO engaged
the services of one of the Big Eight auditors in China, otherwise B8 is coded zero (0). The Big Eight
auditors are Beijing Certified Public Accountants, Beijing Zhonghua Certified Public Accountants,
Shanghai Certified Public Accountants, Shanghai Dahua Certified Public Accountants, Lixin Certified
Public Accountants, Sheko Zhonghua Certified Public Accountants, Shenzhen Zhonghua Certified
Public Accountants, and Sheko Xinde Certified Public Accountants; UND = dummy variable coded
one (1) if the IPO is underwritten by one of the six largest underwriters in China, otherwise UND is
coded zero (0). The largest underwriters are Guotai Securities, Nanfang Securities, Junan Securities,
Shenyin-wanguo Securities, Haitong Securities, and Huaxia Securities; EX = dummy variable coded
one (1) if the IPO is listed on the Shanghai Securities Exchange, otherwise EX is coded zero (0).
5. Empirical Results
5.1 Forecast errors
Table 3 shows the distributional characteristics of forecast errors (FE),
absolute forecast errors (AFE), and superiority measures (SUP, RSUP).
The mean, median, and standard deviations of the errors and superiority
measures are broken down by exchange and by A-and B-shares. The
forecast errors for all partitions are greater than zero and are statistically
significant for the A-shares; the positive signs indicate that actual profits
exceed forecasts and this is consistent with a pessimistic bias in the forecasts. There is less bias in the profit forecasts associated with B-share
issues. The mean forecast errors are higher than those reported in Hong
Kong (Jaggi, 1997) and Singapore (Firth et al., 1995).
© Blackwell Publishers Ltd. 1999.
Profit Forecasts and IPO Firm Valuations
217
Table 3. Summary Statistics of IPO Profit Forecast Accuracy Measures
A-SHARES
SHA (n = 250)
Mean Median
FE
AFE
SUP
RSUP
SZA (n = 197)
σ
Mean Median σ
23.24** 8.64 109.50 20.23**
43.09** 21.38 103.29 36.35**
1.51** 1.33
3.31 1.98**
1.81** 1.61
3.47 2.19**
3.58
15.71
1.51
2.03
TOTAL A (n = 447)
Mean Median
83.76 21.92** 5.20
78.10 40.12** 20.47
3.13 1.72** 1.41
3.54 1.98** 1.69
σ
98.90
93.01
3.24
3.50
B-SHARES
SHB (n = 42)
Mean Median
FE
AFE
SUP
RSUP
12.65
5.63
49.54** 29.73
0.99*
1.13
1.53** 1.31
SZB (n = 43)
σ
92.54
78.79
3.52
3.67
Mean Median σ
16.37*
34.52**
1.32*
2.20**
3.61
15.14
1.47
1.63
TOTAL B (n = 85)
Mean Median
55.31 14.49
4.67
45.93 42.13** 20.57
3.79 1.15*
1.13
3.45 1.86** 1.47
σ
75.99
64.71
3.63
3.55
* statistically significant at the 0.05 level; **statistically significant at the 0.01 level
FE = forecast error = (AP – FP)/|FP|; AFE = absolute forecast error = |FE|; SUP = superiority measure
= ln(((APt – APt–1)/(APt – FPt))2); RSUP = refined superiority measure = ln (((APt – GROt)/(APt –
FPt))2); AP = actual profit; FP = profit forecast given in the IPO prospectus; ln = natural logarithm
operator; GROt = APt–1 multiplied by (APt–1/APt–3) ⁄ .
For definitions of SHA, SHB, SZA, and SZB, see Table 1.
1
2
The mean absolute forecast errors (AFE) are statistically significant for
all partitions and exceed those reported in Hong Kong (Chan et al., 1996;
Jaggi, 1997) and Singapore (Firth et al., 1995). The AFEs are less than or
similar to those reported in Australia, Canada, and New Zealand.8 The
magnitude of AFEs are comparable across A-share and B-share issues and
the absolute errors for Shenzhen listings are lower than for listings made
in Shanghai.
The mean SUP and RSUP scores are greater than zero and this implies
IPO profit forecasts are more accurate than the forecasts derived from
the two time series models. This accords with our expectation. The mean
superiority scores are significantly positive in all partitions of the sample.
A distributional breakdown of the forecast errors is given in Table 4.
As can be seen the variability is quite high with 34 percent of A-shares
© Blackwell Publishers Ltd. 1999.
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Gongmeng Chen and Michael Firth
Table 4. Distribution of Forecast Errors
SHA
Forecast error No.
.100%
(50%, 100%)
(20%, 50%)
(10%, 20%)
(0%, 10%)
(–10%, 0%)
(–20%, –10%)
(–50%, –20%)
(–100%, –50%)
9
25
52
31
50
18
16
31
16
SZA
TOTAL A
SHB
%
No.
%
No.
%
No.
3.629
10.08
20.97
12.5
20.16
7.258
6.45
12.048
6.451
11
20
31
15
49
25
14
23
7
5.641
10.26
15.9
7.692
25.13
12.82
7.18
11.790
3.590
20
45
83
46
99
43
30
54
23
4.515
10.16
18.74
10.38
22.35
9.707
6.77
12.19
5.191
3
2
6
4
9
1
1
4
8
SZB
% No.
7.895
5.263
15.79
10.53
23.68
2.632
2.632
10.53
21.05
5
1
4
2
10
5
3
6
1
%
TOTAL B
No.
%
13.51
8 10.67
2.703 3 4
10.81 10 13.33
5.405 6 8
27.03 19 25.33
13.51
6 8
8.108 4 5.33
16.22 10 13.333
2.703 9 12
Forecast Error (FE) is defined in Table 3. For definitions of SHA, SHB, SZA, and SZB, see Table 1.
having optimistic forecasts (i.e., forecasts exceeding actual) and 39 per
cent of B-shares having optimistic forecasts.
5.2 Cross-sectional model results
Results from the cross-sectional regressions modelling AFE, SUP, and
RSUP are shown in Table 5 (for A-shares) and Table 6 (for B-shares). Model
fits in Table 5 are poor and explain between 6.6 percent and 1.8 percent
of the variability in the accuracy measures. Very few coefficients are
statistically significant in any of the regressions. LSIZE has a positive and
significant coefficient in the model of AFEs. Larger size companies make
less accurate profit forecasts; although this goes against our hypothesis,
it is similar to the findings from some other research (Firth and Smith,
1992). Property companies are associated with poor accuracy and issues
made in 1994 have higher accuracy. The adviser variables (BSIX, B8, and
UND) are not significant; BSIX has a negative sign which is consistent
with our expectation. Research from elsewhere has not modelled SUP and
RSUP measures and so no comparisons can be made. There are very
few significant results in panels B and C. The BSIX variable is positive
and significant in the RSUP analysis; this indicates international Big
Six auditors are associated with IPOs that make superior forecasts. The
negative sign on LEV in the SUP regression implies that IPO forecast
superiority is weaker for companies with higher leverage.
In contrast to the AFE results for A-shares, the model fit for the B-share
AFEs is quite strong with the regression explaining 60 percent of the
© Blackwell Publishers Ltd. 1999.
Profit Forecasts and IPO Firm Valuations
219
Table 5. Cross-Sectional Regression Results Explaining AFE, SUP, and RSUP
Accuracy Measures of A-Share IPOs (equation 6)
Panel A
AFE (×100)
Variable
LSIZE
HOR
LEV
ROA
AGE
OWN
FOR
BSIX
B8
UND
EX
Utilities
Property
Conglomerate
Industrial
1992
1993
1994
1995
1996
Intercept
adjusted R2
12.513
2.794
–47.053
97.628
–0.169
32.664
–15.252
–12.489
15.350
12.988
1.073
–5.256
95.818
23.025
11.856
–24.381
–32.599
–74.964
–70.110
–38.577
–198.115
0.066
Panel B
SUP
(2.194)*
(1.705)
(–1.689)
(1.306)
(–0.103)
(0.544)
(–0.902)
(–0.521)
(1.440)
(1.246)
(0.103)
(–0.256)
(4.239)**
(1.330)
(0.870)
(–0.901)
(–1.275)
(–2.464)*
(–1.854)
(–1.469)
(–1.737)
–0.287
–0.089
–1.793
–2.931
–0.046
2.412
0.858
0.911
–0.018
–0.093
–0.430
–0.819
–0.920
–1.075
–0.852
0.809
0.787
1.779
1.046
1.372
8.131
0.018
(–1.431)
(–1.550)
(–1.833)*
(–1.117)
(–0.790)
(1.143)
(1.444)
(1.081)
(–0.049)
(–0.254)
(–1.178)
(–1.133)
(–1.158)
(–1.768)
(–1.779)
(0.851)
(0.876)
(1.664)
(0.787)
(1.486)
(2.030)
Panel C
RSUP
–0.004
–0.027
–0.918
5.308
0.082
0.379
–0.329
2.596
0.514
0.224
–0.629
–1.090
–1.656
–0.649
–1.123
–0.071
–0.082
1.306
0.820
–0.050
2.835
0.028
(–0.019)
(–0.426)
(–0.823)
(1.766)
(1.305)
(0.160)
(–0.498)
(2.851)**
(1.244)
(0.560)
(–1.580)
(–1.380)
(–1.811)
(–0.974)
(–2.153)*
(–0.069)
(–0.008)
(1.131)
(0.561)
(–0.050)
(0.630)
t-statistics in parentheses
*statistically significant at the 0.05 level; **statistically significant at the 0.01 level.
equation (6): AFE (SUP, RSUP) = β0 + β1LSIZE + β2HOR + β3LEV + β4ROA + β5AGE + β6OWN +
15
20
i=12
i=16
β7FOR + β8BSIX + β9B8 + β10UND + β11EX + Σ βiINDi + Σ βiYEARi
IND = industry dummy variables representing utilities (β12), real estate/property (β13), conglomerate
(β14), and industrial/manufacturing (β15); YEAR = year dummy variables representing IPOs made in
1992 (β16), 1993 (β17), 1994 (β18), 1995 (β19), and 1996 (β20). Other independent variables are defined
in Table 2.
differences in accuracies. HOR has a significant and positive coefficient
in line with our expectation. OWN has a positive coefficient which is
contrary to the hypothesized relationship. ROA and Property sector IPOs
have significant positive coefficients while B-share new issues made in
1995 and 1996 had more accurate forecasts. The model fits (adjusted R2)
for B-share SUP and RSUP accuracy measures are very poor and
significance levels of individual variables are statistically weak.
With the exception of AFEs for B-shares, the explanatory powers of
the models are poor and very few independent variables are statistically
© Blackwell Publishers Ltd. 1999.
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Gongmeng Chen and Michael Firth
Table 6. Cross-Sectional Regression Results Explaining AFE, SUP, and RSUP
Accuracy Measures of B-Share IPOs (equation 7)
Panel A
AFE (×100)
Variable
LSIZE
HOR
LEV
ROA
AGE
OWN
EX
Utilities
Property
Conglomerate
Industrial
1993
1994
1995
1996
Intercept
adjusted R2
–1.967
3.349
–30.731
324.755
0.103
149.062
6.937
3.923
225.700
10.270
11.860
–16.278
–24.283
–56.246
–59.740
8.847
0.601
Panel B
SUP
(–0.256)
–0.971 (–1.425)
(2.042)*
–0.012 (–0.089)
(–0.957)
–1.072 (–0.379)
(3.142)** –10.345 (–1.161)
(0.047)
–0.061 (–0.318)
(1.936)
–6.332 (–0.929)
(0.454)
0.269 (0.200)
(0.130)
–1.878 (–0.704)
(5.500)** –1.078 (–0.337)
(0.326)
–1.589 (–0.569)
(0.432)
–0.667 (–0.274)
(–1.123)
0.335 (0.264)
(–1.443)
–0.410 (–0.282)
(–2.315)*
3.231 (1.511)
(–2.547)*
3.527 (1.710)
(0.055)
24.261 (1.711)
–0.056
Panel C
RSUP
–0.415
–0.095
–3.561
–5.927
–0.339
–5.485
–0.860
–5.092
–2.395
–4.697
–3.835
0.638
1.669
2.593
2.937
18.218
–0.055
(–0.548)
(–0.644)
(–1.232)
(–0.549)
(–1.542)
(–0.705)
(–0.609)
(–1.892)
(–0.747)
(–1.493)
(–1.544)
(0.485)
(0.983)
(1.144)
(1.239)
(1.114)
t-statistics in parentheses
*statistically significant at the 0.05 level; **statistically significant at the 0.01 level.
equation 7: AFE (SUP, RSUP) = β0 + β1LSIZE + β2HOR + β3LEV + β4ROA + β5AGE + β6OWN +
11
15
8
12
β7EX + Σ βiINDi + Σ βiYEARi
IND = industry dummy variables representing utilities (β8), real estate/property (β9), conglomerate
(β10), and industrial/manufacturing (β11); YEAR = year dummy variables representing IPOs made in
1993 (β12), 1994 (β13), 1995 (β14), and 1996 (β15). Other independent variables are defined in Table 2.
significant. The evidence shown in Tables 5 and 6 yield little support for
our hypotheses. One interpretation of the results is that managers do their
best in making forecasts but errors are unavoidable. The errors are random
and have no (or few) systematic relationships with company or share issue
characteristics. While disappointing, the non-significance of the regressions are similar to the results in most other studies conducted outside of
China. Explaining cross-sectional differences in profit forecast accuracy
measures remains a difficult challenge.
5.3 Bias and rationality tests
Table 7 reports the results for the bias and rationality tests based on the
De Bondt and Thaler (1990) research design. Panels A and B report the
results from regression equation 8 for A-shares and B-shares respectively,
© Blackwell Publishers Ltd. 1999.
Profit Forecasts and IPO Firm Valuations
221
Table 7. Tests of Bias and Rationality in IPO Profit Forecasts
Variable
α
β
adjusted R2
Panel A
equation 8
A-Shares
Coefficient
(t-statistic)
Panel B
equation 8
B-Shares
Coefficient
(t-statistic)
Panel C
equation 9
A-Shares
Coefficient
(t-statistic)
Panel D
equation 9
B-Shares
Coefficient
(t-statistic)
–5.145
(–7.244)**
6.511
(119.8)**
0.978
0.082
(1.629)
1.015
(1.25)
0.941
0.655
(3.467)**
0.363
(–9.95)**
0.288
0.082
(0.676)
0.829
(–5.18)**
0.900
**statistically significant at the 0.01 level (for β coefficients, the t-test is for differences from one (1)).
equation 8: (APt–APt–1)/(APt–1) = α + β((FPt – APt–1)/(APt–1))
equation 9: (APt – GROt)/(GROt) = α + β((FPt – GROt)/(GROt))
where: AP = actual profit; FP = forecast profit; GROt = APt multiplied by (APt–1/APt–3) ⁄
1
2
while panels C and D relate to regression equation 9 for A-shares and
B-shares respectively. The results for the A-share samples show that
the hypothesized coefficients for α and β are not observed. The intercept
term, α, is significantly different from zero, being negative in panel A
(implying over optimism) and positive in panel C (implying pessimism).
The β coefficients are significantly greater than one (panel A) and significantly less than one (panel C) implying underreaction and overreaction
to available information, respectively.
The B-share results reported in panels B and D are marginally more
in keeping with expectations. The intercept term, α, is positive but not
significant for both panels B and D. The slope term, β, is not significantly
different from unity in panel B and this implies rationality in the forecasts.
In panel D, however, the slope coefficient is significantly less than unity
implying an underreaction to available information. In general the results
from the bias and rationality tests do not indicate that IPO profit forecasts
are made in unbiased and rational manners.
5.4 Profit forecasts and market valuation
Regression equation 10 relates the market value of IPOs after listing as a
function of the profit forecast and the accuracy of the forecast. The results
are shown in Table 8; panel A relates to A-shares and panel B to B-shares.
The results are in line with the hypothesized relationships. Market
values of both A-shares and B-shares are positively and significantly
© Blackwell Publishers Ltd. 1999.
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Gongmeng Chen and Michael Firth
Table 8. The Relationship between IPO Market Values and Prospectus Profit
Forecasts and Forecast Errors (equation 10)
Panel A: A-Share issues
Variable
PF/BV
FER/BV
EX
FOR
Intercept
adjusted R2
Panel B: B-Share issues
Coefficient
t-statistic
Coefficient
t-statistic
16.526
8.525
1.314
2.779
3.483
0.314
12.777**
7.108**
2.449*
3.547**
6.357
9.181
9.630
–2.399
3.602**
3.626**
–1.931
3.293
0.201
2.267
*statistically significant at the 0.05 level; statistically significant at the 0.01 level
equation 10: MV/BV = β0 + β1PF/BV + β2FER/BV + β3EX + β4FOR
where: MV/BV = market value of the shares at the end of the first day of listing scaled by book value
of net assets; PF/BV = profit forecast published in the prospectus divided by book value of net assets;
FER/BV = forecast error, given by actual profit minus forecast profit, divided by the book value of
net assets; EX = a dummy variable where listings on the Shanghai Securities Exchange are coded one
(1) and those listed on the Shenzhen Stock Exchange are coded zero (0); FOR = a dummy variable
coded one (1) if the A-share company has B-shares either already issued or about to be issued, otherwise FOR is coded zero (0).
related to profit forecasts and forecast accuracy. The results are consistent
with investors being able to accurately predict forecast accuracy and
incorporating this into the pricing of IPOs. Table 8 shows that A-share
issues that concomitantly (or previously) list B-shares are priced more
highly. This result is consistent with the Chinese government only allowing (or ensuring) B-share issues for companies that are successful. Panel A
indicates A-shares are more highly priced in Shanghai (vis-à-vis Shenzhen)
while panel B shows the opposite.
Table 9 gives the regression results for the model of initial returns
(equation 11). The R2 statistics are very poor although this is not unexpected as explaining underpricing is notoriously difficult. For the A-share
sample, a positive and statistically significant coefficient is observed for
FE. This supports our hypothesis that investors are able to correctly predict the sign of the forecast error and use this prediction in pricing shares.
FE is not significant (and in fact has a negative sign) for the B-share
sample.
6. Conclusions
This study is the first to examine profit forecasts in China and our research
procedures include some measures that have not been previously used
© Blackwell Publishers Ltd. 1999.
Profit Forecasts and IPO Firm Valuations
223
Table 9. Initial Stock Returns on IPOs and the Accuracy of Profit Forecasts
(equation 11)
Panel A: A-Share issues
Variable
FE
LSIZE
EX
FOR
Intercept
adjusted R2
Coefficient
0.361
–0.403
0.234
0.046
10.165
0.014
t-statistic
2.223*
–2.186*
0.723
0.092
2.817
Panel B: B-Share issues
Coefficient
t-statistic
–0.057
–0.024
0.115
–1.290
–0.547
1.636
0.571
0.029
0.636
*statistically significant at the 0.05 level
equation 11: IR = β0 + β1FE + β2LSIZE + β3EX + β4FOR
where: IR = the percentage return on the IPO stock on the first day of trading; FE = the percentage
forecast error given by equation 1; LSIZE = log of the total assets of the company; EX = a dummy
variable where listings on the Shanghai Securities Exchange are coded one (1) and those listed on the
Shenzhen Stock Exchange are coded zero (0); FOR = a dummy variable coded one (1) if the A-share
company has B-shares either already issued or about to be issued, otherwise FOR is coded zero (0).
in the context of IPOs. Our study uses basic forecast errors and measures
of forecast superiority over time series models to evaluate accuracy. The
mean forecast errors are positive across all of the partitions based on listing exchange and the A-share and B-share categories. Thus, on average,
IPOs’ actual profits exceed their forecasts. The magnitudes of absolute forecast errors are higher than those reported for Hong Kong and Singapore,
but less than the errors found in Australia, Canada, and New Zealand.
Accurate forecasts are a function of the ease of forecasting (e.g., the
underlying variability and fluctuations of business conditions), the skill
of the forecasters, and the ability and willingness of company executives
to ‘manage’ earnings (Chan et al., 1996; Jelic et al., 1998).9 Forecasts are
found to be more accurate than those based on time series extrapolations
of historical profits. This finding implies IPO forecasts have utility over
and above what investors can glean from historical earnings.
Similar to studies in other countries, cross-sectional regression models
largely fail to identify reasons for differences in forecast errors. We also
apply the models to help explain superiority measures. With the exception
of explaining absolute forecast errors for B-shares, the models have poor
explanatory powers with few, if any, significant independent variables.
While the B-share regression of absolute forecast errors explains 60 percent of the variability in accuracies across companies, support for the
© Blackwell Publishers Ltd. 1999.
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Gongmeng Chen and Michael Firth
hypothesized relationships is weak. While the forecasting period coefficient has the expected positive sign, the positive sign on the ownership
variable is counter-intuitive.
Bias and rationality tests, based on the work of De Bondt and Thaler
(1990) and Capstaff et al. (1995), have weak results. The results from
Table 7 suggest that B-share offerings listed in Shanghai have coefficients
that are consistent with unbiased and rational forecasts but the A-share
IPOs do not. Analyses using market listing prices and initial returns show
that profit forecasts are used to value companies. Further, our research
results indicate that investors anticipate the direction, and to some extent
the magnitude, of forecast errors when pricing A-shares and B-shares.
First day returns on the A-shares are explained, in part, by forecast accuracy; no such relationship is reported for B-shares. In general there is no
evidence that the profit forecast accuracy of B-shares is much different
from that for A-shares.
China is set to continue its economic reforms of the state sector by
pushing ahead with stock market listings of SOE companies. In the future,
privately owned firms established in China are also likely to seek stock
market quotations. Because of inherent uncertainties in investment in China,
potential shareholders will continue to need reliable data and the integrity
of profit forecasts appears indispensible given the lack of independent
alternative sources of information. This study finds that profit forecast
accuracy in Chinese IPOs, while not outstanding, is comparable to that in
some other nations. Comparisons to Hong Kong and Singapore are less
favourable, however, and so continued vigilance is needed to press for
further information disclosures and to stress the importance of forecast
accuracy.
Notes
1. Examples include a background on China’s economic reforms, detailed descriptions
of management and products, and detailed discussions of risks facing the company.
2. B-shares trade on the Shenzhen and Shanghai stock exchanges and are denominated
in Hong Kong dollars (Shenzhen) and U.S. dollars (Shanghai). H-shares trade on the Stock
Exchange of Hong Kong and N-shares trade on the New York Stock Exchange. There are
plans to trade foreign shares on other ‘international’ stock exchanges.
3. A unique characteristic of the market segmentation in China is that the foreign shares
(B-shares) are much cheaper than the domestic shares. In other countries where domestic
and foreign investors are segmented, it is the domestic shares that are cheaper (Poon et al.,
1998b).
4. Current year’s profit is the profit for the calendar year in which the IPO takes place.
All companies in China have a 31 December year end. The length of the forecast horizon
© Blackwell Publishers Ltd. 1999.
Profit Forecasts and IPO Firm Valuations
225
varies from about twelve months (i.e., those issues made in early January) to less than one
month (i.e., those issues made in December).
5. IPOs made in the 1980s and early 1990s often had to wait one or more years before
they were listed.
6. Stock Exchange rules often stipulate that some IPO shares have to be sold to ‘small’
investors and that a certain minimum of shareholders must exist.
7. In recent years the two Exchanges have become more cosmopolitan and there is
strong competition between them for new listings. Historically, the Exchanges principally
attracted IPOs from their geographic hinterland.
8. Forecast errors and absolute forecast errors in Malaysia are less than those reported
by Jelic et al. (1998) but more than those reported by Mohamad et al. (1994). The results
from China are therefore in between the two results from Malaysia.
9. Teoh et al. (1998) conclude that IPO companies in the U.S. engage in earnings
management, where managers use discretionary accruals to change profit numbers toward
some desired outcomes. Aharony et al. (1993) and Friedlan (1994) also find evidence of
earnings management in American IPOs, especially for small size companies.
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