An analysis of Accounting-based valuation models using
Australian IPOs
Lachlan Tuite
The University of Sydney
btui2697@usyd.edu.au
Abstract: Comparable firm multiples are often supported as the primary method to be used
in valuing Initial Public Offerings (IPOs) despite the existence of more sophisticated models.
Empirical research has found relatively weak support for comparable multiples in IPO
settings and there are few studies that investigate a range of alternatives to this method. This
study aims to contribute to the literature by providing such an analysis of a range of
alternative models, and will do so through a value relevance framework. Furthermore, a
review of the literature unveils some inconsistency concerning the value relevance of
management earnings forecasts frequently provided by IPO firms in disclosure documents.
This study will be able to provide some resolution of this inconsistency. Finally, this study
seeks to examine several criticisms of the Edwards-Bell-Feltham-Ohlson (EBFO) model’s
implementation in an Australian IPO setting.
1
1.0 Introduction
An Initial Public Offering (IPO) is when an entity first goes to the public to raise funds. IPOs
are characterised by a higher level of uncertainty than seasoned equity offerings and other
events that might occur during a lifetime of a firm including mergers, acquisitions, and
management buyouts. Such increased uncertainty is due to a large information asymmetry
between the promoters of the issue and prospective buyers. This makes IPOs an interesting
situation in which to explore valuation issues.
This study seeks to resolve three related issues that emerge form the literature. Firstly this
study examines the effectiveness of various accounting-based valuation models in estimating
the value of IPOs. Whilst the comparable multiples approach is widely endorsed by practice
and academia, there are other valuation models that are ostensibly better theoretically
developed than this approach. For example, the Edwards-Bell-Feltham-Ohlson (EBFO)
models are arguably a relatively more complete model, albeit utilising some fairly restrictive
assumptions. Surprisingly there are few tests in the literature examining such an issue, much
less in an IPO environment. Whilst the proposed study does not test the various models
directly, the value relevance approach taken does allow for a statement to be made about the
likely effectiveness of the models.
Secondly, a review of the literature reveals some inconsistency over the relevance of
management earnings forecasts in IPO disclosure documents. The proposed study seeks to
resolve this issue. Thirdly, with respect to the EBFO models, there is much criticism of some
of the assumptions that are made as well as those additional assumptions that researchers have
made in implementing the model empirically. This primarily concerns the linear information
dynamic assumed between earnings of different periods and the corresponding effect on the
value relation. This study seeks to assess whether such concerns are present within the
Australian IPO context.
The following section will discuss the literature relevant for the research question outlined
above. The subsequent section will develop the testable hypothesises and explain the research
method to be used.
2.0 Prior literature
In order to lay the foundation for the rest of the paper, two key areas of the literature will now
be discussed in some detail; accounting-based valuation and IPOs.
2.1 Valuation
There is a vast literature on valuation across three distinct disciplines; accounting, finance and
economics. For the purpose of this study, the accounting-based valuation models are of most
interest. The three models to be empirically tested in this study are the EBFO models, the
comparable multiple models and the residual income valuation model.
2.1.1 EBFO models
The EBFO models attempt to explicitly link accounting information to equity valuation
through a specific dynamic process. These models start with the basic and widely
acknowledged proposition that the equity value of a firm is the summation of the discounted
future stream of dividends, the Present Value of Expected Dividends (PVED) model. With
the aid of the clean surplus assumption, it is possible to convert the PVED model into a model
that accommodates the current book value of equity and abnormal earnings as being the
2
determinants of value. The clean surplus relation states that any change in book value must
be attributed to either earnings or to net distributions (i.e. dividends). This particular
formulation is not new in the literature, with similar models dating back to the beginning of
last century (see Bernard 1995 for more background information).
What makes the EBFO models a novel contribution to the literature is the explicit link
between value and accounting information that the linear information dynamic provides. The
linear information dynamic provides for the relationship between abnormal earnings of
different periods. Specifically the relationship between the year ahead abnormal earnings is
defined as the current abnormal earnings multiplied by a persistence factor plus a factor for
‘other information’ and an error term. The ‘other information’ variable is similarly defined in
an autoregressive-one process 1 . The persistence parameters are then used to estimate firm
value in a closed-form process. This separates the EBFO model from the residual income
valuation model that will be discussed below.
Despite widespread praise of the original Ohlson (1995) model and subsequent efforts, the
empirical evidence on the EBFO models is mixed. Dechow, Hutton and Sloan (1999) found
limited support the EBFO modelling developed by Ohlson (1995) and extended in the
intervening time. However further studies have found fault with the underlying methodology
of the EBFO modelling. Criticism has centred mainly on the linear information dynamic.
Whilst linear information dynamics allows for relatively parsimonious analysis, there is
evidence that such dynamics are not appropriate 2 . Burgstahler and Dichev (1997) found for
instance that equity is a convex function of earnings (book value), holding book value
(earnings) constant. That is, the relevance of earnings and book value depends on the relative
level of earnings and book value. This makes sense intuitively as firms with low or negative
earnings will seek to resurrect their fortunes and thus current earnings would not be expected
to be a good indicator of firm value, which is contingent on future events. It is not clear how
this affects IPOs, characterised as they are by the fact that many such firms seek additional
capital to enlarge current operations i.e. raising funds to continue with current production
technology as opposed to pursuing an alternate process.
In a more direct test of the EBFO models, Myers (1999) did not find support for linear
information dynamics. Myers calculated firm value using variations on the EBFO model and
assessed the estimated parameters against actual parameters and found a significant variation.
Interestingly, Myers also found that the linear information dynamic of the EBFO models did
not provide significantly better estimates than book value alone. Following on from
Burgstahler and Dichev (1997) and Myers (1999) a number of studies have attempted to
develop non-linear information dynamics, based on real-options 3 .
The ‘other information’ component in the EBFO models is often omitted due to this variable
being a priori unknown. Alternatively limited metrics are used as proxy for this ‘other
information’ e.g. Myers (1999) used order backlogs as the proxy for ‘other information’,
although Callen and Segal (2005) suggest that this is not a particularly representative
surrogate. Whilst initially left undefined, later papers discussed methods of estimating this
variable using currently available data, namely earnings forecasts (Liu and Ohlson 2000;
Ohlson 2001). More recent models in the EBFO literature have accounted specifically for
‘other information’ (e.g. Begley and Feltham 2002). Furthermore, the study by Callen and
Segal (2005) suggests that inclusion of an appropriate ‘other information’ variable can
1
Although there have been some attempts to introduce multi-period lags e.g. Morel (1999)
The EBFO model does not rest on linear information dynamics opposed to any other information
dynamic being used (Christensen and Feltham 2003). Thus appropriate modifications may be made
whilst retaining the original model’s intuition.
3
See for example Yee (2000), Zhang (2000), Biddle, Chen and Zhang (2001), and Ashton, Cooke, and
Tippett (2003).
2
3
significantly improve the power of the model, as they find some support for the EBFO model
using the suggestions by Liu and Ohlson (2000) and Ohlson (2001). Although the EBFO
model does not restrict the number of ‘other information’ variables, to avoid cumbersome
analysis, this variable is typically kept to just one representation.
2.1.2 Comparable multiples
Comparable multiples are a popular method for estimating firm value due to their simplicity
as well as their uncanny accuracy (Richardson and Tinaikar 2004). Multiples for instance do
not require the explicit estimation of cost of capital and streams of future dividends or
abnormal earnings and are not subject to the same level of very strict assumptions of other
valuation models. However this is not to say that multiples lack an underlying theory. There
are a number of multiples in common use including the more prominent price-earnings and
market-to-book ratios. Other multiples that may be used include sales-to-book, forecast
earnings-to-price, and cash-to-price 4 .
The use of comparable multiples is predicated on the idea that the relevant ratio captures the
market’s assessment of both risk and growth prospects (Zarowin 1990). This is derived from
the development of the various models. For example, the price-earnings ratio is based on the
assumption that capitalised future earnings are sufficient for estimating price, and thus
implicitly make assumptions about risk and growth. Zarowin (1990) did not find evidence
that multiples were associated with risk, whilst finding only modest evidence for a significant
association with growth. However, Zarowin used Sharpe-Lintner betas to proxy for risk and
there has been considerable evidence that such measures of risk are deficient (Fama and
French 2004). There does not appear to be any subsequent study that has assessed multiples
with respect to other estimations of firm-specific risk.
2.1.3 Residual income valuation model
The residual income valuation (RIV) model defines the value of a firm as the summation of
the book value of the firm and the discounted stream of abnormal earnings of the firm.
Typically a number of periods of abnormal earnings would be explicitly forecasted and
discounted before a final forecast is set in an infinite series, which is to be distinguished from
the closed form EBFO model. As indicated in section 2.1.1, the residual income model has
been present in the literature for some time. Whilst technically any (appropriate) numbers
may be substituted for book value and earnings – there is no restriction that dictates that these
two variables be book value and earnings – as in this instance accounting summary measures
are substituted, this model is labelled an accounting based valuation model. The RIV model
relies on forecasts of future earnings capturing all the information relevant in estimating the
value of the firm.
2.2 Initial Public Offerings (IPOs)
As identified previously, IPOs are characterised by a higher level of uncertainty than other
events that might occur in a firm’s life. This makes sense given that in contrast to some of the
other major events in the firm’s life identified in the introduction, an IPO is an instance where
the firm, coming from relative obscurity, must convince the market to invest. Prior to the
IPO, many firms are almost unknown in investor circles and thus there is a high level
4
Liu, Nissim and Thomas (2002) elaborate on further variations.
4
uncertainty surrounding the typical issue 5 . Given the large information asymmetry between
promoters and investors, there is a large role for signalling information in the valuation
process. The following paragraphs will briefly discuss some significant issues vis-à-vis IPOs
in the provision of management forecasts, IPO valuation, and the role of signalling in IPO
valuation.
2.2.1 Management earnings forecasts
Under Australian corporate law 6 IPO promoters are required to disclose all the information
that investors would reasonably require to make an informed decision about whether or not to
participate in the share offer. Past studies have shown that provision of management forecasts
are almost the de facto standard 7 . However a recent study suggests that the introduction of
legislation in 2000 8 , which relaxed the litigation risk faced by promoters that disclose
forecasts, resulted in less frequent disclosure subsequent to the introduction of the legislation
(Chapple, Clarkson, and Peters 2005). This is consistent with much of the voluntary
disclosure literature (e.g. Verrecchia 1983) that proposes fairly intuitively that firms will
disclose when the costs of non-disclosure are more than the costs of disclosure 9 . In any case
the fact that the management of some IPO firms do disclose management forecasts would
seem to indicate that they at least perceive some value in doing so. The evidence however is
relatively mixed.
Clarkson, Dontoh, Richardson and Sefcik (1992) and Firth (1998), in a Canadian and
Singaporean environment respectively, found a significant association between earnings
forecasts and value. Firth in fact uses a modified version of Clarkson’s et al model and
interacted earnings forecasts with a measure of retained ownership and finds this to be
significantly related to firm value also. This would seem to suggest that the act of providing
an earnings forecasts induces the firm to emphasize that such a signal is credible by retaining
more of the shares in the company. However contrary to the studies listed above, How and
Yeo (2001) did not find earnings forecasts to be value relevant. Whilst How and Yeo (2001)
performed their test in an Australian environment in contrast to the previous studies, the
sample was taken from the period before the introduction of the CLERP 1999 legislation.
Given the similar between-country litigation environments at that time this does not appear to
be the cause of differentiation. Chapple et al (2005) find significant but relatively weak
results for the value relevance of earnings forecast in contrast to How and Yeo (2001) in the
period before the introduction of CLERP 1999. Following the introduction of the legislation
this relationship is found to be significantly stronger. Perhaps, as Chapple et al (2005) note,
this is due only to firms that have a reasonable basis for disclosing forecast earnings doing so.
5
There are of course exceptions where well known firms have publicly listed and where analyst
following would be considerably higher and consequently there would be less uncertainty about the
firms future prospects.
6
In particular s710 of the Corporations Act 2001
7
See for example How and Yeo (2001) and Lee, Taylor and Taylor (2006)
8
Corporate Law Economic Reform Program Act 1999 – amongst other amendments, this act amended
the Corporations Law with respect to liability for inadequate disclosure and the oversight role of ASIC.
9
Here the costs of disclosure and non-disclosure include for example proprietary costs through
revealing information to competitors, changes in firm cost of capital, and normal charges associated
with disclosure such as audit fees and so forth.
Strictly speaking a reduction in litigation risk should increase the probability of disclosure. However as
Chapple et al (2005) discuss, the reduction in litigation risk applied to omissions as well as inclusions
(i.e. provision of forecast). A reduction of risk with respect to omissions should affect a decrease in
disclosure. As Chapple et al found that frequency of disclosure had dropped significantly this would
suggest that the later effect is more powerful, and is thus consistent with the discretionary disclosure
literature.
5
2.2.2 IPO valuation
Industry practice for valuing IPOs favours the comparable multiples approach outlined in
section 2.1.2 above due to the simplicity in use and the availability of required data (Kim and
Ritter 1999). However recent studies have found that such models have relatively poor
explanatory power (Kim and Ritter 1999 in the USA; How, Lam, and Yeo 2007 in Australia).
Although as Kim and Ritter (1999) noted, it would be concerning if the explanatory power
was very high. Even though comparable firms are chosen due to their similarities to the IPO
firm, the firm under analysis would have many firm-specific factors that distinguish it from
the comparable firm.
Cotter, Goyen and Hegarty (2005) performed a direct test of various valuation models in an
IPO context including the price-earnings multiple and the residual income valuation model 10
and subsequently found that neither model provides a particularly accurate estimate of IPO
price. There does not appear to be any direct test of the EBFO model in an IPO context in the
literature. This is not altogether surprising due to the complexities involved in calculating the
parameters required to estimate value using the model in a ‘seasoned’ market setting,
notwithstanding the assumptions necessary to translate the EBFO model to an IPO setting 11 .
2.2.3 Signalling and IPOs
It has been mentioned a few times in this paper that IPOs are characterised by a large degree
of information asymmetries. Such an environment induces the promoters of the IPO to signal
information about the quality of their offering as otherwise investors may find it difficult to
differentiate between ostensibly similar share offerings. Signals however are only credible
where creation is costly 12 . The provision of management earnings forecasts is an example of
such a signal, albeit one that appears to be open to more abuse than other signals, perhaps due
to a lag between any deception (intentional or otherwise) and punishment, but also due to the
difficult task of providing a forecast about future uncertain events. There are heavy penalties
for firms that disclose forecasts that are later deemed to be misleading. However, whilst the
earnings signal is significant, it is likely that there are a great many other signals that affect
firm value in concert with earnings forecasts, both in modifying type relationships such as in
Firth (1998) as well as by themselves.
A number of other signals have been proposed in the literature that would be expected to have
a significant impact on price. These factors include underwriter prestige (Krinsky and
Rotenberg 1989), audit quality (Feltham, Hughes, and Simunic 1991), other institutional
choices, and retained ownership (Leland and Pyle 1977). However these relationships are not
necessarily straight forward as illustrated by Lee, Stokes, Taylor and Walter (2003) with
respect to the auditing signal.
3.0 Hypothesis development and method
This section will develop the hypothesises corresponding to the research questions outlined in
the introduction and the method to be employed to carry this out. The research takes a value
relevance approach to testing the various models outlined above. The general models will be
10
Whilst Cotter et al (2005) label the residual income model under the EBFO tag, the model they
employ does not contain any information dynamic that is characteristic of the EBFO models.
11
See Myers (1999) for an example of the steps involved. Note that Myers uses some fifteen years of
data to estimate the required parameters. This is clearly not available for IPOs, and thus for an
operationalisation of this model in an IPO context it is likely that market data would have to be used as
a proxy for missing IPO data.
12
See Riley (2001) for a comprehensive review of the signaling and screening literature.
6
presented first, followed by a discussion of key empirical issues including scaling and
collection of data.
The first research question concerns the apparent effectiveness of various accounting-based
valuation approaches in valuing IPOs. As discussed above, whilst the comparable multiples
approach is widely endorsed in the literature, there does not appear to be many studies that
explicitly test this approach with respect to other methods available 13 . As discussed above,
support for the comparable multiples method is through their simplicity and the ability to
avoid explicit forecast of a series of earnings and calculation of appropriate discount rate in
apparent contrast to the other models outlined in this paper. Despite this notable attraction,
the multiples method lacks any detailed modelling of the behaviour of summary accounting
information that characterises, in various degrees, the other two models outlined in this paper.
The EBFO model, as discussed previously, contains an ‘other information’ component. In
most modelling this is featured as a single variable. However as Myers (1999) demonstrated,
additional ‘other information’ variables may be modelled with a corresponding increase in
complexity. Based on the discussion about signalling factors in the IPO environment, it is
appropriate to augment these models with additional factors that are expected to determine
value. Many of the following factors would only be expected to influence the initial value
and would not persist for long.
In the review of prior literature above it was noted that later formulations of the EBFO model
suggested that forecasts of future earnings may be used to derive ‘other information’. For
instance, the difference between current earnings and one period ahead forecasted earnings
would be expected to give some insight into the persistence of current projects and growth of
future projects (Begley and Feltham 2002). Other factors are also envisioned to have some
informational role in determining value.
It is suggested that given auditors have a lot to lose by not performing a sufficiently tight
audit, they will not allow the client firm to pursue overly aggressive accounting policies
(Feltham, Hughes, and Simunic 1991). This would be expected to induce investors to place
more reliance on accounts audited by high quality auditors. A similar case may be made for
underwriters, who in their presence convey a degree of certainty about the offer (Hughes
1986). Leland and Pyle (1977) suggest that the level of retained ownership sends a signal to
prospective investors. Specifically the willingness to retain some ownership in the firm
indicates that they believe future value will be forthcoming i.e. they are all in the same boat.
The operating history of a firm indicates that firm’s ability to operate with a degree of success
over a period of time and provides an important signal to potential investors. Also, under the
same general disclosure section explained above, IPO firms are expected to disclose risk
factors faced by the firm. Thus the number of risk factors identified in the disclosure
document is expected to influence the investor’s beliefs about firm value. A further measure
for uncertainty in the post-issue leverage of the IPO firm, defined as total liabilities over total
assets.
A direct analysis of the various models would involve directly calculating the various models,
estimating the parameters of the EBFO model for instance. However this study takes a value
relevance approach. Part of the reason for this decision is time constraints. Whilst estimating
the parameters is certainly possible for IPOs, the processes involved are quite complex and
time consuming across the entire sample, performing the same process over one or two cases
13
There are however studies that test the comparable multiples model in isolation e.g. Kim and Ritter
(1999) and How et al (2007). Cotter et al (2005) extends this somewhat by also investigation the RIV
model.
7
however would be less demanding 14 . In assessing the accounting-based valuation models, the
models are split into simple and complex models. Simple models are the comparable
multiples models, whereas complex models are the RIV and EBFO models. As the RIV
models are essentially an earlier stage in the development of the EBFO model, it is
appropriate to characterise models in this way. Such characterisation allows the first
hypothesis to be stated:
H1: Complex models outperform simple models of accounting-based valuation in estimating
the value of Australian IPOs.
Initially it would be instructive to examine the models with respect to their original theoretical
development i.e. leaving the multiples without additional information. As discussed above
the EBFO model actively incorporates such elements. However this does not say much about
the particular models as only very rarely would such implementation be used in practice and
thus using these results to reject or not reject H1 would be akin to setting up a straw man to be
knocked down. Cotter et al (2005) suggested that investment banks take a variety of factors
into consideration alongside the comparable multiple. Thus, to truly test the comparable
multiples model, it is necessary to include the range of other factors identified above with
respect to ‘other information’ in the EBFO model.
Pt = α1M + α2SEG + α3MEG + α4AUD + α5UND + α6LP + α7HIST
+ α8RISKF + α9LEV + ε
(1) 15
Pt = δ1bvt + δ2xat + δ3ΔF1 + δ4ΔF2 + δ5AUD + δ6UND + δ7LP + δ8HIST +
δ9RISKF + δ10LEV + ε
(2)
where P = either the offer or end of first day listing price, M = relevant multiplier (i.e.
either earnings, one-period ahead earnings, book value, and sales), bv = book value,
xa = abnormal earnings, SEG = Short-term earnings growth, MEG = Medium-term
earnings growth, ΔF# = change between # period abnormal earnings and the prior
period, auditor (AUD), underwriter (UND), retained ownership measured as in
Leland and Pyle (1977) (LP), operating history (HISTORY), risk factors identified in
report (RISKF) and a measure of leverage (LEV defined as total liabilities divided by
total assets).
The second model above ostensibly tests for the EBFO model, although significant
coefficients on delta one and two also provide support for RIV model as well, albeit
imprecisely. There is however another way to distinguish between the EBFO and RIV
models and determine which of the models provides a better explanation of value. Firstly it is
necessary to recall the above discussion about the development of the EBFO model. This
development led to a close-form valuation model being the function of several factors
including book value, abnormal earnings, ‘other information’ and the associated persistence
factors on abnormal earnings and ‘other information’. It is thus suggested that these
14
It is important to stress that across all the models examined in this study a number of assumptions are
taken in order to facilitate an empirical analysis. These assumptions would be expected to be improved
upon in either a more focused study or in practice and would subsequently improve the power of the
respective models.
15
An intercept is not included in this model. Whilst, and as discussed later, it would be expected that
there would be other information that would be associated with valuation in combination with the
multiple, at this stage this model is testing this multiplier alone. Also note that here the coefficient on
M is effectively the multiplier in question.
This model will have to be adjusted in order to reflect industry variation in multipliers.
8
persistence factors communicate a more sophisticated understanding about valuation than the
simpler RIV model. In order to test this assertion, following Begley and Feltham’s (2002)
approach, capitalised earnings are calculated with this measure replacing delta’s two through
four of model two. This is then compared with a formulation of the model as presented in
model two. The model with the capitalised earnings then effectively identifies as a simple
RIV type analysis. It is expected that the EBFO type analysis will yield a better coefficient of
determination (r2), indicating the value of modelling persistence and growth as is the case in
EBFO type models.
It is further possible to provide some statement about the likely fit of linear information
dynamics in the IPO case. As outlined a few times now, EBFO modelling allows for a
closed-form valuation model to be established, with abnormal earnings for instance being
multiplied by a function of various persistence factors and the firm’s discount rate. This
structure allows the coefficients found in the regression analysis to be reversed out in order to
obtain estimates for the persistence parameters. In the EBFO modelling several restrictions
are placed on these parameters in order to reflect certain assumptions e.g. the persistence on
abnormal earnings is restricted to less than one, corresponding to the pattern expected of
abnormal earnings (declining over time due to competition). Thus it is possible to assess how
likely the implied parameters are given the discount rate, and thus to give some statement
about the apparent ability of the linear information dynamic to describe the valuation process.
This leads to the second hypothesis:
H2: The coefficients in the regression testing the EBFO model are within the range of
expectations given linear information dynamics.
As discussed above in section 2.2.1, there seems to be some conflict in the literature about the
relevance of earnings forecasts. Clarkson et al (1992) and Firth (1998), in foreign settings
find that earnings forecasts are value relevant. However in an Australian study How and Yeo
(2001) found no relevance once several risk factors have been controlled for. Chapple et al
(2005) using a similar test design as How and Yeo and again in an Australian environment
found earnings forecasts to be value relevant either side of the introduction of CLERP 1999.
This is an odd discrepancy as the voluntary disclosure literature suggests that the firm would
only provide the forecast if it was in some way beneficial to the firm. This discrepancy makes
a return to this issue worthwhile. The inconsistency also perhaps points to a more complex
relationship between earnings forecast and value. For example Firth (1998) suggested that the
ownership signal is incremental to the forecast information i.e. the importance of the retained
ownership signal is contingent on a forecast being provided. Following this intuition, there
may be a number of factors have a modifying relationship with earnings forecasts. In fact,
any of the factors identified above in model two could potentially interact with the provision
of earnings forecasts in such a relationship. From this the following hypothesises are formed:
H3a: Management earnings forecasts are value relevant.
H3b: There is a modifying relationship between some of the factors identified in models one
and two and provision of management earnings forecasts.
In order to test H3a and H3b, model two is used as a base; the continuous variables for
abnormal earnings are replaced with categorical variables (one for presence and zero for
absence), leading to model three presented below. To test the respective hypothesises the
appropriate variables will be included or excluded i.e. in order to test H3a the modifying
relationship (attached to zeta six) will be dropped from the following regression model:
Pt = ζ1bvt + ζ2xat + ζ3EF1 + ζ4EF2 + ζ5LP + ζ6Yh*EF1 + ζ7AUD + ζ8HIST + ζ9UND +
ζ10GROWTH + ζ11RISKF + ζ11LEV + ε
(3)
9
In section two, the literature review, a few paragraphs were spent on discussing the linear
information dynamic as it is this component of the EBFO modelling that appears to have
become the leading criticism of the model. The essential message from this literature was that
if a firm is performing poorly currently it is unlikely they will for too much longer as it would
be expected that the firm would pursue some course of action aimed at performing better in
the future. Thus the weighting placed on earnings or book value for instance depends on the
firm’s relative performance. Burgstahler and Dichev (1997) found such a relationship as
discussed above. However this relationship was found amongst firms that had already listed
on a stock exchange. IPO firms however might be expected to be different. IPO firms after
all are raising funds with the aim to increase the scale of their operation amongst other things.
That is IPO firms are firms that one might categorise as highly adaptive firms, in the
Burgstahler and Dichev (1997) terminology. If this is the case then, when adopting a similar
test to Burgstahler and Dichev there should be no significant difference between the
partitioned variables in the equation below. The equations are followed by the associated
hypothesis.
P/BVt-1 = γ1 + γ2DM + γ3DH + γ4(xat/bvt-1) + γ5(xat/bvt-1)DM +
γ6(xat/bvt-1)DH + ε
(4)
P/xat = λ1(bvt-1/xat) + λ2(bvt-1/xat)DM + λ3(bvt-1/xat)DH + λ4 + λ5DM +
λ6DH + ε
(5)
where Dx = dummy indicating medium or high (low implied) book value or earnings
respectively for model five and six.
H4: Using the approach by Burgstahler and Dichev (1997) there is no significant difference
between the different partitions indicating no convex relationship on these terms.
However this is not to suggest that IPOs are not characterised by non-linear dynamics at all.
There might be a number of factors that interact with earnings or book value in a non-linear
fashion. There are a number of other possible factors that might cause the relation between
value and earnings and book values to be non-linear. At this stage of the research project
these factors are still being analysed and so this process will not be discussed here in great
detail. Work to date has involved investigating the possible role of distinguishing between
tangible and intangible assets as a basis of some non-linear relation, either through a partition
similar to models four and five above or through separating book value into those two
components and investigating that effect. Similarly the possibility of higher order variables is
being investigated with the aim of producing a testable hypothesis and corresponding model.
3.1 Cost of capital
Both the RIV and EBFO model require the use of abnormal earnings. Abnormal earnings as
its title suggests are earnings in excess of normal earnings. Abnormal earnings are calculated
as earnings before interest and tax less a capital charge reflecting both the cost of debt and
equity. Interestingly many studies investigating the EBFO models use blanket discount rates
(e.g. Dechow, Hutton, and Sloan 1999; Begley and Feltham 2002). This is either done
through simply declaring a rate across section and time or by assuming a common market
premium and allowing the risk-free rate to change through time. Whilst sensitivity tests
performed in some of these analysis’s indicate that this is not a major concern, Beaver (1999)
noted that given that risk is known to vary by firm, a more realistic representation of risk is
called for and would add more explanatory power to the models.
An alternative to the blanket approach is to let cost of capital vary across industry using
industry betas as Cotter et al (2005) did. Whilst not perfect, this method does introduce some
10
variation across the sample in any one time period. Calculating beta for individual IPOs is
clearly not feasible as beta is of course a measure of the firm’s movement with the market,
which IPO firms do not, by definition, have a history of. Another issue overlooked by
researchers is the cost of debt. CAPM or similar models calculate the cost of equity, however
a firm’s cost of capital is a composite of the cost of debt and equity. Thus researchers
implicitly assume that all the companies in the sample are all-equity companies.
At this stage the source and type of cost of capital is yet to be determined and will be
dependent on the ability to collect the data of over the sample period. Despite the apparent
weakness of some concepts of risk it is likely that some trade-off will be necessary in order to
conduct empirical tests.
3.2 Scaling issues
In research of this type (EBFO and RIV models) scale issues become a concern. As
researchers attempt to isolate effects using large data sets it becomes apparent that size
appears to be a driver in large amount of events – a not altogether unsurprising conclusion.
Therefore, given this is not the factor under analysis, it is important to control for this effect.
Barth and Clinch (2007) identified types of scale effects that can impede statistical inferences
in capital market tests; multiplicative and additive correlated omitted variables, regression
parameters that vary with scale, survivorship and scale-related heteroscedasticity.
Barth and Clinch (2007) found that none of the currently proposed diagnostics for detecting
scaling issues is particularly effective at doing so. In testing different specifications against
simulated data they find that deflating by number of shares performs comparably better than
other specifications, although this method does not dominate the other methods. Importantly
for this study this specification performed well in mitigating additive scale issues. An IPO is
where a firm raises capital and so it is of no surprise that book value affected by the issue will
be related to the value of the firm – this is the additive scale problem. Consequently this
specification will be used throughout the empirical tests 16 . Other specifications introduced in
Barth and Clinch will be used for completeness, although will not comprise the main part of
analysis.
3.3 Data
Much of the data required for the above analysis will be hand-collected off the relevant IPO
prospectus. IPO were identified through examining the Australian Stock Exchange (ASX)
new listings. Mining firms were excluded as mining companies typically do not disclose
earnings forecasts as other components of the prospectus are more important to potential
investors, specifically the geological report. Trusts and pooled development funds were also
excluded as these firms have a different structure. The sample period under analysis is from
July 2001 till June 2005. A cursory examination suggests that in excess of 200 observations
are available in this time frame once the exclusions have been taken into account.
The exceptions to collecting data from disclosure documents are the measures of industryspecific risk and the measure of the risk free rate. Currently a source for the industry risk
measure has not been found whilst the risk free rate will be one of either the rate of return on
thirty-year bonds or the cash rate.
Large accounting firms are so identified as being one of the big four/five i.e. Ernst and
Young, Deloitte Touche Tohmatsu, KPMG, Arthur Anderson or PricewaterhouseCoopers.
Prestigious underwriting firms will be identified as firms that have a national presence and/or
16
As the firms under analysis are IPOs, post-listing shares will be used.
11
that are bank-backed. It is unclear at this stage how many underwriters will then be
categorised as not prestigious. A cursory overview of the prospectus documents within the
sample indicates that those that employ underwriters generally seem to employ those firms
that the above categorisation would label prestigious firms. This appears to be less the case
with mining firms that seem to employ more localised underwriting firms. However, as
discussed above, mining firms are excluded from the analysis.
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