Description
The purpose of this study is to examine the usefulness of pre-production cash expenditure
forecasts issued by Australian mining explorers in their quarterly cash-flow reports.
Accounting Research Journal
The reliability of mandatory cash expenditure forecasts provided by Australian mining
exploration companies in quarterly cash flow reports
Gerry Gallery J odie Nelson
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To cite this document:
Gerry Gallery J odie Nelson, (2008),"The reliability of mandatory cash expenditure forecasts provided by
Australian mining exploration companies in quarterly cash flow reports", Accounting Research J ournal, Vol.
21 Iss 3 pp. 263 - 287
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The reliability of mandatory cash
expenditure forecasts provided
by Australian mining exploration
companies in quarterly
cash ?ow reports
Gerry Gallery and Jodie Nelson
School of Accountancy, Queensland University of Technology,
Brisbane, Australia
Abstract
Purpose – The purpose of this study is to examine the usefulness of pre-production cash expenditure
forecasts issued by Australian mining explorers in their quarterly cash-?ow reports.
Design/methodology/approach – Usefulness is determined by examining compliance and the
reliability of forecasts (accuracy and bias) for a sample of 1,760 forecasts issued by 481 explorers in
2005/2006. The cross-sectional variation in reliability is examined using regression analysis.
Findings – The ?ndings reveal a high level of compliance but signi?cant inaccuracies (median
forecast error of around 50 percent of actual expenditure for exploration and evaluation expenditure
and 85 percent for development expenditure), and some evidence of forecast bias. Forecast inaccuracy
is more prevalent in ?rms that have poorer performance, greater ?nancial slack, greater cash-?ow
volatility, no ?nancial leverage, and for ?rms that are smaller, in the pre-development stage, and in the
mineral (non-oil and gas) sub-industry.
Research limitations/implications – The analysis of forecast usefulness is con?ned to
compliance and reliability. Further research could consider the value-relevance and predictive
ability of these forecasts.
Practical implications – The ?ndings question the usefulness of mandatory forecasting by
showing that the information role of forecasts in capital markets is impaired when ?rms have little
discretion over the forecast decision, timing and speci?city.
Originality/value – This is the ?rst study to examine mandatory cash expenditure forecasts and
makes a signi?cant contribution to the small literature on mandatory ?nancial forecasts.
Keywords Financial forecasting, Cash ?ow, Financial reporting, Australia, Mining industry
Paper type Research paper
1. Introduction
Our study examines the usefulness of forecasted pre-production costs in the quarterly
cash ?ow reports of Australian mining exploration companies, and their relation to
?rm-speci?c characteristics. Three related issues motivate our study. First, numerous
corporate scandals have focused regulators’ attention around the world on
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1030-9616.htm
The authors thank Malik Mirza, Natalie Gallery, participants at the 2008 Paci?c Basin Finance
Economics Accounting Management Conference held at QUT, and an anonymous referee for
their helpful comments and suggestions. The authors also thank Tahlee Fong for her expert
research assistance.
Mandatory cash
expenditure
forecasts
263
Accounting Research Journal
Vol. 21 No. 3, 2008
pp. 263-287
qEmerald Group Publishing Limited
1030-9616
DOI 10.1108/10309610810922503
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strengthening corporate governance and disclosure regulations (Coglianese et al.,
2004). Sound ?nancial disclosures mitigate agency problems by reducing information
asymmetry between management and shareholders. However, where ?nancial
disclosures are poor, the opposite can occur; information asymmetry may increase,
some market participants may be misled, and the ?rm’s cost of equity and shareholder
wealth may be adversely affected (Healy and Palepu, 2001). A higher probability of
poorer quality disclosure is likely to be observed in situations where regulations
prescribe the disclosure of forward-looking information by companies operating in
uncertain environments.
Second, the mining industry plays a signi?cant role in the Australian economy,
representing approximately 20 percent of market capitalisation and about one third of
all Australian Securities Exchange (ASX, 2008) listed companies. The mining industry
is characterised by high-operating risk and information asymmetry, leading to
high-price volatility. In this environment, public disclosure regarding outcomes from
exploration and production activities can have a signi?cant impact on stock prices.
Since mid-2005, the Australian Securities and Investments Commission (ASIC, 2006)
has increased its surveillance of small mining company disclosures. This increased
surveillance followed concerns about inadequate disclosures in 2005 and 2006 and
claims that surveillance efforts by the ASX were inadequate due to the lack of
resources and expertise for monitoring mining company disclosures[1].
Third, the ASX requires listed mining exploration companies to issue quarterly
cash ?ow reports in accordance with listing rule (LR) 5.3 and Appendix 5B. A unique
additional disclosure in these reports is a requirement to forecast future cash out?ows
relating to pre-production expenditure. The release of forward-looking information has
the potential to expand the information set available to investors and its disclosure
may be viewed as one dimension of ?nancial reporting quality since a ?nancial report
containing such information is more likely to be perceived as being of higher quality
(Ajinkya et al., 2005; Karamanou and Vafeas, 2005). However, the uncertain operating
environment of mining exploration companies raises the issue of forecast reliability.
Scott (2003) contends, that to highlight the uncertain nature and improve the reliability
of forward-looking information, companies should only forecast for the period that
such information can be reasonably estimated and in doing so, disclose underlying
forecast assumptions[2]. However, despite this common view on when and how
forecasts should be provided, the LR 5.3 does not permit any forecasting discretion and
does not require disclosure of assumptions by mining explorers in their Appendix 5B
cash ?ow reports[3].
Given the unusual nature of cash expenditure forecasts and the fact that they have
been required for more than a decade, it could be expected that some research would
have been conducted on the usefulness of such forecasts, however, we are unable to
identify any prior research on this issue. The absence of research provides an
opportunity to extend the disclosure literature to mandatory cash expenditure
forecasts. Three research questions are considered:
RQ1. What is the nature of the Appendix 5B cash ?ow forecasts?
RQ2. How accurate are the forecasts, and are they biased?
RQ3. What ?rm-speci?c characteristics in?uence the accuracy and bias of the
forecasts?
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The characteristics examined include performance, ?nancial slack, cash ?ow volatility,
leverage, size (as measured by total assets), age and experience, and sub-industry
(minerals versus and oil and gas).
Our ?ndings show that most mining exploration entities comply with the
requirement to disclose forecasts. Nearly, 90 percent of entities provide evaluation and
exploration expenditure forecasts and nearly 20 percent of these same ?rms also
provide development expenditure forecasts. However, despite the high level of
compliance, our results reveal signi?cant inaccuracies and some bias in the forecasts.
On both a quarterly and pooled basis, the median forecast error is approximately
50 percent of the actual expenditure for exploration and evaluation (EE) expenditure,
and approximately 85 percent of the actual expenditure for development expenditure.
Our ?ndings reveal that forecast inaccuracy is more prevalent in ?rms that have
poorer performance, greater ?nancial slack, greater cash-?ow volatility, no ?nancial
leverage, and in ?rms that are smaller, in the pre-development stage, and in the mineral
(non-oil and gas) sub-industry. We also ?nd evidence that some of the same factors
in?uence forecast bias. The signi?cant inaccuracies and considerable variation across
?rms challenge the wisdom of mandating such forward-looking information for these
type of entities.
The remainder of this paper is organised as follows. Section 2 provides an overview
of the ASX LR as well as extant literature on ?nancial forecasting by managers.
Section 3 provides an overview of related research. Section 4 outlines the research
questions and expectations. Section 5 presents the sample selection and research
design. Section 6 presents the summary statistics and main results of the study.
Section 7 summarises and discusses the implications of the study.
2. Institutional background and quarterly reporting requirements
Disclosures relating to exploration and development activities are governed by the
Australasian Code for Reporting of Mineral Resources and Ore Reserves (JORC
Code)[4]. This code was developed to ensure that mining and exploration companies
report all information necessary for stakeholders to evaluate the activities of the
company (ASX, 2008). The ASX requires listed mining exploration companies to issue
quarterly activity and cash ?ow reports in accordance with ASX LR 5.3 and Appendix
5A and 5B (the JORC Code). With the exception of commitments test entities[5], mining
exploration companies are the only companies in Australia required to provide
quarterly reports in addition to their annual reporting requirements (Gallery et al.,
2004). In most jurisdictions outside North America, annual and semi-annual reports
have been the traditional means for conveying detailed ?nancial and non-?nancial
information to stakeholders.
2.1 ASX listing rule 5.3
On July 1, 1996, the ASXamended LR5.3 to require all listed mining exploration entities
to issue quarterly cash ?owreports, known as Appendix 5Breports. The purpose of this
change was to inform the market on “how the entity’s activities have been ?nanced for
the past quarter and the effect on its cash position” (Australian Stock Exchange – ASX,
2001). The cash ?ow report must be lodged as soon as the information is available or
within one month after the end of each quarter of its ?nancial year (ASX, 2001).
A director or company secretary must complete a compliance statement attesting to
Mandatory cash
expenditure
forecasts
265
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the fact that the information contained in the 5B report has been prepared under
accounting policies that comply with accounting standards, as de?ned in the
Corporations Act 2001 or other standards acceptable to the ASX, and provides a true
and fair view of the matters disclosed. However, unlike annual and semi-annual cash
?owstatements, there is no requirement for the 5Breport to be audited or reviewed by an
external auditor. The format of the cash ?ow report is speci?ed in Appendix 5B, which
contains a proforma cash ?ow statement, some additional ?nancial information, and
limited note disclosures. This format has been modelled on accounting standard AASB
107 Cash Flow Statements[6], which guides the presentation and preparation of annual
cash ?owstatements by reporting entities in Australia. One stated bene?t of a cash ?ow
statement is to inform investors of the “amount, timing, and certainty of future cash
?ows” (AASB, 2007b, para. 5).
Although modelled on AASB 107, the prescribed content of Appendix 5B cash
?ow statements contain a number of notable variations from the AASB 107 version.
First, while the Appendix 5B cash ?ow statement contains the general categories of
cash ?ows (cash ?ows from operating, investing and ?nancing), the line items
within these categories are more detailed and suited to mining companies. For
example, individual line item disclosures are required for cash out?ows for EE,
development, production, and administration activities. Second, other supplementary
information that is not required under AASB 107 must be reported, including details
of related party transactions, securities, and non-cash ?nancing activities, and for the
next quarter, the estimated cash out?ows relating to EE activities (Item 4.1) and
development activities (Item 4.2). Also, an entity wanting to disclose additional
information is encouraged to do so, in a note or notes attached to the report
(Appendix 5B, Note 1)[7].
Interestingly, cash expenditure forecasts are not required to be disclosed under
Australia’s GAAP or other corporate regulations. Although the ASX does not provide
a rationale for their LR requirement, it is presumed that the speculative nature of the
industry calls for greater disclosure in the form of ?nancial forecasts to better inform
investors of the amount, timing, and certainty of future cash ?ows.
2.2 Accounting for EE costs
In Australia, EE costs are accounted for in accordance with AASB 6 Exploration for
and Evaluation of Mineral Resources[8]. Unsurprisingly, most mining exploration
companies exercise their discretion permitted under this standard (AASB, 2007a, para.
Aus 7.1) and capitalise rather than immediately expense EE costs. Hence, these costs
are treated as assets in the balance sheet rather than expenses in the income statement,
which is unlike other costs of a similar nature, such as research and development
expenditure generated in the research phase[9].
The accounting treatment of pre-production costs may provide managers with
incentives to focus their spending on exploration, evaluation and development costs,
rather than other costs. Lilien and Pastena (1982) contend that ?rms that incur large
amounts of pre-production costs have a greater incentive to capitalise these costs
than expense them in order to avoid signi?cant negative impacts on their income
statement and balance sheet. Hence, from a ?nancial statement perspective, when
forecasting pre-production costs, managers have no strong incentive to understate
their forecasts.
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3. Related research
Prior research on ?nancial forecasting by managers largely examines voluntary earnings
forecast incentives (Healy and Palepu, 2001). Only a few studies have examined
management cash forecasts. For example, Wasley and Wu (2006) investigate managers’
incentives inthe USAtoprovide voluntarycash?owforecasts. Ina studyof 2090 forecasts
appearing in press releases frommid-1979 to October 2003, they report that management
issue cash ?owforecasts to signal goodnews incash ?ows and thus, mitigate the negative
impact of bad news in earnings, lend credibility to good news in earnings, and signal
economic viability for young ?rms. They also ?nd that cash ?ow forecasts are important
in meeting investor demandfor this type of information. Unlike prior studies on voluntary
earnings forecasts, which reveal that managers tendto disclose badnews, especially when
the risks of litigation are high (Skinner, 1994, 1997; Francis et al., 1994) and job security
is threatened (Brennan, 1999; Warner et al., 1988; Weisbach, 1988), Wasley and Wu
conclude that different incentives, other than litigation risk, drive the disclosure of
different types of ?nancial information, including cash ?ow information.
There is no known research on mandatory cash ?ow forecasts; however, there are
some studies on mandatory sales and earnings forecasts. For example, Kato et al.
(2006) investigate the accuracy of annual sales and earnings forecasts (as single-point
estimates) issued under the Japanese Stock Exchange Timely Disclosure Rules
(Kessan-Tannsin). In accordance with these rules (as prescribed in the Stock Exchange
Act) listed entities must provide “signi?cant”[10] forecast revisions at interim
announcement dates. Based on a sample of 35,639 management forecasts issued from
1997 to 2006, their results reveal that managers initially set overly optimistic forecasts
at the beginning of the year (especially for ?rms with poor pro?tability), and then issue
downwards forecast revisions to meet realisations.
Kato et al.’s (2006) ?ndings indicate that management forecasts are consistently
biased from one year to the next, possibly because:
.
managers are not exposed to the same high levels of litigation risk as managers
in other countries, such as the USA; and
.
reputationcosts are not suf?cient to penalise managerial opportunisminforecasting.
Kato et al. conclude that despite their consistent optimism, management forecasts in
Japan affect stock prices (albeit these effects are smaller than those observed in the
USA) and are informative about future earnings[11].
In addition to the institutional differences, the relevance of the prior forecast
research to our study is limited by the reporting period (i.e. annual cash ?ows as
opposed to quarterly cash ?ows), type of forecasts required (i.e. net cash ?ows versus
cash out?ows), and/or by the regulatory regime (i.e. voluntary versus mandatory
forecasts). Nevertheless, the ?ndings are important to this study because they show
that, unlike prior earnings forecast ?ndings, the disclosure of cash ?ow information is
potentially motivated by different incentives.
4. Research questions and expectations
4.1 Compliance
Our ?rst research question is: what is the nature of the Appendix 5B cash ?ow
forecasts? Given that these forecasts are mandatory, we focus on the compliance issue
in addressing this question. It is well accepted that routine compliance with disclosure
Mandatory cash
expenditure
forecasts
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regulations requires implementation of effective enforcement mechanisms (La Porta
et al., 2006). The ASX has implemented a market surveillance unit that monitors
company compliance with LRs and provides suspension and expulsion measures for
non-compliance. Nevertheless, a comprehensive investigation of the ASX web site,
company queries, and disciplinary activities from 2001 to 2006 found no evidence of
ASX action with respect to incomplete or inaccurate information in 5B cash ?ow
reports. This lack of enforcement suggests that the forecasting decision and the quality
of forecasts ultimately depend upon managerial discretion[12]. Thus, we expect
non-compliance is likely to be prevalent under these circumstances.
4.2 Forecasting accuracy and bias
Our second research question is: how accurate are the forecasts, and are they biased?
In examining the factors associated with forecast accuracy, most prior studies
associate the frequency and quality of earnings forecasts with attempts to
minimise litigation costs. For example, Baginski et al. (2002) show that bad-news
?rms are more inclined to provide forecasts when the risks of litigation are relatively
high. Bamber and Cheon (1998) ?nd that managers are less inclined to issue speci?c
earnings forecasts when exposure to legal liability and proprietary information costs
are high. Similarly, Skinner (1994) documents that to minimise litigation costs, good
earnings-related news tends to be disclosed as point or range estimates while bad news
disclosures tend to be disclosed in qualitative statements and related to quarterly
earnings announcements.
Reputation costs and credibility concerns are also important considerations in
encouraging accuracy and deterring managers from issuing biased forecasts (Skinner,
1994; Hong et al., 2000; Hong and Kubik, 2003; Rogers and Stocken, 2005). If managers
obtain a reputation for unreliable forecasts, the credibility of their forecasts will
decline, making it less likely that stock prices will respond positively to their forecasts
and harder to convince investors of their managerial ability (Hutton and Stocken, 2007;
Karamanou and Vafeas, 2005). Over 90 percent of US managers surveyed by Graham
et al. (2005) agreed or strongly agreed that voluntarily disclosures promote a reputation
for transparent reporting (La Porta et al., 2006). The ?ndings of Kato et al. (2006) with
respect to mandatory management earnings forecasts in Japan, and Gallery et al. (2008)
with respect to voluntary management earnings forecasts in Australian initial public
offer prospectuses, provide some support for the in?uence of reputation in lower
litigation environments. Both studies show that managers attempt to walk down prior
earnings forecasts through forecast revisions prior to the earnings realisation dates.
In the case of mining exploration companies, cash expenditure forecasts in 5Breports
are issued in point form. These speci?c forecasts encompass a narrower range of
outcomes, which increases the likelihood of inaccuracy. The lack of enforcement and the
associated low litigation risk reduces incentives for managers to devote resources to
providing better quality forecasts. While forecasts are expected to be inaccurate, it is not
clear what incentives managers have to bias their cash expenditure forecasts other than
to meet budgeted expenditure outlays. Also, unlike the Japanese stock exchange and
Australian IPO forecasts, the ASX does not require or explicitly encourage mining
explorers to issue forecast revisions prior to reporting the actual realised out?ows in the
subsequent 5B report. Hence, managers have little opportunity to correct biases in cash
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expenditure forecasts. Therefore, apart from a desire to meet targets or budgets, we do
not expect exploration ?rms to exhibit optimistic biases in their expenditure forecasts.
4.3 Forecast accuracy and ?rm-characteristics
Our third research question is: what ?rm-speci?c characteristics in?uence the accuracy
and bias of the forecasts? In examining this question we consider relevant factors from
prior disclosure research, namely: performance, ?nancial slack, ?nancial leverage, cash
?ow volatility, ?rm age and experience, size, and sub-industry membership[13].
4.3.1 Performance. Disclosure research generally shows that better performing ?rms
produce more frequent and better quality forecasts. Early US research reveals that
management earnings forecasts tend to be more frequent when ?rmperformance is high
rather than low (Penman, 1980; Verrecchia, 1983; Lev and Penman, 1990, Lang and
Lundholm, 1993). However, in more recent times, concerns about increased litigation
risk in the US showthat management forecasts have shifted frombeing more frequently
associated with good performers (a “good news” ?rmbias) to more frequently associated
with poorer performers (a “bad news” ?rmbias) (Skinner, 1994; Kasznik and Lev, 1995).
Outside the USA where litigation risk is lower, the good news bias tends to persist
(Baginski et al., 2002), and there is some evidence that better performing ?rms tend to
produce more accurate and less optimistically biased forecasts (Kato et al., 2006). For
similar reasons we expect that better performing mining explorers will produce cash
expenditure forecasts with similar properties. Also, in the case of these ?rms, a high cash
burn rate contributes to an ongoing need for future funding. The more a ?rm can
generate cash from internal sources, the less uncertainty associated with future cash
?ows. Hence, we expect that better performing ?rms (as measured by cash performance)
are more likely to have more accurate expenditure forecasts.
4.3.2 Financial slack. Suf?cient cash holdings are necessary for ?rms to fund
working capital requirements and investments in positive NPV projects. Observable
differences in cash holdings across ?rms are a natural outcome of differences in the
cost of external ?nancing, capital constraints, and the level of ?nancial distress (Myers,
1984; Myers and Majluf, 1984; Almeida et al., 2004). However, high-cash balances can
induce managers to overinvest in negative NPV projects, which bene?t managers at
the expense of shareholders (Easterbrook, 1984; Jensen, 1986). Furthermore, managers
can more readily consume liquid assets for private gain than ?xed assets (Myers and
Rajan, 1998). This inef?ciency in cash expenditure, or agency view, is generally
consistent with the empirical evidence (Blanchard et al., 1994; Harford, 1999; Opler et al.,
1999). However, the evidence is not conclusive on the optimal level of cash holdings or
the impact of alternative governance characteristics and disclosure on the level of
?rms’ cash holdings (Mikkelson and Partch, 2003; Kalcheva and Lins, 2007).
In addition to the agency concerns of excessive cash holdings, mining exploration
companies face dif?culty in quickly raising external funds due to their size and
uncertain operating environments. Therefore, the failure to meet budgets and estimates
may be related to a combination of agency and non-agency related factors such as poor
planning and cost control. The agency and non-agency arguments both lead to similar
predictions. Where cash holdings are large, managers are more likely to waste cash on
less productive activities (i.e. non-exploration or development activities, such as
administration and inef?cient related-party transactions). In these circumstances,
managers are likely to produce more inaccurate forecasts. In contrast, in companies with
Mandatory cash
expenditure
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lowlevels of cash holdings, managers are more likely to be focused on allocating cash to
productive activities and conserving cash resources. Hence, it is expected that where
?nancial slack is low, managers will issue more accurate expenditure forecasts.
4.3.3 Financial leverage. As leverage increases, lenders demand more information to
ensure effective monitoring of the ?rm in order to assess the probability of a ?rm
meeting its debt obligations (Jensen and Meckling, 1976). However, empirical studies
present mixed ?ndings with respect to the association between leverage variables and
more frequent and better quality disclosure. Some researchers document a positive
association between leverage and the voluntary disclosure of information (Ferguson
et al., 2002; Bradbury, 1992), while others fail to ?nd any signi?cant association (Malone
et al., 1993; Wallace et al., 1994). Contrary to these studies, Meek et al. (1995) report a
signi?cant, negative relationship for US, UK, and continental European multinational
corporations when disclosure is voluntary. A major assumption is that leverage
variables (typically the debt-to-equity or debt-to-asset ratio) accurately proxy for the
underlying ?nancial risk across sample companies regardless of other cross-sectional
differences such as ?rm size, asset structure, operating risk, and industry. In the case of
mining exploration companies only a minority (approximately one third) have any form
of debt in their capital structure. Explorers that have secured debt funding typically
have a higher portion of assets in place, face lower uncertainty about their prospects, and
have agreed to debtholder monitoring. Thus, ?rms with ?nancial leverage are likely to
produce more accurate cash expenditure forecasts than ?rms without ?nancial leverage.
4.3.4 Cash ?ow volatility. Where investors demand information about cash ?ows,
managers have incentives to issue cash ?owforecasts (Wasley and Wu, 2006). However,
where the ?rm’s cash ?ows are more volatile, it can be more dif?cult for managers to
make accurate forecasts (Wasley and Wu). Prior earnings forecast literature contends
that when earnings are highly volatile, managers face a greater risk of making an
inaccurate forecast (Baginski et al., 2002; Kato et al., 2006). In the case of cash ?ows, they
can be more volatile than earnings because of the smoothing effect of accruals in
earnings and because managers can engage in earnings management to reduce earnings
volatility (Wasley and Wu). In the case of mining exploration companies, they operate in
an uncertain environment that contributes to less predictable and more volatile cash
?ows than many other entities. It is therefore expected that as cash ?ow volatility
increases, explorers produce less accurate cash expenditure forecasts.
4.3.5 Firm age and experience. Prior studies provide mixed results on the relationship
between disclosure and ?rmage. Image and reputation are both important considerations
for older, well-established companies, and accordingly, they have been found to disclose
more information than younger companies (Owusu-Ansah, 1998). However, Chen et al.
(2002) ?nd a negative association between disclosure and ?rm age under a quarterly
earnings announcement regime. They argue that investors demand more useful
information fromyounger ?rms because their earnings and production activities are more
uncertain. Consistent with this argument, Wasley and Wu (2006) reveal that younger
?rms issue cash ?ow information to signal economic viability and therefore assist with
raising external capital. However, younger explorers in the Australian market may not be
able to provide suf?ciently accurate cash forecasts to credibly signal to potential fund
providers. Also, age may not successfully capture an explorer’s ability to predict future
cash outlays if the ?rm has been unsuccessful in explorations activities over a number of
years. A more relevant measure is likely to be a ?rm’s stage of operation. If a ?rm has
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progressed to the development stage it is more likely to be able to predict future cash EE
expenditure than ?rms at the exploration stage. We therefore expect that both age and
experience are likely to lead to more accurate forecasts.
4.3.6 Size. Firm size is likely to be closely related to age and experience and also
capture other cross-sectional differences in ?rms. Prior research ?nds that larger ?rms
are more likely to voluntarily disclose earnings forecasts compared to smaller ?rms
(Cox, 1987; Choon et al., 2000). In the IPO setting, ?rm size is also argued to have a
negative relationship with forecast error as larger ?rms have a greater capacity to
absorb the impact of unexpected events, more diverse operations, and more
sophisticated forecasting techniques (Chapple et al., 2005; Hartnett and Romcke, 2000).
In terms of mandated information, empirical evidence suggests that larger companies
disclose more adequate information, as opposed to smaller companies because their
competitive advantage is less likely to be threatened (Owusu-Ansah, 1998)[14].
Therefore, it is expected that larger mining exploration ?rms are more likely to provide
accurate forecasts than their smaller counterparts.
4.3.7 Sub-industry category. Several studies in the disclosure literature have
indicated that industry membership can in?uence a ?rm’s disclosure practices
(Hope, 2003; Dye and Sridhar, 1995). For example, ?rms in high-risk industries may
disclose more information in order to better distinguish themselves from competitors in
the same industry. With regard to cash ?ow forecasts, the accuracy of the forecast is
likely to differ across industries. In the Australian mining industry, there are two major
sub-industries: minerals (materials) and oil and gas (energy). The mineral explorers
typically face greater uncertainty in their exploration activities because of the more
diverse nature of their operations. However, oil and gas ?rms face greater dif?culty in
estimating and extracting hydrocarbon reserves relative to resources measurement and
extraction in the minerals industry (Sykes, 2001). In responding to the uncertainty facing
oil and gas ?rms, the ASX imposes additional disclosure obligations on these ?rms[15].
Owing to these differences, it is expected that oil and gas explorers will provide less
accurate cash expenditure forecasts relative to the mineral explorers.
5. Data and research design
5.1 Sample selection and data sources
The sample comprises all mining explorations companies listed on the ASX that
lodged Appendix 5B reports between September 30, 2005 and July 31, 2006. For most
companies, the study period spans four consecutive quarterly reporting periods, which
varies depending on the company’s respective balance date. A total of 481 companies
are identi?ed as representing the entire population of companies subject to Appendix
5B quarterly cash ?ow reporting by the ASX. Within this population of ?rms, 371
?rms are in the GICS material (minerals) sector (GICS Codes 15101010-15105020) and
110 are in the energy sector (oil and gas) sector (GICS Codes 10101010-10102050).
Where an entity is not listed or has not provided a forecast for a certain quarter, the
quarterly observation is excluded from testing procedures, yielding a total of 1,760
cash ?ow reports (1,377 for the Materials and 383 for energy ?rms).
All quarterly cash ?ow information (including lodgement dates, listing/de-listing
date) was hand-collected from announcements (including Appendix 5Bs reports) and
other information obtained through the Aspect Huntley DatAnalysis database.
Company ?nancial data from the annual ?nancial reports were hand-collected from a
Mandatory cash
expenditure
forecasts
271
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combination of the Connect 4 Annual Reports Collection and Aspect Huntley
FinAnalysis databases.
5.2 Research design
Our research design uses summary statistics to examine research question one
(compliance) and research question two (accuracy and bias). Multivariate regression
procedures are used to examine research question three (factors associated with
accuracy and bias). The following sub-section explains the procedures used to measure
the variables and test expected associations.
5.2.1 Measuring forecasting accuracy and bias. Following prior research, accuracy
or forecast error (FERROR) is measured as the absolute value of the signed cash
expenditure forecast error de?ated by the actual (realised) cash expenditure[16]:
jForecasted cash expenditure 2Actual cash expenditurej
Actual cash expenditure
This measure captures the magnitude of the cash ?ow forecast error and is useful in
measuring the percentage error. However, the measure does not capture the economic
signi?cance of the error. Following prior research (Kato et al., 2006) we therefore use an
alternative metric (using total assets as a de?ator) in regression analysis to capture the
economic signi?cance of the error, calculated as follows:
jForecasted cash expenditure 2Actual cash expenditurej
Total assets
t21
Forecast bias (FBIAS) is the relative (unsigned) directional forecast error. As cash
expenditure is treated as an out?ow (a negative value), a positive forecast error (i.e. the
actual exceeds the forecast) indicates an underestimation or conservative forecast, and a
negative forecast error (i.e. the forecast exceeds the actual) indicates an overestimation
or optimistic forecast. If managers issue cash expenditure forecasts based on true
expectations and these expectations are unbiased, it is expected that on average, forecast
error will be statistically indistinguishable from zero.
5.2.2 Research model. Mining exploration companies are required to forecast cash
out?ows for EE costs, as well as development costs. Hence, the model below will be
used to test research question three where the dependent variable is either forecast
accuracy (equation (1)) or bias (equation (2)) for EE costs, or development costs, and the
independent variables are the previously explained ?rm-speci?c factors that are
expected to explain error and bias:
FERROR
it
¼ a
1
þa
2
PERFORM
it
þa
3
FINSLACK
it
þa
4
CFVOL
it
þa
5
LEVDUM
it
þa
6
AGE
it
þa
7
AGEEXP
it
þa
8
SIZE
þa
9
INDDUM
it
þ1
it
ð1Þ
FBIAS
it
¼ b
1
þb
2
PERFORM
it
þb
3
FINSLACK
it
þb
4
CFVOL
it
þb
5
LEVDUM
it
þb
6
AGE
it
þb
7
SIZE
it
þb
8
INDDUM
it
þ1
it
ð2Þ
where dependent variables: FERROR, jforecasted cash expenditure-actual cash
expenditurej de?ated by lagged total assets; and FBIAS, forecasted cash
expenditure-actual cash expenditure de?ated by lagged total assets (signed values):
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Independent variables: PERFORM, net operating cash ?ows de?ated by average
total assets.
.
FINSLACK, total cash at the end of the current quarter de?ated by average total
assets.
.
CFVOL, standard deviation of net operating cash ?ows over four prior quarters
de?ated by lagged total assets.
.
LEVDUM, one if the company has ?nancial leverage (interest-bearing debt) and
zero otherwise.
.
AGE, number of years between the listing and the current quarterly reporting
date.
.
AGEEXP, one if the company has both actual exploration/evaluation and
development expenditure in the same quarter, and zero otherwise; SIZE, natural
logarithm of average total assets for the current ?scal year (t) and prior year
(t 2 1).
.
INDDUM, one if the company is in the energy sector and zero if the company is in
the materials sector.
6. Results
6.1 Descriptive statistics
Table I reports the number of cash expenditure reports and forecasts issued for EE
costs (Panel A) and developments costs (Panel B) over each of the four quarters and an
average for all quarters. Of the 481 ?rms in the sample (371 for materials and 110 for
energy) an average of 440 reports (344 for materials and 96 for energy) were lodged
with the ASX over the study period. Panel A and B further shows high compliance
with the requirement to provide forecasts. For EE (development) forecasts, only
7.03 percent (6.21 percent) on average fail to provide a forecast when there is actual
expenditure recorded in the subsequent quarter. Although most ?rms provide EE
forecasts (93.81 percent for material ?rms and 91.06 percent for energy ?rms), only a
minority have reached the development expenditure forecast stage (18.94 percent for
material ?rms and 41.82 percent for energy ?rms). The difference between the two
industries highlights the need to control for sub-industry type. Overall, these results
show that contrary to our expectation for research question one, there is a strong
culture of compliance with the ASX’s Appendix 5B forecasting requirements.
Table II presents the descriptive statistics for forecasting accuracy (error and bias)
for EE (Panel A) and development expenditure (Panel B). In both panels statistics for
error and bias de?ated by:
(1) actual cash out?ows; and
(2) by lagged total assets are reported.
In Panel A, the mean (median) error for the pooled observations (ERROR Pooled) is
210.3 percent (46.8 percent), indicating that companies are issuing signi?cantly inaccurate
forecasts. More than half the sample issues forecasts with a median error of nearly
50 percent. Similar results are reported across each of the four quarters. In contrast, the
median EE forecast bias is only 22.9 percent, which indicates a small optimistic bias (i.e.
forecast expenditure is greater than actual expenditure). The statistics for EE forecast
Mandatory cash
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forecasts
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Table I.
Number of forecasts
issued by quarter
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error and bias calculated using the total asset de?ator further show that the error is also
economically signi?cant. The mean (median) forecast error is 18.1 percent (15 percent) of
total reported assets for the prior period. In contrast, the EE forecast bias is almost zero
relative to total assets. Thus, these results show that in answer to research question two,
EE expenditure forecasts are signi?cantly inaccurate but not materially biased.
The Appendix provides an example of a typical sample company’s quarterly
expenditure forecasts extracted from Appendix 5B reports for the March 2006 quarter
(the ?rst period after listing) to the June 2008 quarter. The forecasts are compared with
subsequent actual reported expenditure to estimate the forecast error. Over almost all
quarters the errors are material (from6.3 to 327.4 percent) and are optimistically biased.
Variable N Mean Median SD Variable Mean Median SD
Panel A: Exploration and evaluation expenditure
Forecast error de?ated by actual expenditure Forecast bias de?ated by actual expenditure
ERROR Q1 351 1.102 0.471 2.584 BIAS Q1 20.652 0.032 2.733
ERROR Q2 378 3.769 0.411 46.862 BIAS Q2 23.349 0.041 46.894
ERROR Q3 397 1.423 0.532 3.544 BIAS Q3 21.024 20.129 3.680
ERROR Q4 405 2.081 0.462 10.460 BIAS Q4 21.708 20.086 10.528
ERROR Pooled 1,531 2.103 0.468 23.997 BIAS Pooled 21.694 20.029 24.029
Forecast error de?ated by lagged total assets Forecast bias de?ated by lagged total assets
ERROR Q1 369 0.165 0.140 0.109 BIAS Q1 0.002 0.000 0.063
ERROR Q2 391 0.173 0.147 0.129 BIAS Q2 0.006 0.000 0.083
ERROR Q3 417 0.188 0.154 0.140 BIAS Q3 20.009 20.005 0.093
ERROR Q4 422 0.196 0.159 0.148 BIAS Q4 20.005 20.004 0.099
ERROR Pooled 1,599 0.181 0.150 0.133 BIAS Pooled 20.002 20.002 0.086
Panel B: Development expenditure
Forecast error de?ated by actual expenditure Forecast bias de?ated by actual expenditure
ERROR Q1 79 13.587 0.873 105.954 BIAS Q1 212.725 0.296 106.062
ERROR Q2 86 19.814 0.787 161.526 BIAS Q2 219.009 0.257 161.624
ERROR Q3 90 2.284 0.858 6.485 BIAS Q3 21.584 20.021 6.693
ERROR Q4 87 2.818 0.864 8.861 BIAS Q4 22.116 0.116 9.057
ERROR Pooled 342 9.439 0.856 95.697 BIAS Pooled 28.674 0.173 95.770
Forecast error de?ated by lagged total assets Forecast bias de?ated by lagged total assets
ERROR Q1 109 0.188 0.152 0.174 BIAS Q1 20.018 20.002 0.110
ERROR Q2 110 0.217 0.172 0.180 BIAS Q2 20.009 20.002 0.137
ERROR Q3 124 0.240 0.181 0.204 BIAS Q3 20.053 20.008 0.157
ERROR Q4 121 0.269 0.209 0.208 BIAS Q4 20.039 20.008 0.173
ERROR Pooled 464 0.230 0.174 0.194 BIAS Pooled 20.031 20.005 0.148
Notes: Forecasts are sourced from 1,760 quarterly cash ?ow reports issued by 481 ?rms during the
2005/2006 ?scal year. ERROR is a measure of cash expenditure forecast accuracy and is measured as
the absolute value of forecasted cash out?ows less actual reported cash out?ows for the respective
quarter (Q1-4) de?ated by the absolute value of the actual out?ows in Panel A and de?ated by lagged
total assets in Panel B. BIAS is a measure of cash expenditure forecast bias and is measured as the
signed ERROR or relative directional forecast error for the respective quarter, and is calculated as
forecasted cash out?ows less actual reported cash out?ows, de?ated by the value of the actual
out?ows for the respective quarter (Q1-4) in Panel A and de?ated by lagged total assets in Panel B;
ERROR Pooled includes all quarterly ERROR observations; BIAS Pooled includes all quarterly BIAS
observations. The ERROR and BIAS measures shown in Panel B (i.e. using the lagged asset de?ators)
are used in regression analysis (the variable names are shown as FERROR and FBIAS in the
regression models)
Table II.
Descriptive statistics –
error and bias
Mandatory cash
expenditure
forecasts
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An examination of notes provided in the covering letter attached to the Appendix 5Bs
revealed that delays in obtaining exploration permits where frequently cited as reasons
for the failure to commence exploration activities on a number of mining claims.
However, forecasts were not explained or quali?ed and there was no indication provided
as to why the forecasts were continually inaccurate and optimistically biased.
Table II Panel B shows development expenditure forecast error consistent with
those reported for the EE forecast error. The median forecast error for the pooled
observations is 85.6 percent. Contrary to the ?ndings for the EE forecasts, a signi?cant
positive forecast bias is evident for most of the quarterly ?gures and the pooled
observations (median error of 17.3 percent). This suggests that managers may be
conservative in under-estimating their development expenditure relative to realised
cash out?ows. However, when the economic signi?cance is considered (Panel B), the
error for the pooled observations remains material with a mean (median) of 23 percent
(17.39 percent), but not the bias.
Table III further explores error and bias by displaying the signed error over
percentiles for the full sample and the industry sub-samples (material and energy). The
statistics reveal that the negative EE forecast error and bias shown in Table II are
evident in both sub-industries, but it is more pronounced in the energy sector.
Approximately, 87 percent of ?rms in this sector have errors greater than positive or
negative 50 percent and the negative errors dominate (58.4 percent are greater than
250 percent of realized cash ?ows). Thus, as expected, ?rms in the energy sector are
more likely to produce inaccurate forecasts that overestimate their actual expenditure
(negatively biased forecasts). In contrast, Panel B shows that similar biases are not
evident for development expenditure forecasts.
Table IV provides the descriptive statistics for the independent variables entering in
the regression model. The data were obtained from the most recent annual report prior
to the release of each quarterly report. The sample size for most of the variables is less
than the total 481 observations due to missing data for some of the variables.
The statistics reveal that most ?rms are small (median total assets of $7.029 million),
are performing poorly with negative net cash ?ow from operations, have relatively
large amounts of their assets in the form of cash (median FINSLACK is 37.4 percent of
total assets), have higher cash ?ow volatility, and are relatively young (median AGE is
4.74 years). Also, most ?rms have no debt (67.41 percent of the sample), are yet to reach
the development expenditure stage (83.65 percent of the sample), and are in the
material sub-industry (77.13 percent of the sample).
6.2 Multiple regression results
6.2.1 Exploration and evaluation forecast expenditure. Table Vpresents the results from
estimating the model for EE forecasting error (Panel A) and bias (Panel B). Results are
reported for each quarter and for the pooled observations[17]. Consistent withthe ?ndings
of prior studies (Penman, 1980; Verrecchia, 1983; Lev and Penman, 1990), the Panel A
pooled results show that the performance (PERFORM) coef?cient is negative (20.344)
and signi?cant ( p , 0.01), indicating that better performing ?rms provide more accurate
forecasts. The ?nancial slack (FINSLACK) and cash ?ow volatility (CFVOL) coef?cients
are positive (0.041 and 0.020) and signi?cant ( p , 0.01); indicating that ?rms with greater
cash holdings and greater cash ?ow volatility are more likely to exhibit greater EE
forecasting errors. While the ?rm age (AGE) coef?cient is not signi?cant, the experience
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dummy coef?cient (AGEEXP) is negative (20.039) and signi?cant ( p , 0.01) indicating
that companies with more experience, that is, they have reached the development stage,
are more likely to produce more accurate EE forecasts. Similarly the ?nancial leverage
(LEVDUM) coef?cient is negative (20.016) and signi?cant ( p , 0.05), which suggests
that the presence of debtholders may helpto mitigate forecast inaccuracy. Also, consistent
with prior research, larger ?rms are more likely to issue more accurate forecasts than
smaller ?rms (the SIZE coef?cient ¼ 20.012; p , 0.05). Finally, the negative and
signi?cant industry dummy (INDDUM) coef?cient (0.035; p , 0.01) indicates that
consistent with the results reported in Table III, ?rms in the materials sub-industry are
more likely to provide more accurate EE forecasts than those in the energy sector.
Apart from company age, the ?ndings for EE forecast accuracy provide strong
support for the expected explanators of EE forecast error and in combination, the
variables have signi?cant explanatory power (adjusted R
2
¼ 37.9 percent). Similar
results are evident in the quarterly models.
All Firms Materials Energy
Signed error
(percent)
No. of forecasts
(percent)
No. of forecasts
(percent)
No. of forecasts
(percent)
Panel A: Exploration and evaluation expenditure
Negative , 2 50.01 483 (31.55) 361 (29.64) 122 (58.37)
240.01 to 250 44 (2.87) 36 (2.95) 8 (3.83)
230.01 to 240 47 (3.07) 40 (3.28) 7 (3.35)
220.01 to 230 70 (4.57) 59 (4.84) 11 (5.26)
210.01 to 220 78 (5.09) 62 (5.09) 16 (7.65)
20.01 to 210 68 (4.44) 52 (4.27) 16 (7.65)
Positive 0 to 10 109 (7.12) 93 (7.63) 16 (7.65)
10.01 to 20 95 (6.20) 76 (6.24) 19 (9.09)
20.01 to 30 110 (7.18) 96 (7.88) 14 (6.70)
30.01 to 40 93 (6.07) 83 (6.81) 10 (4.78)
40.01 to 50 89 (5.81) 74 (6.07) 15 (7.18)
.50.01 245 (16.00) 186 (15.27) 59 (28.23)
Total 1,531 1,218 313
Panel B: Development expenditure
Negative ,250.01 107 (31.29) 63 (30.14) 44 (33.08)
240.01 to 250 3 (0.88) 1 (0.48) 2 (1.50)
230.01 to 240 5 (1.46) 3 (1.44) 2 (1.50)
220.01 to 230 10 (2.92) 6 (2.87) 4 (3.01)
210.01 to 220 10 (2.92) 7 (3.35) 3 (2.26)
20.01 to 210 14 (4.09) 9 (4.31) 5 (3.76)
Positive 0 to 10 12 (3.51) 7 (3.35) 5 (3.76)
10.01 to 20 13 (3.80) 6 (2.87) 7 (5.26)
20.01 to 30 14 (4.09) 9 (4.31) 5 (3.76)
30.01 to 40 11 (3.22) 4 (1.91) 7 (5.26)
40.01 to 50 16 (4.68) 11 (5.26) 5 (3.76)
.50.01 126 (36.84) 82 (39.23) 44 (33.08)
Total 342 209 133
Notes: Forecasts are sourced from 1,760 quarterly cash ?ow reports issued by 481 ?rms during the
2005/2006 ?scal year. Signed error (or bias) is measured as the difference between forecasted cash
out?ows and the actual reported cash out?ows, de?ated by the value of the actual out?ows
Table III.
Number of cash ?ow
expenditure forecasts
classi?ed by percentage
error (signed)
Mandatory cash
expenditure
forecasts
277
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The results in Table V, Panel B for EEforecast bias are modest in comparison with Panel
A. Given the weak evidence of bias previously reported it is not surprising that the results
for EE forecast bias reveal fewer signi?cant coef?cients and a lower model explanatory
power (adjusted R
2
¼ 8.30 percent in the pooled model). Only the coef?cients for ?rm
performance (PERFORM), ?nancial slack (FINSLACK), pre-production stage experience
(AGEEXP) and size (SIZE) are signi?cant. These results nevertheless reveal that better
performance, greater ?nancial slack, and pre-production stage experience induce an
optimistic (overestimation) EE forecast bias, while larger size induces a conservative
(underestimation) bias.
6.2.2 Development forecast expenditure. Table VI presents the results of estimating
the model for development forecasting error (Panel A) and bias (Panel B). Consistent
with the results reported for EE forecast error, Panel A shows in the pooled results that
better performing ?rms (PERFORM), those with greater ?nancial slack (FINSLACK),
and greater cash ?ow volatility (CFVOL) are more likely to issue inaccurate forecasts.
The size (SIZE), ?rm age (AGE), industry (IND) and leverage (LEV) coef?cients have
no signi?cant in?uence on the Development forecast error.
Panel B shows that only the coef?cients for performance (PERFORM) and ?nancial
slack (FINSLACK) are negative and signi?cant. These results indicate that ?rms that
are performing poorly and have lower cash holdings are more likely to overstate their
development forecasts than ?rms that are performing well and have greater cash
holdings. While these results are consistent with the results reported for EE forecast
bias, none of the other variables display signi?cant coef?cients. The weaker results in
Panel A: Test variables – continuous
n Mean Median SD Minimum Maximum
Total assets ($million) 479 14.503 7.029 28.087 0.18 326.12
Total assets (logged) SIZE) 479 8.902 8.858 1.101 5.18 12.70
Cash ?ow performance (PERFORM) 463 20.116 20.081 0.126 20.50 0.13
Financial slack (FINSLACK) 463 0.526 0.374 0.507 20.03 2.00
Cash ?ow volatility (CFVOL) 448 23.118 23.215 1.158 26.73 5.073
Age in years (AGE) 481 8.590 4.740 9.069 0.000 38.05
Frequencies
0 1
n Percent n Percent
Panel B: Test variable – dichotomous
Leverage dummy (LEVDUM) 448 302 67.41 146 32.59
Experience dummy (AGEEXP) 422 353 83.65 69 16.35
Panel C: Control variable – dichotomous
Industry dummy (INDDUM) 481 371 77.13 110 22.87
Notes: Data are sourced from annual results for the population of 481 explorers for 2005/2006 ?scal
year. In Panel A, SIZE is the natural logarithm of average total assets for the current ?scal year (t) and
prior year (t 2 1); PERFORM is net operating cash ?ow de?ated by average total assets for t and
t 2 1; FINSLACK is total cash at end of quarter de?ated by average total assets; CFVOL is the
standard deviation of net operating cash ?ows over four prior quarters de?ated by lagged total assets;
and AGE is the number of years between listing and the current quarterly reporting date; de?ated by
lagged total assets. In Panel B, LEVDUM is equal to one if the company has ?nancial leverage
(interest-bearing debt) and zero otherwise; and AGEEXP is equal to one if the company has both actual
exploration/evaluation and development expenditure in the same quarter, and zero otherwise. In Panel
C; INDDUM is equal to one if the company is in the energy sector and zero if it is in the materials sector
Table IV.
Descriptive statistics –
independent variables
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Table V.
Cash ?ow forecast
accuracy and bias -
exploration and
evaluation expenditure
Mandatory cash
expenditure
forecasts
279
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Table VI.
Cash ?ow forecast
accuracy and bias –
development expenditure
ARJ
21,3
280
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Table VI are possibly explained by the smaller sample of ?rms that have reached the
development stage in the pre-production activities. Nevertheless, even with this
smaller sample, the results show that forecast accuracy can be explained by well
recognised ?rm speci?c differences.
7. Discussion and conclusion
This study investigates the reliability of cash expenditure forecasts issued mining
exploration companies. Prior research has examined voluntary cash ?ow reporting
by managers (Wasley and Wu, 2006), and voluntary (Skinner, 1994) and mandatory
(Kato et al., 2006) management earnings forecasts. However, as cash ?ow forecasts are
not mandated in other jurisdictions, and as this issue has not been previously
examined in Australia, this is the ?rst known study to identify and examine such a
mandatory setting. The context of the study is the ASX’s (2001) Appendix 5B quarterly
cash ?ow report regime. This setting is particularly interesting because the ASX’s
mandatory regime imposed on mining exploration companies only requires cash
expenditure forecasts, not comprehensive cash ?ow (net cash ?ow) forecasts.
We investigate three research questions relating to the quality of these cash
expenditure forecasts:
(1) What is the nature of the Appendix 5B cash ?ow forecasts?
(2) How accurate are the forecasts and are they biased?
(3) What ?rm-speci?c characteristics in?uence the accuracy and bias of the
forecasts?
We examine these questions using the available Appendix 5B cash expenditure
forecasts (1,760 cash expenditure observations) provided by the population of mining
exploration entities listed on the ASX between September 30, 2005 and July 31, 2006.
Our ?ndings showthat most mining exploration entities comply with the requirement
to disclose forecasts with more than 90 percent of entities providing evaluation and
exploration expenditure forecasts, and nearly 20 percent of these same ?rms provide
development expenditure forecasts. In contrast, similar ?ndings are not observed for
forecast accuracyandbias. Onbotha quarterlyandpooledbasis, the medianforecast error
is approximately 50 percent of the actual expenditure for EE expenditure, and
approximately 85 percent of the actual expenditure for development expenditure. These
?ndings indicate that ?rms have considerable dif?culty in forecasting one-quarter-ahead
pre-production expenditure. Our ?ndings reveal that forecast inaccuracy is more
prevalent in ?rms that have poorer performance, greater ?nancial slack, greater cash-?ow
volatility, and no ?nancial leverage. Also, forecasts are less accurate for ?rms that are
smaller, in the pre-development stage, and in the mineral (non-oil and gas) sub-industry.
We also ?nd evidence that some of the same ?rm-speci?c factors in?uence forecast bias.
Overall, our results clearly show that, on average, the mandatory cash expenditure
forecasts required by the ASX are unreliable and, contrary to the objectives of cash
?ow reports (AASB 107, para. 5), do not appear to provide information to better inform
investors of the “amount, timing, and certainty” of future cash ?ows[18]. As a
consequence, these forecasts may adversely affect the investment decisions of
investors. The fact that many forecasts are signi?cantly inaccurate, together with the
considerable variation we observe across ?rms in the extractive industry, challenge the
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wisdom of mandating such forward-looking information for ?rms in uncertain
operating environments.
Given their potential to mislead, we suggest that cash expenditure forecasts should
not remain mandatory. Regardless of whether they continue to be mandated or are
made voluntary, we suggest that ?rms be required to disclose information about the
underlying estimates and assumptions used in deriving the forecasts. Such additional
disclosures would be consistent with best practice observed in other contexts, such as
in initial public offer prospectus documents. Additionally, the ASX should require
?rms to provide explanations when forecasts vary materially from estimates.
Finally, our study adds to the small body of literature that reveals the limitations of
mandatory forecasting. As in other studies (Kato et al., 2006) our ?ndings show that the
information role of forecasts in capital markets is impaired when ?rms have little
discretion over the decision to forecast and the characteristics of the forecast.
Notes
1. For example, in 2006 CuDeco Ltd announced to the ASX a signi?cant copper discovery,
leading to its share price increasing from 29 cents to ten dollars in just eight weeks.
Following an ASX investigation the discovery was subsequently shown to be overstated.
The ASX was heavily criticised for its slow response (West and Andrusiak, 2006).
2. The ASIC adopts a similar “reasonable grounds” position with respect to forecasts in
prospectuses (ASIC, 2002).
3. Additional disclosures can be provided in notes to the cash ?ow report.
4. The Code is issued by the Joint Ore Reserves Committee of The Australasian Institute of
Mining and Metallurgy, Australian Institute of Geoscientists and Minerals Council of
Australia.
5. Commitments test entities are those companies that are allowed to list on the ASX without a
‘binding contract’, provided they make commitments to spend their cash in accordance with
their business objectives (Gallery et al., 2004).
6. AASB 107 is the equivalent of IAS 7 Cash Flow Statements issued by the International
Accounting Standard Setting Board (IASB).
7. Although entities frequent provide additional information in coversheets and notes, a careful
scrutiny of a large sample of 5B reports failed to ?nd any evidence of attempts to justify
forecasts or to explain forecast errors.
8. AASB 6 is the equivalent of IFRS 6 Explorations and Evaluations of Mineral Resources
issued by the IASB.
9. Under AASB 138 Intangible Assets all research costs must be expensed (para. 54) because in
the research phase, companies cannot demonstrate that an asset exists that will generate
probable future income bene?ts (para. 55). Similarly with mining exploration and evaluation
costs, there is on average, a low probability that these costs will result in a recoverable
reserve from which the company will generate economic bene?ts, yet the company can
initially capitalise these costs (development expenditure is generally recognised in
accordance with AASB, 2007c).
10. ‘Signi?cant’ revisions in management forecast estimates are de?ned as changes in estimated
sales of 10 percent or more and/or changes in estimated earnings of 30 percent or more.
11. There is also evidence that mandatory and voluntary earnings forecasts in Australian IPO
prospectuses are materially inaccurate, optimistically biased, and tend to be walked down
prior to the earnings realisation date (Gallery et al., 2008).
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12. Arguably the absence of regulatory monitoring and sanctions for non-compliance is
tantamount to having voluntary or no regulation at all (Fung et al., 2004; Lopez-De-Silanes,
2003).
13. We also test these same factors in examining forecast bias but we make no directional
predictions about their in?uence on bias.
14. Disclosure is considered “adequate” if it is relevant to the needs of users, capable of ful?lling
those needs, and timely.
15. Oil and gas ?rms must provide a hydrocarbon report (inclusive of pre-hydrocarbon reserve
stage details) as part of their quarterly Appendix 5B reports (ASX Listing Rule 5.9-5.17).
16. Actual rather than forecast cash expenditure is used as a de?ator because a number of ?rms
forecast zero cash expenditure. Appendix 5 provides an example of how this forecast error is
calculated.
17. A panel data random effects regression procedure is used to estimate the pooled model.
Robust standard errors are used in estimating the reported coef?cients.
18. It is possible that the forecasts may be at least partially informative in some contexts. To
more comprehensively assess the usefulness of the forecasts would require a comparative
approach using alternative predictive models (e.g. historical versus forecast cash ?ows) and
an assessment of the value-relevance of the forecasts to investors. We leave this relative
value-relevance issue to further research.
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(The Appendix Table is shown on the next page.)
Corresponding author
Gerry Gallery can be contacted at: [email protected]
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Table AI.
Mandatory cash
expenditure
forecasts
287
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doc_133459528.pdf
The purpose of this study is to examine the usefulness of pre-production cash expenditure
forecasts issued by Australian mining explorers in their quarterly cash-flow reports.
Accounting Research Journal
The reliability of mandatory cash expenditure forecasts provided by Australian mining
exploration companies in quarterly cash flow reports
Gerry Gallery J odie Nelson
Article information:
To cite this document:
Gerry Gallery J odie Nelson, (2008),"The reliability of mandatory cash expenditure forecasts provided by
Australian mining exploration companies in quarterly cash flow reports", Accounting Research J ournal, Vol.
21 Iss 3 pp. 263 - 287
Permanent link to this document:http://dx.doi.org/10.1108/10309610810922503
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Marion R. Hutchinson, Majella Percy, Leyal Erkurtoglu, (2008),"An investigation of the association between
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The reliability of mandatory cash
expenditure forecasts provided
by Australian mining exploration
companies in quarterly
cash ?ow reports
Gerry Gallery and Jodie Nelson
School of Accountancy, Queensland University of Technology,
Brisbane, Australia
Abstract
Purpose – The purpose of this study is to examine the usefulness of pre-production cash expenditure
forecasts issued by Australian mining explorers in their quarterly cash-?ow reports.
Design/methodology/approach – Usefulness is determined by examining compliance and the
reliability of forecasts (accuracy and bias) for a sample of 1,760 forecasts issued by 481 explorers in
2005/2006. The cross-sectional variation in reliability is examined using regression analysis.
Findings – The ?ndings reveal a high level of compliance but signi?cant inaccuracies (median
forecast error of around 50 percent of actual expenditure for exploration and evaluation expenditure
and 85 percent for development expenditure), and some evidence of forecast bias. Forecast inaccuracy
is more prevalent in ?rms that have poorer performance, greater ?nancial slack, greater cash-?ow
volatility, no ?nancial leverage, and for ?rms that are smaller, in the pre-development stage, and in the
mineral (non-oil and gas) sub-industry.
Research limitations/implications – The analysis of forecast usefulness is con?ned to
compliance and reliability. Further research could consider the value-relevance and predictive
ability of these forecasts.
Practical implications – The ?ndings question the usefulness of mandatory forecasting by
showing that the information role of forecasts in capital markets is impaired when ?rms have little
discretion over the forecast decision, timing and speci?city.
Originality/value – This is the ?rst study to examine mandatory cash expenditure forecasts and
makes a signi?cant contribution to the small literature on mandatory ?nancial forecasts.
Keywords Financial forecasting, Cash ?ow, Financial reporting, Australia, Mining industry
Paper type Research paper
1. Introduction
Our study examines the usefulness of forecasted pre-production costs in the quarterly
cash ?ow reports of Australian mining exploration companies, and their relation to
?rm-speci?c characteristics. Three related issues motivate our study. First, numerous
corporate scandals have focused regulators’ attention around the world on
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1030-9616.htm
The authors thank Malik Mirza, Natalie Gallery, participants at the 2008 Paci?c Basin Finance
Economics Accounting Management Conference held at QUT, and an anonymous referee for
their helpful comments and suggestions. The authors also thank Tahlee Fong for her expert
research assistance.
Mandatory cash
expenditure
forecasts
263
Accounting Research Journal
Vol. 21 No. 3, 2008
pp. 263-287
qEmerald Group Publishing Limited
1030-9616
DOI 10.1108/10309610810922503
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strengthening corporate governance and disclosure regulations (Coglianese et al.,
2004). Sound ?nancial disclosures mitigate agency problems by reducing information
asymmetry between management and shareholders. However, where ?nancial
disclosures are poor, the opposite can occur; information asymmetry may increase,
some market participants may be misled, and the ?rm’s cost of equity and shareholder
wealth may be adversely affected (Healy and Palepu, 2001). A higher probability of
poorer quality disclosure is likely to be observed in situations where regulations
prescribe the disclosure of forward-looking information by companies operating in
uncertain environments.
Second, the mining industry plays a signi?cant role in the Australian economy,
representing approximately 20 percent of market capitalisation and about one third of
all Australian Securities Exchange (ASX, 2008) listed companies. The mining industry
is characterised by high-operating risk and information asymmetry, leading to
high-price volatility. In this environment, public disclosure regarding outcomes from
exploration and production activities can have a signi?cant impact on stock prices.
Since mid-2005, the Australian Securities and Investments Commission (ASIC, 2006)
has increased its surveillance of small mining company disclosures. This increased
surveillance followed concerns about inadequate disclosures in 2005 and 2006 and
claims that surveillance efforts by the ASX were inadequate due to the lack of
resources and expertise for monitoring mining company disclosures[1].
Third, the ASX requires listed mining exploration companies to issue quarterly
cash ?ow reports in accordance with listing rule (LR) 5.3 and Appendix 5B. A unique
additional disclosure in these reports is a requirement to forecast future cash out?ows
relating to pre-production expenditure. The release of forward-looking information has
the potential to expand the information set available to investors and its disclosure
may be viewed as one dimension of ?nancial reporting quality since a ?nancial report
containing such information is more likely to be perceived as being of higher quality
(Ajinkya et al., 2005; Karamanou and Vafeas, 2005). However, the uncertain operating
environment of mining exploration companies raises the issue of forecast reliability.
Scott (2003) contends, that to highlight the uncertain nature and improve the reliability
of forward-looking information, companies should only forecast for the period that
such information can be reasonably estimated and in doing so, disclose underlying
forecast assumptions[2]. However, despite this common view on when and how
forecasts should be provided, the LR 5.3 does not permit any forecasting discretion and
does not require disclosure of assumptions by mining explorers in their Appendix 5B
cash ?ow reports[3].
Given the unusual nature of cash expenditure forecasts and the fact that they have
been required for more than a decade, it could be expected that some research would
have been conducted on the usefulness of such forecasts, however, we are unable to
identify any prior research on this issue. The absence of research provides an
opportunity to extend the disclosure literature to mandatory cash expenditure
forecasts. Three research questions are considered:
RQ1. What is the nature of the Appendix 5B cash ?ow forecasts?
RQ2. How accurate are the forecasts, and are they biased?
RQ3. What ?rm-speci?c characteristics in?uence the accuracy and bias of the
forecasts?
ARJ
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The characteristics examined include performance, ?nancial slack, cash ?ow volatility,
leverage, size (as measured by total assets), age and experience, and sub-industry
(minerals versus and oil and gas).
Our ?ndings show that most mining exploration entities comply with the
requirement to disclose forecasts. Nearly, 90 percent of entities provide evaluation and
exploration expenditure forecasts and nearly 20 percent of these same ?rms also
provide development expenditure forecasts. However, despite the high level of
compliance, our results reveal signi?cant inaccuracies and some bias in the forecasts.
On both a quarterly and pooled basis, the median forecast error is approximately
50 percent of the actual expenditure for exploration and evaluation (EE) expenditure,
and approximately 85 percent of the actual expenditure for development expenditure.
Our ?ndings reveal that forecast inaccuracy is more prevalent in ?rms that have
poorer performance, greater ?nancial slack, greater cash-?ow volatility, no ?nancial
leverage, and in ?rms that are smaller, in the pre-development stage, and in the mineral
(non-oil and gas) sub-industry. We also ?nd evidence that some of the same factors
in?uence forecast bias. The signi?cant inaccuracies and considerable variation across
?rms challenge the wisdom of mandating such forward-looking information for these
type of entities.
The remainder of this paper is organised as follows. Section 2 provides an overview
of the ASX LR as well as extant literature on ?nancial forecasting by managers.
Section 3 provides an overview of related research. Section 4 outlines the research
questions and expectations. Section 5 presents the sample selection and research
design. Section 6 presents the summary statistics and main results of the study.
Section 7 summarises and discusses the implications of the study.
2. Institutional background and quarterly reporting requirements
Disclosures relating to exploration and development activities are governed by the
Australasian Code for Reporting of Mineral Resources and Ore Reserves (JORC
Code)[4]. This code was developed to ensure that mining and exploration companies
report all information necessary for stakeholders to evaluate the activities of the
company (ASX, 2008). The ASX requires listed mining exploration companies to issue
quarterly activity and cash ?ow reports in accordance with ASX LR 5.3 and Appendix
5A and 5B (the JORC Code). With the exception of commitments test entities[5], mining
exploration companies are the only companies in Australia required to provide
quarterly reports in addition to their annual reporting requirements (Gallery et al.,
2004). In most jurisdictions outside North America, annual and semi-annual reports
have been the traditional means for conveying detailed ?nancial and non-?nancial
information to stakeholders.
2.1 ASX listing rule 5.3
On July 1, 1996, the ASXamended LR5.3 to require all listed mining exploration entities
to issue quarterly cash ?owreports, known as Appendix 5Breports. The purpose of this
change was to inform the market on “how the entity’s activities have been ?nanced for
the past quarter and the effect on its cash position” (Australian Stock Exchange – ASX,
2001). The cash ?ow report must be lodged as soon as the information is available or
within one month after the end of each quarter of its ?nancial year (ASX, 2001).
A director or company secretary must complete a compliance statement attesting to
Mandatory cash
expenditure
forecasts
265
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the fact that the information contained in the 5B report has been prepared under
accounting policies that comply with accounting standards, as de?ned in the
Corporations Act 2001 or other standards acceptable to the ASX, and provides a true
and fair view of the matters disclosed. However, unlike annual and semi-annual cash
?owstatements, there is no requirement for the 5Breport to be audited or reviewed by an
external auditor. The format of the cash ?ow report is speci?ed in Appendix 5B, which
contains a proforma cash ?ow statement, some additional ?nancial information, and
limited note disclosures. This format has been modelled on accounting standard AASB
107 Cash Flow Statements[6], which guides the presentation and preparation of annual
cash ?owstatements by reporting entities in Australia. One stated bene?t of a cash ?ow
statement is to inform investors of the “amount, timing, and certainty of future cash
?ows” (AASB, 2007b, para. 5).
Although modelled on AASB 107, the prescribed content of Appendix 5B cash
?ow statements contain a number of notable variations from the AASB 107 version.
First, while the Appendix 5B cash ?ow statement contains the general categories of
cash ?ows (cash ?ows from operating, investing and ?nancing), the line items
within these categories are more detailed and suited to mining companies. For
example, individual line item disclosures are required for cash out?ows for EE,
development, production, and administration activities. Second, other supplementary
information that is not required under AASB 107 must be reported, including details
of related party transactions, securities, and non-cash ?nancing activities, and for the
next quarter, the estimated cash out?ows relating to EE activities (Item 4.1) and
development activities (Item 4.2). Also, an entity wanting to disclose additional
information is encouraged to do so, in a note or notes attached to the report
(Appendix 5B, Note 1)[7].
Interestingly, cash expenditure forecasts are not required to be disclosed under
Australia’s GAAP or other corporate regulations. Although the ASX does not provide
a rationale for their LR requirement, it is presumed that the speculative nature of the
industry calls for greater disclosure in the form of ?nancial forecasts to better inform
investors of the amount, timing, and certainty of future cash ?ows.
2.2 Accounting for EE costs
In Australia, EE costs are accounted for in accordance with AASB 6 Exploration for
and Evaluation of Mineral Resources[8]. Unsurprisingly, most mining exploration
companies exercise their discretion permitted under this standard (AASB, 2007a, para.
Aus 7.1) and capitalise rather than immediately expense EE costs. Hence, these costs
are treated as assets in the balance sheet rather than expenses in the income statement,
which is unlike other costs of a similar nature, such as research and development
expenditure generated in the research phase[9].
The accounting treatment of pre-production costs may provide managers with
incentives to focus their spending on exploration, evaluation and development costs,
rather than other costs. Lilien and Pastena (1982) contend that ?rms that incur large
amounts of pre-production costs have a greater incentive to capitalise these costs
than expense them in order to avoid signi?cant negative impacts on their income
statement and balance sheet. Hence, from a ?nancial statement perspective, when
forecasting pre-production costs, managers have no strong incentive to understate
their forecasts.
ARJ
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3. Related research
Prior research on ?nancial forecasting by managers largely examines voluntary earnings
forecast incentives (Healy and Palepu, 2001). Only a few studies have examined
management cash forecasts. For example, Wasley and Wu (2006) investigate managers’
incentives inthe USAtoprovide voluntarycash?owforecasts. Ina studyof 2090 forecasts
appearing in press releases frommid-1979 to October 2003, they report that management
issue cash ?owforecasts to signal goodnews incash ?ows and thus, mitigate the negative
impact of bad news in earnings, lend credibility to good news in earnings, and signal
economic viability for young ?rms. They also ?nd that cash ?ow forecasts are important
in meeting investor demandfor this type of information. Unlike prior studies on voluntary
earnings forecasts, which reveal that managers tendto disclose badnews, especially when
the risks of litigation are high (Skinner, 1994, 1997; Francis et al., 1994) and job security
is threatened (Brennan, 1999; Warner et al., 1988; Weisbach, 1988), Wasley and Wu
conclude that different incentives, other than litigation risk, drive the disclosure of
different types of ?nancial information, including cash ?ow information.
There is no known research on mandatory cash ?ow forecasts; however, there are
some studies on mandatory sales and earnings forecasts. For example, Kato et al.
(2006) investigate the accuracy of annual sales and earnings forecasts (as single-point
estimates) issued under the Japanese Stock Exchange Timely Disclosure Rules
(Kessan-Tannsin). In accordance with these rules (as prescribed in the Stock Exchange
Act) listed entities must provide “signi?cant”[10] forecast revisions at interim
announcement dates. Based on a sample of 35,639 management forecasts issued from
1997 to 2006, their results reveal that managers initially set overly optimistic forecasts
at the beginning of the year (especially for ?rms with poor pro?tability), and then issue
downwards forecast revisions to meet realisations.
Kato et al.’s (2006) ?ndings indicate that management forecasts are consistently
biased from one year to the next, possibly because:
.
managers are not exposed to the same high levels of litigation risk as managers
in other countries, such as the USA; and
.
reputationcosts are not suf?cient to penalise managerial opportunisminforecasting.
Kato et al. conclude that despite their consistent optimism, management forecasts in
Japan affect stock prices (albeit these effects are smaller than those observed in the
USA) and are informative about future earnings[11].
In addition to the institutional differences, the relevance of the prior forecast
research to our study is limited by the reporting period (i.e. annual cash ?ows as
opposed to quarterly cash ?ows), type of forecasts required (i.e. net cash ?ows versus
cash out?ows), and/or by the regulatory regime (i.e. voluntary versus mandatory
forecasts). Nevertheless, the ?ndings are important to this study because they show
that, unlike prior earnings forecast ?ndings, the disclosure of cash ?ow information is
potentially motivated by different incentives.
4. Research questions and expectations
4.1 Compliance
Our ?rst research question is: what is the nature of the Appendix 5B cash ?ow
forecasts? Given that these forecasts are mandatory, we focus on the compliance issue
in addressing this question. It is well accepted that routine compliance with disclosure
Mandatory cash
expenditure
forecasts
267
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regulations requires implementation of effective enforcement mechanisms (La Porta
et al., 2006). The ASX has implemented a market surveillance unit that monitors
company compliance with LRs and provides suspension and expulsion measures for
non-compliance. Nevertheless, a comprehensive investigation of the ASX web site,
company queries, and disciplinary activities from 2001 to 2006 found no evidence of
ASX action with respect to incomplete or inaccurate information in 5B cash ?ow
reports. This lack of enforcement suggests that the forecasting decision and the quality
of forecasts ultimately depend upon managerial discretion[12]. Thus, we expect
non-compliance is likely to be prevalent under these circumstances.
4.2 Forecasting accuracy and bias
Our second research question is: how accurate are the forecasts, and are they biased?
In examining the factors associated with forecast accuracy, most prior studies
associate the frequency and quality of earnings forecasts with attempts to
minimise litigation costs. For example, Baginski et al. (2002) show that bad-news
?rms are more inclined to provide forecasts when the risks of litigation are relatively
high. Bamber and Cheon (1998) ?nd that managers are less inclined to issue speci?c
earnings forecasts when exposure to legal liability and proprietary information costs
are high. Similarly, Skinner (1994) documents that to minimise litigation costs, good
earnings-related news tends to be disclosed as point or range estimates while bad news
disclosures tend to be disclosed in qualitative statements and related to quarterly
earnings announcements.
Reputation costs and credibility concerns are also important considerations in
encouraging accuracy and deterring managers from issuing biased forecasts (Skinner,
1994; Hong et al., 2000; Hong and Kubik, 2003; Rogers and Stocken, 2005). If managers
obtain a reputation for unreliable forecasts, the credibility of their forecasts will
decline, making it less likely that stock prices will respond positively to their forecasts
and harder to convince investors of their managerial ability (Hutton and Stocken, 2007;
Karamanou and Vafeas, 2005). Over 90 percent of US managers surveyed by Graham
et al. (2005) agreed or strongly agreed that voluntarily disclosures promote a reputation
for transparent reporting (La Porta et al., 2006). The ?ndings of Kato et al. (2006) with
respect to mandatory management earnings forecasts in Japan, and Gallery et al. (2008)
with respect to voluntary management earnings forecasts in Australian initial public
offer prospectuses, provide some support for the in?uence of reputation in lower
litigation environments. Both studies show that managers attempt to walk down prior
earnings forecasts through forecast revisions prior to the earnings realisation dates.
In the case of mining exploration companies, cash expenditure forecasts in 5Breports
are issued in point form. These speci?c forecasts encompass a narrower range of
outcomes, which increases the likelihood of inaccuracy. The lack of enforcement and the
associated low litigation risk reduces incentives for managers to devote resources to
providing better quality forecasts. While forecasts are expected to be inaccurate, it is not
clear what incentives managers have to bias their cash expenditure forecasts other than
to meet budgeted expenditure outlays. Also, unlike the Japanese stock exchange and
Australian IPO forecasts, the ASX does not require or explicitly encourage mining
explorers to issue forecast revisions prior to reporting the actual realised out?ows in the
subsequent 5B report. Hence, managers have little opportunity to correct biases in cash
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expenditure forecasts. Therefore, apart from a desire to meet targets or budgets, we do
not expect exploration ?rms to exhibit optimistic biases in their expenditure forecasts.
4.3 Forecast accuracy and ?rm-characteristics
Our third research question is: what ?rm-speci?c characteristics in?uence the accuracy
and bias of the forecasts? In examining this question we consider relevant factors from
prior disclosure research, namely: performance, ?nancial slack, ?nancial leverage, cash
?ow volatility, ?rm age and experience, size, and sub-industry membership[13].
4.3.1 Performance. Disclosure research generally shows that better performing ?rms
produce more frequent and better quality forecasts. Early US research reveals that
management earnings forecasts tend to be more frequent when ?rmperformance is high
rather than low (Penman, 1980; Verrecchia, 1983; Lev and Penman, 1990, Lang and
Lundholm, 1993). However, in more recent times, concerns about increased litigation
risk in the US showthat management forecasts have shifted frombeing more frequently
associated with good performers (a “good news” ?rmbias) to more frequently associated
with poorer performers (a “bad news” ?rmbias) (Skinner, 1994; Kasznik and Lev, 1995).
Outside the USA where litigation risk is lower, the good news bias tends to persist
(Baginski et al., 2002), and there is some evidence that better performing ?rms tend to
produce more accurate and less optimistically biased forecasts (Kato et al., 2006). For
similar reasons we expect that better performing mining explorers will produce cash
expenditure forecasts with similar properties. Also, in the case of these ?rms, a high cash
burn rate contributes to an ongoing need for future funding. The more a ?rm can
generate cash from internal sources, the less uncertainty associated with future cash
?ows. Hence, we expect that better performing ?rms (as measured by cash performance)
are more likely to have more accurate expenditure forecasts.
4.3.2 Financial slack. Suf?cient cash holdings are necessary for ?rms to fund
working capital requirements and investments in positive NPV projects. Observable
differences in cash holdings across ?rms are a natural outcome of differences in the
cost of external ?nancing, capital constraints, and the level of ?nancial distress (Myers,
1984; Myers and Majluf, 1984; Almeida et al., 2004). However, high-cash balances can
induce managers to overinvest in negative NPV projects, which bene?t managers at
the expense of shareholders (Easterbrook, 1984; Jensen, 1986). Furthermore, managers
can more readily consume liquid assets for private gain than ?xed assets (Myers and
Rajan, 1998). This inef?ciency in cash expenditure, or agency view, is generally
consistent with the empirical evidence (Blanchard et al., 1994; Harford, 1999; Opler et al.,
1999). However, the evidence is not conclusive on the optimal level of cash holdings or
the impact of alternative governance characteristics and disclosure on the level of
?rms’ cash holdings (Mikkelson and Partch, 2003; Kalcheva and Lins, 2007).
In addition to the agency concerns of excessive cash holdings, mining exploration
companies face dif?culty in quickly raising external funds due to their size and
uncertain operating environments. Therefore, the failure to meet budgets and estimates
may be related to a combination of agency and non-agency related factors such as poor
planning and cost control. The agency and non-agency arguments both lead to similar
predictions. Where cash holdings are large, managers are more likely to waste cash on
less productive activities (i.e. non-exploration or development activities, such as
administration and inef?cient related-party transactions). In these circumstances,
managers are likely to produce more inaccurate forecasts. In contrast, in companies with
Mandatory cash
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lowlevels of cash holdings, managers are more likely to be focused on allocating cash to
productive activities and conserving cash resources. Hence, it is expected that where
?nancial slack is low, managers will issue more accurate expenditure forecasts.
4.3.3 Financial leverage. As leverage increases, lenders demand more information to
ensure effective monitoring of the ?rm in order to assess the probability of a ?rm
meeting its debt obligations (Jensen and Meckling, 1976). However, empirical studies
present mixed ?ndings with respect to the association between leverage variables and
more frequent and better quality disclosure. Some researchers document a positive
association between leverage and the voluntary disclosure of information (Ferguson
et al., 2002; Bradbury, 1992), while others fail to ?nd any signi?cant association (Malone
et al., 1993; Wallace et al., 1994). Contrary to these studies, Meek et al. (1995) report a
signi?cant, negative relationship for US, UK, and continental European multinational
corporations when disclosure is voluntary. A major assumption is that leverage
variables (typically the debt-to-equity or debt-to-asset ratio) accurately proxy for the
underlying ?nancial risk across sample companies regardless of other cross-sectional
differences such as ?rm size, asset structure, operating risk, and industry. In the case of
mining exploration companies only a minority (approximately one third) have any form
of debt in their capital structure. Explorers that have secured debt funding typically
have a higher portion of assets in place, face lower uncertainty about their prospects, and
have agreed to debtholder monitoring. Thus, ?rms with ?nancial leverage are likely to
produce more accurate cash expenditure forecasts than ?rms without ?nancial leverage.
4.3.4 Cash ?ow volatility. Where investors demand information about cash ?ows,
managers have incentives to issue cash ?owforecasts (Wasley and Wu, 2006). However,
where the ?rm’s cash ?ows are more volatile, it can be more dif?cult for managers to
make accurate forecasts (Wasley and Wu). Prior earnings forecast literature contends
that when earnings are highly volatile, managers face a greater risk of making an
inaccurate forecast (Baginski et al., 2002; Kato et al., 2006). In the case of cash ?ows, they
can be more volatile than earnings because of the smoothing effect of accruals in
earnings and because managers can engage in earnings management to reduce earnings
volatility (Wasley and Wu). In the case of mining exploration companies, they operate in
an uncertain environment that contributes to less predictable and more volatile cash
?ows than many other entities. It is therefore expected that as cash ?ow volatility
increases, explorers produce less accurate cash expenditure forecasts.
4.3.5 Firm age and experience. Prior studies provide mixed results on the relationship
between disclosure and ?rmage. Image and reputation are both important considerations
for older, well-established companies, and accordingly, they have been found to disclose
more information than younger companies (Owusu-Ansah, 1998). However, Chen et al.
(2002) ?nd a negative association between disclosure and ?rm age under a quarterly
earnings announcement regime. They argue that investors demand more useful
information fromyounger ?rms because their earnings and production activities are more
uncertain. Consistent with this argument, Wasley and Wu (2006) reveal that younger
?rms issue cash ?ow information to signal economic viability and therefore assist with
raising external capital. However, younger explorers in the Australian market may not be
able to provide suf?ciently accurate cash forecasts to credibly signal to potential fund
providers. Also, age may not successfully capture an explorer’s ability to predict future
cash outlays if the ?rm has been unsuccessful in explorations activities over a number of
years. A more relevant measure is likely to be a ?rm’s stage of operation. If a ?rm has
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progressed to the development stage it is more likely to be able to predict future cash EE
expenditure than ?rms at the exploration stage. We therefore expect that both age and
experience are likely to lead to more accurate forecasts.
4.3.6 Size. Firm size is likely to be closely related to age and experience and also
capture other cross-sectional differences in ?rms. Prior research ?nds that larger ?rms
are more likely to voluntarily disclose earnings forecasts compared to smaller ?rms
(Cox, 1987; Choon et al., 2000). In the IPO setting, ?rm size is also argued to have a
negative relationship with forecast error as larger ?rms have a greater capacity to
absorb the impact of unexpected events, more diverse operations, and more
sophisticated forecasting techniques (Chapple et al., 2005; Hartnett and Romcke, 2000).
In terms of mandated information, empirical evidence suggests that larger companies
disclose more adequate information, as opposed to smaller companies because their
competitive advantage is less likely to be threatened (Owusu-Ansah, 1998)[14].
Therefore, it is expected that larger mining exploration ?rms are more likely to provide
accurate forecasts than their smaller counterparts.
4.3.7 Sub-industry category. Several studies in the disclosure literature have
indicated that industry membership can in?uence a ?rm’s disclosure practices
(Hope, 2003; Dye and Sridhar, 1995). For example, ?rms in high-risk industries may
disclose more information in order to better distinguish themselves from competitors in
the same industry. With regard to cash ?ow forecasts, the accuracy of the forecast is
likely to differ across industries. In the Australian mining industry, there are two major
sub-industries: minerals (materials) and oil and gas (energy). The mineral explorers
typically face greater uncertainty in their exploration activities because of the more
diverse nature of their operations. However, oil and gas ?rms face greater dif?culty in
estimating and extracting hydrocarbon reserves relative to resources measurement and
extraction in the minerals industry (Sykes, 2001). In responding to the uncertainty facing
oil and gas ?rms, the ASX imposes additional disclosure obligations on these ?rms[15].
Owing to these differences, it is expected that oil and gas explorers will provide less
accurate cash expenditure forecasts relative to the mineral explorers.
5. Data and research design
5.1 Sample selection and data sources
The sample comprises all mining explorations companies listed on the ASX that
lodged Appendix 5B reports between September 30, 2005 and July 31, 2006. For most
companies, the study period spans four consecutive quarterly reporting periods, which
varies depending on the company’s respective balance date. A total of 481 companies
are identi?ed as representing the entire population of companies subject to Appendix
5B quarterly cash ?ow reporting by the ASX. Within this population of ?rms, 371
?rms are in the GICS material (minerals) sector (GICS Codes 15101010-15105020) and
110 are in the energy sector (oil and gas) sector (GICS Codes 10101010-10102050).
Where an entity is not listed or has not provided a forecast for a certain quarter, the
quarterly observation is excluded from testing procedures, yielding a total of 1,760
cash ?ow reports (1,377 for the Materials and 383 for energy ?rms).
All quarterly cash ?ow information (including lodgement dates, listing/de-listing
date) was hand-collected from announcements (including Appendix 5Bs reports) and
other information obtained through the Aspect Huntley DatAnalysis database.
Company ?nancial data from the annual ?nancial reports were hand-collected from a
Mandatory cash
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combination of the Connect 4 Annual Reports Collection and Aspect Huntley
FinAnalysis databases.
5.2 Research design
Our research design uses summary statistics to examine research question one
(compliance) and research question two (accuracy and bias). Multivariate regression
procedures are used to examine research question three (factors associated with
accuracy and bias). The following sub-section explains the procedures used to measure
the variables and test expected associations.
5.2.1 Measuring forecasting accuracy and bias. Following prior research, accuracy
or forecast error (FERROR) is measured as the absolute value of the signed cash
expenditure forecast error de?ated by the actual (realised) cash expenditure[16]:
jForecasted cash expenditure 2Actual cash expenditurej
Actual cash expenditure
This measure captures the magnitude of the cash ?ow forecast error and is useful in
measuring the percentage error. However, the measure does not capture the economic
signi?cance of the error. Following prior research (Kato et al., 2006) we therefore use an
alternative metric (using total assets as a de?ator) in regression analysis to capture the
economic signi?cance of the error, calculated as follows:
jForecasted cash expenditure 2Actual cash expenditurej
Total assets
t21
Forecast bias (FBIAS) is the relative (unsigned) directional forecast error. As cash
expenditure is treated as an out?ow (a negative value), a positive forecast error (i.e. the
actual exceeds the forecast) indicates an underestimation or conservative forecast, and a
negative forecast error (i.e. the forecast exceeds the actual) indicates an overestimation
or optimistic forecast. If managers issue cash expenditure forecasts based on true
expectations and these expectations are unbiased, it is expected that on average, forecast
error will be statistically indistinguishable from zero.
5.2.2 Research model. Mining exploration companies are required to forecast cash
out?ows for EE costs, as well as development costs. Hence, the model below will be
used to test research question three where the dependent variable is either forecast
accuracy (equation (1)) or bias (equation (2)) for EE costs, or development costs, and the
independent variables are the previously explained ?rm-speci?c factors that are
expected to explain error and bias:
FERROR
it
¼ a
1
þa
2
PERFORM
it
þa
3
FINSLACK
it
þa
4
CFVOL
it
þa
5
LEVDUM
it
þa
6
AGE
it
þa
7
AGEEXP
it
þa
8
SIZE
þa
9
INDDUM
it
þ1
it
ð1Þ
FBIAS
it
¼ b
1
þb
2
PERFORM
it
þb
3
FINSLACK
it
þb
4
CFVOL
it
þb
5
LEVDUM
it
þb
6
AGE
it
þb
7
SIZE
it
þb
8
INDDUM
it
þ1
it
ð2Þ
where dependent variables: FERROR, jforecasted cash expenditure-actual cash
expenditurej de?ated by lagged total assets; and FBIAS, forecasted cash
expenditure-actual cash expenditure de?ated by lagged total assets (signed values):
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Independent variables: PERFORM, net operating cash ?ows de?ated by average
total assets.
.
FINSLACK, total cash at the end of the current quarter de?ated by average total
assets.
.
CFVOL, standard deviation of net operating cash ?ows over four prior quarters
de?ated by lagged total assets.
.
LEVDUM, one if the company has ?nancial leverage (interest-bearing debt) and
zero otherwise.
.
AGE, number of years between the listing and the current quarterly reporting
date.
.
AGEEXP, one if the company has both actual exploration/evaluation and
development expenditure in the same quarter, and zero otherwise; SIZE, natural
logarithm of average total assets for the current ?scal year (t) and prior year
(t 2 1).
.
INDDUM, one if the company is in the energy sector and zero if the company is in
the materials sector.
6. Results
6.1 Descriptive statistics
Table I reports the number of cash expenditure reports and forecasts issued for EE
costs (Panel A) and developments costs (Panel B) over each of the four quarters and an
average for all quarters. Of the 481 ?rms in the sample (371 for materials and 110 for
energy) an average of 440 reports (344 for materials and 96 for energy) were lodged
with the ASX over the study period. Panel A and B further shows high compliance
with the requirement to provide forecasts. For EE (development) forecasts, only
7.03 percent (6.21 percent) on average fail to provide a forecast when there is actual
expenditure recorded in the subsequent quarter. Although most ?rms provide EE
forecasts (93.81 percent for material ?rms and 91.06 percent for energy ?rms), only a
minority have reached the development expenditure forecast stage (18.94 percent for
material ?rms and 41.82 percent for energy ?rms). The difference between the two
industries highlights the need to control for sub-industry type. Overall, these results
show that contrary to our expectation for research question one, there is a strong
culture of compliance with the ASX’s Appendix 5B forecasting requirements.
Table II presents the descriptive statistics for forecasting accuracy (error and bias)
for EE (Panel A) and development expenditure (Panel B). In both panels statistics for
error and bias de?ated by:
(1) actual cash out?ows; and
(2) by lagged total assets are reported.
In Panel A, the mean (median) error for the pooled observations (ERROR Pooled) is
210.3 percent (46.8 percent), indicating that companies are issuing signi?cantly inaccurate
forecasts. More than half the sample issues forecasts with a median error of nearly
50 percent. Similar results are reported across each of the four quarters. In contrast, the
median EE forecast bias is only 22.9 percent, which indicates a small optimistic bias (i.e.
forecast expenditure is greater than actual expenditure). The statistics for EE forecast
Mandatory cash
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Number of forecasts
issued by quarter
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error and bias calculated using the total asset de?ator further show that the error is also
economically signi?cant. The mean (median) forecast error is 18.1 percent (15 percent) of
total reported assets for the prior period. In contrast, the EE forecast bias is almost zero
relative to total assets. Thus, these results show that in answer to research question two,
EE expenditure forecasts are signi?cantly inaccurate but not materially biased.
The Appendix provides an example of a typical sample company’s quarterly
expenditure forecasts extracted from Appendix 5B reports for the March 2006 quarter
(the ?rst period after listing) to the June 2008 quarter. The forecasts are compared with
subsequent actual reported expenditure to estimate the forecast error. Over almost all
quarters the errors are material (from6.3 to 327.4 percent) and are optimistically biased.
Variable N Mean Median SD Variable Mean Median SD
Panel A: Exploration and evaluation expenditure
Forecast error de?ated by actual expenditure Forecast bias de?ated by actual expenditure
ERROR Q1 351 1.102 0.471 2.584 BIAS Q1 20.652 0.032 2.733
ERROR Q2 378 3.769 0.411 46.862 BIAS Q2 23.349 0.041 46.894
ERROR Q3 397 1.423 0.532 3.544 BIAS Q3 21.024 20.129 3.680
ERROR Q4 405 2.081 0.462 10.460 BIAS Q4 21.708 20.086 10.528
ERROR Pooled 1,531 2.103 0.468 23.997 BIAS Pooled 21.694 20.029 24.029
Forecast error de?ated by lagged total assets Forecast bias de?ated by lagged total assets
ERROR Q1 369 0.165 0.140 0.109 BIAS Q1 0.002 0.000 0.063
ERROR Q2 391 0.173 0.147 0.129 BIAS Q2 0.006 0.000 0.083
ERROR Q3 417 0.188 0.154 0.140 BIAS Q3 20.009 20.005 0.093
ERROR Q4 422 0.196 0.159 0.148 BIAS Q4 20.005 20.004 0.099
ERROR Pooled 1,599 0.181 0.150 0.133 BIAS Pooled 20.002 20.002 0.086
Panel B: Development expenditure
Forecast error de?ated by actual expenditure Forecast bias de?ated by actual expenditure
ERROR Q1 79 13.587 0.873 105.954 BIAS Q1 212.725 0.296 106.062
ERROR Q2 86 19.814 0.787 161.526 BIAS Q2 219.009 0.257 161.624
ERROR Q3 90 2.284 0.858 6.485 BIAS Q3 21.584 20.021 6.693
ERROR Q4 87 2.818 0.864 8.861 BIAS Q4 22.116 0.116 9.057
ERROR Pooled 342 9.439 0.856 95.697 BIAS Pooled 28.674 0.173 95.770
Forecast error de?ated by lagged total assets Forecast bias de?ated by lagged total assets
ERROR Q1 109 0.188 0.152 0.174 BIAS Q1 20.018 20.002 0.110
ERROR Q2 110 0.217 0.172 0.180 BIAS Q2 20.009 20.002 0.137
ERROR Q3 124 0.240 0.181 0.204 BIAS Q3 20.053 20.008 0.157
ERROR Q4 121 0.269 0.209 0.208 BIAS Q4 20.039 20.008 0.173
ERROR Pooled 464 0.230 0.174 0.194 BIAS Pooled 20.031 20.005 0.148
Notes: Forecasts are sourced from 1,760 quarterly cash ?ow reports issued by 481 ?rms during the
2005/2006 ?scal year. ERROR is a measure of cash expenditure forecast accuracy and is measured as
the absolute value of forecasted cash out?ows less actual reported cash out?ows for the respective
quarter (Q1-4) de?ated by the absolute value of the actual out?ows in Panel A and de?ated by lagged
total assets in Panel B. BIAS is a measure of cash expenditure forecast bias and is measured as the
signed ERROR or relative directional forecast error for the respective quarter, and is calculated as
forecasted cash out?ows less actual reported cash out?ows, de?ated by the value of the actual
out?ows for the respective quarter (Q1-4) in Panel A and de?ated by lagged total assets in Panel B;
ERROR Pooled includes all quarterly ERROR observations; BIAS Pooled includes all quarterly BIAS
observations. The ERROR and BIAS measures shown in Panel B (i.e. using the lagged asset de?ators)
are used in regression analysis (the variable names are shown as FERROR and FBIAS in the
regression models)
Table II.
Descriptive statistics –
error and bias
Mandatory cash
expenditure
forecasts
275
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An examination of notes provided in the covering letter attached to the Appendix 5Bs
revealed that delays in obtaining exploration permits where frequently cited as reasons
for the failure to commence exploration activities on a number of mining claims.
However, forecasts were not explained or quali?ed and there was no indication provided
as to why the forecasts were continually inaccurate and optimistically biased.
Table II Panel B shows development expenditure forecast error consistent with
those reported for the EE forecast error. The median forecast error for the pooled
observations is 85.6 percent. Contrary to the ?ndings for the EE forecasts, a signi?cant
positive forecast bias is evident for most of the quarterly ?gures and the pooled
observations (median error of 17.3 percent). This suggests that managers may be
conservative in under-estimating their development expenditure relative to realised
cash out?ows. However, when the economic signi?cance is considered (Panel B), the
error for the pooled observations remains material with a mean (median) of 23 percent
(17.39 percent), but not the bias.
Table III further explores error and bias by displaying the signed error over
percentiles for the full sample and the industry sub-samples (material and energy). The
statistics reveal that the negative EE forecast error and bias shown in Table II are
evident in both sub-industries, but it is more pronounced in the energy sector.
Approximately, 87 percent of ?rms in this sector have errors greater than positive or
negative 50 percent and the negative errors dominate (58.4 percent are greater than
250 percent of realized cash ?ows). Thus, as expected, ?rms in the energy sector are
more likely to produce inaccurate forecasts that overestimate their actual expenditure
(negatively biased forecasts). In contrast, Panel B shows that similar biases are not
evident for development expenditure forecasts.
Table IV provides the descriptive statistics for the independent variables entering in
the regression model. The data were obtained from the most recent annual report prior
to the release of each quarterly report. The sample size for most of the variables is less
than the total 481 observations due to missing data for some of the variables.
The statistics reveal that most ?rms are small (median total assets of $7.029 million),
are performing poorly with negative net cash ?ow from operations, have relatively
large amounts of their assets in the form of cash (median FINSLACK is 37.4 percent of
total assets), have higher cash ?ow volatility, and are relatively young (median AGE is
4.74 years). Also, most ?rms have no debt (67.41 percent of the sample), are yet to reach
the development expenditure stage (83.65 percent of the sample), and are in the
material sub-industry (77.13 percent of the sample).
6.2 Multiple regression results
6.2.1 Exploration and evaluation forecast expenditure. Table Vpresents the results from
estimating the model for EE forecasting error (Panel A) and bias (Panel B). Results are
reported for each quarter and for the pooled observations[17]. Consistent withthe ?ndings
of prior studies (Penman, 1980; Verrecchia, 1983; Lev and Penman, 1990), the Panel A
pooled results show that the performance (PERFORM) coef?cient is negative (20.344)
and signi?cant ( p , 0.01), indicating that better performing ?rms provide more accurate
forecasts. The ?nancial slack (FINSLACK) and cash ?ow volatility (CFVOL) coef?cients
are positive (0.041 and 0.020) and signi?cant ( p , 0.01); indicating that ?rms with greater
cash holdings and greater cash ?ow volatility are more likely to exhibit greater EE
forecasting errors. While the ?rm age (AGE) coef?cient is not signi?cant, the experience
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dummy coef?cient (AGEEXP) is negative (20.039) and signi?cant ( p , 0.01) indicating
that companies with more experience, that is, they have reached the development stage,
are more likely to produce more accurate EE forecasts. Similarly the ?nancial leverage
(LEVDUM) coef?cient is negative (20.016) and signi?cant ( p , 0.05), which suggests
that the presence of debtholders may helpto mitigate forecast inaccuracy. Also, consistent
with prior research, larger ?rms are more likely to issue more accurate forecasts than
smaller ?rms (the SIZE coef?cient ¼ 20.012; p , 0.05). Finally, the negative and
signi?cant industry dummy (INDDUM) coef?cient (0.035; p , 0.01) indicates that
consistent with the results reported in Table III, ?rms in the materials sub-industry are
more likely to provide more accurate EE forecasts than those in the energy sector.
Apart from company age, the ?ndings for EE forecast accuracy provide strong
support for the expected explanators of EE forecast error and in combination, the
variables have signi?cant explanatory power (adjusted R
2
¼ 37.9 percent). Similar
results are evident in the quarterly models.
All Firms Materials Energy
Signed error
(percent)
No. of forecasts
(percent)
No. of forecasts
(percent)
No. of forecasts
(percent)
Panel A: Exploration and evaluation expenditure
Negative , 2 50.01 483 (31.55) 361 (29.64) 122 (58.37)
240.01 to 250 44 (2.87) 36 (2.95) 8 (3.83)
230.01 to 240 47 (3.07) 40 (3.28) 7 (3.35)
220.01 to 230 70 (4.57) 59 (4.84) 11 (5.26)
210.01 to 220 78 (5.09) 62 (5.09) 16 (7.65)
20.01 to 210 68 (4.44) 52 (4.27) 16 (7.65)
Positive 0 to 10 109 (7.12) 93 (7.63) 16 (7.65)
10.01 to 20 95 (6.20) 76 (6.24) 19 (9.09)
20.01 to 30 110 (7.18) 96 (7.88) 14 (6.70)
30.01 to 40 93 (6.07) 83 (6.81) 10 (4.78)
40.01 to 50 89 (5.81) 74 (6.07) 15 (7.18)
.50.01 245 (16.00) 186 (15.27) 59 (28.23)
Total 1,531 1,218 313
Panel B: Development expenditure
Negative ,250.01 107 (31.29) 63 (30.14) 44 (33.08)
240.01 to 250 3 (0.88) 1 (0.48) 2 (1.50)
230.01 to 240 5 (1.46) 3 (1.44) 2 (1.50)
220.01 to 230 10 (2.92) 6 (2.87) 4 (3.01)
210.01 to 220 10 (2.92) 7 (3.35) 3 (2.26)
20.01 to 210 14 (4.09) 9 (4.31) 5 (3.76)
Positive 0 to 10 12 (3.51) 7 (3.35) 5 (3.76)
10.01 to 20 13 (3.80) 6 (2.87) 7 (5.26)
20.01 to 30 14 (4.09) 9 (4.31) 5 (3.76)
30.01 to 40 11 (3.22) 4 (1.91) 7 (5.26)
40.01 to 50 16 (4.68) 11 (5.26) 5 (3.76)
.50.01 126 (36.84) 82 (39.23) 44 (33.08)
Total 342 209 133
Notes: Forecasts are sourced from 1,760 quarterly cash ?ow reports issued by 481 ?rms during the
2005/2006 ?scal year. Signed error (or bias) is measured as the difference between forecasted cash
out?ows and the actual reported cash out?ows, de?ated by the value of the actual out?ows
Table III.
Number of cash ?ow
expenditure forecasts
classi?ed by percentage
error (signed)
Mandatory cash
expenditure
forecasts
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The results in Table V, Panel B for EEforecast bias are modest in comparison with Panel
A. Given the weak evidence of bias previously reported it is not surprising that the results
for EE forecast bias reveal fewer signi?cant coef?cients and a lower model explanatory
power (adjusted R
2
¼ 8.30 percent in the pooled model). Only the coef?cients for ?rm
performance (PERFORM), ?nancial slack (FINSLACK), pre-production stage experience
(AGEEXP) and size (SIZE) are signi?cant. These results nevertheless reveal that better
performance, greater ?nancial slack, and pre-production stage experience induce an
optimistic (overestimation) EE forecast bias, while larger size induces a conservative
(underestimation) bias.
6.2.2 Development forecast expenditure. Table VI presents the results of estimating
the model for development forecasting error (Panel A) and bias (Panel B). Consistent
with the results reported for EE forecast error, Panel A shows in the pooled results that
better performing ?rms (PERFORM), those with greater ?nancial slack (FINSLACK),
and greater cash ?ow volatility (CFVOL) are more likely to issue inaccurate forecasts.
The size (SIZE), ?rm age (AGE), industry (IND) and leverage (LEV) coef?cients have
no signi?cant in?uence on the Development forecast error.
Panel B shows that only the coef?cients for performance (PERFORM) and ?nancial
slack (FINSLACK) are negative and signi?cant. These results indicate that ?rms that
are performing poorly and have lower cash holdings are more likely to overstate their
development forecasts than ?rms that are performing well and have greater cash
holdings. While these results are consistent with the results reported for EE forecast
bias, none of the other variables display signi?cant coef?cients. The weaker results in
Panel A: Test variables – continuous
n Mean Median SD Minimum Maximum
Total assets ($million) 479 14.503 7.029 28.087 0.18 326.12
Total assets (logged) SIZE) 479 8.902 8.858 1.101 5.18 12.70
Cash ?ow performance (PERFORM) 463 20.116 20.081 0.126 20.50 0.13
Financial slack (FINSLACK) 463 0.526 0.374 0.507 20.03 2.00
Cash ?ow volatility (CFVOL) 448 23.118 23.215 1.158 26.73 5.073
Age in years (AGE) 481 8.590 4.740 9.069 0.000 38.05
Frequencies
0 1
n Percent n Percent
Panel B: Test variable – dichotomous
Leverage dummy (LEVDUM) 448 302 67.41 146 32.59
Experience dummy (AGEEXP) 422 353 83.65 69 16.35
Panel C: Control variable – dichotomous
Industry dummy (INDDUM) 481 371 77.13 110 22.87
Notes: Data are sourced from annual results for the population of 481 explorers for 2005/2006 ?scal
year. In Panel A, SIZE is the natural logarithm of average total assets for the current ?scal year (t) and
prior year (t 2 1); PERFORM is net operating cash ?ow de?ated by average total assets for t and
t 2 1; FINSLACK is total cash at end of quarter de?ated by average total assets; CFVOL is the
standard deviation of net operating cash ?ows over four prior quarters de?ated by lagged total assets;
and AGE is the number of years between listing and the current quarterly reporting date; de?ated by
lagged total assets. In Panel B, LEVDUM is equal to one if the company has ?nancial leverage
(interest-bearing debt) and zero otherwise; and AGEEXP is equal to one if the company has both actual
exploration/evaluation and development expenditure in the same quarter, and zero otherwise. In Panel
C; INDDUM is equal to one if the company is in the energy sector and zero if it is in the materials sector
Table IV.
Descriptive statistics –
independent variables
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o
d
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1
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t
¼
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1
þ
b
2
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M
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b
3
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4
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5
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6
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b
7
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P
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b
8
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b
9
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N
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D
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M
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1
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t
;
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o
d
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l
2
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B
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t
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b
1
þ
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2
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b
3
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A
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b
4
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F
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5
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6
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7
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8
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b
9
I
N
D
D
U
M
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t
þ
1
i
t
Table V.
Cash ?ow forecast
accuracy and bias -
exploration and
evaluation expenditure
Mandatory cash
expenditure
forecasts
279
D
o
w
n
l
o
a
d
e
d
b
y
P
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D
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C
H
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2
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4
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(
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6
1
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Table VI are possibly explained by the smaller sample of ?rms that have reached the
development stage in the pre-production activities. Nevertheless, even with this
smaller sample, the results show that forecast accuracy can be explained by well
recognised ?rm speci?c differences.
7. Discussion and conclusion
This study investigates the reliability of cash expenditure forecasts issued mining
exploration companies. Prior research has examined voluntary cash ?ow reporting
by managers (Wasley and Wu, 2006), and voluntary (Skinner, 1994) and mandatory
(Kato et al., 2006) management earnings forecasts. However, as cash ?ow forecasts are
not mandated in other jurisdictions, and as this issue has not been previously
examined in Australia, this is the ?rst known study to identify and examine such a
mandatory setting. The context of the study is the ASX’s (2001) Appendix 5B quarterly
cash ?ow report regime. This setting is particularly interesting because the ASX’s
mandatory regime imposed on mining exploration companies only requires cash
expenditure forecasts, not comprehensive cash ?ow (net cash ?ow) forecasts.
We investigate three research questions relating to the quality of these cash
expenditure forecasts:
(1) What is the nature of the Appendix 5B cash ?ow forecasts?
(2) How accurate are the forecasts and are they biased?
(3) What ?rm-speci?c characteristics in?uence the accuracy and bias of the
forecasts?
We examine these questions using the available Appendix 5B cash expenditure
forecasts (1,760 cash expenditure observations) provided by the population of mining
exploration entities listed on the ASX between September 30, 2005 and July 31, 2006.
Our ?ndings showthat most mining exploration entities comply with the requirement
to disclose forecasts with more than 90 percent of entities providing evaluation and
exploration expenditure forecasts, and nearly 20 percent of these same ?rms provide
development expenditure forecasts. In contrast, similar ?ndings are not observed for
forecast accuracyandbias. Onbotha quarterlyandpooledbasis, the medianforecast error
is approximately 50 percent of the actual expenditure for EE expenditure, and
approximately 85 percent of the actual expenditure for development expenditure. These
?ndings indicate that ?rms have considerable dif?culty in forecasting one-quarter-ahead
pre-production expenditure. Our ?ndings reveal that forecast inaccuracy is more
prevalent in ?rms that have poorer performance, greater ?nancial slack, greater cash-?ow
volatility, and no ?nancial leverage. Also, forecasts are less accurate for ?rms that are
smaller, in the pre-development stage, and in the mineral (non-oil and gas) sub-industry.
We also ?nd evidence that some of the same ?rm-speci?c factors in?uence forecast bias.
Overall, our results clearly show that, on average, the mandatory cash expenditure
forecasts required by the ASX are unreliable and, contrary to the objectives of cash
?ow reports (AASB 107, para. 5), do not appear to provide information to better inform
investors of the “amount, timing, and certainty” of future cash ?ows[18]. As a
consequence, these forecasts may adversely affect the investment decisions of
investors. The fact that many forecasts are signi?cantly inaccurate, together with the
considerable variation we observe across ?rms in the extractive industry, challenge the
Mandatory cash
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wisdom of mandating such forward-looking information for ?rms in uncertain
operating environments.
Given their potential to mislead, we suggest that cash expenditure forecasts should
not remain mandatory. Regardless of whether they continue to be mandated or are
made voluntary, we suggest that ?rms be required to disclose information about the
underlying estimates and assumptions used in deriving the forecasts. Such additional
disclosures would be consistent with best practice observed in other contexts, such as
in initial public offer prospectus documents. Additionally, the ASX should require
?rms to provide explanations when forecasts vary materially from estimates.
Finally, our study adds to the small body of literature that reveals the limitations of
mandatory forecasting. As in other studies (Kato et al., 2006) our ?ndings show that the
information role of forecasts in capital markets is impaired when ?rms have little
discretion over the decision to forecast and the characteristics of the forecast.
Notes
1. For example, in 2006 CuDeco Ltd announced to the ASX a signi?cant copper discovery,
leading to its share price increasing from 29 cents to ten dollars in just eight weeks.
Following an ASX investigation the discovery was subsequently shown to be overstated.
The ASX was heavily criticised for its slow response (West and Andrusiak, 2006).
2. The ASIC adopts a similar “reasonable grounds” position with respect to forecasts in
prospectuses (ASIC, 2002).
3. Additional disclosures can be provided in notes to the cash ?ow report.
4. The Code is issued by the Joint Ore Reserves Committee of The Australasian Institute of
Mining and Metallurgy, Australian Institute of Geoscientists and Minerals Council of
Australia.
5. Commitments test entities are those companies that are allowed to list on the ASX without a
‘binding contract’, provided they make commitments to spend their cash in accordance with
their business objectives (Gallery et al., 2004).
6. AASB 107 is the equivalent of IAS 7 Cash Flow Statements issued by the International
Accounting Standard Setting Board (IASB).
7. Although entities frequent provide additional information in coversheets and notes, a careful
scrutiny of a large sample of 5B reports failed to ?nd any evidence of attempts to justify
forecasts or to explain forecast errors.
8. AASB 6 is the equivalent of IFRS 6 Explorations and Evaluations of Mineral Resources
issued by the IASB.
9. Under AASB 138 Intangible Assets all research costs must be expensed (para. 54) because in
the research phase, companies cannot demonstrate that an asset exists that will generate
probable future income bene?ts (para. 55). Similarly with mining exploration and evaluation
costs, there is on average, a low probability that these costs will result in a recoverable
reserve from which the company will generate economic bene?ts, yet the company can
initially capitalise these costs (development expenditure is generally recognised in
accordance with AASB, 2007c).
10. ‘Signi?cant’ revisions in management forecast estimates are de?ned as changes in estimated
sales of 10 percent or more and/or changes in estimated earnings of 30 percent or more.
11. There is also evidence that mandatory and voluntary earnings forecasts in Australian IPO
prospectuses are materially inaccurate, optimistically biased, and tend to be walked down
prior to the earnings realisation date (Gallery et al., 2008).
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12. Arguably the absence of regulatory monitoring and sanctions for non-compliance is
tantamount to having voluntary or no regulation at all (Fung et al., 2004; Lopez-De-Silanes,
2003).
13. We also test these same factors in examining forecast bias but we make no directional
predictions about their in?uence on bias.
14. Disclosure is considered “adequate” if it is relevant to the needs of users, capable of ful?lling
those needs, and timely.
15. Oil and gas ?rms must provide a hydrocarbon report (inclusive of pre-hydrocarbon reserve
stage details) as part of their quarterly Appendix 5B reports (ASX Listing Rule 5.9-5.17).
16. Actual rather than forecast cash expenditure is used as a de?ator because a number of ?rms
forecast zero cash expenditure. Appendix 5 provides an example of how this forecast error is
calculated.
17. A panel data random effects regression procedure is used to estimate the pooled model.
Robust standard errors are used in estimating the reported coef?cients.
18. It is possible that the forecasts may be at least partially informative in some contexts. To
more comprehensively assess the usefulness of the forecasts would require a comparative
approach using alternative predictive models (e.g. historical versus forecast cash ?ows) and
an assessment of the value-relevance of the forecasts to investors. We leave this relative
value-relevance issue to further research.
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(The Appendix Table is shown on the next page.)
Corresponding author
Gerry Gallery can be contacted at: [email protected]
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Appendix. Central Petroleum Limited (CTP) forecast errors calculated from
Appendix 5B estimated cash expenditure out?ows: March 2006 to June 2008
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Table AI.
Mandatory cash
expenditure
forecasts
287
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doc_133459528.pdf