Exploration intensity, analysts’ private information development and their forecast performance

Abstract

This study examines whether analysts in the extractive industries in Australia adjust their private information searching and processing in response to the complexity of information about a firm’s exploration and evaluation (E&E) activities. We find that both the proportion of private information in their forecasts and the accuracy of their forecasts increase with the intensity of E&E activities. Additional analyses reveal that this effect is more pronounced for firms with substantial E&E activities but limited production activities, and that analysts’ private information development activities are mainly related to the capitalized E&E expenditures. Our results provide guidance for both investors and future standard setters. They show that investors can benefit from analysts’ expertise in situations of high information asymmetry. They also provide evidence of the advantage of distinguishing successful from unsuccessful investments in resource exploration when accounting for E&E expenditures, which may inform future decisions about accounting for intangible assets.

Keywords:

• analysts
• intangible assets
• exploration and evaluation expenditures
• extractive industries

JEL Classification:

• G14
• G24
• L72
• M41

Acknowledgements

We appreciate insightful comments from Mark Clatworthy and Ed Lee (Guest editors of the Accounting and Business Research special issue on financial analysts) and two anonymous reviewers. We also would like to thank Dan Dhaliwal, Neil Fargher, Wolfgang Schultze, Terry Walter and participants at the 2012 American Accounting Association (AAA) annual meeting, the 2013 European Accounting Association (EAA) conference and the 2013 Accounting & Finance Association of Australia and New Zealand (AFAANZ) conference for their comments. Data from Institutional Brokers Estimate System (IBES) are gratefully acknowledged. All errors and omissions are the responsibility of the authors.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. For example, Poskitt (2005) reports that the share prices of extractive companies are highly sensitive to changes in the probability of discovering an economic mineral or oil and gas deposit. Ferguson and Crockett (2003) and Bird et al. (2013) conclude that investors who have little geological expertise may rely more on media reports or exploration announcements with positive adjectives because of the complexity of geological information. In particular, Ferguson and Crockett (2003, p. 103) point out that routine mining company disclosures include “discussion of complex variables such as metal purity, the width of the drilling intercepts and the depth below the surface where the intercept occurs”, and could also contain “highly technical factors including the geochemical composition of the mineralization of the discovery” and “other complexities”.

2. Private information refers to information about future earnings that is developed by the individual, arising from analysts’ information processing skills and different forecast modelling techniques, as opposed to common information that is known to all analysts (Barron et al. 1998). An example of private information acquisition and processing is analysts’ site visits to extractive companies’ operations and exploration grounds: on 28 October 2008, the Australian Financial Review (p. 21) reported that “analysts will visit Olympic Dam on the last day of a week-long tour of BHP's operations, which began yesterday with a trip to Karratha in the Pilbara and briefings on the company's iron ore and petroleum divisions. Today they travel south to take in the company's nickel operations at Mount Keith and the troubled Ravensthorpe plant, before heading to South Australia on Thursday.” Using their specialized knowledge, analysts evaluate future prospects of these E&E activities and provide guidance to investors. For example, analysts from Citigroup said that the share price of Energy Resources of Australia (ERA) could rise almost 70% to hit the target price of $23.30 a share set by the brokerage house if ERA’s project hurdles could be cleared following a recent visit to the company’s Ranger mine in the Northern Territory (Australian Financial Review, 30 June 2010, p. 26).

3. Analysts’ average (mean or median) forecast also refers to analysts’ consensus forecast. We use the term “analysts’ average forecast” as opposed to “analysts’ consensus forecast” to avoid confusion regarding our use of the analyst consensus construct developed by Barron et al. (1998) to measure the ratio of analysts’ common information to their total information.

4. The extractive industries are central to the ASX, with total market capitalization of $310 billion and over 1000 listed extractive companies, representing 28% of total market capitalization and 49% of all ASX-listed companies by number (ASX 2013a).

5. E&E expenditures are expenditures incurred by an entity in connection with the exploration for and evaluation of mineral resources before the technical feasibility and commercial viability of extracting a mineral resource are demonstrable (IASB 2004).

6. The IASB Discussion Paper – Extractive Activities was developed by a research team comprising members from the national accounting standard setters in Australia, Canada, Norway and South Africa. The Discussion Paper outlined a revised framework for accounting for extractive activities. After considering 141 comment letters received on the Discussion Paper in December 2012, the IASB decided to discontinue the project in favor of a broader intangible assets project which includes extractive activities as part of a broader consideration of intangible assets and research and development activities.

7. Exploration is the detailed examination of a geographical area of interest that has shown sufficient mineral-producing potential to merit further exploration. Exploration activities include: conducting topographical, geological, geochemical and geophysical studies; and carrying out exploratory drilling, trenching and sampling activities. Evaluation activities involve determining the technical feasibility and commercial viability of mineral deposits that have been found through exploration (IASB 2010).

8. Development is the establishment of access to the mineral reserve and other preparations for commercial production. Development activities often continue during production. Production involves the extraction of the natural resources from the earth and the related processes necessary to make the produced resource marketable or transportable (IASB 2010).

9. The costs of exploration are for discovering resources; the costs of evaluation are for proving the technical feasibility and commercial viability of any resources found. In comparison, the costs of development relate to gaining access to the resources after the decision has been made to develop the mine. The costs of production are the cost of producing the saleable product on a commercial scale and includes all extraction and treatment costs (PricewaterhouseCoopers 2007).

10. To a large extent, IFRS 6 allows companies to carry over to IFRS their previous GAAP practice.

11. An area of interest refers to an individual geological area whereby the presence of a mineral deposit or an oil or natural gas field is considered favourable or has been proven to exist (AASB 2004).

12. Paragraphs 7.1 and 7.2 of AASB 6 require that for each area of interest, E&E costs shall either be: “(i) expensed as incurred; or (ii) partially or fully capitalized, and recognized as an E&E asset if the following conditions are satisfied. (a) the rights to tenure of the area of interest are current, and (b) at least one of the following two conditions is also met: (i) the E&E expenditures are expected to be recouped through successful development and exploitation, or by sale and (ii) E&E activities in the area of interest have not at the reporting date reached a stage of reasonable assessment to determine the recoverable reserves, but active operations are continuing.” (Similar criteria appear in the pre-IFRS equivalent AASB 1022 (1989) of Australian GAAP).

13. It is the nation’s largest single export sector. In 2012–2013, mineral and energy exports accounted for an estimated 86% (A$175 billion) of Australian commodity exports, and 58% of total goods and services exports (BREE 2013). During that period, the mineral resources industries accounted for 8.6% (A$122 billion) of Australia’s gross domestic product (ABS 2013), and at 266,000 employees, more than 50% above the level of three years earlier (BREE 2013).

14. Market manipulation allegations were made against Reef Mining NL, Diversified Mineral Resources NL and Diamond Rose NL. Insider trading allegations were made against Mt Kersey Mining NL and Carpenter Pacific Resources NL.

15. Using ranked and weighted geological aspects such as proximity to mines, deposits and the significance of the camp and the commodity sought, the Geoscience Factor Method is a subjective, matrix-based valuation methodology for mineral exploration properties that do not contain exploitable resources. The market approach method is based on the value of recent (cash- or share-based) transactions that are similar in terms of scope, time, place and commodity. The appraised value method is based on the premise that a mineral exploration property is worth meaningful past exploration expenditures (in dollars of the day) plus warranted future costs (i.e. expenditure base). Readers are referred to Kilburn (1990), Thompson (2000) and Lilford and Minnitt (2005) for a more detailed discussion of valuation methodologies on mineral properties.

16. The results are similar using the sample of firm-years with at least two analysts following.

17. For firms that capitalize E&E expenditures, their total assets on an “as-if-expensing” basis are estimated by subtracting the value of E&E assets from reported total assets.

18. For example, capitalized E&E expenditures is estimated by subtracting the value of impairment loss for E&E assets, the value of E&E assets written off, the value of E&E assets disposed of, the value of E&E assets transferred to other accounts, and also subtracting the opening balance of E&E assets from the closing balance of E&E assets. If the value of estimated capitalized E&E expenditures is negative, the firm-year observation is excluded. There are 42 firm-year observations with negative values of estimated capitalized E&E expenditures.

19. The results are similar using the mean EPS forecast.

20. Using h and s as measures of COMMON and PRIVATE without taking square roots obtains similar results.

21. The market-to-book ratio measures not only growth opportunities but also intangible assets. The inclusion of this control variable potentially reduces the power of our tests.

22. Total assets are re-stated on an “as-if-expensing” basis for firms that capitalize E&E expenditures.

23. Actual earnings per share is re-stated on an “as-if-expensing” basis for firms that capitalize E&E expenditures.

24. Book value of equity is re-stated on an “as-if-expensing” basis for firms that capitalize E&E expenditures.

25. Barron et al. (2002) report that the mean (median) and standard deviation of CONSENSUS for the US firms with intangible assets are 0.752 (0.7877) and 0.2465, respectively (p. 303).

26. In Chen (2010)’s sample of Australian companies with analysts following during 1987–2007, analysts’ mean (median) forecast error is 0.927% (0.885%) of share price.

27. We report results of all estimations after winsorizing the continuous independent variables at the 1% and 99% levels, and controlling for firm and year fixed effects.

28. The results are similar when earnings volatility is measured as the standard deviation of the annual earnings divided by total assets for the past five years.

29. Using the annual E&E expenditures scaled by market value of equity rather than total assets at the end of year t−1, yields consistent results for all AFE regressions in the main and additional analyses.

30. 21% = −0.1 × 0.073/0.034.

31. , where N is the number of analysts’ forecasts; D is the sample variance of the analysts’ individual forecasts; and SE is the squared error in the mean forecast, calculated as the squared difference between the mean forecast and actual earnings, that is, (Actual EPS−Mean EPS Forecast)² (Barron et al. 1998).

32. Consistent with Barth et al. (2001), we do not control for the firm fixed effect because EFFORT is a rather sticky measure that is quite firm specific. The sample for the effort analysis includes 604 firm-years after excluding 11 influential observations. A closer look at these observations reveals potential data errors: analysts of these firm-years follow more than 40 firms on average.

33. The classification of Capitalizers or Expensers is on yearly basis. It does not carry over to a subsequent period. In other words, we re-classify firm-years based on whether firms capitalize their annual E&E expenditures for a subsequent period.

34. The number of Capitalizer and Expenser firms in total (142) exceeds the number of firms in the final sample (131) because a firm may be classified as both an Expenser and a Capitalizer during the sample period if the firm changed their E&E asset recognition policy. The results are similar if these firms are removed from the sample. 

35. Total assets is re-stated on an “as-if-expensing” basis for Capitalizers.

36. Scaling capitalized and expensed E&E expenditures by market value of equity rather than total assets yields similar results.

37. We also re-classify Producers and Non-Producers based on the median value of REV only, and partition the sample into two sub-samples (i.e. HRev and LRev). The group with operating revenue below the median value shows a significant association between the intensity of E&E activities and the properties of analysts’ information environment.

38. This analysis is based on the sample partitioning using the median values of REV and EXPL. The interactions between EXPL and the indicator variables, which indicate firm-years from one of the four groups based on the median values of REV and EXPL, only capture the relations between EXPL and the properties of analysts’ forecasts within individual sub-samples. If the properties of analysts’ forecasts vary systematically between these sub-samples, for instance, between high E&E firms and low E&E firms, the regression models (5) would not have captured the variation in those forecast properties.