R&D profitability, intensity and market-to-book: evidence from Australia
Bibliographic record
Abstract
Purpose The purpose of this paper is to assess the financial disclosure vis-á-vis economic reality of research and development (R&D) expensed by Australian firms under the pre-2005 Australian generally accepted accounting principles (A-GAAP) regime via the lens of market-to-book. Design/methodology/approach The authors estimated firms' R&D profit rate, measured R&D revenue intensity and modelled the impacts of these and related economic factors, via economic and financial disclosure channels, on market-to-book using data for 1988-2004. Findings R&D, on average, was profit neutral and had undetectable impacts on market-to-book whether via equity valuation or financial disclosure. Research limitations/implications Market-to-book's information content is best viewed as conditional on the reference disclosure regime. Australian firms' typically at best minimal R&D profitability is an international anomaly. Data limitations in terms of the generating process and availability mean that R&D's impact on market-to-book via financial reporting is not definitively determined. Practical implications Restrictive rules on the capitalization of intangible asset-related expenditures under A-GAAP apparently did not adversely impact market-to-book's economic information. AIFRS's more permissive rule risks compromising market-to-book's reliability in such a role. Originality/value For Australia, the paper is anticipated to be the first to estimate the profit rate of R&D, measure the intensity of R&D, and model R&D's influence on the market-to-book ratio. It develops a framework for the economic and financial reporting impacts of investments on a key indicator of firms' financial standing and contributes to the debate on identifiable intangibles' disclosure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".