A Better Estimate of Internally Generated Intangible Capital
Bibliographic record
Abstract
Internally developed intangibles are not included in reported assets under U.S. generally accepted accounting principles. The omission of this increasingly important class of assets reduces the usefulness and relevance of financial statement analysis, conducted with reported values of equity and assets. Recent studies overcome this deficiency by capitalizing research and development (R&D) expenses to estimate the value of knowledge capital and by capitalizing selling, general, and administrative (SG&A) expenses to estimate the value of organization capital. Those two estimates are then added to reported values for financial statement analysis. For convenience, many studies rely on two rules of thumb and assume them to be equally applicable in all instances: (1) 30% of SG&A and 100% of R&D expenses are investments, and (2) the useful life of SG&A and R&D investments is three and five years, respectively. We propose a more flexible approach by estimating the capitalization and amortization parameters on an industry–year–specific basis. Our modified values of total assets and equity, inclusive of the value of capitalized intangibles, exhibit greater association with future returns and investments compared with as-reported values and values estimated with uniform rules of thumb. We provide a better estimate of intangible capital for the consumers of financial statements. This paper was accepted by Ranjani Krishnan, accounting. Funding: A. Srivastava and R. Zhao acknowledge financial support from the Social Sciences and Humanities Research Council of Canada. A. Srivastava acknowledges financial support from the Canada Research Chairs Program of the Government of Canada. A. Iqbal acknowledges financial support from the Canadian Securities Institute Research Foundation and Chartered Professional Accountants (Alberta) Education Foundation. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.01703 .
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".