The impact of intangible intensity on the amount and quality of accruals, the tone of narrative disclosures, and the stock of capital assets
Bibliographic record
Abstract
My dissertation consists of three studies about the impact of intangible intensity on the amount and quality of accruals, the tone of narrative disclosures, and the stock of capital assets. In my first study, presented in Chapter 2, I examine whether the amounts of accruals, their composition (components such as working capital, long-term, conditionally conservative, nonarticulating, and financial accruals), and their properties (alleviating timing difference between the occurrence of economic events and cash flows, as well as accruals’ ability to predict cash flows and earnings) differ for knowledge versus physical firms. I find that, as a percentage of assets and revenues, the absolute value of accruals is larger for knowledge firms than for physical firms. This pattern indicates that accounting for knowledge firms requires at least as much judgment and estimates as for physical firms. Meanwhile, accruals’ timing mitigating role is more prone to estimation errors for knowledge firms. Despite higher errors, knowledge firms’ accruals are as predictive of future earnings and cash flows as for physical firms, at least by the time knowledge firms become large and mature. This study contributes to the ongoing debate on the changing usefulness of accrual accounting vis-à-vis cash accounting, as the composition of listed firms shifts toward knowledge firms. I show that accrual accounting remains prevalent and useful despite this shift. In my second study, presented in Chapter 3, I estimate investment and maintenance portions of research and development (R&D) and MainSG&A (SG&A minus R&D), and their amortization rates, on an industry-year–specific basis. My modified book value, inclusive of capitalized intangibles, exhibits greater association with future returns, investments, and bankruptcies, relative to as-reported and mechanically estimated book values. I provide a better estimate of book values of assets and equity for consumers of financial statements. In my third study, presented in Chapter 4, I develop a model that uses the level of uncertainty in the narrative disclosures of the loss-reporting firms to predict their future earnings. I find that the level of uncertainty in narrative disclosures contains incremental information about the future performance of the loss-reporting firms. This information is economically significant as a size-adjusted hedged portfolio based on this information provides abnormal returns. In additional analysis, I find that the level of uncertainty in the narrative disclosures is more informative for young firms and those that report special items and research and development expenditures.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".