Is R&D Really That Special? A <scp>Fixed‐Cost</scp> Explanation for the Empirical Patterns of R&D Firms<sup>†</sup>
Bibliographic record
Abstract
ABSTRACT I propose an explanation for the positive relation between R&D, future earnings, and future stock returns based on the fixed‐cost qualities of R&D. If R&D is relatively fixed over short horizons, demand shocks realized by some R&D firms will push these firms into R&D intensity levels that are suboptimal as common scale proxies—market equity, assets, and sales—respond more quickly to demand shocks than R&D. In response, R&D firms realizing negative demand shocks reduce future expenses and capital expenditures, producing higher future profitability on lower sales growth. Consistent with the fixed‐cost hypothesis, I find the higher future profits of high R&D firms are explained by cost cutting, not revenue growth. Collectively, the restructuring of cost and capital structures of the subset of high R&D firms realizing demand shocks explains the future profit and investment patterns of R&D firms, while the fixed‐cost qualities of R&D seem to explain patterns in future stock returns. My results have implications for literatures that examine how decisions on R&D investment levels affect future firm performance, growth, and stock returns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".