The Impact of Options Trading on the Relationship between Research Quotient and Firm Performance
Bibliographic record
Abstract
The performance of innovation could be counted by the number of patent. Patent information enables a firm to estimate R&D efficiency and stock market value. Nonetheless, patents is not universal because more than 50% companies in COMPUSTAT do not patent their new products. Since patents have some drawbacks, Cooper, Knott, and Yang (2015) use Research Quotient (RQ) as an indicator of firm innovation because RQ measures the productivity of the R&D department, which produces a new innovative product and transforms it into revenues. In this paper, we examine the impact of option trading on the relation between RQ and stock market return (or firm value). We find that RQ has the positive impact on firm value, proxy by market-to-book (MTB) value. The option dummy, which is the firm with option trading, has significantly positive impact on the relation between RQ and firm value and insignificantly positive impact on the relation between RQ and future stock return. Nonetheless, interaction term of RQ and option volume has positive and significant impacts on MTB and future stock return.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".