Do Institutional Investors Influence R&D Investment Policy in Firms with High Information Asymmetry?
Bibliographic record
Abstract
This paper seeks to determine if institutional investors influence corporate research and development (R&D) investment policies by encouraging R&D investment in firms with high information asymmetry. The effect of changes in institutional investor levels to subsequent changes in R&D investment levels are examined using firm and year fixed effect regressions and difference-GMM regressions. Increased institutional ownership leads to increased R&D investment and this relationship is stronger in firms with higher information asymmetry. Institutional investors encourage higher R&D investment primarily in firms with high information asymmetry indicating they have an advantage in discerning the value of R&D investments in such firms. Institutions are an important and increasing force in U.S. stock ownership. The results in this paper indicate that institutional investors have an advantage in discerning the value of R&D investments in firms with high information asymmetry. The presence of institutional investors encourages the management of such firms to make long-term investments in R&D.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".