The Linkage between Corporate Research and Development Intensity and Stock Returns: Empirical Evidence
Bibliographic record
Abstract
Research and development (R&D) is a significant driver of innovation that leads to superior performance. The present study attempts to examine the relationship between R&D intensity and a firm’s performance at both aggregate and industry levels in the emerging market of India using a battery of R&D intensity measures and stock market returns as a measure of a firm’s performance. The study was conducted on 1097 companies from six R&D-intensive industries. The Fama-French portfolio formation method was used to evaluate the stock market performance of R&D-intensive firms for both equal-weighted (EW) and value-weighted (VW) returns. The findings suggest that R&D intensity and stock returns show a positive relationship. A long–short strategy in R&D-intense firms has yielded 1.43% (t = 4.22) per month in the sample. In general, the results suggest an undervaluation of highly R&D-intensive firms that investors can exploit for above-average returns. The effect is not homogeneous across return schemes (equal-weighted and value-weighted) or across industries. R&D growth measures and R&D capital are not found to have significant impacts on stock returns. Both the market firm size and age are included as control variables, and the results reveal that the relationship is robust to these control variables. The sub-periods ranging from 2000 to 2007 and 2008 to 2019 have been considered in the present study and the results are consistent with the overall sample. The study fills the existing empirical void for R&D intensity and stock returns in relation to the emerging market of India.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".