Can R&D investment reduce the impact of <scp>COVID</scp>‐19 on firm performance?—Evidence from India
Bibliographic record
Abstract
This study examines whether investing in R&D reduces the impact of exogenous shocks like the COVID-19 on stock market performance and accounting performance of manufacturing firms in India. For the sample of listed manufacturing firms, the paper finds that the firms engaged in R&D activities had lower negative cumulative abnormal return than those firms that did not invest in R&D in the pre-pandemic period using multiple event windows. The result suggests that R&D investments can lower value erosion for the shareholders during a severe crisis period. Further, using a difference-in-difference fixed effects model, the study finds that manufacturing firms engaged in R&D activities in the pre-pandemic period exhibited higher return on sales and growth of total income during the pandemic quarter vis-à-vis the non-R&D firms. The favorable accounting performance indicates the possibility of firm-level R&D being associated with the firm's ability to adjust its functioning during a crisis, thereby reducing the effect of the crisis. Finally, the study documents that government intervention to reduce the spread of the virus had a differential impact on firms based on their industry of operation. The findings have implications for investors, corporate managers, and policymakers in 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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".