R&D Investment and Market Reactions in Non-crisis and Crisis Periods: Evidence from Taiwan
Bibliographic record
Abstract
This study examines market reactions to firms with different level of R&D expenditure. In particular, we investigate whether R&D investment in an uncertain environment, such as during the global financial crisis of 2008, will aggravate the level of information asymmetry and increase the likelihood of undervaluation on R&D stocks. We use a sample of Taiwanese firms and classify the sample into four portfolios: no R&D, low R&D, middle R&D and high R&D firms, and estimate abnormal returns using the Fama and French three factor and Carhart four factor models. We find that the no R&D portfolio has the highest positive and significant abnormal returns in the non-crisis period (2000-2007), while the high R&D portfolio has the highest abnormal return in crisis period (2008-2011). Our multivariate analysis provides supporting evidence that high R&D firms have a greater extent of information asymmetry than no R&D firms during the crisis period, while no R&D firms bear a high risk of low growth potential in non-crisis period. Similar results are obtained either by equal-weighted or value-weighted portfolio returns. Recent studies propose that investors may misprice high-tech firms. Our results provide international evidence that investors react differently to no R&D and R&D intensive firms, and R&D investment in crisis period will aggravate information asymmetry and the extent to which investors underestimate the value of R&D stocks.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".