A Study on the Effect of Research and Development Expenditures on Firm Value and Corporate Performance
Bibliographic record
Abstract
In the modern economy, research and development (R&D) investment is crucial for a company's innovation and long-term growth. This study examines the impact of R&D expenditures on firm performance, specifically Tobin’s Q and stock returns, using data from major global markets: the U.S. (NYSE, NASDAQ), the U.K. (London), Canada (Toronto), and South Korea (KOSPI) from 2015 to 2021. Using time series and cross-sectional tests, along with two-stage least squares (2SLS) regression for verification, the results show that R&D investments positively affect corporate performance (Tobin’s Q) in the NYSE, NASDAQ, and KOSPI markets, but have negative or no effects in London and Toronto. Panel regression results suggest R&D expenditures generally negatively impact stock returns, while 2SLS regression indicates positive effects in NYSE, NASDAQ, and KOSPI, with insignificant impacts in London and Toronto. Overall, R&D expenditures positively influence corporate performance, though outcomes vary by exchanges. These findings highlight the importance of understanding regional differences in R&D investment strategies, offering valuable insights for companies and investors.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".