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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".