The Influence Of Stock Valuation On Firm Level Investment: Signal Or Noise?
Bibliographic record
Abstract
Do stock price increases influence firm-level investment decisions? Studies suggest that stock price movements may reflect adjustments in the beliefs of outsiders about the prospects of a firm and may therefore contain information or considerations that are new to the firm. However, it remains unclear if or when firms can use price movements as informational inputs in investment decisions as some suggest that the market is a sideshow, where trading is done with little or no impact on firm decisions, and that firms are better informed than outsiders about the value of their investments. This paper studies the firm and industry factors that influence a firm’s information environment, and thus, the informational relationship between stock prices and investments. We find that firm characteristics such as strategy uniqueness and complexity decrease the potential informativeness of stock prices for investment. In addition, higher analyst coverage and dedicated institutional investors negatively influence the relationship between stock prices and investment. Finally, industry context can shape how stock price changes can influence investments. Specifically, higher industry R&D intensity and competition decrease, and higher industry demand uncertainty increase the informativeness of stock prices for firms’ investment decisions.
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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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".