Do Bubbles Lead to Overinvestment? A Revealed Preference Approach
Bibliographic record
Abstract
Many economists believe that the stock market plays an important role in efficiently allocating capital to its most productive uses. This standard story of the stock market was called into question by events in the late 1990s, when some observers believed that stock market overvaluation – or a bubble - led to overinvestment. Both the standard and overinvestment stories involve discount rates and, to differentiate between the two stories, this paper examines the discount rates used by firms in making their investment decisions. We use a revealed preference approach that relies on the pattern of investment spending – combined with investment theory – to estimate the discount rates used by managers. The standard story predicts that firms with high stock prices and good investment opportunities should have discount rates that do not differ systematically from the risk-adjusted market rate. The overinvestment story predicts that firms with high stock prices and poor investment opportunities should have discount rates consistently below the market rate. Based on a panel dataset of over 50,000 firm-year observations, we find support for both stories. The behavior of high stock price firms with good measured investment opportunities is best described by the standard story, while the overinvestment story provides the most appropriate interpretation of the behavior of high stock price firms with poor investment opportunities. Firms in this latter category accumulate between 15.1% and 45.2% too much capital. These estimates suggest that, even before they burst, bubbles adversely affect economic activity by misallocating capital.
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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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".