Eliminating Laboratory Asset Bubbles by Paying Interest on Cash
Bibliographic record
Abstract
The seminal work of Smith Suchanek and Williams (1988) finds price bubbles are frequently observed in an experimental asset market where a single asset with a finite\nlifetime is traded. Ever since, many studies have been carried out to understand the reason why bubbles occur in such an environment and to find mechanisms to eliminate\nbubbles.In this paper, we introduce an interest-bearing savings account to the experimental asset market. We find bubbles disappear with high interest rates. The effect of\nthe interest rate potentially works in two ways. First, the savings account increases the opportunity cost of buying shares, which in turn, reduces the incentive to speculate and alleviates the “active participation” problem as raised in Lei, Noussair and Plott (2001). Second, fixing the dividend process and terminal value of the asset, the time trend of the fundamental value of the asset becomes positive with a high interest rate. An increasing\nfundamental value is more compatible with subjects’ perception that asset prices tend to be flat or increasing in the long run. Therefore, subjects are more likely to follow the fundamental value when they trade and over-pricing is lessened. To disentangle the effects through the two channels, we run a second set of experiments with high interest rate but a lower terminal value to induce the fundamental value of the asset to decrease over time. Bubbles reappear in these sessions, which suggests the time path of the fundamental value is more important for reducing bubbles.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".