Bursting the bitcoin bubble: Do market prices reflect fundamental bitcoin value?
Bibliographic record
Abstract
This paper develops a theoretical model of the bitcoin market and demonstrates that the bitcoin’s volatile and explosive price path is a consequence of the Bitcoin protocol’s system of supply management. The model implies that the marginal cost of mining the target supply of bitcoins is the fundamental value of the bitcoin since it corresponds to an equilibrium in the Bitcoin protocol and the rent-seeking tournament among miners. The data provide strong empirical evidence of cointegration between the bitcoin’s price and the marginal cost of mining the target supply of bitcoins, demonstrating the existence of their long-run equilibrium relationship. Current bubble detection techniques indicate that there is no evidence of explosive departures in the price of the bitcoin from its model-implied fundamental value. Since the raw price data exhibit explosive behavior, the apparent bubbles in the price of the bitcoin can be attributed to its nonstationary market fundamentals. • The bitcoin’s price dynamics result from the protocol’s interference in the market. • The Bitcoin protocol works against the self-correcting mechanism of the market. • Adjustments of the difficulty result in volatile and explosive behavior in the price. • There is cointegration between the bitcoin’s price and the marginal cost of mining. • Apparent bubbles in the price can be attributed to nonstationary market fundamentals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.013 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".