A Supply and Demand Framework for Bitcoin Price Forecasting
Bibliographic record
Abstract
We develop a flexible supply and demand equilibrium framework that can be used to develop pricing models to forecast Bitcoin’s price trajectory based on its fixed, inelastic supply and evolving demand dynamics. This approach integrates Bitcoin’s unique monetary attributes with demand drivers such as institutional adoption and long-term holding patterns. Using the April 2024 halving as a baseline, we explore model scenarios with varying assumptions about growth in adoption and supply-side constraints, calibrated to real-world data. Our findings indicate that institutional and sovereign accumulation can significantly influence price trajectories, with increasing demand intensifying the impact of Bitcoin’s constrained liquidity. Forecasts suggest that modest withdrawals from liquid supply to strategic reserves could lead to substantial price appreciation over the medium term, while higher withdrawal levels may induce volatility due to supply scarcity. These results highlight Bitcoin’s potential as a long-term investment and underline the importance of integrating economic fundamentals into forward-looking portfolio strategies. Our framework provides flexibility for testing different market scenarios, demand curve functional forms, and parameterizations, offering a tool for investors and policymakers considering Bitcoin’s role as a strategic asset. By advancing a fundamentals-based approach, this study contributes to the broader understanding of how Bitcoin’s supply–demand dynamics influence market behavior.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".