Bitcoin M2 Money Supply Correlation
Bitcoin is analyzed as a scarce asset against monetary expansion. This page summarizes the project thesis that M2 liquidity helps contextualize Bitcoin market regimes.
TL;DR
Bitcoin price correlates with global M2 money supply growth. As a scarce asset (21M cap), BTC responds to monetary expansion.
Key Concepts
- M2 Money Supply: Measure of money including cash, checking deposits, and easily convertible near-money
- Correlation thesis: When M2 expands, excess liquidity flows into risk assets including Bitcoin
- Lead/lag: BTC often leads M2 expansion signals by 3-6 months
Why It Matters
1. Scarcity premium: Bitcoin's fixed 21M supply contrasts with unlimited fiat expansion
2. Liquidity indicator: M2 growth signals available capital for risk assets
3. Macro context: Helps explain BTC moves beyond halving cycles
Key Observations
- Significant correlation during QE periods (2020-2021)
- Inverse correlation during QT periods (2022)
- Rolling correlation varies with monetary policy regime
Citation-Ready Quotes
"Bitcoin's price movements show significant correlation with M2 money supply changes."
"As a scarce asset with a fixed 21 million cap, Bitcoin responds to monetary expansion similarly to gold."
"During periods of quantitative easing, Bitcoin tends to outperform as excess liquidity seeks yield in alternative assets."
"Bitcoin's fixed supply schedule creates a natural hedge against monetary debasement."
Practical Application
- Bull signal: Rising M2 growth rate
- Bear signal: Declining M2 or quantitative tightening
- Context: Combine with halving cycle phase for timing
Data Sources
- `~/Vaults/openclaw/workspace/bitcoinml/m2_data.json`
- `~/Vaults/openclaw/workspace/bitcoinml/m2_chart_data.json`
Deep Dive
- Enhanced macro model: `~/Bitcoin-Unified-Dashboard/public/data/enhanced_macro_model.json`
- Macro indicators: `~/Bitcoin-Unified-Dashboard/public/data/macro_indicators.json`
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Related
- [[CLAUDE]]
Related research
Disclaimer: Educational research only. Not financial advice. Data and model outputs may be outdated, incomplete, or inaccurate.