Bitcoin and the dollar: deciphering the hidden patterns between M2 and currency fluctuations

What truly drives Bitcoin in the global markets? The answer is not as simple as it seems. A deep analysis reveals that the relationship between Bitcoin, the money supply M2, and the US dollar index (DXY) follows much more complex patterns than investors typically assume.

Data reveals correlations with temporal surprises

Numbers speak for themselves: Bitcoin maintains a historical correlation of 0.78 with M2, but with an 84-day lag. This means that changes in the money supply are not immediately reflected in Bitcoin’s price but generate delayed effects. On the other hand, the relationship with the dollar index is inverse (-0.58), indicating that when the dollar strengthens, Bitcoin tends to weaken.

To contextualize the importance of these monetary movements, consider that fluctuations equivalent to 3,000,000 yen to USD in strong currency markets can cause significant volatility in correlated digital assets.

The 2025 break: change of rules

What’s fascinating is what happened in 2025. Bitcoin’s correlation with M2 experienced a dramatic shift: it went from 0.89 before the peak of the bullish cycle to -0.49 afterward. This change of sign suggests that M2 stopped acting as a driver and began exerting opposing pressure.

The 180-day rolling correlation with M2 reached a maximum of 0.94 at the end of 2024 but plummeted to -0.12 in November 2025. This abrupt drop indicates that the influence of the money supply on subsequent rallies is fading, at least temporarily.

Two forces, two speeds

Analysis suggests that M2 and DXY operate on different time scales. M2 acts as a long-term trend driver and slow-moving factor, shaping the overall market direction. The DXY, in contrast, is a short-term volatility factor, generating rapid fluctuations but less directive.

Toward a dynamic and adaptive approach

Analysts warn that using fixed lag strategies is obsolete. Instead, they recommend a dynamic framework that recognizes different market phases and adjusts lag parameters in real time. This allows traders to anticipate correlation changes before they happen, rather than reacting after signals have already disappeared.

The key is understanding that these relationships are not constant: they are conditional, evolve with market cycles, and require continuous monitoring.