Influencers on X have recently highlighted the rising M2 money supply and a softening dollar as indicators that Bitcoin is on the cusp of a significant price increase. While these graphical representations of data generate high engagement, they oversimplify a more intricate relationship. Both factors play a role in influencing Bitcoin’s price, but not in the straightforward manner often portrayed.
Research indicates that money printing, which expands the global M2 money supply, typically leads Bitcoin price movements by approximately 12 weeks. The rationale behind this timing is that once more liquidity is injected into the global economy, it takes a while for that additional money to find its way into Bitcoin. The analysis reveals that the strongest correlation between Bitcoin and M2 occurs over an 84-day lag, guiding the focus for deeper examination.
The interplay between liquidity and the dollar follows a dual-timing mechanism. Bitcoin reacts to both elements, yet they seldom align perfectly. An analysis of daily price data over the past year shows diverse interactions among Bitcoin, global M2 supply (adjusted forward by 84 days), and the DXY dollar index. This interaction is complex; while liquidity often correlates with price movements during slower market shifts, the dollar tends to apply pressure more immediately.
Across the current year, correlations reveal Bitcoin’s alignment with M2 stands at 0.78 (with an 84-day lag) and 0.77 (for the forward version). Meanwhile, Bitcoin’s relation to the DXY is −0.58, indicating that an increase in dollar value inversely affects Bitcoin. Furthermore, the M2 supply and the dollar exhibit an inverse relationship of −0.71.
These statistics provide a backdrop rather than a daily predictive model, as the relationships tend to trend over longer periods. Daily correlations present a more limited view, with negligible correlations (0.02 for Bitcoin versus M2 and 0.04 for Bitcoin versus DXY), suggesting that the common notion of a direct relationship—where an increase in the dollar leads to a decrease in Bitcoin—does not hold true on a daily basis.
When evaluating lagged returns, findings show that Bitcoin’s price movements are most closely connected with prior moves in liquidity approximately six weeks prior, while it shows an inverse correlation with DXY about a month earlier. The most effective correlations occur when M2 leads by 42 days (0.16) and when DXY leads by 33 days (−0.20).
This is akin to a multi-faceted dynamic where liquidity functions like a slow-moving force, providing gradual momentum during bullish phases, while the dollar functions as a throttle—immediately constraining Bitcoin’s price during downturns. The strength of these connections varies depending on the prevailing market conditions.
A marked shift in correlation values occurs around Bitcoin’s peak in 2025. Prior to this high, correlations with M2 were strong (0.89) and similarly high for the forward-shifted M2 (0.87), while showing an inverse correlation of −0.58 with DXY. After the peak, liquidity correlations flip, becoming negative (around −0.49 for both M2 versions), yet the inverse relationship with the dollar remains robust.
Moreover, a 180-day rolling correlation analysis demonstrates that the relationship between Bitcoin and lagged M2 peaked at 0.94 on December 26, 2024, before declining to lows of −0.16 by September 30, 2025. This trajectory highlights the pattern of Bitcoin’s price aligning with M2 increases in bullish phases, followed by diverging as the dollar strengthens.
In summary, the data illustrates that neither M2 nor DXY fully explains Bitcoin’s behavior. Instead, their relationships are conditional and adaptable over time, with liquidity contributing to longer-term trends when the dollar is stable or weak. Conversely, a strong dollar tends to exert immediate downward pressure, complicating Bitcoin’s response.
To effectively interpret these dynamics, the recommended approach involves observing the trends of liquidity and the dollar over rolling one to three months, allowing for flexibility in lag times. This nuanced perspective suggests that many of the recent correlations reflect not fixed connections but a framework that shifts as market conditions evolve. The past year has yielded varied states, with correlations fluidly mirroring changing circumstances.
