Global M2 Can’t Predict Bitcoin Price, Says Quant Analyst

Sina—co-founder of the hedge fund 21st Capital—publicly dismantled a popular Bitcoin price model promoted by Real Vision CEO Raoul Pal, calling it a textbook case of data illiteracy and overfitting.

Model in question shows a strong correlation between Bitcoin and Global M2—a measure of global money supply—by shifting M2 data forward by a set number of weeks, typically 10 to 12, to supposedly “predict” Bitcoin’s expected price movement. Raoul Pal used this chart in his argument that macro-liquidity conditions are what drive the crypto cycle, and current market behavior is forecastable using monetary growth.

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Sina is a data scientist with a PhD who has taught data analytics to undergraduates and graduates. says The model fails to hold up under scrutiny “This is a terrible failure of not understanding overfitting,” He said this in a video that was posted on X.com, 24 June. “What I’m seeing doesn’t even pass the first month of a first-year data analytics course.”

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Sina points to the correlation that exists between Bitcoin and Global M2 because of the way the data is collected. “tortured” Fit historical patterns “If I’m allowed to play with the data and arbitrarily move things around, I can definitely find great matches between pockets of data,” He said that the flexibility of analysts is what gives them an illusion of accuracy.

He said that the Global M2 Data is not inherently reliable. flawed. It’s compiled by multiplying various central banks’ M2 figures by exchange rates—mixing fast-reporting economies like the US with countries that have data delays of weeks or even months. The result is a false impression about global liquidity fluctuations. “It seems to be moving on a daily basis, but it’s actually mixing frequent and infrequent updates,” Sina said. “It’s not a true signal.”

Sina says that it is more important to point out the failure of the model when one zooms in beyond selected chart slices. When Raoul Pal Sina, among others, has shown how small changes to lead times or scales can produce dramatic differences in outcomes. “Let’s try a lead of 80 days. That doesn’t look good. What about 108? Ah, now the tops align—so let’s zoom in again and pretend it works,” “He said in a sarcastic way.” “This is not modeling. This is playing.”

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He highlighted how each adjustment to the model—shifting from a 12-week lead to 10 weeks, to 108 days—exposes its lack of systematic foundation. “If you don’t have a proper model, you fail to predict the future,” Sina said. “This is classic overfitting. You force the data to match historical behavior, but you lose any generalizability.”

Sina explained the idea by comparing it to fitting curves through noisy sine waves. An organized model ignores noise and captures only the main pattern. An overfit model, by contrast, attempts to match every small fluctuation—resulting in poor predictive performance when new data arrives. “Overfitting looks better, but it models noise. And noise doesn’t repeat,” “He said”

Sina also wondered if Bitcoin would actually drive liquidity rather than follow it. “If you look at the last cycle, Bitcoin topped first. Liquidity topped 145 days later,” he added. It is a contradiction to the Global M2’s implied causality and it calls its entire basis as an innovative tool into question.

He concluded his remarks bluntly: “You have to be very careful with overfitting. It looks matching, but it’s forcibly fit on historical data. You have no idea about the predictive accuracy of this thing.”

Bitcoin was trading at $106,952 as of press time.

Featured Image created using DALL.E and chart from TradingView.com