Sina points out that the apparent correlation between Bitcoin and Global M2 only exists because the data has been \u201ctortured\u201d to fit historical patterns. \u201cIf I\u2019m allowed to play with the data and arbitrarily move things around, I can definitely find great matches between pockets of data,\u201d he said, warning that this flexibility is exactly what allows analysts to create the illusion of predictive accuracy.<\/p>\n
The primary issue, he explained, is that the Global M2 data itself is inherently flawed. It\u2019s compiled by multiplying various central banks\u2019 M2 figures by exchange rates\u2014mixing fast-reporting economies like the US with countries that have data delays of weeks or even months. This creates a misleading impression of daily fluctuations in global liquidity. \u201cIt seems to be moving on a daily basis, but it\u2019s actually mixing frequent and infrequent updates,\u201d Sina said. \u201cIt\u2019s not a true signal.\u201d<\/p>\n
More importantly, Sina argues that the model fails the moment one zooms out from selective chart slices. While Raoul Pal and others have showcased examples of tightly aligned tops and bottoms between Bitcoin and Global M2, Sina demonstrated how minor tweaks in lead time or scale can yield dramatically different outcomes. \u201cLet\u2019s try a lead of 80 days. That doesn\u2019t look good. What about 108? Ah, now the tops align\u2014so let\u2019s zoom in again and pretend it works,\u201d he said sarcastically. \u201cThis is not modeling. This is playing.\u201d<\/p>\n
He highlighted how each adjustment to the model\u2014shifting from a 12-week lead to 10 weeks, to 108 days\u2014exposes its lack of systematic foundation. \u201cIf you don\u2019t have a proper model, you fail to predict the future,\u201d Sina said. \u201cThis is classic overfitting. You force the data to match historical behavior, but you lose any generalizability.\u201d<\/p>\n
To illustrate the concept, Sina compared it to fitting a curve through a noisy sine wave. A well-structured model captures the core pattern and ignores noise. An overfit model, by contrast, attempts to match every small fluctuation\u2014resulting in poor predictive performance when new data arrives. \u201cOverfitting looks better, but it models noise. And noise doesn\u2019t repeat,\u201d he said.<\/p>\n
Sina also questioned whether Bitcoin might actually lead liquidity, not follow it. \u201cIf you look at the last cycle, Bitcoin topped first. Liquidity topped 145 days later,\u201d he said. This reverses the causality implied by the Global M2 model and calls into question its entire premise as a forward-looking tool.<\/p>\n
His conclusion was blunt: \u201cYou have to be very careful with overfitting. It looks matching, but it\u2019s forcibly fit on historical data. You have no idea about the predictive accuracy of this thing.\u201d<\/p>\n
At press time, Bitcoin traded at $106,952.<\/p>\n
![\"Bitcoin](\"https:\/\/www.newsbtc.com\/wp-content\/uploads\/2025\/06\/BTCUSDT_2025-06-25_14-17-54.png?resize=1024%2C454\")
<\/p>\n","protected":false},"excerpt":{"rendered":"
Sina\u2014co-founder of the hedge fund 21st Capital\u2014publicly dismantled a popular Bitcoin price model promoted by Real Vision CEO Raoul Pal, calling it a textbook case of data illiteracy and overfitting. The model in question draws a close correlation between Bitcoin and Global M2\u2014a measure of global money supply\u2014by shifting M2 data forward by a set…<\/p>\n","protected":false},"author":1,"featured_media":5500,"comment_status":"","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[245],"tags":[22,28,29,32,33,34,2125,1900],"class_list":["post-5499","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bitcoin-news","tag-bitcoin","tag-bitcoin-news","tag-bitcoin-price","tag-btc","tag-btc-news","tag-btc-price","tag-global-m2","tag-m2"],"_links":{"self":[{"href":"https:\/\/ad-doge.com\/blog\/wp-json\/wp\/v2\/posts\/5499","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ad-doge.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ad-doge.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ad-doge.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ad-doge.com\/blog\/wp-json\/wp\/v2\/comments?post=5499"}],"version-history":[{"count":0,"href":"https:\/\/ad-doge.com\/blog\/wp-json\/wp\/v2\/posts\/5499\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ad-doge.com\/blog\/wp-json\/wp\/v2\/media\/5500"}],"wp:attachment":[{"href":"https:\/\/ad-doge.com\/blog\/wp-json\/wp\/v2\/media?parent=5499"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ad-doge.com\/blog\/wp-json\/wp\/v2\/categories?post=5499"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ad-doge.com\/blog\/wp-json\/wp\/v2\/tags?post=5499"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}