Bitcoin’s Stock-to-Flow Model May Not Be Used in Future
Market data and charts are central fundamentals used by investors to analyze crypto assets available on the market. And though the crypto market has existed for more than a decade, the fact that the industry entered the mainstream just a few years ago makes a comprehensive study of the market using long-term statistics and methods (such as Bitcoin‘s Stock-to-Flow model) difficult.
Inherent volatility of the crypto market allows enthusiasts to develop their own prediction models for crypto assets in order to understand price movements of Bitcoin or any other crypto asset.
The Stock-to-Flow ratio model for Bitcoin has been a well-known prediction model throughout the past year. It determined Bitcoin’s future price on the basis of scarcity value. However, the Stock-to-Flow model predicts that Bitcoin will cross $50,000 after 2020. This prediction contributed to the controversial reception from the community.
Pierre Rochard, Bitcoin supporter at Kraken, recently discussed the Stock-to-Flow model for Bitcoin, asserting that such projections are popular with “price cults”.
On the latest edition of Stephan Livera’s podcast, Rochard stated that many influential crypto personalities are staking their reputation with such imposing projections derived from pseudo-math, adding that such models are likely to disappear very soon.
Although Bitcoin’s S2F model faced many a criticism by many in the community, Rochard believes that it has been precise mainly because people can take a look at more than 10 years of price history, while highlighting the correlation with Bitcoin’s valuations. He further noted:
“When we’re talking about quantitatively what has been the relationship between Stock-to-Flow and Bitcoin’s price. That’s history, And so, there’s nothing wrong with using publicly available data to run a regression and to see this statistical relationship.”
Rochard also noted that such Bitcoin prediction models are quite reflexive and circular, which makes them a self-balancing system. Thus, it is hard to determine why models such as S2F have been so accurate, since it is hard to separate its fundamentals from other factors.