Bitcoin Price Models Struggle to Beat Simple Forecasting

The complex world of Bitcoin price forecasting has accumulated a colorful collection of methods. Basic scarcity models convert the halving schedule into a price, while on-chain models turn address or transaction activity into value. Power-law charts draw an ascending corridor through Bitcoin's history, and machine-learning systems feed market and macroeconomic data into complex software.

However, most of these approaches enter the price-prediction contest against a simple opponent: naive forecasts that use only current market information. A naive model can predict today's price with a small percentage error even when it has learned almost nothing about direction or return.

A review of Bitcoin prediction research by Carlos Baquero found that no model had demonstrated durable superiority over the naive benchmark at horizons of one to six months across several market regimes. The review examined 23 papers based on their methods, influence, or use of genuine out-of-sample evaluation.