Bitcoin Price Prediction: Peer-Reviewed Evidence and Social Media Discourse

arXiv:2606.00071v1

Announce Type: cross

Abstract:

Despite the proliferation of hundreds of academic studies and ongoing debates on social media regarding Bitcoin price forecasting, the field remains devoid of consensus on fundamental issues. A primary question persists: Is it possible for any model to consistently outperform a simple "today's price" baseline when forecasting horizons range from one to six months? This paper surveys the peer-reviewed literature, classifying existing studies by their evaluation methods, and juxtaposes these academic findings with the substantive, albeit informal, discussions occurring on X/Twitter.

The resulting analysis presents a sobering perspective. Research indicates that, across various market regimes, no peer-reviewed study has demonstrated robust superiority over the naive baseline at short-to-medium horizons. While predictability exists on a daily basis, it does not hold for hourly or monthly intervals and may be negated by transaction costs. Furthermore, the stock-to-flow model has not withstood formal out-of-sample testing, and valuations based on Metcalfe’s Law have been dismissed as spurious. Although the Bitcoin price power law offers empirical appeal, it has not yet undergone formal distributional testing.

Concurrently, practitioners on social media have raised valid statistical concerns—such as violations in ordinary least squares (OLS) assumptions, overfitting in backtests, and spurious regressions—that the academic community has largely failed to formalize. To advance the field, we identify key areas for future research and advocate for stricter methodological standards. These include implementing walk-forward evaluation, utilizing multi-regime holdout windows, comparing against naive baselines, including zero in hyperparameter grids, and applying Diebold-Mariano significance testing. We argue that the sector’s most critical requirement is not the creation of additional models, but rather the adoption of superior evaluation techniques.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC