Bitcoin forecasting: daily edges exist, but 1–6 month prediction remains mostly unproven

I just posted a survey paper on arXiv about Bitcoin price prediction, comparing the peer-reviewed literature with the much more active debate happening on X/Twitter.

The short version: the field has produced hundreds of papers, but the evidence is much weaker than the volume suggests.

The central question:

Can any model reliably beat the naive baseline — “tomorrow / next month / next quarter will be close to today’s price” — across multiple Bitcoin market regimes?

My conclusion: at 1–6 month horizons, I could not find a peer-reviewed study showing robust superiority over the naive baseline across bull, bear, and sideways markets.

Key takeaways:

Methodological issues
Many studies rely on single train/test splits, in-sample metrics, or omit comparison to the naive baseline.

Evidence of daily predictability does not automatically imply predictability at monthly horizons.

The Bitcoin power law remains a compelling hypothesis but requires stronger statistical validation.

Empirical findings
Some studies report short-term predictive signals from technical indicators, sentiment, and market microstructure data.

I found no peer-reviewed evidence that any model robustly beats the naive baseline across bull, bear, and sideways markets at 1–6 month horizons.

Stock-to-flow has not held up in formal out-of-sample testing.

Metcalfe-style valuation models become less convincing once endogeneity is addressed.

Community critique
Social media critics often highlight legitimate concerns about overfitting and spurious regressions that are underrepresented in formal research.

Social media discussions also lack a reliable mechanism for retiring models that fail out of sample.

The paper argues Bitcoin forecasting does not need another LSTM, transformer, or AI model unless it can pass basic standards:

Walk-forward or rolling-window evaluation

Testing across multiple market regimes

Explicit comparison to the naive baseline

Statistical significance testing

Economic significance after fees and costs

Public code, data, and exact splits

The main open question is not “which model is best?” but: Can any model reliably beat the naive baseline across multiple Bitcoin regimes?

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