Abstract

This study develops a leakage-free hybrid Markov–Random Forest framework for cryptocurrency price forecasting and evaluates it on Bitcoin and Ethereum. Daily OHLCV features are lagged by one trading day to prevent look-ahead bias, while regime labels are assigned from observed price changes using a two-state Markov chain with increasing and decreasing states. Regime-specific Random Forest models are then tuned independently via time-series cross-validation, allowing the predictive structure to adapt to regime-specific market conditions. The empirical results exhibit clear asset dependence. For Ethereum, the hybrid model outperforms the standalone Random Forest on magnitude-based metrics, attaining lower MAE and RMSE while also delivering a modest improvement in directional accuracy. Regime-specific tuning further identifies distinct optimal hyperparameter configurations across the increasing and decreasing states, suggesting that Ethereum’s upward and downward dynamics are structurally heterogeneous and can be better captured through regime-aware learning. By contrast, for Bitcoin, the standalone Random Forest delivers superior magnitude forecasting performance, while the regime-specific models differ only in tree depth and share the remaining tuning parameters, indicating that regime conditioning adds limited incremental value in a more persistent market. Statistical tests reinforce these findings. For Ethereum, Diebold–Mariano tests show that the hybrid significantly outperforms the standalone Random Forest under squared loss, while the absolute-loss comparison is only marginal. Across both assets, directional accuracy remains close to random chance, confirming the limited predictability of next-day price direction from lagged OHLCV features. Overall, the hybrid framework is most valuable when regime-specific dynamics are sufficiently distinct, offering improved forecasting performance and greater interpretability than a single global model.

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