Causal Modelling of Cryptocurrency Price Movements Using Discretisation-Aware Bayesian Networks

TL;DR

This paper develops discretisation-aware Bayesian network models to analyze and predict key factors influencing cryptocurrency prices, emphasizing interpretability and performance in highly volatile markets.

Contribution

It introduces a structured procedure for building multiple Bayesian network models with various discretisation methods, improving predictive accuracy and interpretability in cryptocurrency markets.

Findings

Equal interval discretisation with two bins performs best.

Distinct price-driving patterns are identified for each cryptocurrency.

Bayesian networks provide interpretable causal insights into market dynamics.

Abstract

This study identifies the key factors influencing the price movements of major cryptocurrencies, Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether, using Bayesian networks (BNs). This study addresses two key challenges: modelling price movements in highly volatile cryptocurrency markets and enhancing predictive performance through discretisation-aware Bayesian Networks. It analyses both macro-financial indicators (gold, oil, MSCI, S and P 500, USDX) and social media signals (tweet volume) as potential price drivers. Moreover, since discretisation is a critical step in the effectiveness of BNs, we implement a structured procedure to build 54 BNs models by combining three discretisation methods (equal interval, equal quantile, and k-means) with several bin counts. These models are evaluated using four metrics, including balanced accuracy, F1 score, area under the ROC curve and a composite score. Results show that equal interval with two bins consistently yields the best predictive performance. We also provide deeper insights into each network's structure through inference, sensitivity, and influence strength analyses. These analyses reveal distinct price-driving patterns for each cryptocurrency, underscore the importance of coin-specific analysis, and demonstrate the value of BNs for interpretable causal modelling in volatile cryptocurrency markets.