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

arXiv: https://arxiv.org/abs/2303.16148 · HTML: https://mlsanity.com/paper/2303.16148/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 78 references that are in the map.
- First posted on arXiv 26 Mar 2023.

## Builds on (most cited first, 1 of 1)

- [A Tutorial on Learning With Bayesian Networks](https://mlsanity.com/paper/2002.00269/) (2020)

## Built on by (most cited first, 2 of 2)

- [Dynamic Bayesian Networks for Predicting Cryptocurrency Price Directions: Uncovering Causal Relationships](https://mlsanity.com/paper/2306.08157/) (2023)
- [A Framework for Empowering Reinforcement Learning Agents with Causal Analysis: Enhancing Automated Cryptocurrency Trading](https://mlsanity.com/paper/2310.09462/) (2023)

## Abstract (arXiv)

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.