# Dynamic Bayesian Networks for Predicting Cryptocurrency Price Directions: Uncovering Causal Relationships

arXiv: https://arxiv.org/abs/2306.08157 · HTML: https://mlsanity.com/paper/2306.08157/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 84 references that are in the map.
- First posted on arXiv 13 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [A Tutorial on Learning With Bayesian Networks](https://mlsanity.com/paper/2002.00269/) (2020)
- [PreBit -- A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin](https://mlsanity.com/paper/2206.00648/) (2022)
- [Causal Modelling of Cryptocurrency Price Movements Using Discretisation-Aware Bayesian Networks](https://mlsanity.com/paper/2303.16148/) (2023)
- [Stock Trading Using PE ratio: A Dynamic Bayesian Network Modeling on Behavioral Finance and Fundamental Investment](https://mlsanity.com/paper/1706.02985/) (2017)

## Built on by (most cited first, 1 of 1)

- [A Framework for Empowering Reinforcement Learning Agents with Causal Analysis: Enhancing Automated Cryptocurrency Trading](https://mlsanity.com/paper/2310.09462/) (2023)

## Abstract (arXiv)

Cryptocurrencies have gained popularity across various sectors, especially in finance and investment. Despite their growing popularity, cryptocurrencies can be a high-risk investment due to their price volatility. The inherent volatility in cryptocurrency prices, coupled with the effects of external global economic factors, makes predicting their price movements challenging. To address this challenge, we propose a dynamic Bayesian network (DBN)-based approach to uncover potential causal relationships among various features including social media data, traditional financial market factors, and technical indicators. Six popular cryptocurrencies, Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether are studied in this work. The proposed model's performance is compared to five baseline models of auto-regressive integrated moving average, support vector regression, long short-term memory, random forests, and support vector machines. The results show that while DBN performance varies across cryptocurrencies, with some cryptocurrencies exhibiting higher predictive accuracy than others, the DBN significantly outperforms the baseline models.