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

ArXiv ID: 2306.08157 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Dynamic Bayesian Network, Cryptocurrency, Price Prediction, Causal Inference, Time Series Analysis, Cryptocurrency

Complexity vs Empirical Score

• Math Complexity: 5.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail
• Why: The paper uses advanced probabilistic models (Dynamic Bayesian Networks) which requires mathematical sophistication, while also implementing a full comparison with multiple baselines and diverse datasets (six cryptocurrencies, multiple feature sets), indicating strong empirical testing.

  flowchart TD
    A["Research Goal<br>Predict Crypto Price Directions<br>& Uncover Causal Relationships"] --> B["Data Collection & Preprocessing<br>6 Cryptos + Social Media + Financial + Technical Indicators"]
    B --> C["Model Development<br>Dynamic Bayesian Network DBN"]
    C --> D["Computational Process<br>Time Series Analysis & Causal Inference"]
    D --> E["Baseline Comparison<br>ARIMA, SVR, LSTM, RF, SVM"]
    D --> E
    E --> F["Key Findings/Outcomes<br>DBN Outperforms Baselines<br>Causal Relationships Identified"]