Dynamic Bayesian Networks for Predicting Cryptocurrency Price Directions: Uncovering Causal Relationships
This work addresses the problem of high-risk investment due to price volatility for cryptocurrency investors, but it is incremental as it applies an existing method (DBN) to a new domain with specific data.
The paper tackled predicting cryptocurrency price directions by proposing a dynamic Bayesian network (DBN) to uncover causal relationships among features like social media data and financial factors, and it found that the DBN significantly outperformed baseline models such as ARIMA and LSTM, though performance varied across six cryptocurrencies.
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.