Causal Feature Engineering of Price Directions of Cryptocurrencies using
  Dynamic Bayesian Networks
Rasoul Amirzadeh Asef Nazari Dhananjay Thiruvady Mong Shan Ee
Cryptocurrencies have gained popularity across various sectors, especially in
finance and investment. The popularity is partly due to their unique
specifications originating from blockchain-related characteristics such as
privacy, decentralisation, and untraceability. Despite their growing
popularity, cryptocurrencies remain a high-risk investment due to their price
volatility and uncertainty. The inherent volatility in cryptocurrency prices,
coupled with internal cryptocurrency-related factors and external influential
global economic factors makes predicting their prices and price movement
directions challenging. Nevertheless, the knowledge obtained from predicting
the direction of cryptocurrency prices can provide valuable guidance for
investors in making informed investment decisions. To address this issue, this
paper proposes a dynamic Bayesian network (DBN) approach, which can model
complex systems in multivariate settings, to predict the price movement
direction of five popular altcoins (cryptocurrencies other than Bitcoin) in the
next trading day. The efficacy of the proposed model in predicting
cryptocurrency price directions is evaluated from two perspectives. Firstly,
our proposed approach is compared to two baseline models, namely an
auto-regressive integrated moving average and support vector regression.
Secondly, from a feature engineering point of view, the impact of twenty-three
different features, grouped into four categories, on the DBN's prediction
performance is investigated. The experimental results demonstrate that the DBN
significantly outperforms the baseline models. In addition, among the groups of
features, technical indicators are found to be the most effective predictors of
cryptocurrency price directions.