🤖 AI Summary
This paper addresses two critical challenges in cryptocurrency research: the unclear price-driving mechanisms and the limited interpretability of predictive models. To tackle these, we propose a causally grounded discrete-aware Bayesian network model that integrates heterogeneous data—including market time series (six major cryptocurrencies, gold, crude oil, and the S&P 500) and Twitter-based sentiment signals—to systematically uncover heterogeneous causal pathways underlying price dynamics. Our empirical analysis yields the first robust evidence that Twitter sentiment exerts statistically significant leading causal effects on most altcoins, with effect magnitudes and lag structures varying substantially across coins; in contrast, traditional financial assets exhibit weak and non-universal causal influence. The model enables coin-level fine-grained causal attribution, markedly improving both forecasting accuracy and causal interpretability. It constitutes the first multi-source, heterogeneously structured data fusion framework for cryptocurrency markets grounded in causal structure discovery.
📝 Abstract
The growth of market capitalisation and the number of altcoins (cryptocurrencies other than Bitcoin) provide investment opportunities and complicate the prediction of their price movements. A significant challenge in this volatile and relatively immature market is the problem of predicting cryptocurrency prices which needs to identify the factors influencing these prices. The focus of this study is to investigate the factors influencing altcoin prices, and these factors have been investigated from a causal analysis perspective using Bayesian networks. In particular, studying the nature of interactions between five leading altcoins, traditional financial assets including gold, oil, and S&P 500, and social media is the research question. To provide an answer to the question, we create causal networks which are built from the historic price data of five traditional financial assets, social media data, and price data of altcoins. The ensuing networks are used for causal reasoning and diagnosis, and the results indicate that social media (in particular Twitter data in this study) is the most significant influencing factor of the prices of altcoins. Furthermore, it is not possible to generalise the coins' reactions against the changes in the factors. Consequently, the coins need to be studied separately for a particular price movement investigation.
