Exploring the Influence of News Articles on Bitcoin Price with Machine Learning In recent years, cryptocurrencies have become more and more popular around the world, and they are being accepted and used by more countries. Cryptocurrencies are decentralized, and they form an emerging market that is different from stocks. At present, there is already much work around the stock price prediction using news articles, but there are few papers on the cryptocurrency market. In this paper, we aim to research the effects of news articles on bitcoin prices. We extract features from news articles with both commonly used text feature extraction algorithms (e.g., NGram and TFIDF) and SentiGraph, which is a novel text representation method we propose. SentiGraph takes advantages of sentiment analysis and transforms a news article into a graph. Compared with previous feature extraction methods, our experiment results show that this new approach is superior on the prediction accuracy, which also demonstrates the impacts of news articles on the bitcoin price.

ChainNet: Learning on Blockchain Graphs with Topological Features With emergence of blockchain technologies and the associated cryptocurrencies, such as Bitcoin, understanding network dynamics behind Blockchain graphs has become a rapidly evolving research direction. Unlike other financial networks, such as stock and currency trading, blockchain based cryptocurrencies have the entire transaction graph accessible to the public (i.e., all transactions can be downloaded and analyzed). A natural question is then to ask whether dynamics of the transaction graph impacts price of the underlying cryptocurrency. We show that standard graph features such as degree distribution of the transaction graph may not be sufficient to capture network dynamics and its potential impact on fluctuations of Bitcoin price. In contrast, topological features computed from the blockchain graph using the tools of persistent homology, are found to exhibit higher utility for predicting Bitcoin price dynamics.