Bitcoin Price Factors: Natural Language Processing Approach

I propose a new methodology to construct interpretable, fundamental-based pricing factors from news to explain Bitcoin returns. Each news article from a specialized cryptocurrency website is classified in a semi-supervised manner into one of the few predefined topics. Topic sentiments become factors contributing to the price variation. I use a cutting-edge NLP algorithm (SBERT network) to embed linguistic data into a vector space, which allows the application of an intuitive classification rule. This approach permits the exclusion of news pieces that describe the price movements per se from the analysis, thus mitigating endogeneity concerns. I show that non-endogenous news contains fundamental information about Bitcoin. Thus I reject the concept of Bitcoin price being based on pure speculation and show that Bitcoin returns are partially explained by fundamental topics. Among those, the adoption of cryptocurrencies and blockchain technology is the most important aspect. On top of that, I study the media expressed attitude toward Bitcoin from the functions of money perspective. I show that investors consider Bitcoin as the store of value rather than the medium of exchange.