Forecasting Cryptocurrency Prices Using Deep Learning: Integrating
  Financial, Blockchain, and Text Data
Vincent Gurgul Stefan Lessmann Wolfgang Karl Härdle
This paper explores the application of Machine Learning (ML) and Natural
Language Processing (NLP) techniques in cryptocurrency price forecasting,
specifically Bitcoin (BTC) and Ethereum (ETH). Focusing on news and social
media data, primarily from Twitter and Reddit, we analyse the influence of
public sentiment on cryptocurrency valuations using advanced deep learning NLP
methods. Alongside conventional price regression, we treat cryptocurrency price
forecasting as a classification problem. This includes both the prediction of
price movements (up or down) and the identification of local extrema. We
compare the performance of various ML models, both with and without NLP data
integration. Our findings reveal that incorporating NLP data significantly
enhances the forecasting performance of our models. We discover that
pre-trained models, such as Twitter-RoBERTa and BART MNLI, are highly effective
in capturing market sentiment, and that fine-tuning Large Language Models
(LLMs) also yields substantial forecasting improvements. Notably, the BART MNLI
zero-shot classification model shows considerable proficiency in extracting
bullish and bearish signals from textual data. All of our models consistently
generate profit across different validation scenarios, with no observed decline
in profits or reduction in the impact of NLP data over time. The study
highlights the potential of text analysis in improving financial forecasts and
demonstrates the effectiveness of various NLP techniques in capturing nuanced
market sentiment.