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Deep Learning and NLP in Cryptocurrency Forecasting: Integrating Financial, Blockchain, and Social Media Data

Vincent Gurgul, Stefan Lessmann, Wolfgang Karl Härdle

TL;DR

These findings demonstrate that integrating textual data into cryptocurrency price forecasting not only improves forecasting accuracy but also consistently enhances the profitability and Sharpe ratio across various validation scenarios, particularly when applying deep learning NLP techniques.

Abstract

We introduce novel approaches to cryptocurrency price forecasting, leveraging Machine Learning (ML) and Natural Language Processing (NLP) techniques, with a focus on Bitcoin and Ethereum. By analysing news and social media content, primarily from Twitter and Reddit, we assess the impact of public sentiment on cryptocurrency markets. A distinctive feature of our methodology is the application of the BART MNLI zero-shot classification model to detect bullish and bearish trends, significantly advancing beyond traditional sentiment analysis. Additionally, we systematically compare a range of pre-trained and fine-tuned deep learning NLP models against conventional dictionary-based sentiment analysis methods. Another key contribution of our work is the adoption of local extrema alongside daily price movements as predictive targets, reducing trading frequency and portfolio volatility. Our findings demonstrate that integrating textual data into cryptocurrency price forecasting not only improves forecasting accuracy but also consistently enhances the profitability and Sharpe ratio across various validation scenarios, particularly when applying deep learning NLP techniques. The entire codebase of our experiments is made available via an online repository: https://anonymous.4open.science/r/crypto-forecasting-public

Deep Learning and NLP in Cryptocurrency Forecasting: Integrating Financial, Blockchain, and Social Media Data

TL;DR

These findings demonstrate that integrating textual data into cryptocurrency price forecasting not only improves forecasting accuracy but also consistently enhances the profitability and Sharpe ratio across various validation scenarios, particularly when applying deep learning NLP techniques.

Abstract

We introduce novel approaches to cryptocurrency price forecasting, leveraging Machine Learning (ML) and Natural Language Processing (NLP) techniques, with a focus on Bitcoin and Ethereum. By analysing news and social media content, primarily from Twitter and Reddit, we assess the impact of public sentiment on cryptocurrency markets. A distinctive feature of our methodology is the application of the BART MNLI zero-shot classification model to detect bullish and bearish trends, significantly advancing beyond traditional sentiment analysis. Additionally, we systematically compare a range of pre-trained and fine-tuned deep learning NLP models against conventional dictionary-based sentiment analysis methods. Another key contribution of our work is the adoption of local extrema alongside daily price movements as predictive targets, reducing trading frequency and portfolio volatility. Our findings demonstrate that integrating textual data into cryptocurrency price forecasting not only improves forecasting accuracy but also consistently enhances the profitability and Sharpe ratio across various validation scenarios, particularly when applying deep learning NLP techniques. The entire codebase of our experiments is made available via an online repository: https://anonymous.4open.science/r/crypto-forecasting-public
Paper Structure (22 sections, 1 equation, 12 figures, 21 tables)

This paper contains 22 sections, 1 equation, 12 figures, 21 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Evolution of Forecasting Techniques
  4. Application of NLP Methods
  5. Target and Feature Selection
  6. Methodology
  7. Time Series Modelling with Neural Networks
  8. Deep Learning Approaches for NLP
  9. Choice of Target Variables and Trading Strategy
  10. Experimental Design
  11. Data Collection and Preprocessing
  12. Model Development and Optimisation
  13. Performance Metrics and Model Evaluation
  14. Results and Analysis
  15. Comparison of Forecasting Performance
  16. ...and 7 more sections

Figures (12)

  • Figure 1: The standard RNN model architecture goodfellow_2016_deep
  • Figure 2: LSTM cell architecture
  • Figure 3: Comparison of information decay in RNN and LSTM networks graves_2012_supervised
  • Figure 4: CNN time series model architecture ismailfawaz_2019_deep
  • Figure 5: TFT model architecture
  • ...and 7 more figures