Crypto Forecasting with Deep NLP and Market Data

Summary

The study examines whether news and social media text can improve Bitcoin and Ethereum price forecasts when combined with financial and blockchain data. It compares pre-trained and fine-tuned language models with dictionary-based sentiment methods, and uses BART MNLI zero-shot classification to identify bullish or bearish content. The forecast targets include local price extrema as well as daily movements, a choice intended to reduce trading frequency and portfolio volatility.

The authors report that adding textual features improves forecast accuracy and that deep learning NLP methods also raise profitability and Sharpe ratio across the validation scenarios they considered. The document does not provide detailed sample sizes, evaluation metrics, baseline results, or market-period information, so the strength and generality of those findings cannot be assessed from this description alone. It describes an empirical forecasting approach, not evidence that the signals will remain profitable live or after trading costs.

Key ideas

• The study combines financial and blockchain features with news and social media text for Bitcoin and Ethereum forecasting.
• It compares deep learning NLP models with dictionary-based sentiment analysis.
• BART MNLI zero-shot classification is used to categorize text as bullish or bearish.
• Using local extrema as forecast targets is intended to reduce trading frequency and portfolio volatility.
• The authors report improved forecasting and trading metrics, but the description omits validation details and cost analysis.

Tags

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


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









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

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.