Forecasting Cryptocurrency Prices Using Deep Learning: Integrating Financial, Blockchain, and Text Data
This paper uses deep learning and NLP to forecast cryptocurrency prices, integrating financial, blockchain, and text data.
What it examines
This paper explores the use of Machine Learning and NLP techniques for forecasting Bitcoin and Ethereum prices, focusing on the impact of public sentiment from news and social media. It aims to enhance prediction accuracy by integrating financial, blockchain, and text data.
What it concludes
The research demonstrates the potential of NLP in enhancing financial forecasts, suggesting applications in trading strategies and market analysis. Future research could explore more advanced NLP techniques and their long-term impact on market efficiency.
Evidence objects
The research demonstrates the potential of NLP in enhancing financial forecasts, suggesting applications in trading strategies and market analysis. Future research could explore more advanced NLP techniques and their long-term impact on market efficiency.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Abstract: 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 conve… ▽ More 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. △ Less
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