Incorporating financial news for forecasting Bitcoin prices based on long short-term memory networks

Johannes Jakubik, Abdolreza Nazemi, Andreas Geyer-Schulz and Frank J. Fabozzi

Quantitative Finance, 2023, vol. 23, issue 2, 335-349

Abstract: In this paper, we investigate how a deep learning machine learning model can be applied to improve Bitcoin price forecasting and trading by incorporating unstructured information from financial news. The two-stage model we propose that includes financial news significantly outperforms machine learning models without financial news. In the first stage, we leverage long short-term memory (LSTM) networks to extract structured information from financial news. In the second stage, we apply machine learning models with structured input from financial news to the prediction of Bitcoin prices. In addition to the superior performance relative to machine learning models without input from financial news, we find that the out-of-time rate of return attained with the proposed forecasting system is substantially higher than for a buy-and-hold strategy. Our study highlights how combining deep learning and financial news offers investors and traders support for the monetization of unstructured data in finance.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:23:y:2023:i:2:p:335-349

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DOI: 10.1080/14697688.2022.2130085

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