Incorporating Financial News for Forecasting Bitcoin Prices Based on Long Short-Term Memory Networks

34 Pages Posted: 2 Dec 2020 Last revised: 9 Apr 2021

See all articles by Johannes Jakubik

Johannes Jakubik

Karlsruhe Institute of Technology

Abdolreza Nazemi

Karlsruhe Institute of Technology

Andreas Geyer-Schulz

Karlsruhe Institute of Technology

Frank J. Fabozzi

EDHEC Business School

Date Written: November 19, 2020

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 outperforms other machine learning models significantly. 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 a second LSTM network with structured input from financial news to the prediction of Bitcoin prices. In addition to the superior performance relative to other machine learning models, we find that the out-of-time rate of return attained with the proposed deep learning model 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.

Keywords: Bitcoin price forecasting, sentiment analysis, deep learning, financial news, Bitcoin trading

JEL Classification: G14, G10, C14

Suggested Citation

Jakubik, Johannes and Nazemi, Abdolreza and Geyer-Schulz, Andreas and Fabozzi, Frank J., Incorporating Financial News for Forecasting Bitcoin Prices Based on Long Short-Term Memory Networks (November 19, 2020). Available at SSRN: https://ssrn.com/abstract=3733398 or http://dx.doi.org/10.2139/ssrn.3733398

Johannes Jakubik (Contact Author)

Karlsruhe Institute of Technology ( email )

Kaiserstraße 12
Karlsruhe, Baden Württemberg 76131
Germany

Abdolreza Nazemi

Karlsruhe Institute of Technology ( email )

Kaiserstraße 12
Karlsruhe, Baden Württemberg 76131
Germany

Andreas Geyer-Schulz

Karlsruhe Institute of Technology ( email )

Kaiserstraße 12
Karlsruhe, Baden Württemberg 76131
Germany

Frank J. Fabozzi

EDHEC Business School ( email )

France
215 598-8924 (Phone)

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