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research-article

Multisource financial sentiment analysis for detecting Bitcoin price change indications using deep learning

Authors:

Nikolaos Passalis,

Loukia Avramelou,

Avraam Tsantekidis,

Stavros Doropoulos,

Giorgos Makris,

Anastasios TefasAuthors Info & Claims

Neural Computing and Applications, Volume 34, Issue 22

Pages 19441 - 19452

https://doi.org/10.1007/s00521-022-07509-6

Published: 01 November 2022 Publication History

Abstract

The success of deep learning (DL) in various areas, such as computer vision, fueled the interest in several novel DL-enabled applications, such as financial trading, which could potentially surpass the previously used approaches. Indeed, there has been a plethora of DL-based trading methods proposed in recent years. Despite the success of these methods, they typically rely on a very restricted set of information, usually employing only price-related information. As a result, they ignore sentiment-related information, which can have a profound impact and be a strong predictor of various assets, such as cryptocurencies. The contribution of this paper is multifold. First, we examine whether the use of sentiment information, as extracted by various online sources, including news articles, is beneficial when training DL agents for trading. Then, given the difficulty of training reliable sentiment extractors for financial applications, we evaluate the impact of using different DL models as sentiment extractors, as well as employ an unsupervised training pipeline for further improving their performance. Finally, we propose an effective multisource sentiment fusion approach that can improve the performance over the rest of the evaluated approaches. The conducted experiments have been performed using several different configurations and models, ranging from multilayer perceptrons (MLPs) to convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to provide a reliable evaluation of sentiment-aware DL-based trading strategies providing evidence that sentiment information might be a stronger predictor compared to the information provided by the actual price time series for Bitcoin.

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Cited By

Kang YZhao JYang XFan BXie W(2024)A hybrid style transfer with whale optimization algorithm model for textual adversarial attackNeural Computing and Applications10.1007/s00521-023-09278-236:8(4263-4280)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s00521-023-09278-2

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Information & Contributors

Information

Published In

cover image Neural Computing and Applications

Neural Computing and Applications Volume 34, Issue 22

Nov 2022

1022 pages

ISSN:0941-0643

EISSN:1433-3058

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 November 2022

Accepted: 01 June 2022

Received: 02 December 2021

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EU and Greek National Funds (Operational Program Competitiveness, Entrepreneurship and Innovation)

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Cited By

Kang YZhao JYang XFan BXie W(2024)A hybrid style transfer with whale optimization algorithm model for textual adversarial attackNeural Computing and Applications10.1007/s00521-023-09278-236:8(4263-4280)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s00521-023-09278-2

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