Crypto volatility forecasting: mounting a HAR, sentiment, and machine learning horserace

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Brauneis, A ORCID: https://orcid.org/0000-0001-6965-5492 and Sahiner, M, 2024. Crypto volatility forecasting: mounting a HAR, sentiment, and machine learning horserace. Asia-Pacific Financial Markets. ISSN 1387-2834

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Official URL: https://doi.org/10.1007/s10690-024-09510-6

Abstract

The relationship between investor sentiment and cryptocurrency market volatility remains an area of growing interest in empirical finance. In this study, we present an innovative forecasting approach by utilizing a unique dataset of AI-generated sentiment from a comprehensive database of crypto market news. In a horserace fashion, we first evaluate the Heterogeneous Autoregressive (HAR) model and then compare its forecasting performance to five advanced machine learning (ML) methods. ML performs reasonably well and improves the accuracy of the benchmark HAR model. Interestingly, including sentiment does not improve the forecasting accuracy of the HAR model. However, our findings highlight that investor sentiment seems to influence crypto market volatility in a nonlinear fashion that can (only) be captured by ML methods. In other words, LightGBM, XGBoost, and LSTM models show enhanced predictive accuracy when sentiment data is incorporated, improving no-sentiment forecasts in 54.17% of the cases studied. Overall, our results emphasize the significant potential of integrating machine learning and sentiment analysis as a promising avenue for improved forecasting, offering potential benefits for risk management strategies and provide valuable insights for researchers and practitioners.

Item Type:	Journal article
Publication Title:	Asia-Pacific Financial Markets
Creators:	Brauneis, A. and Sahiner, M.
Publisher:	Springer
Date:	13 December 2024
ISSN:	1387-2834
Identifiers:	Number Type 10.1007/s10690-024-09510-6 DOI 2333151 Other
Rights:	This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Divisions:	Schools > Nottingham Business School
Record created by:	Jonathan Gallacher
Date Added:	15 Jan 2025 09:02
Last Modified:	15 Jan 2025 09:03
URI:	https://irep.ntu.ac.uk/id/eprint/52848