Leveraging Explainable AI to Support Cryptocurrency Investors

Jacopo Fior (), Luca Cagliero and Paolo Garza ()
Additional contact information
Jacopo Fior: Dipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca Degli Abruzzi, 24, 10129 Torino, Italy
Luca Cagliero: Dipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca Degli Abruzzi, 24, 10129 Torino, Italy
Paolo Garza: Dipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca Degli Abruzzi, 24, 10129 Torino, Italy

Future Internet, 2022, vol. 14, issue 9, 1-19

Abstract: In the last decade, cryptocurrency trading has attracted the attention of private and professional traders and investors. To forecast the financial markets, algorithmic trading systems based on Artificial Intelligence (AI) models are becoming more and more established. However, they suffer from the lack of transparency, thus hindering domain experts from directly monitoring the fundamentals behind market movements. This is particularly critical for cryptocurrency investors, because the study of the main factors influencing cryptocurrency prices, including the characteristics of the blockchain infrastructure, is crucial for driving experts’ decisions. This paper proposes a new visual analytics tool to support domain experts in the explanation of AI-based cryptocurrency trading systems. To describe the rationale behind AI models, it exploits an established method, namely SHapley Additive exPlanations, which allows experts to identify the most discriminating features and provides them with an interactive and easy-to-use graphical interface. The simulations carried out on 21 cryptocurrencies over a 8-year period demonstrate the usability of the proposed tool.

Keywords: quantitative trading; cryptocurrencies; blockchain (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1999-5903/14/9/251/pdf (application/pdf)
https://www.mdpi.com/1999-5903/14/9/251/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:14:y:2022:i:9:p:251-:d:896429

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().