LSTM-Based Dynamic Linguistic Decision-Making for Cryptocurrency Selection

Pablo Poblete-Arrué, Romina Torres, Víctor Salazar-Vasquez, Gustavo Gatica

Producción científica: Contribución a los tipos de informe/libroContribución a la conferenciarevisión exhaustiva


Analyzing the overwhelming number of options available in the cryptocurrency market through technical analysis becomes unfeasible, whether expert investor or not. Similarly, challenging is the application of fundamental analysis in such markets. To address these complexities, this paper proposes a novel method to assist investors in navigating the overwhelming array of cryptocurrency options by employing a ‘buy-and-sell’ strategy. The approach incorporates a dynamic daily ranking system generated through LSTM neural network predictions and a dynamic linguistic decision-making model (DLDM). Simulated on a dataset of 68 cryptocurrencies observed from March to May 2018, the method surpasses state-of-the-art returns by over 300% when considering combinations of Day Profitability, Day Variability, and one of Open, Close, Low, or High attributes. Furthermore, by using a unitary constant as the third attribute, it achieves even higher returns, outperforming the state-of-the-art by more than 1700%. Comparatively, the proposed method easily outshines alternative strategies such as random selection, Bitcoin buy-and-hold, and equitable investment in all cryptocurrencies, which yielded returns of 3%, -34%, and 54%, respectively. The integration of LSTM predictions and DLDM showcases a potent tool for making informed decisions in the dynamic cryptocurrency market, especially crucial given the multitude of investment options and prevalence of non-expert investors. This paper presents a powerful approach to cryptocurrency investment, leveraging LSTM predictions and dynamic linguistic decision-making to provide investors with a competitive edge. The method's superior performance against published strategies showcases its potential for effectively tackling the complexities in cryptocurrency market, benefiting investors, despite experienced or not.

Idioma originalInglés
Título de la publicación alojadaProceedings of International Conference on Information Technology and Applications - ICITA 2023
EditoresAbrar Ullah, Sajid Anwar, Davide Calandra, Raffaele Di Fuccio
EditorialSpringer Science and Business Media Deutschland GmbH
Número de páginas14
ISBN (versión impresa)9789819983230
EstadoPublicada - 2024
Evento17th International Conference on Information Technology and Applications, ICITA 2023 - Lisbon, Portugal
Duración: 20 oct. 202222 oct. 2022

Serie de la publicación

NombreLecture Notes in Networks and Systems
ISSN (versión impresa)2367-3370
ISSN (versión digital)2367-3389


Conferencia17th International Conference on Information Technology and Applications, ICITA 2023

Áreas temáticas de ASJC Scopus

  • Ingeniería de control y sistemas
  • Procesamiento de senales
  • Redes de ordenadores y comunicaciones


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