| Texto completo | |
| Autor(es): |
Sbrana, Attilio
;
de Castro, Paulo Andre Lima
Número total de Autores: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | COMPUTATIONAL ECONOMICS; v. N/A, p. 35-pg., 2023-09-22. |
| Resumo | |
In this paper, we propose a novel approach for forecasting cryptocurrency portfolios, harnessing modified versions of the N-BEATS deep learning architecture, integrated with convolutional network layers, Transformer mechanisms, and the Mish activation function. Our thorough evaluation, featuring an extensive sample size exceeding 4 million portfolio test samples, shows these variations outperforming traditional and other deep learning forecasting methods across various metrics. Particularly noteworthy is our N-BEATS Perceiver model, a Transformer-based variation, which not only delivers superior forecast accuracy but also exhibits a robust risk profile with less downside. Furthermore, the model performs exceptionally well under the TOPSIS method across a broad spectrum of portfolio evaluation parameters, making it a valuable asset for both portfolio selection and risk management in the dynamic cryptocurrency market. (AU) | |
| Processo FAPESP: | 22/01524-2 - Inteligência artificial em finanças digitais e cryptomercados: desafios e oportunidades |
| Beneficiário: | PAULO ANDRE LIMA DE CASTRO |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |