| Autor: |
Matheus José Silva de Souza, Fahad W. Almudhaf, Bruno Miranda Henrique, Ana Beatriz Silveira Negredo, Danilo Guimarães Franco Ramos, Vinicius Amorim Sobreiro, Herbert Kimura |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
Journal of Finance and Data Science, Vol 5, Iss 2, Pp 83-98 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2405-9188 |
| DOI: |
10.1016/j.jfds.2019.01.002 |
| Popis: |
This paper aims to investigate how Machine Learning (ML) techniques perform in the prediction of cryptocurrency prices. We answer if Support Vector Machines (SVM) and Artificial Neural Networks (ANN) based strategies can generate abnormal risk-adjusted returns when applied to Bitcoin, the largest decentralized digital currency in terms of market capitalization. Findings indicate that traders are able to earn conservative returns on the risk adjusted basis, even accounting for transaction costs, when using SVM. Furthermore, the study suggests that ANN can explore short run informational inefficiencies to generate abnormal profits, being able to beat even buy-and-hold during strong bull trends. Keywords: Bitcoins, Machine learning (ML), Artificial neural network (ANN), Support vector machine regression (SVM), JEL Code: G11, G15, G17 |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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