Can artificial intelligence enhance the Bitcoin bonanza
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 de...
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doaj-fb0504422054491a930c694bc7009c982021-02-02T04:23:13ZengKeAi Communications Co., Ltd.Journal of Finance and Data Science2405-91882019-06-01528398Can artificial intelligence enhance the Bitcoin bonanzaMatheus José Silva de Souza0Fahad W. Almudhaf1Bruno Miranda Henrique2Ana Beatriz Silveira Negredo3Danilo Guimarães Franco Ramos4Vinicius Amorim Sobreiro5Herbert Kimura6University of Brasília, Department of Economics, Campus Darcy Ribeiro, Brasília, Federal District, 70910-900, BrazilKuwait University, Department of Finance & Financial Institutions, College of Business Administration, P.O Box 5486 Safat, 13055, Kuwait; Corresponding author.University of Brasília, Department of Management, Campus Darcy Ribeiro, Brasília, Federal District, 70910-900, BrazilUniversity of Brasília, Department of Economics, Campus Darcy Ribeiro, Brasília, Federal District, 70910-900, BrazilUniversity of Brasília, Department of Management, Campus Darcy Ribeiro, Brasília, Federal District, 70910-900, BrazilUniversity of Brasília, Department of Management, Campus Darcy Ribeiro, Brasília, Federal District, 70910-900, BrazilUniversity of Brasília, Department of Management, Campus Darcy Ribeiro, Brasília, Federal District, 70910-900, BrazilThis 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, G17http://www.sciencedirect.com/science/article/pii/S2405918818300953 |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Matheus José Silva de Souza Fahad W. Almudhaf Bruno Miranda Henrique Ana Beatriz Silveira Negredo Danilo Guimarães Franco Ramos Vinicius Amorim Sobreiro Herbert Kimura |
spellingShingle |
Matheus José Silva de Souza Fahad W. Almudhaf Bruno Miranda Henrique Ana Beatriz Silveira Negredo Danilo Guimarães Franco Ramos Vinicius Amorim Sobreiro Herbert Kimura Can artificial intelligence enhance the Bitcoin bonanza Journal of Finance and Data Science |
author_facet |
Matheus José Silva de Souza Fahad W. Almudhaf Bruno Miranda Henrique Ana Beatriz Silveira Negredo Danilo Guimarães Franco Ramos Vinicius Amorim Sobreiro Herbert Kimura |
author_sort |
Matheus José Silva de Souza |
title |
Can artificial intelligence enhance the Bitcoin bonanza |
title_short |
Can artificial intelligence enhance the Bitcoin bonanza |
title_full |
Can artificial intelligence enhance the Bitcoin bonanza |
title_fullStr |
Can artificial intelligence enhance the Bitcoin bonanza |
title_full_unstemmed |
Can artificial intelligence enhance the Bitcoin bonanza |
title_sort |
can artificial intelligence enhance the bitcoin bonanza |
publisher |
KeAi Communications Co., Ltd. |
series |
Journal of Finance and Data Science |
issn |
2405-9188 |
publishDate |
2019-06-01 |
description |
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 |
url |
http://www.sciencedirect.com/science/article/pii/S2405918818300953 |
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