Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/93817 https://doi.org/10.47743/saeb-2020-0024 |
Resumo: | This study investigates the use of several trading strategies, based on Machine Learning methods, to profit on the risk premium of the Nordic electricity base-load week futures. The information set is only composed by financial data from January 02, 2006 to November 15, 2017. The results point out that the Support Vector Machine is the best method, but, most importantly, they highlight that all individual models are valuable, in the sense that their combination provides a robust trading procedure, generating an average profit of at least 26% per year, after considering trading costs and liquidity constraints. The results are robust to the different data partitions, and there is no evidence that the profitability of the trading strategies has decreased in recent years. We claim that this market allows for profitable speculation, namely by using combinations of non-linear signal extraction techniques. |
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Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity FuturesNord Poolelectricity futuresrisk premiummachine learningtradingThis study investigates the use of several trading strategies, based on Machine Learning methods, to profit on the risk premium of the Nordic electricity base-load week futures. The information set is only composed by financial data from January 02, 2006 to November 15, 2017. The results point out that the Support Vector Machine is the best method, but, most importantly, they highlight that all individual models are valuable, in the sense that their combination provides a robust trading procedure, generating an average profit of at least 26% per year, after considering trading costs and liquidity constraints. The results are robust to the different data partitions, and there is no evidence that the profitability of the trading strategies has decreased in recent years. We claim that this market allows for profitable speculation, namely by using combinations of non-linear signal extraction techniques.2020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/93817http://hdl.handle.net/10316/93817https://doi.org/10.47743/saeb-2020-0024eng2501196025013165Sebastião, Helder Miguel Correia VirtuosoGodinho, Pedro Manuel CortesãoWestgaard, Sjurinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-05-25T10:20:08Zoai:estudogeral.uc.pt:10316/93817Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:12:42.524463Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures |
title |
Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures |
spellingShingle |
Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures Sebastião, Helder Miguel Correia Virtuoso Nord Pool electricity futures risk premium machine learning trading |
title_short |
Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures |
title_full |
Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures |
title_fullStr |
Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures |
title_full_unstemmed |
Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures |
title_sort |
Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures |
author |
Sebastião, Helder Miguel Correia Virtuoso |
author_facet |
Sebastião, Helder Miguel Correia Virtuoso Godinho, Pedro Manuel Cortesão Westgaard, Sjur |
author_role |
author |
author2 |
Godinho, Pedro Manuel Cortesão Westgaard, Sjur |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Sebastião, Helder Miguel Correia Virtuoso Godinho, Pedro Manuel Cortesão Westgaard, Sjur |
dc.subject.por.fl_str_mv |
Nord Pool electricity futures risk premium machine learning trading |
topic |
Nord Pool electricity futures risk premium machine learning trading |
description |
This study investigates the use of several trading strategies, based on Machine Learning methods, to profit on the risk premium of the Nordic electricity base-load week futures. The information set is only composed by financial data from January 02, 2006 to November 15, 2017. The results point out that the Support Vector Machine is the best method, but, most importantly, they highlight that all individual models are valuable, in the sense that their combination provides a robust trading procedure, generating an average profit of at least 26% per year, after considering trading costs and liquidity constraints. The results are robust to the different data partitions, and there is no evidence that the profitability of the trading strategies has decreased in recent years. We claim that this market allows for profitable speculation, namely by using combinations of non-linear signal extraction techniques. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/93817 http://hdl.handle.net/10316/93817 https://doi.org/10.47743/saeb-2020-0024 |
url |
http://hdl.handle.net/10316/93817 https://doi.org/10.47743/saeb-2020-0024 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
25011960 25013165 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799134022436126720 |