Forecasting sovereign bonds markets using machine learning: forecasting the portuguese government bond using machine learning approach

Detalhes bibliográficos
Autor(a) principal: Vieira, Tiago Alexandre Rodrigues de Sousa
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/112036
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management
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spelling Forecasting sovereign bonds markets using machine learning: forecasting the portuguese government bond using machine learning approachPortuguese Government BondsMachine LearningGenetic ProgrammingLong-Short-Term MemoryFinancial MarketsSDG 8 - Decent work and economic growthDissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementFinancial markets, due to their non-linear, volatile and complex nature turn any type of forecasting into a difficult task, as the classical statistical methods are no longer adequate. Many factors exist that can influence the government bonds yields and how these bonds behave. The consequence of the behaviour of these bonds are extended over geographies and individuals. As the financial markets grow bigger, more investors are trying to develop systematic approaches that are intended to predict prices and movements. Machine Learning algorithms already proven their value in predicting and finding patterns in many subjects. When it comes to financial markets, Machine Learning is not a new tool. It is already widely used to predict behaviours and trends with some degree of success. This dissertation aims to study the application of two Machine Learning algorithms - Genetic Programming (GP) and Long Short-Term Memory (LSTM) - to the Portuguese Government 10Y Bond and try to forecast the yield with accuracy. The construction of the predictive models is based on historical information of the bond and on other important factors that influence its behaviour, extracted through the Bloomberg Portal. In order to analyse the quality of the two models, the results of each algorithm will be compared. An analysis will be presented regarding the quality of the results from both algorithms and the respective time cost. In the end, each model will be discussed and conclusions will be taken about which one can be the answer to the main question of this study, which is “What will the Yield of the Portuguese Government 10Y Bond be on T+1?”. The results obtained showed that Genetic Programming can create a model with higher accuracy. However, Long Short-Term Memory should not be ignored because it can also point to good results. Regarding execution time, velocity is a problem when it comes to Genetic Programming. This algorithm takes more time to execute compared to LSTM. Long Short-Term Memory is considerably quicker to get results. In order to take the right decision about which model to choose one must keep in mind the priorities. In case accuracy is the priority, Genetic Programming will be the answer. Nevertheless, when velocity is the priority Long Short-Term Memory should be the choice.Castelli, MauroRUNVieira, Tiago Alexandre Rodrigues de Sousa2021-02-18T16:01:56Z2021-01-132021-01-13T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/112036TID:202642160enginfo: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:RCAAP2024-03-11T04:55:47Zoai:run.unl.pt:10362/112036Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:42:03.938229Repositório 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 Forecasting sovereign bonds markets using machine learning: forecasting the portuguese government bond using machine learning approach
title Forecasting sovereign bonds markets using machine learning: forecasting the portuguese government bond using machine learning approach
spellingShingle Forecasting sovereign bonds markets using machine learning: forecasting the portuguese government bond using machine learning approach
Vieira, Tiago Alexandre Rodrigues de Sousa
Portuguese Government Bonds
Machine Learning
Genetic Programming
Long-Short-Term Memory
Financial Markets
SDG 8 - Decent work and economic growth
title_short Forecasting sovereign bonds markets using machine learning: forecasting the portuguese government bond using machine learning approach
title_full Forecasting sovereign bonds markets using machine learning: forecasting the portuguese government bond using machine learning approach
title_fullStr Forecasting sovereign bonds markets using machine learning: forecasting the portuguese government bond using machine learning approach
title_full_unstemmed Forecasting sovereign bonds markets using machine learning: forecasting the portuguese government bond using machine learning approach
title_sort Forecasting sovereign bonds markets using machine learning: forecasting the portuguese government bond using machine learning approach
author Vieira, Tiago Alexandre Rodrigues de Sousa
author_facet Vieira, Tiago Alexandre Rodrigues de Sousa
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
RUN
dc.contributor.author.fl_str_mv Vieira, Tiago Alexandre Rodrigues de Sousa
dc.subject.por.fl_str_mv Portuguese Government Bonds
Machine Learning
Genetic Programming
Long-Short-Term Memory
Financial Markets
SDG 8 - Decent work and economic growth
topic Portuguese Government Bonds
Machine Learning
Genetic Programming
Long-Short-Term Memory
Financial Markets
SDG 8 - Decent work and economic growth
description Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management
publishDate 2021
dc.date.none.fl_str_mv 2021-02-18T16:01:56Z
2021-01-13
2021-01-13T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/112036
TID:202642160
url http://hdl.handle.net/10362/112036
identifier_str_mv TID:202642160
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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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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