Data science applications to investment management: leveraging in alternative data sets and unconventional tetchniques to enhance portfolio performance.

Màster universitari en Banca i Finances (UPF Barcelona School of Management) Curs 2019-2020

Detalles Bibliográficos
Autor: Ayala González, Jonathan
Tipo de recurso: tesis de maestría
Fecha de publicación:2020
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/45818
Acceso en línea:http://hdl.handle.net/10230/45818
Access Level:acceso abierto
Palabra clave:Treball de fi de màster – Curs 2019-2020
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spelling Data science applications to investment management: leveraging in alternative data sets and unconventional tetchniques to enhance portfolio performance.Ayala González, JonathanTreball de fi de màster – Curs 2019-2020Màster universitari en Banca i Finances (UPF Barcelona School of Management) Curs 2019-2020Mentor: Luz Parrondo“On the other hand, investing is a unique kind of casino—one where you cannot lose in the end, so long as you play only by the rules that put the odds squarely in your favour.” (Benjamin Graham - The Intelligent Investor, 1949). This paper provides a theoretical and practical approach to the uses of data science as a mechanism to support investment decisions. Although data science applications in investment management are quite varied and numerous, this paper focuses on a data typology currently being widely used by large investment institutions worldwide: The Alternative Data Sets. Concretely, the focus is put on the uses of consumption data and 10-K filings as valuable sources of information to support investment decisions. Overall, results show that, despite data science and algorithm-based tools are essential to process and understand underlying business information, these techniques are not by themselves sufficient to develop a consistent investment strategy but, instead, can be employed as very useful systems to boost the understanding of the business that underlays every stock in the market.202020202020info:eu-repo/semantics/masterThesisapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/45818reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licensehttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/458182026-05-29T05:05:01Z
dc.title.none.fl_str_mv Data science applications to investment management: leveraging in alternative data sets and unconventional tetchniques to enhance portfolio performance.
title Data science applications to investment management: leveraging in alternative data sets and unconventional tetchniques to enhance portfolio performance.
spellingShingle Data science applications to investment management: leveraging in alternative data sets and unconventional tetchniques to enhance portfolio performance.
Ayala González, Jonathan
Treball de fi de màster – Curs 2019-2020
title_short Data science applications to investment management: leveraging in alternative data sets and unconventional tetchniques to enhance portfolio performance.
title_full Data science applications to investment management: leveraging in alternative data sets and unconventional tetchniques to enhance portfolio performance.
title_fullStr Data science applications to investment management: leveraging in alternative data sets and unconventional tetchniques to enhance portfolio performance.
title_full_unstemmed Data science applications to investment management: leveraging in alternative data sets and unconventional tetchniques to enhance portfolio performance.
title_sort Data science applications to investment management: leveraging in alternative data sets and unconventional tetchniques to enhance portfolio performance.
dc.creator.none.fl_str_mv Ayala González, Jonathan
author Ayala González, Jonathan
author_facet Ayala González, Jonathan
author_role author
dc.subject.none.fl_str_mv Treball de fi de màster – Curs 2019-2020
topic Treball de fi de màster – Curs 2019-2020
description Màster universitari en Banca i Finances (UPF Barcelona School of Management) Curs 2019-2020
publishDate 2020
dc.date.none.fl_str_mv 2020
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/45818
url http://hdl.handle.net/10230/45818
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,198674

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