Koneoppiminen rahoitusmarkkinoiden ennustamisessa

Tutkielma käsittelee koneoppimisen soveltuvuutta rahoitusmarkkinoiden ennustamiseen käsitellen erityisesti eri algoritmeja sekä niiden yhdistelmiä ja syötteen optimointia. Tulokset osoittavat, että tehokkaiden markkinoiden hypoteesin heikot ehdot eivät ole aina toteutuneet täydellisesti ja erityises...

Täydet tiedot

Bibliografiset tiedot
Päätekijä: Leskinen, Jarre
Muut tekijät: Informaatioteknologian tiedekunta, Faculty of Information Technology, Informaatioteknologia, Information Technology, Jyväskylän yliopisto, University of Jyväskylä
Aineistotyyppi: Kandityö
Kieli:fin
Julkaistu: 2019
Aiheet:
Linkit: https://jyx.jyu.fi/handle/123456789/63989
_version_ 1826225800071872512
author Leskinen, Jarre
author2 Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä
author_facet Leskinen, Jarre Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä Leskinen, Jarre Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä
author_sort Leskinen, Jarre
datasource_str_mv jyx
description Tutkielma käsittelee koneoppimisen soveltuvuutta rahoitusmarkkinoiden ennustamiseen käsitellen erityisesti eri algoritmeja sekä niiden yhdistelmiä ja syötteen optimointia. Tulokset osoittavat, että tehokkaiden markkinoiden hypoteesin heikot ehdot eivät ole aina toteutuneet täydellisesti ja erityisesti tukivektorikone sekä hybriditoteutukset syötteen optimointiin vaikuttavat lupaavilta. Koneoppimista voidaan hyödyntää tähän ongelmaan ja muihin satunnaisuutta sisältäviin ongelmiin. Tutkimuksessa esitetään myös parannusehdotuksia käsitellyille malleille sekä mahdollisia kohteita jatkotutkimukselle. This study researches whether machine learning could be utilized in forecasting the financial markets. Different types of algorithms are researched and different combinations of those including optimizing the input data. The results suggest that the market is not always weak form efficient. Especially support vector machine and hybrid models with input optimizing show promising results. Machine learning can be utilized for this problem and other problems which include randomness by nature. The study also suggests improvements for the studied models and possible areas for further research.
first_indexed 2019-09-20T09:14:44Z
format Kandityö
free_online_boolean 1
fullrecord [{"key": "dc.contributor.advisor", "value": "M\u00f6nk\u00f6l\u00e4, Sanna", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Leskinen, Jarre", "language": "", "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2019-05-16T05:57:08Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2019-05-16T05:57:08Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2019", "language": "", "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/63989", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Tutkielma k\u00e4sittelee koneoppimisen soveltuvuutta rahoitusmarkkinoiden ennustamiseen k\u00e4sitellen erityisesti eri algoritmeja sek\u00e4 niiden yhdistelmi\u00e4 ja sy\u00f6tteen optimointia. Tulokset osoittavat, ett\u00e4 tehokkaiden markkinoiden hypoteesin heikot ehdot eiv\u00e4t ole aina toteutuneet t\u00e4ydellisesti ja erityisesti tukivektorikone sek\u00e4 hybriditoteutukset sy\u00f6tteen optimointiin vaikuttavat lupaavilta. Koneoppimista voidaan hy\u00f6dynt\u00e4\u00e4 t\u00e4h\u00e4n ongelmaan ja muihin satunnaisuutta sis\u00e4lt\u00e4viin ongelmiin. Tutkimuksessa esitet\u00e4\u00e4n my\u00f6s parannusehdotuksia k\u00e4sitellyille malleille sek\u00e4 mahdollisia kohteita jatkotutkimukselle.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "This study researches whether machine learning could be utilized in forecasting the financial markets. Different types of algorithms are researched and different combinations of those including optimizing the input data. The results suggest that the market is not always weak form efficient. Especially support vector machine and hybrid models with input optimizing show promising results. Machine learning can be utilized for this problem and other problems which include randomness by nature. The study also suggests improvements for the studied models and possible areas for further research.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Paivi Vuorio (paelvuor@jyu.fi) on 2019-05-16T05:57:08Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2019-05-16T05:57:08Z (GMT). No. of bitstreams: 0\n Previous issue date: 2019", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "21", "language": "", "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.language.iso", "value": "fin", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.subject.other", "value": "tekninen analyysi", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "Koneoppiminen rahoitusmarkkinoiden ennustamisessa", "language": "", "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "bachelor thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-201905162634", "language": "", "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Bachelor's thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Kandidaatinty\u00f6", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Informaatioteknologian tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Information Technology", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Informaatioteknologia", "language": "fi", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Information Technology", "language": "en", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Tietotekniikka", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Mathematical Information Technology", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "yvv.contractresearch.funding", "value": "0", "language": "", "element": "contractresearch", "qualifier": "funding", "schema": "yvv"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_7a1f", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.accesslevel", "value": "openAccess", "language": null, "element": "rights", "qualifier": "accesslevel", "schema": "dc"}, {"key": "dc.type.publication", "value": "bachelorThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.subject.oppiainekoodi", "value": "602", "language": "", "element": "subject", "qualifier": "oppiainekoodi", "schema": "dc"}, {"key": "dc.subject.yso", "value": "algoritmit", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "rahoitusmarkkinat", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "geneettiset algoritmit", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "koneoppiminen", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "neuroverkot", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "osakkeet", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "indikaattorit", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://rightsstatements.org/page/InC/1.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}]
id jyx.123456789_63989
language fin
last_indexed 2025-02-18T10:55:55Z
main_date 2019-01-01T00:00:00Z
main_date_str 2019
online_boolean 1
online_urls_str_mv {"url":"https:\/\/jyx.jyu.fi\/bitstreams\/1d76aeb0-6e94-42e7-9299-6c7bd6bd33ec\/download","text":"URN:NBN:fi:jyu-201905162634.pdf","source":"jyx","mediaType":"application\/pdf"}
publishDate 2019
record_format qdc
source_str_mv jyx
spellingShingle Leskinen, Jarre Koneoppiminen rahoitusmarkkinoiden ennustamisessa tekninen analyysi Tietotekniikka Mathematical Information Technology 602 algoritmit rahoitusmarkkinat geneettiset algoritmit koneoppiminen neuroverkot osakkeet indikaattorit
title Koneoppiminen rahoitusmarkkinoiden ennustamisessa
title_full Koneoppiminen rahoitusmarkkinoiden ennustamisessa
title_fullStr Koneoppiminen rahoitusmarkkinoiden ennustamisessa Koneoppiminen rahoitusmarkkinoiden ennustamisessa
title_full_unstemmed Koneoppiminen rahoitusmarkkinoiden ennustamisessa Koneoppiminen rahoitusmarkkinoiden ennustamisessa
title_short Koneoppiminen rahoitusmarkkinoiden ennustamisessa
title_sort koneoppiminen rahoitusmarkkinoiden ennustamisessa
title_txtP Koneoppiminen rahoitusmarkkinoiden ennustamisessa
topic tekninen analyysi Tietotekniikka Mathematical Information Technology 602 algoritmit rahoitusmarkkinat geneettiset algoritmit koneoppiminen neuroverkot osakkeet indikaattorit
topic_facet 602 Mathematical Information Technology Tietotekniikka algoritmit geneettiset algoritmit indikaattorit koneoppiminen neuroverkot osakkeet rahoitusmarkkinat tekninen analyysi
url https://jyx.jyu.fi/handle/123456789/63989 http://www.urn.fi/URN:NBN:fi:jyu-201905162634
work_keys_str_mv AT leskinenjarre koneoppiminenrahoitusmarkkinoidenennustamisessa