{"id":"https://openalex.org/W4407143111","doi":"https://doi.org/10.3390/a18020081","title":"Machine Learning Models to Predict Google Stock Prices","display_name":"Machine Learning Models to Predict Google Stock Prices","publication_year":2025,"publication_date":"2025-02-03","ids":{"openalex":"https://openalex.org/W4407143111","doi":"https://doi.org/10.3390/a18020081"},"language":"en","primary_location":{"id":"doi:10.3390/a18020081","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a18020081","pdf_url":"https://www.mdpi.com/1999-4893/18/2/81/pdf?version=1738566028","source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Algorithms","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1999-4893/18/2/81/pdf?version=1738566028","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114583017","display_name":"Cosmina Elena Bucura","orcid":null},"institutions":[{"id":"https://openalex.org/I25217355","display_name":"University of Pavia","ror":"https://ror.org/00s6t1f81","country_code":"IT","type":"education","lineage":["https://openalex.org/I25217355"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Cosmina Elena Bucura","raw_affiliation_strings":["Department of Economics and Management, University of Pavia, 27100 Pavia, Italy"],"raw_orcid":"https://orcid.org/0009-0002-6141-5288","affiliations":[{"raw_affiliation_string":"Department of Economics and Management, University of Pavia, 27100 Pavia, Italy","institution_ids":["https://openalex.org/I25217355"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051364218","display_name":"Paolo Giudici","orcid":"https://orcid.org/0000-0002-4198-0127"},"institutions":[{"id":"https://openalex.org/I25217355","display_name":"University of Pavia","ror":"https://ror.org/00s6t1f81","country_code":"IT","type":"education","lineage":["https://openalex.org/I25217355"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Paolo Giudici","raw_affiliation_strings":["Department of Economics and Management, University of Pavia, 27100 Pavia, Italy"],"raw_orcid":"https://orcid.org/0000-0002-4198-0127","affiliations":[{"raw_affiliation_string":"Department of Economics and Management, University of Pavia, 27100 Pavia, Italy","institution_ids":["https://openalex.org/I25217355"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5051364218"],"corresponding_institution_ids":["https://openalex.org/I25217355"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01932838,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"18","issue":"2","first_page":"81","last_page":"81"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.988099992275238,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.968500018119812,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.6399588584899902},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5490900278091431},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4600607454776764},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4361489415168762},{"id":"https://openalex.org/keywords/stock-price","display_name":"Stock price","score":0.4354398548603058},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4284461438655853},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.26859375834465027},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12297287583351135}],"concepts":[{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.6399588584899902},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5490900278091431},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4600607454776764},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4361489415168762},{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.4354398548603058},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4284461438655853},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.26859375834465027},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12297287583351135},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/a18020081","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a18020081","pdf_url":"https://www.mdpi.com/1999-4893/18/2/81/pdf?version=1738566028","source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Algorithms","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:41b8604bbfac4be09d93830bd8c3d8d3","is_oa":true,"landing_page_url":"https://doaj.org/article/41b8604bbfac4be09d93830bd8c3d8d3","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Algorithms, Vol 18, Iss 2, p 81 (2025)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1999-4893/18/2/81/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/a18020081","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Pages: 81","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/a18020081","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a18020081","pdf_url":"https://www.mdpi.com/1999-4893/18/2/81/pdf?version=1738566028","source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Algorithms","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4407143111.pdf","grobid_xml":"https://content.openalex.org/works/W4407143111.grobid-xml"},"referenced_works_count":7,"referenced_works":["https://openalex.org/W2188293297","https://openalex.org/W2295598076","https://openalex.org/W2512625237","https://openalex.org/W2885195348","https://openalex.org/W3024761859","https://openalex.org/W4200454878","https://openalex.org/W4323852252"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"The":[0,24],"aim":[1],"of":[2,26,51],"this":[3],"paper":[4],"is":[5,29,70,76,87,100],"to":[6,55,57,91],"predict":[7,58],"Google":[8,59],"stock":[9,60],"price":[10],"using":[11],"different":[12],"datasets":[13],"and":[14,18,43],"machine":[15],"learning":[16],"models,":[17],"understand":[19],"which":[20],"models":[21,33],"perform":[22],"better.":[23],"novelty":[25],"our":[27],"approach":[28],"that":[30,48],"we":[31],"compare":[32],"not":[34],"only":[35],"by":[36,41],"predictive":[37],"accuracy":[38],"but":[39],"also":[40],"explainability":[42],"robustness.":[44],"Our":[45],"findings":[46],"show":[47],"the":[49,52,64,68,72,77,83,88,96,101],"choice":[50],"best":[53,78,102],"model":[54,86,99],"employ":[56],"prices":[61],"depends":[62],"on":[63],"desired":[65],"objective.":[66],"If":[67],"goal":[69],"accuracy,":[71],"recurrent":[73],"neural":[74],"network":[75],"model,":[79],"while,":[80],"for":[81,94],"robustness,":[82],"Ridge":[84],"regression":[85],"most":[89],"resilient":[90],"changes":[92],"and,":[93],"explainability,":[95],"Gradient":[97],"Boosting":[98],"choice.":[103]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
