{"id":"https://openalex.org/W4366459230","doi":"https://doi.org/10.48550/arxiv.2304.08883","title":"Parameterized Neural Networks for Finance","display_name":"Parameterized Neural Networks for Finance","publication_year":2023,"publication_date":"2023-04-18","ids":{"openalex":"https://openalex.org/W4366459230","doi":"https://doi.org/10.48550/arxiv.2304.08883"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2304.08883","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2304.08883","pdf_url":"https://arxiv.org/pdf/2304.08883","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2304.08883","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030030665","display_name":"Daniel Oeltz","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Oeltz, Daniel","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010757293","display_name":"Jan Hamaekers","orcid":"https://orcid.org/0000-0001-5399-043X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hamaekers, Jan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5027645582","display_name":"Kay F. Pilz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pilz, Kay F.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5030030665"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9761999845504761,"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":0.9761999845504761,"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/T10320","display_name":"Neural Networks and Applications","score":0.9006999731063843,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.9211539030075073},{"id":"https://openalex.org/keywords/parameterized-complexity","display_name":"Parameterized complexity","score":0.7075775861740112},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6738268733024597},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6555016040802002},{"id":"https://openalex.org/keywords/asset","display_name":"Asset (computer security)","score":0.6541246175765991},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5671901702880859},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5662522912025452},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5588127970695496},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5239003896713257},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48786139488220215},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.47578465938568115},{"id":"https://openalex.org/keywords/mathematical-finance","display_name":"Mathematical finance","score":0.47192907333374023},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4288170337677002},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3478241562843323},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.3326289653778076},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.18338599801063538},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1316288709640503},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.1192452609539032},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.08421280980110168}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.9211539030075073},{"id":"https://openalex.org/C165464430","wikidata":"https://www.wikidata.org/wiki/Q1570441","display_name":"Parameterized complexity","level":2,"score":0.7075775861740112},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6738268733024597},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6555016040802002},{"id":"https://openalex.org/C76178495","wikidata":"https://www.wikidata.org/wiki/Q4808784","display_name":"Asset (computer security)","level":2,"score":0.6541246175765991},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5671901702880859},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5662522912025452},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5588127970695496},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5239003896713257},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48786139488220215},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.47578465938568115},{"id":"https://openalex.org/C93373587","wikidata":"https://www.wikidata.org/wiki/Q335632","display_name":"Mathematical finance","level":2,"score":0.47192907333374023},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4288170337677002},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3478241562843323},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.3326289653778076},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.18338599801063538},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1316288709640503},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.1192452609539032},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.08421280980110168},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:2304.08883","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2304.08883","pdf_url":"https://arxiv.org/pdf/2304.08883","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},{"id":"pmh:oai:RePEc:arx:papers:2304.08883","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"preprint"},{"id":"doi:10.48550/arxiv.2304.08883","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2304.08883","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2304.08883","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2304.08883","pdf_url":"https://arxiv.org/pdf/2304.08883","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W3009056573","https://openalex.org/W4297676672","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W4376166922","https://openalex.org/W4378510483","https://openalex.org/W4221142204"],"abstract_inverted_index":{"We":[0],"discuss":[1],"and":[2,80,101,137],"analyze":[3],"a":[4,11,15,25,29,51,62,71],"neural":[5],"network":[6],"architecture,":[7],"that":[8,117],"enables":[9],"learning":[10,24,46],"model":[12,27,48],"class":[13,49],"for":[14,28,69,84],"set":[16],"of":[17,56,95,107,126],"different":[18,58,85],"data":[19,31,53,59],"samples":[20],"rather":[21],"than":[22],"just":[23,61],"single":[26],"specific":[30,73],"sample.":[32],"In":[33],"this":[34,120,123],"sense,":[35],"it":[36],"may":[37,145],"help":[38],"to":[39,66,93,129,147],"reduce":[40],"the":[41,47,77,91,96,105,115,160],"overfitting":[42],"problem,":[43],"since,":[44],"after":[45],"over":[50],"larger":[52],"sample":[54],"consisting":[55],"such":[57],"sets,":[60],"few":[63],"parameters":[64],"need":[65,146],"be":[67],"adjusted":[68],"modeling":[70],"new,":[72],"problem.":[74],"After":[75],"analyzing":[76],"method":[78],"theoretically":[79],"by":[81],"regression":[82],"examples":[83],"one-dimensional":[86],"problems,":[87],"we":[88],"finally":[89],"apply":[90],"approach":[92],"one":[94],"standard":[97],"problems":[98],"asset":[99,135],"managers":[100,136],"banks":[102,138],"are":[103,140],"facing:":[104],"calibration":[106],"spread":[108],"curves.":[109],"The":[110],"presented":[111],"results":[112],"clearly":[113],"show":[114],"potential":[116],"lies":[118],"within":[119],"method.":[121],"Furthermore,":[122],"application":[124],"is":[125],"particular":[127],"interest":[128],"financial":[130],"practitioners,":[131],"since":[132],"nearly":[133],"all":[134],"which":[139],"having":[141],"solutions":[142],"in":[143],"place":[144],"adapt":[148],"or":[149],"even":[150],"change":[151],"their":[152],"current":[153],"methodologies":[154],"when":[155],"ESG":[156],"ratings":[157],"additionally":[158],"affect":[159],"bond":[161],"spreads.":[162]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}