{"id":"https://openalex.org/W3012344804","doi":null,"title":"Forecasting the USD/SEK exchange rate using deep neural networks","display_name":"Forecasting the USD/SEK exchange rate using deep neural networks","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W3012344804","mag":"3012344804"},"language":"en","primary_location":{"id":"pmh:oai:lup-student-papers.lub.lu.se:8992603","doi":null,"is_oa":true,"landing_page_url":"http://lup.lub.lu.se/student-papers/record/8992603","pdf_url":"http://lup.lub.lu.se/student-papers/record/8992603","source":{"id":"https://openalex.org/S4306402420","display_name":"Lund University Publications Student Papers (Lund University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I187531555","host_organization_name":"Lund University","host_organization_lineage":["https://openalex.org/I187531555"],"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":"M2"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://lup.lub.lu.se/student-papers/record/8992603","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041455408","display_name":"Thomas Hamfelt","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Thomas Hamfelt","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5041455408"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"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":0.9976000189781189,"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":0.9976000189781189,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.002099999925121665,"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/T11490","display_name":"Hydrological Forecasting Using AI","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/exchange-rate-forecasting","display_name":"exchange rate forecasting","score":0.9079999923706055},{"id":"https://openalex.org/keywords/time-series-forecasting","display_name":"time series forecasting","score":0.6240000128746033},{"id":"https://openalex.org/keywords/feedforward-neural-networks","display_name":"feedforward neural networks","score":0.7630000114440918},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"long short-term memory","score":0.8550000190734863},{"id":"https://openalex.org/keywords/multi-step-forecasting","display_name":"multi-step forecasting","score":0.7369999885559082}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6885446906089783},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.6735454797744751},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6704110503196716},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6626551151275635},{"id":"https://openalex.org/C75778745","wikidata":"https://www.wikidata.org/wiki/Q342626","display_name":"Lag","level":2,"score":0.5904819369316101},{"id":"https://openalex.org/C2776988154","wikidata":"https://www.wikidata.org/wiki/Q66100","display_name":"Exchange rate","level":2,"score":0.589813232421875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5699604153633118},{"id":"https://openalex.org/C2778775528","wikidata":"https://www.wikidata.org/wiki/Q5135432","display_name":"Closing (real estate)","level":2,"score":0.5438987612724304},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5348348617553711},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.47586482763290405},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47492557764053345},{"id":"https://openalex.org/C47702885","wikidata":"https://www.wikidata.org/wiki/Q5441227","display_name":"Feedforward neural network","level":3,"score":0.4660671055316925},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4627895653247833},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4086892306804657},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.26695477962493896},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.14560940861701965},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.13750845193862915},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:lup-student-papers.lub.lu.se:8992603","doi":null,"is_oa":true,"landing_page_url":"http://lup.lub.lu.se/student-papers/record/8992603","pdf_url":"http://lup.lub.lu.se/student-papers/record/8992603","source":{"id":"https://openalex.org/S4306402420","display_name":"Lund University Publications Student Papers (Lund University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I187531555","host_organization_name":"Lund University","host_organization_lineage":["https://openalex.org/I187531555"],"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":"M2"},{"id":"mag:3012344804","doi":null,"is_oa":false,"landing_page_url":"https://lup.lub.lu.se/student-papers/record/8992603/file/8992604.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:lup-student-papers.lub.lu.se:8992603","doi":null,"is_oa":true,"landing_page_url":"http://lup.lub.lu.se/student-papers/record/8992603","pdf_url":"http://lup.lub.lu.se/student-papers/record/8992603","source":{"id":"https://openalex.org/S4306402420","display_name":"Lund University Publications Student Papers (Lund University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I187531555","host_organization_name":"Lund University","host_organization_lineage":["https://openalex.org/I187531555"],"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":"M2"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3012344804.pdf","grobid_xml":"https://content.openalex.org/works/W3012344804.grobid-xml"},"referenced_works_count":3,"referenced_works":["https://openalex.org/W1598796236","https://openalex.org/W2103496339","https://openalex.org/W2267236547"],"related_works":["https://openalex.org/W2958786696","https://openalex.org/W4360978920","https://openalex.org/W3133816294","https://openalex.org/W4226200912","https://openalex.org/W3200576881","https://openalex.org/W7019270589","https://openalex.org/W4393989244","https://openalex.org/W4327795417","https://openalex.org/W3029872365","https://openalex.org/W7015864528"],"abstract_inverted_index":{"This":[0],"thesis":[1],"is":[2,27,58,105,135,155],"about":[3],"predicting":[4],"the":[5,13,23,30,68,80,83,86,89,93,112,147],"average":[6],"ten":[7],"minute":[8],"closing":[9],"bid":[10],"price":[11],"of":[12],"USD/SEK":[14],"exchange":[15,113],"rate":[16,114],"by":[17,96],"applying":[18],"deep":[19],"learning":[20],"methods.":[21],"First,":[22],"time":[24,75,127],"lag":[25],"method":[26],"applied":[28],"for":[29],"vanilla":[31],"Feedforward":[32],"Neural":[33],"Network":[34],"(FNN)":[35],"to":[36,50,74,108,122],"undertake":[37,51],"one-step":[38,52],"prediction.":[39,55],"Secondly,":[40],"three":[41],"univariate":[42],"Long":[43],"Short-Term":[44],"Memory":[45],"(LSTM)":[46],"models":[47],"are":[48,72],"used":[49,142],"and":[53,61,70,78,137],"multi-step":[54],"Each":[56],"network":[57],"theoretically":[59],"described":[60],"motivated.":[62],"The":[63],"results":[64,87],"indicate":[65],"that":[66,79,107],"both":[67],"FNN":[69],"LSTM":[71,81,90],"applicable":[73],"series":[76,128],"prediction":[77],"outperforms":[82],"FNN.":[84],"Furthermore,":[85],"suggests":[88],"can":[91],"outperform":[92],"na\u00efve":[94],"predictor":[95],"a":[97],"small":[98,150],"margin":[99],"but":[100],"it":[101],"remains":[102],"uncertain.":[103],"It":[104],"concluded":[106],"detect":[109],"structure":[110],"in":[111],"much":[115],"more":[116],"computing":[117],"power":[118],"might":[119],"be":[120,141],"required":[121],"learn":[123],"from":[124],"significantly":[125],"longer":[126],"as":[129,143],"input.":[130],"Finally,":[131],"some":[132],"economic":[133],"theory":[134],"reviewed":[136],"presented":[138],"which":[139],"could":[140],"potential":[144],"inputs":[145],"improving":[146],"results.":[148],"A":[149],"discussion":[151],"on":[152],"overlooked":[153],"biases":[154],"also":[156],"included.":[157]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2020-03-23T00:00:00"}