{"id":"https://openalex.org/W4396923707","doi":"https://doi.org/10.33138/2957-0506.2024.7.443","title":"LSTM-ARIMA AS A HYBRID APPROACH IN ALGORITHMIC INVESTMENT STRATEGIES","display_name":"LSTM-ARIMA AS A HYBRID APPROACH IN ALGORITHMIC INVESTMENT STRATEGIES","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4396923707","doi":"https://doi.org/10.33138/2957-0506.2024.7.443"},"language":"en","primary_location":{"id":"doi:10.33138/2957-0506.2024.7.443","doi":"https://doi.org/10.33138/2957-0506.2024.7.443","is_oa":true,"landing_page_url":"https://doi.org/10.33138/2957-0506.2024.7.443","pdf_url":"https://doi.org/10.33138/2957-0506.2024.7.443","source":{"id":"https://openalex.org/S4404676341","display_name":"Working Papers.","issn_l":"2957-0506","issn":["2957-0506"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Working Papers","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://doi.org/10.33138/2957-0506.2024.7.443","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5098535605","display_name":"Kamil Kashif","orcid":"https://orcid.org/0009-0004-0666-581X","observed_orcids":["https://orcid.org/0009-0004-0666-581X"]},"institutions":[{"id":"https://openalex.org/I4654613","display_name":"University of Warsaw","ror":"https://ror.org/039bjqg32","country_code":"PL","type":"education","lineage":["https://openalex.org/I4654613"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Kamil Kashif","raw_affiliation_strings":["University of Warsaw , Faculty of Economic Sciences"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Warsaw , Faculty of Economic Sciences","institution_ids":["https://openalex.org/I4654613"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031588007","display_name":"Robert \u015alepaczuk","orcid":"https://orcid.org/0000-0001-5227-2014","observed_orcids":["https://orcid.org/0000-0001-5227-2014"]},"institutions":[{"id":"https://openalex.org/I4654613","display_name":"University of Warsaw","ror":"https://ror.org/039bjqg32","country_code":"PL","type":"education","lineage":["https://openalex.org/I4654613"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Robert \u015alepaczuk","raw_affiliation_strings":["Department of Quantitative Finance and Machine Learning","University of Warsaw , Faculty of Economic Sciences , Quantitative Finance Research Group ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Quantitative Finance and Machine Learning","institution_ids":[]},{"raw_affiliation_string":"University of Warsaw , Faculty of Economic Sciences , Quantitative Finance Research Group ,","institution_ids":["https://openalex.org/I4654613"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4654613"],"apc_list":null,"apc_paid":null,"fwci":2.7033,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":{"value":0.90339726,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"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":1.0,"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":1.0,"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/T10047","display_name":"Financial Markets and Investment Strategies","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.0,"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/hybrid-forecasting","display_name":"hybrid forecasting","score":0.6869999766349792},{"id":"https://openalex.org/keywords/algorithmic-trading-strategies","display_name":"algorithmic trading strategies","score":0.8700000047683716},{"id":"https://openalex.org/keywords/s-p-500","display_name":"S&P 500","score":0.949999988079071},{"id":"https://openalex.org/keywords/ftse-100","display_name":"FTSE 100","score":1.0},{"id":"https://openalex.org/keywords/cac-40","display_name":"CAC 40","score":1.0},{"id":"https://openalex.org/keywords/long-only-strategy","display_name":"long-only strategy","score":0.5720000267028809},{"id":"https://openalex.org/keywords/long-short-strategies","display_name":"long-short strategies","score":0.9959999918937683},{"id":"https://openalex.org/keywords/walk-forward-validation","display_name":"walk-forward validation","score":0.6949999928474426},{"id":"https://openalex.org/keywords/random-search-hyperparameter-tuning","display_name":"random search hyperparameter tuning","score":0.7680000066757202},{"id":"https://openalex.org/keywords/risk-adjusted-returns","display_name":"risk-adjusted returns","score":0.7699999809265137},{"id":"https://openalex.org/keywords/information-ratio","display_name":"information ratio","score":0.7929999828338623}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.8331769704818726},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6363241672515869},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6209720373153687},{"id":"https://openalex.org/C199728807","wikidata":"https://www.wikidata.org/wiki/Q2578557","display_name":"Equity (law)","level":2,"score":0.5377078056335449},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.4617815613746643},{"id":"https://openalex.org/C139938925","wikidata":"https://www.wikidata.org/wiki/Q1501898","display_name":"Sharpe ratio","level":3,"score":0.44191572070121765},{"id":"https://openalex.org/C27548731","wikidata":"https://www.wikidata.org/wiki/Q88272","display_name":"Investment (military)","level":3,"score":0.42317235469818115},{"id":"https://openalex.org/C103144560","wikidata":"https://www.wikidata.org/wiki/Q2670999","display_name":"Investment strategy","level":3,"score":0.4221475124359131},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42084550857543945},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4010326862335205},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4010116159915924},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.38332024216651917},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.25232452154159546},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1949261724948883},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.12301793694496155},{"id":"https://openalex.org/C2780821815","wikidata":"https://www.wikidata.org/wiki/Q5340806","display_name":"Portfolio","level":2,"score":0.09620991349220276},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C183582576","wikidata":"https://www.wikidata.org/wiki/Q184783","display_name":"Market liquidity","level":2,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.33138/2957-0506.2024.7.443","doi":"https://doi.org/10.33138/2957-0506.2024.7.443","is_oa":true,"landing_page_url":"https://doi.org/10.33138/2957-0506.2024.7.443","pdf_url":"https://doi.org/10.33138/2957-0506.2024.7.443","source":{"id":"https://openalex.org/S4404676341","display_name":"Working Papers.","issn_l":"2957-0506","issn":["2957-0506"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Working Papers","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2406.18206","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2406.18206","pdf_url":"https://arxiv.org/pdf/2406.18206","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2406.18206","doi":"https://doi.org/10.48550/arxiv.2406.18206","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2406.18206","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.33138/2957-0506.2024.7.443","doi":"https://doi.org/10.33138/2957-0506.2024.7.443","is_oa":true,"landing_page_url":"https://doi.org/10.33138/2957-0506.2024.7.443","pdf_url":"https://doi.org/10.33138/2957-0506.2024.7.443","source":{"id":"https://openalex.org/S4404676341","display_name":"Working Papers.","issn_l":"2957-0506","issn":["2957-0506"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Working Papers","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4396923707.pdf","grobid_xml":"https://content.openalex.org/works/W4396923707.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7112622654","https://openalex.org/W4389125078","https://openalex.org/W4389124877","https://openalex.org/W4210596068","https://openalex.org/W4205861619","https://openalex.org/W7111566263","https://openalex.org/W4402363419","https://openalex.org/W3202715085","https://openalex.org/W4317397801","https://openalex.org/W7113619684"],"abstract_inverted_index":{"This":[0,23],"study":[1],"focuses":[2],"on":[3,81,110,117],"building":[4],"an":[5],"algorithmic":[6,210],"investment":[7,144,211],"strategy":[8,149,167],"employing":[9],"a":[10],"hybrid":[11,197],"approach":[12],"that":[13,92,178],"combines":[14],"LSTM":[15,27],"and":[16,61,97,129,150,158],"ARIMA":[17,44],"models":[18,204],"referred":[19],"to":[20,28,72,133,163],"as":[21],"LSTM-ARIMA.":[22],"unique":[24],"algorithm":[25,50,181],"uses":[26,93],"produce":[29],"final":[30],"predictions":[31,45],"but":[32],"boost":[33],"results":[34],"of":[35,77,103,136,140],"this":[36],"RNN":[37],"by":[38],"adding":[39],"the":[40,82,99,104,155,160,165,169,179,184,189,208],"residuals":[41],"obtained":[42],"from":[43,69],"among":[46],"other":[47,185],"inputs.":[48],"The":[49,75,101,175],"is":[51,79,86,107],"tested":[52],"across":[53,187],"three":[54],"equity":[55,151,161,190],"indices":[56,191],"(S&P":[57],"500,":[58],"FTSE":[59],"100,":[60],"CAC":[62],"40)":[63],"using":[64,94],"daily":[65],"frequency":[66],"data":[67],"spanning":[68],"January,":[70],"2000":[71],"August,":[73],"2023.":[74],"architecture":[76],"testing":[78],"based":[80,109],"walk-forward":[83],"procedure":[84],"which":[85],"applied":[87],"for":[88,125,207],"hyperparameter":[89],"tunning":[90],"phase":[91],"Random":[95],"Search":[96],"backtesting":[98],"algorithms.":[100],"selection":[102],"optimal":[105,209],"model":[106],"determined":[108],"adequately":[111],"selected":[112],"performance":[113,156],"metrics":[114,157],"combining":[115],"focused":[116],"risk-adjusted":[118],"return":[119],"measures.":[120],"We":[121],"considered":[122],"two":[123,137],"strategies":[124],"each":[126,148],"algorithm:":[127],"Long-Only":[128],"Long-Short":[130],"in":[131,205],"order":[132],"present":[134],"situation":[135],"various":[138],"groups":[139],"investors":[141],"with":[142,168],"different":[143],"policy":[145],"restrictions.":[146],"For":[147],"index,":[152],"we":[153],"compute":[154],"visualize":[159],"curve":[162],"identify":[164],"best":[166],"highest":[170],"modified":[171],"information":[172],"ratio":[173],"(\ud835\udc3c\ud835\udc3c\ud835\udc3c\ud835\udc3c**).":[174],"findings":[176],"conclude":[177],"LSTM-ARIMA":[180],"outperforms":[182],"all":[183,188],"algorithms":[186],"what":[192],"confirms":[193],"strong":[194],"potential":[195],"behind":[196],"ML-TS":[198],"(machine":[199],"learning":[200],"-":[201],"time":[202],"series)":[203],"searching":[206],"strategies.":[212]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2024-05-16T00:00:00"}