{"id":"https://openalex.org/W7214943644","doi":"https://doi.org/10.30784/epfad.1916011","title":"The Effect of Liquidity on Deep Learning Errors","display_name":"The Effect of Liquidity on Deep Learning Errors","publication_year":2026,"publication_date":"2026-09-30","ids":{"openalex":"https://openalex.org/W7214943644","doi":"https://doi.org/10.30784/epfad.1916011"},"language":"tr","primary_location":{"id":"doi:10.30784/epfad.1916011","doi":"https://doi.org/10.30784/epfad.1916011","is_oa":true,"landing_page_url":"https://doi.org/10.30784/epfad.1916011","pdf_url":"https://dergipark.org.tr/en/download/article-file/5845252","source":{"id":"https://openalex.org/S4210237676","display_name":"Ekonomi Politika ve Finans Arastirmalari Dergisi","issn_l":"2587-151X","issn":["2587-151X"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["doaj","erih-plus"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ekonomi Politika ve Finans Arastirmalari Dergisi","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://dergipark.org.tr/en/download/article-file/5845252","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109260544","display_name":"Salih R\u0131dvan Y\u0131lmaz","orcid":"https://orcid.org/0000-0002-3044-840X","observed_orcids":["https://orcid.org/0000-0002-3044-840X"]},"institutions":[{"id":"https://openalex.org/I4210103784","display_name":"Small and Medium Enterprises Development Organization","ror":"https://ror.org/025rbqk82","country_code":"TR","type":"government","lineage":["https://openalex.org/I4210103784"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Salih R\u0131dvan Y\u0131lmaz","raw_affiliation_strings":["KOSGEB"],"raw_orcid":"https://orcid.org/0000-0002-3044-840X","affiliations":[{"raw_affiliation_string":"KOSGEB","institution_ids":["https://openalex.org/I4210103784"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027977004","display_name":"Nurullah U\u00e7kun","orcid":"https://orcid.org/0000-0001-5073-5644","observed_orcids":["https://orcid.org/0000-0001-5073-5644"]},"institutions":[{"id":"https://openalex.org/I51209816","display_name":"Eski\u015fehir Osmangazi University","ror":"https://ror.org/01dzjez04","country_code":"TR","type":"education","lineage":["https://openalex.org/I51209816"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Nurullah U\u00e7kun","raw_affiliation_strings":["Eski\u015fehir Osmangazi \u00dcniversitesi"],"raw_orcid":"https://orcid.org/0000-0001-5073-5644","affiliations":[{"raw_affiliation_string":"Eski\u015fehir Osmangazi \u00dcniversitesi","institution_ids":["https://openalex.org/I51209816"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.66427274,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"11","issue":"3","first_page":"1010","last_page":"1035"},"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":9.999999747378752e-05,"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/market-liquidity","display_name":"market liquidity","score":0.9520000219345093},{"id":"https://openalex.org/keywords/deep-learning-prediction","display_name":"deep learning prediction","score":0.6859999895095825},{"id":"https://openalex.org/keywords/lstm","display_name":"LSTM","score":0.718999981880188},{"id":"https://openalex.org/keywords/stock-price-prediction","display_name":"stock price prediction","score":0.6620000004768372},{"id":"https://openalex.org/keywords/prediction-error","display_name":"prediction error","score":0.6600000262260437},{"id":"https://openalex.org/keywords/amihud-illiquidity-measure","display_name":"Amihud illiquidity measure","score":0.9399999976158142},{"id":"https://openalex.org/keywords/trading-volume","display_name":"trading volume","score":0.9869999885559082},{"id":"https://openalex.org/keywords/macroeconomic-factors","display_name":"macroeconomic factors","score":0.9430000185966492},{"id":"https://openalex.org/keywords/bist-30-index","display_name":"BIST 30 index","score":0.7870000004768372},{"id":"https://openalex.org/keywords/forecast-calibration","display_name":"forecast calibration","score":0.7059999704360962},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.5260000228881836}],"concepts":[{"id":"https://openalex.org/C183582576","wikidata":"https://www.wikidata.org/wiki/Q184783","display_name":"Market liquidity","level":2,"score":0.748199999332428},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.7088000178337097},{"id":"https://openalex.org/C2777402642","wikidata":"https://www.wikidata.org/wiki/Q2557224","display_name":"Explanatory power","level":2,"score":0.5198000073432922},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.4909999966621399},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4731999933719635},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.4722999930381775},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45239999890327454},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42910000681877136},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.4205000102519989},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.41600000858306885},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.3962000012397766},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3880000114440918},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.3506999909877777},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3393000066280365},{"id":"https://openalex.org/C137703641","wikidata":"https://www.wikidata.org/wiki/Q4874480","display_name":"Bayesian vector autoregression","level":3,"score":0.3246999979019165},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3124000132083893},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.3098999857902527},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.29510000348091125},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C162144332","wikidata":"https://www.wikidata.org/wiki/Q1665305","display_name":"Instrumental variable","level":2,"score":0.2888999879360199},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C106159729","wikidata":"https://www.wikidata.org/wiki/Q2294553","display_name":"Financial economics","level":1,"score":0.2766000032424927},{"id":"https://openalex.org/C133029050","wikidata":"https://www.wikidata.org/wiki/Q385593","display_name":"Vector autoregression","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C163068380","wikidata":"https://www.wikidata.org/wiki/Q3409313","display_name":"Economic forecasting","level":2,"score":0.2680000066757202}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.30784/epfad.1916011","doi":"https://doi.org/10.30784/epfad.1916011","is_oa":true,"landing_page_url":"https://doi.org/10.30784/epfad.1916011","pdf_url":"https://dergipark.org.tr/en/download/article-file/5845252","source":{"id":"https://openalex.org/S4210237676","display_name":"Ekonomi Politika ve Finans Arastirmalari Dergisi","issn_l":"2587-151X","issn":["2587-151X"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["doaj","erih-plus"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ekonomi Politika ve Finans Arastirmalari Dergisi","raw_type":"journal-article"},{"id":"pmh:oai:dergipark.org.tr:article/1916011","doi":null,"is_oa":false,"landing_page_url":"https://dergipark.org.tr/en/pub/epfad/article/1916011","pdf_url":null,"source":{"id":"https://openalex.org/S4306401840","display_name":"DergiPark (Istanbul University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67581229","host_organization_name":"Istanbul University","host_organization_lineage":["https://openalex.org/I67581229"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Ekonomi Politika ve Finans Ara\u015ft\u0131rmalar\u0131 Dergisi, Vol. 11 No. 3","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.30784/epfad.1916011","doi":"https://doi.org/10.30784/epfad.1916011","is_oa":true,"landing_page_url":"https://doi.org/10.30784/epfad.1916011","pdf_url":"https://dergipark.org.tr/en/download/article-file/5845252","source":{"id":"https://openalex.org/S4210237676","display_name":"Ekonomi Politika ve Finans Arastirmalari Dergisi","issn_l":"2587-151X","issn":["2587-151X"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["doaj","erih-plus"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ekonomi Politika ve Finans Arastirmalari Dergisi","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/W7214943644.pdf","grobid_xml":"https://content.openalex.org/works/W7214943644.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7140201745","https://openalex.org/W3093690785","https://openalex.org/W7154905038","https://openalex.org/W4415153197","https://openalex.org/W4306174257","https://openalex.org/W7161237243","https://openalex.org/W7127380733","https://openalex.org/W7133194268","https://openalex.org/W7116287337","https://openalex.org/W7161309080"],"abstract_inverted_index":{"This":[0],"study":[1],"investigates":[2],"the":[3,12,35,92,105,129,152,177],"impact":[4],"of":[5,15,94,151,179],"market":[6,49],"liquidity":[7,96,144,181],"and":[8,47,74,87,98,125,166,171],"macroeconomic":[9,100,165],"variables":[10],"on":[11,34],"forecasting":[13,187],"performance":[14],"deep":[16,184],"learning":[17],"models":[18,188],"in":[19],"financial":[20,186],"markets.":[21],"The":[22],"primary":[23],"objective":[24],"is":[25,59,83,146,161],"to":[26,90,121,189],"forecast":[27,191],"price":[28],"movements":[29],"for":[30,138,149],"ten":[31,113,153],"stocks":[32],"listed":[33],"BIST":[36],"30":[37],"index":[38],"using":[39,61,85],"a":[40],"single-layer":[41],"Long":[42],"Short-Term":[43],"Memory":[44],"(LSTM)":[45],"model":[46,107],"identify":[48],"conditions":[50,182],"associated":[51,134],"with":[52,115,135,155],"prediction":[53,136],"failures.":[54],"A":[55],"two-stage":[56],"empirical":[57],"framework":[58],"employed":[60],"daily":[62,78],"data":[63],"covering":[64],"2006\u20132025.":[65],"First,":[66],"LSTM":[67,106],"hyperparameters":[68],"are":[69,132],"optimized":[70],"through":[71],"Bayesian":[72],"optimization":[73],"walk-forward":[75],"validation.":[76],"Second,":[77],"Absolute":[79],"Percentage":[80],"Error":[81],"(APE)":[82],"modeled":[84],"OLS-HAC":[86],"EGARCH-X":[88],"regressions":[89],"examine":[91],"effects":[93],"stock-level":[95],"measures":[97],"global":[99,167],"variables.":[101],"Results":[102],"show":[103],"that":[104],"generates":[108],"well-calibrated":[109],"forecasts":[110],"across":[111],"all":[112],"equities,":[114,154],"MAPE":[116],"values":[117],"ranging":[118],"from":[119],"3.29%":[120],"8.40%.":[122],"Trading":[123],"volume":[124],"illiquidity,":[126],"measured":[127],"by":[128],"Amihud":[130],"ratio,":[131],"positively":[133],"errors":[137],"most":[139],"stocks.":[140],"At":[141],"least":[142],"one":[143],"proxy":[145],"statistically":[147],"significant":[148],"nine":[150],"consistent":[156],"coefficient":[157],"signs":[158],"where":[159],"significance":[160],"observed.":[162],"In":[163],"contrast,":[164],"indicators":[168],"exhibit":[169],"limited":[170],"stock-specific":[172],"effects.":[173],"These":[174],"findings":[175],"highlight":[176],"importance":[178],"incorporating":[180],"into":[183],"learning-based":[185],"improve":[190],"reliability.":[192]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2026-10-01T00:00:00"}