{"id":"https://openalex.org/W7225833868","doi":"https://doi.org/10.48550/arxiv.2611.14117","title":"Fine-Tuning an Order-Flow Foundation Model for Sub-Second Forecasting","display_name":"Fine-Tuning an Order-Flow Foundation Model for Sub-Second Forecasting","publication_year":2026,"publication_date":"2026-11-10","ids":{"openalex":"https://openalex.org/W7225833868","doi":"https://doi.org/10.48550/arxiv.2611.14117"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2611.14117","doi":"https://doi.org/10.48550/arxiv.2611.14117","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2611.14117","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"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2611.14117","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119757225","display_name":"Fedor Shabashev","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Shabashev, Fedor","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5119757225"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"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":0.9404000043869019,"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":0.9404000043869019,"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":0.05700000002980232,"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/T11995","display_name":"FinTech, Crowdfunding, Digital Finance","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.715499997138977},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.67330002784729},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.5827000141143799},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5611000061035156},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4821999967098236},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.4675999879837036},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44999998807907104},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3718999922275543},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35679998993873596},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3540000021457672},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.3086000084877014},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.30379998683929443},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C12186640","wikidata":"https://www.wikidata.org/wiki/Q6815743","display_name":"Memory model","level":3,"score":0.2653000056743622},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2611.14117","doi":"https://doi.org/10.48550/arxiv.2611.14117","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2611.14117","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:10.48550/arxiv.2611.14117","doi":"https://doi.org/10.48550/arxiv.2611.14117","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2611.14117","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"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Generative":[0],"models":[1,12],"have":[2],"become":[3],"the":[4,72,77,108,112,125,131,138,144],"standard":[5],"approach":[6],"in":[7,100,148],"language":[8],"and":[9,17,67,80,150],"vision.":[10],"These":[11,153],"are":[13,135,147],"typically":[14],"pretrained":[15,73,158],"first":[16,51],"then":[18],"fine-tuned.":[19],"Recent":[20],"work":[21],"demonstrates":[22],"how":[23],"to":[24,35,75,116,124],"pretrain":[25,52],"an":[26],"autoregressive":[27],"generative":[28,55,159],"model":[29,44,56,74,89,113,160],"on":[30,57,97],"Level-3":[31,58],"order-flow":[32,59],"data.":[33],"However,":[34],"our":[36,87],"knowledge,":[37],"no":[38],"one":[39],"has":[40],"fine-tuned":[41,88,121],"such":[42,93],"a":[43,53,157,165],"yet":[45],"for":[46],"downstream":[47],"tasks.":[48],"Here":[49],"we":[50,70],"300M-parameter":[54],"data":[60],"collected":[61],"from":[62],"two":[63],"crypto":[64],"exchanges,":[65],"Bitstamp":[66],"Bitfinex.":[68],"Then":[69],"fine-tune":[71],"predict":[76],"microprice":[78,102,133],"return":[79],"its":[81],"realized":[82],"volatility.":[83],"We":[84],"show":[85],"that":[86,156],"outperforms":[90],"classical":[91],"baselines,":[92],"as":[94],"gradient":[95],"boosting":[96],"handcrafted":[98],"features,":[99],"predicting":[101],"returns":[103],"over":[104],"sub-second":[105],"horizons.":[106],"Furthermore,":[107],"predictive":[109],"ability":[110],"of":[111,128],"transfers":[114],"zero-shot":[115],"assets":[117],"it":[118],"was":[119],"never":[120],"on.":[122],"Due":[123],"short-term":[126],"nature":[127],"these":[129],"forecasts,":[130],"predicted":[132],"moves":[134],"smaller":[136],"than":[137],"bid-ask":[139],"spread":[140],"plus":[141],"fees,":[142],"so":[143],"natural":[145],"applications":[146],"execution":[149],"market":[151],"making.":[152],"results":[154],"indicate":[155],"can":[161],"be":[162],"turned":[163],"into":[164],"competitive":[166],"short-horizon":[167],"price":[168],"forecaster":[169],"via":[170],"fine-tuning.":[171]},"counts_by_year":[],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2026-11-12T00:00:00"}
