{"id":"https://openalex.org/W7155387014","doi":"https://doi.org/10.48550/arxiv.2604.20374","title":"Towards Event-Aware Forecasting in DeFi: Insights from On-chain Automated Market Maker Protocols","display_name":"Towards Event-Aware Forecasting in DeFi: Insights from On-chain Automated Market Maker Protocols","publication_year":2026,"publication_date":"2026-04-22","ids":{"openalex":"https://openalex.org/W7155387014","doi":"https://doi.org/10.48550/arxiv.2604.20374"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.20374","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20374","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.20374","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5056935939","display_name":"Huaiyu Jia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jia, Huaiyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134385673","display_name":"Jiehshun You","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"You, Jiehshun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106722630","display_name":"Yizhi Luo","orcid":"https://orcid.org/0009-0004-8321-0181"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Yizhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134435207","display_name":"Jingyu Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jingyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134393853","display_name":"Shuo Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Shuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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":0.5239999890327454,"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":0.5239999890327454,"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.07999999821186066,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"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/T10270","display_name":"Blockchain Technology Applications and Security","score":0.07479999959468842,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/weighting","display_name":"Weighting","score":0.6290000081062317},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5335999727249146},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5307000279426575},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.47609999775886536},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.47450000047683716},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.45739999413490295},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.40639999508857727},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.3912000060081482}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6310999989509583},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6290000081062317},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5335999727249146},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5307000279426575},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.47609999775886536},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.47450000047683716},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.45739999413490295},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.40639999508857727},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4036000072956085},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3912000060081482},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3767000138759613},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.36399999260902405},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.35190001130104065},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.32109999656677246},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.3077999949455261},{"id":"https://openalex.org/C76178495","wikidata":"https://www.wikidata.org/wiki/Q4808784","display_name":"Asset (computer security)","level":2,"score":0.29589998722076416},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2955999970436096},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C2781215313","wikidata":"https://www.wikidata.org/wiki/Q3493345","display_name":"SPARK (programming language)","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C2780021719","wikidata":"https://www.wikidata.org/wiki/Q282283","display_name":"Prediction market","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.25589999556541443}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.20374","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20374","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.20374","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20374","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.49682387709617615}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Automated":[0],"Market":[1],"Makers":[2],"(AMMs),":[3],"as":[4],"a":[5,18,76,99,192],"core":[6],"infrastructure":[7],"of":[8,121,181,187,219],"decentralized":[9],"finance":[10],"(DeFi),":[11],"uniquely":[12],"drive":[13],"on-chain":[14,33,104,220],"asset":[15],"pricing":[16],"through":[17],"deterministic":[19],"reserve":[20,40],"ratio":[21],"mechanism.":[22],"Unlike":[23],"traditional":[24,149],"markets,":[25],"AMM":[26],"price":[27,57,221],"dynamics":[28,69],"is":[29],"triggered":[30],"largely":[31],"by":[32,44,155,178],"events":[34],"(e.g.,":[35],"swap)":[36],"that":[37,169],"change":[38],"the":[39,67,71,142,148,157,174,185,199,205,214],"ratio,":[41],"rather":[42],"than":[43],"continuous":[45],"responses":[46],"to":[47],"off-chain":[48],"information.":[49],"This":[50,202],"makes":[51],"event-level":[52],"analysis":[53],"crucial":[54],"for":[55,90,195,212],"understanding":[56],"formation":[58],"mechanisms":[59],"in":[60,198],"AMMs.":[61],"However,":[62],"existing":[63],"research":[64],"generally":[65],"neglects":[66],"micro-structural":[68],"at":[70],"AMMs":[72,110,200],"level,":[73],"lacking":[74],"both":[75],"comprehensive":[77],"dataset":[78,100],"covering":[79],"multiple":[80],"protocols":[81],"with":[82,118,159],"fine-grained":[83],"event":[84,105,188],"classification":[85],"and":[86,116,124,209,216,225],"an":[87,131,179],"effective":[88],"framework":[89,211],"event-aware":[91,196],"modeling.":[92],"To":[93],"fill":[94],"this":[95,170],"gap,":[96],"we":[97,129],"construct":[98],"containing":[101],"8.9":[102],"million":[103],"records":[106],"from":[107],"four":[108],"representative":[109],"protocols:":[111],"Pendle,":[112],"Uniswap":[113],"v3,":[114],"Aave":[115],"Morpho,":[117],"precise":[119],"annotations":[120],"transaction":[122],"type":[123,189],"block":[125,143],"height":[126],"timestamps.":[127],"Furthermore,":[128],"propose":[130],"Uncertainty":[132],"Weighted":[133],"Mean":[134],"Squared":[135],"Error":[136],"(UWM)":[137],"loss":[138,171],"function,":[139],"which":[140],"incorporates":[141],"interval":[144],"regression":[145],"term":[146],"into":[147],"Time-Point":[150],"Process":[151],"(TPP)":[152],"objective":[153],"function":[154,172],"weighting":[156],"uncertainty":[158],"homoscedasticity.":[160],"Extensive":[161],"experiments":[162],"on":[163],"eight":[164],"advanced":[165],"TPP":[166],"architectures":[167],"demonstrate":[168],"reduces":[173],"time":[175],"prediction":[176,197],"error":[177],"average":[180],"56.41\\%":[182],"while":[183],"maintaining":[184],"accuracy":[186],"prediction,":[190],"establishing":[191],"robust":[193],"benchmark":[194],"ecosystem.":[201],"work":[203],"provides":[204],"necessary":[206],"data":[207],"foundation":[208],"methodological":[210],"modeling":[213],"discreteness":[215],"event-driven":[217],"characteristics":[218],"discovery.":[222],"All":[223],"datasets":[224],"source":[226],"code":[227],"are":[228],"publicly":[229],"available.":[230],"https://github.com/yosen-king/Deep-AMM-Events":[231]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-24T00:00:00"}
