{"id":"https://openalex.org/W4280628761","doi":"https://doi.org/10.1109/cifer52523.2022.9776160","title":"Iterative Filtering Algorithms for Computing Consensus Analyst Estimates","display_name":"Iterative Filtering Algorithms for Computing Consensus Analyst Estimates","publication_year":2022,"publication_date":"2022-05-01","ids":{"openalex":"https://openalex.org/W4280628761","doi":"https://doi.org/10.1109/cifer52523.2022.9776160"},"language":"en","primary_location":{"id":"doi:10.1109/cifer52523.2022.9776160","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cifer52523.2022.9776160","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066463495","display_name":"Kheng Kua","orcid":null},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Kheng Kua","raw_affiliation_strings":["University of New South Wales,School of Computer Science and Engineering,Sydney,Australia","School of Computer Science and Engineering, University of New South Wales, Sydney, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of New South Wales,School of Computer Science and Engineering,Sydney,Australia","institution_ids":["https://openalex.org/I31746571"]},{"raw_affiliation_string":"School of Computer Science and Engineering, University of New South Wales, Sydney, Australia","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079126989","display_name":"Aleksandar Ignjatovi\u0107","orcid":"https://orcid.org/0000-0001-7427-4934"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Aleksandar Ignjatovic","raw_affiliation_strings":["University of New South Wales,School of Computer Science and Engineering,Sydney,Australia","School of Computer Science and Engineering, University of New South Wales, Sydney, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of New South Wales,School of Computer Science and Engineering,Sydney,Australia","institution_ids":["https://openalex.org/I31746571"]},{"raw_affiliation_string":"School of Computer Science and Engineering, University of New South Wales, Sydney, Australia","institution_ids":["https://openalex.org/I31746571"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I31746571"],"apc_list":null,"apc_paid":null,"fwci":1.8985,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.8595879,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9950000047683716,"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":0.9950000047683716,"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":0.9932000041007996,"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":0.9918000102043152,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6733939051628113},{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.5862963795661926},{"id":"https://openalex.org/keywords/iterative-and-incremental-development","display_name":"Iterative and incremental development","score":0.5473402738571167},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4546104967594147},{"id":"https://openalex.org/keywords/equity","display_name":"Equity (law)","score":0.44562166929244995},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.4450685381889343},{"id":"https://openalex.org/keywords/iterative-method","display_name":"Iterative method","score":0.42412686347961426},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39115315675735474},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36794760823249817},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3672383725643158}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6733939051628113},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.5862963795661926},{"id":"https://openalex.org/C143587482","wikidata":"https://www.wikidata.org/wiki/Q1543216","display_name":"Iterative and incremental development","level":2,"score":0.5473402738571167},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4546104967594147},{"id":"https://openalex.org/C199728807","wikidata":"https://www.wikidata.org/wiki/Q2578557","display_name":"Equity (law)","level":2,"score":0.44562166929244995},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.4450685381889343},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.42412686347961426},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39115315675735474},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36794760823249817},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3672383725643158},{"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/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cifer52523.2022.9776160","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cifer52523.2022.9776160","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals","score":0.41999998688697815}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1506983033","https://openalex.org/W1992462783","https://openalex.org/W2055958150","https://openalex.org/W2058738785","https://openalex.org/W2058775147","https://openalex.org/W2081432842","https://openalex.org/W2088010151","https://openalex.org/W2158000180","https://openalex.org/W2160841369","https://openalex.org/W2248129317","https://openalex.org/W3121296121","https://openalex.org/W3124972276","https://openalex.org/W3125179425","https://openalex.org/W3125209766"],"related_works":["https://openalex.org/W2607642827","https://openalex.org/W2141090006","https://openalex.org/W1971373454","https://openalex.org/W2121460486","https://openalex.org/W2057477010","https://openalex.org/W2120980534","https://openalex.org/W2375297922","https://openalex.org/W2387953094","https://openalex.org/W2391323571","https://openalex.org/W2171985077"],"abstract_inverted_index":{"In":[0],"equity":[1],"investment":[2],"management,":[3],"sell":[4],"side":[5],"analysts":[6,44],"serve":[7],"an":[8,24,50,57],"important":[9],"role":[10],"in":[11,23],"forecasting":[12],"metrics":[13],"of":[14,63,122,125,131,148,162],"companies\u2019":[15],"financial":[16],"performance.":[17],"These":[18],"estimates":[19,65,94,111],"are":[20],"often":[21],"produced":[22],"opaque":[25],"manner,":[26],"namely,":[27],"the":[28,32,46,77,92,128,146,159,163,168],"process":[29],"upon":[30,158],"which":[31],"estimate":[33],"is":[34,38,118],"initiated":[35],"or":[36],"revised":[37],"not":[39],"directly":[40],"observable.":[41],"With":[42],"multiple":[43,53],"covering":[45,52],"same":[47],"company,":[48],"and":[49,69,135],"analyst":[51,64,110],"companies,":[54],"we":[55,103],"have":[56],"n-m":[58],"relationship.":[59],"The":[60,151],"systematic":[61,68],"capture":[62],"provide":[66],"a":[67,98,105],"quantitative":[70],"proxy":[71],"for":[72,90,108],"market":[73],"sentiment.":[74],"Thus":[75],"far":[76],"academic":[78],"literature":[79],"analysing":[80],"this":[81,101,149],"dataset":[82],"has":[83],"resolved":[84],"to":[85,95,127,144,167],"use":[86],"relatively":[87],"simple":[88,169],"methods":[89,156],"aggregating":[91,109],"individual":[93],"arrive":[96],"at":[97],"consensus":[99,164],"estimate.In":[100],"paper":[102],"propose":[104],"novel":[106],"method":[107],"utilising":[112],"iterative":[113,154],"filtering":[114,155],"algorithms.":[115],"This":[116],"work":[117],"inspired":[119],"by":[120],"applications":[121],"such":[123],"classes":[124],"algorithms":[126],"robust":[129],"aggregation":[130],"sensor":[132],"network":[133],"data":[134],"online":[136],"reviews.":[137],"We":[138],"conduct":[139],"experiments":[140],"using":[141],"real-world":[142],"datasets":[143],"demonstrate":[145],"efficacy":[147],"approach.":[150],"results":[152],"suggest":[153],"improve":[157],"forecast":[160,165],"accuracy":[161],"compared":[166],"mean":[170],"consensus.":[171]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
