{"id":"https://openalex.org/W3154430708","doi":"https://doi.org/10.1109/icassp39728.2021.9414430","title":"HCGM-Net: A Deep Unfolding Network for Financial Index Tracking","display_name":"HCGM-Net: A Deep Unfolding Network for Financial Index Tracking","publication_year":2021,"publication_date":"2021-05-13","ids":{"openalex":"https://openalex.org/W3154430708","doi":"https://doi.org/10.1109/icassp39728.2021.9414430","mag":"3154430708"},"language":"en","primary_location":{"id":"doi:10.1109/icassp39728.2021.9414430","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414430","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5070268822","display_name":"Ruben Pauwels","orcid":"https://orcid.org/0000-0002-9462-7546"},"institutions":[{"id":"https://openalex.org/I13469542","display_name":"Vrije Universiteit Brussel","ror":"https://ror.org/006e5kg04","country_code":"BE","type":"education","lineage":["https://openalex.org/I13469542"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Ruben Pauwels","raw_affiliation_strings":["Vrije Universiteit Brussel, Brussels, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vrije Universiteit Brussel, Brussels, Belgium","institution_ids":["https://openalex.org/I13469542"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076766934","display_name":"Evaggelia Tsiligianni","orcid":"https://orcid.org/0000-0003-2876-5788"},"institutions":[{"id":"https://openalex.org/I194019607","display_name":"University of Ioannina","ror":"https://ror.org/01qg3j183","country_code":"GR","type":"education","lineage":["https://openalex.org/I194019607"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Evaggelia Tsiligianni","raw_affiliation_strings":["University of Ioannina, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Ioannina, Greece","institution_ids":["https://openalex.org/I194019607"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043511500","display_name":"Nikos Deligiannis","orcid":"https://orcid.org/0000-0001-9300-5860"},"institutions":[{"id":"https://openalex.org/I13469542","display_name":"Vrije Universiteit Brussel","ror":"https://ror.org/006e5kg04","country_code":"BE","type":"education","lineage":["https://openalex.org/I13469542"]},{"id":"https://openalex.org/I4210114974","display_name":"IMEC","ror":"https://ror.org/02kcbn207","country_code":"BE","type":"nonprofit","lineage":["https://openalex.org/I4210114974"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Nikos Deligiannis","raw_affiliation_strings":["Vrije Universiteit Brussel, Brussels, Belgium","imec, Leuven, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vrije Universiteit Brussel, Brussels, Belgium","institution_ids":["https://openalex.org/I13469542"]},{"raw_affiliation_string":"imec, Leuven, Belgium","institution_ids":["https://openalex.org/I4210114974"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3910","last_page":"3914"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/computer-science","display_name":"Computer science","score":0.7480236887931824},{"id":"https://openalex.org/keywords/index","display_name":"Index (typography)","score":0.6773137450218201},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.6061365008354187},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.5901157259941101},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.568723201751709},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5154200792312622},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48848211765289307},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.45199066400527954},{"id":"https://openalex.org/keywords/stock-market-index","display_name":"Stock market index","score":0.4440366327762604},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.406578928232193},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38276761770248413},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3380786180496216},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32141822576522827}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7480236887931824},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.6773137450218201},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.6061365008354187},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.5901157259941101},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.568723201751709},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5154200792312622},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48848211765289307},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.45199066400527954},{"id":"https://openalex.org/C88389905","wikidata":"https://www.wikidata.org/wiki/Q223371","display_name":"Stock market index","level":4,"score":0.4440366327762604},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.406578928232193},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38276761770248413},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3380786180496216},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32141822576522827},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C2780762169","wikidata":"https://www.wikidata.org/wiki/Q5905368","display_name":"Horse","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp39728.2021.9414430","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414430","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W905619","https://openalex.org/W2074051020","https://openalex.org/W2105630053","https://openalex.org/W2115706991","https://openalex.org/W2118103795","https://openalex.org/W2135046866","https://openalex.org/W2136885855","https://openalex.org/W2345557152","https://openalex.org/W2760945190","https://openalex.org/W2766452532","https://openalex.org/W2798458649","https://openalex.org/W2806329290","https://openalex.org/W2956103957","https://openalex.org/W2969478734","https://openalex.org/W2977745911","https://openalex.org/W2988998293","https://openalex.org/W3011730771","https://openalex.org/W3049253011","https://openalex.org/W3098294986","https://openalex.org/W6600043445","https://openalex.org/W6677645113","https://openalex.org/W6750284896"],"related_works":["https://openalex.org/W4377865163","https://openalex.org/W3193857078","https://openalex.org/W2888956734","https://openalex.org/W3208304128","https://openalex.org/W2990652064","https://openalex.org/W3134659362","https://openalex.org/W3123122373","https://openalex.org/W4386639254","https://openalex.org/W2360554747","https://openalex.org/W3208509893"],"abstract_inverted_index":{"Tracking":[0],"the":[1,14,25,29,76,80,93,100],"performance":[2],"of":[3,11,28,95],"a":[4,9,17,55,62,66,88],"financial":[5,107],"index":[6,15,51],"by":[7],"selecting":[8],"subset":[10],"assets":[12],"composing":[13],"is":[16,99],"problem":[18],"that":[19,75],"raises":[20],"several":[21],"difficulties":[22],"due":[23],"to":[24,40,84],"large":[26],"size":[27],"stock":[30],"market.":[31],"Typically,":[32],"optimisation":[33],"algorithms":[34],"with":[35],"high":[36,85],"complexity":[37],"are":[38],"employed":[39],"address":[41],"such":[42],"problems.":[43],"In":[44],"this":[45,98],"paper,":[46],"we":[47,59],"focus":[48],"on":[49],"sparse":[50],"tracking":[52],"and":[53],"employ":[54],"Frank-Wolfe-based":[56],"algorithm":[57],"which":[58],"translate":[60],"into":[61],"deep":[63,70,102],"neural":[64],"network,":[65],"strategy":[67],"known":[68],"as":[69],"unfolding.":[71],"Numerical":[72],"experiments":[73],"show":[74],"learned":[77],"model":[78],"outperforms":[79],"iterative":[81],"algorithm,":[82],"leading":[83],"accuracy":[86],"at":[87],"low":[89],"computational":[90],"cost.":[91],"To":[92],"best":[94],"our":[96],"knowledge,":[97],"first":[101],"unfolding":[103],"design":[104],"proposed":[105],"for":[106],"data":[108],"processing.":[109]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}