{"id":"https://openalex.org/W4403617594","doi":"https://doi.org/10.1109/tsp.2025.3576781","title":"QuantFactor REINFORCE: Mining Steady Formulaic Alpha Factors With Variance-Bounded REINFORCE","display_name":"QuantFactor REINFORCE: Mining Steady Formulaic Alpha Factors With Variance-Bounded REINFORCE","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4403617594","doi":"https://doi.org/10.1109/tsp.2025.3576781"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2025.3576781","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2025.3576781","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2409.05144","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Junjie Zhao","orcid":"https://orcid.org/0000-0003-0718-4183"},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junjie Zhao","raw_affiliation_strings":["Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Department of Computer Science and Engineering, Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-0718-4183","affiliations":[{"raw_affiliation_string":"Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Department of Computer Science and Engineering, Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China","institution_ids":["https://openalex.org/I3045169105"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chengxi Zhang","orcid":"https://orcid.org/0009-0009-5876-5632"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengxi Zhang","raw_affiliation_strings":["Department of Modern Physics, University of Science and Technology of China, Hefei, China"],"raw_orcid":"https://orcid.org/0009-0009-5876-5632","affiliations":[{"raw_affiliation_string":"Department of Modern Physics, University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102335040","display_name":"Min Qin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210107834","display_name":"Development Fund","ror":"https://ror.org/01rkhv978","country_code":"NO","type":"nonprofit","lineage":["https://openalex.org/I4210107834"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Min Qin","raw_affiliation_strings":["Shenzhen Yujin Hedge Fund Company Ltd., Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0005-6316-6125","affiliations":[{"raw_affiliation_string":"Shenzhen Yujin Hedge Fund Company Ltd., Shenzhen, China","institution_ids":["https://openalex.org/I4210107834"]}]},{"author_position":"last","author":{"id":null,"display_name":"Peng Yang","orcid":"https://orcid.org/0000-0001-5333-6155"},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Yang","raw_affiliation_strings":["Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Department of Computer Science and Engineering, Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-5333-6155","affiliations":[{"raw_affiliation_string":"Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Department of Computer Science and Engineering, Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China","institution_ids":["https://openalex.org/I3045169105"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":12.108,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.97990574,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"73","issue":null,"first_page":"2448","last_page":"2463"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9832000136375427,"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"}},"topics":[{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9832000136375427,"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"}},{"id":"https://openalex.org/T10260","display_name":"Software Engineering Research","score":0.964900016784668,"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"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9361000061035156,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/bounded-function","display_name":"Bounded function","score":0.6934074759483337},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.6930007934570312},{"id":"https://openalex.org/keywords/alpha","display_name":"Alpha (finance)","score":0.5714987516403198},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5051468014717102},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.44551515579223633},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3971947133541107},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.28390005230903625},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.1932339072227478},{"id":"https://openalex.org/keywords/accounting","display_name":"Accounting","score":0.05305075645446777}],"concepts":[{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.6934074759483337},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.6930007934570312},{"id":"https://openalex.org/C64943373","wikidata":"https://www.wikidata.org/wiki/Q2651003","display_name":"Alpha (finance)","level":4,"score":0.5714987516403198},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5051468014717102},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.44551515579223633},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3971947133541107},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.28390005230903625},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.1932339072227478},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.05305075645446777},{"id":"https://openalex.org/C171606756","wikidata":"https://www.wikidata.org/wiki/Q506132","display_name":"Psychometrics","level":2,"score":0.0},{"id":"https://openalex.org/C49453240","wikidata":"https://www.wikidata.org/wiki/Q1592163","display_name":"Construct validity","level":3,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/tsp.2025.3576781","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2025.3576781","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2409.05144","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.05144","pdf_url":"https://arxiv.org/pdf/2409.05144","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:RePEc:arx:papers:2409.05144","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"preprint"},{"id":"doi:10.48550/arxiv.2409.05144","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2409.05144","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2409.05144","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.05144","pdf_url":"https://arxiv.org/pdf/2409.05144","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4071115985","display_name":null,"funder_award_id":"62331014","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7929321301","display_name":null,"funder_award_id":"62272210","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8555519613","display_name":null,"funder_award_id":"62250710682","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4403617594.pdf"},"referenced_works_count":60,"referenced_works":["https://openalex.org/W4404238369","https://openalex.org/W2912400541","https://openalex.org/W2582533304","https://openalex.org/W2885054548","https://openalex.org/W4253377838","https://openalex.org/W4394585837","https://openalex.org/W4386142022","https://openalex.org/W6846251138","https://openalex.org/W4313892575","https://openalex.org/W4313199264","https://openalex.org/W4375870051","https://openalex.org/W7006806681","https://openalex.org/W4386512054","https://openalex.org/W4381795742","https://openalex.org/W2951360122","https://openalex.org/W3183695018","https://openalex.org/W3198149465","https://openalex.org/W4319986958","https://openalex.org/W4407690929","https://openalex.org/W2899189920","https://openalex.org/W2767101511","https://openalex.org/W3091349911","https://openalex.org/W2064675550","https://openalex.org/W4385245566","https://openalex.org/W6788108678","https://openalex.org/W6852791562","https://openalex.org/W4392029873","https://openalex.org/W3106281374","https://openalex.org/W4316038443","https://openalex.org/W4285011703","https://openalex.org/W2463050987","https://openalex.org/W4200371786","https://openalex.org/W4200321348","https://openalex.org/W4283315103","https://openalex.org/W6774270251","https://openalex.org/W6783347321","https://openalex.org/W2119717200","https://openalex.org/W6683195989","https://openalex.org/W4239207475","https://openalex.org/W3093528669","https://openalex.org/W6794588271","https://openalex.org/W6634413486","https://openalex.org/W6848605334","https://openalex.org/W32403112","https://openalex.org/W6627932998","https://openalex.org/W4285796263","https://openalex.org/W4248246871","https://openalex.org/W7007678527","https://openalex.org/W6804601995","https://openalex.org/W1498436455","https://openalex.org/W6638018090","https://openalex.org/W6692846177","https://openalex.org/W4407828227","https://openalex.org/W2238750598","https://openalex.org/W3121958425","https://openalex.org/W6881868219","https://openalex.org/W6870695199","https://openalex.org/W3166499964","https://openalex.org/W4313532289","https://openalex.org/W2795659063"],"related_works":["https://openalex.org/W3013085049","https://openalex.org/W1560496689","https://openalex.org/W1992739770","https://openalex.org/W4386900933","https://openalex.org/W4229058035","https://openalex.org/W3125672081","https://openalex.org/W2006389905","https://openalex.org/W4297797135","https://openalex.org/W2904666330","https://openalex.org/W1563551083"],"abstract_inverted_index":{"Alpha":[0],"factor":[1,32],"mining":[2,33,249],"aims":[3],"to":[4,18,135,158,182,203,215,239,244],"discover":[5],"investment":[6],"signals":[7],"from":[8,84],"the":[9,40,53,94,110,124,137,151,159,185,193,205,226,245,253],"historical":[10],"financial":[11],"market":[12,218],"data,":[13],"which":[14,156,251],"can":[15,162,212],"be":[16],"used":[17],"predict":[19],"asset":[20],"returns":[21,232,242],"and":[22,58,79,87,235],"gain":[23],"excess":[24,241],"profits.":[25],"Powerful":[26],"deep":[27,76],"learning":[28,120],"methods":[29,61],"for":[30,49,70],"alpha":[31,45,73,113,209,247],"lack":[34],"interpretability,":[35,51],"making":[36,169],"them":[37],"unacceptable":[38],"in":[39,109,150,217],"risk-sensitive":[41],"real":[42,222],"markets.":[43],"Formulaic":[44],"factors":[46,74,114,210,248],"are":[47,62],"preferred":[48],"their":[50],"while":[52],"search":[54],"space":[55],"is":[56,68,128,180,196],"complex":[57],"powerful":[59],"explorative":[60],"urged.":[63],"Recently,":[64],"a":[65,117,199,236],"promising":[66],"framework":[67],"proposed":[69,227],"generating":[71],"formulaic":[72],"using":[75],"reinforcement":[77,119],"learning,":[78],"quickly":[80],"gained":[81],"research":[82],"focuses":[83],"both":[85],"academia":[86],"industries.":[88],"This":[89],"paper":[90],"first":[91],"argues":[92],"that":[93,211],"originally":[95],"employed":[96],"policy":[97,138],"training":[98],"method,":[99],"i.e.,":[100],"Proximal":[101],"Policy":[102],"Optimization":[103],"(PPO),":[104],"faces":[105],"several":[106],"important":[107],"issues":[108],"context":[111],"of":[112,190,207],"mining.":[115],"Herein,":[116],"novel":[118],"algorithm":[121,127,171,228],"based":[122],"on":[123,221],"well-known":[125],"REINFORCE":[126,130,170],"proposed.":[129],"employs":[131],"Monte":[132],"Carlo":[133],"sampling":[134],"estimate":[136],"gradient\u2014yielding":[139],"unbiased":[140],"but":[141],"high":[142,166,188],"variance":[143,167,189],"estimates.":[144],"The":[145],"minimal":[146],"environmental":[147],"variability":[148],"inherent":[149],"underlying":[152],"state":[153],"transition":[154],"function,":[155],"adheres":[157],"Dirac":[160],"distribution,":[161],"help":[163],"alleviate":[164],"this":[165],"issue,":[168],"more":[172],"appropriate":[173],"than":[174],"PPO.":[175],"A":[176],"new":[177],"dedicated":[178],"baseline":[179],"designed":[181],"theoretically":[183],"reduce":[184],"commonly":[186],"suffered":[187],"REINFORCE.":[191],"Moreover,":[192],"information":[194],"ratio":[195],"introduced":[197],"as":[198],"reward":[200],"shaping":[201],"mechanism":[202],"encourage":[204],"generation":[206],"steady":[208],"better":[213],"adapt":[214],"changes":[216],"volatility.":[219],"Evaluations":[220],"assets":[223],"data":[224],"indicate":[225],"boosts":[229],"correlation":[230],"with":[231],"by":[233],"3.83%,":[234],"stronger":[237],"ability":[238],"obtain":[240],"compared":[243],"latest":[246],"methods,":[250],"meets":[252],"theoretical":[254],"results":[255],"well.":[256]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2024-10-22T00:00:00"}