{"id":"https://openalex.org/W4206454720","doi":"https://doi.org/10.1145/3488378","title":"Forecasting the Market with Machine Learning Algorithms: An Application of NMC-BERT-LSTM-DQN-X Algorithm in Quantitative Trading","display_name":"Forecasting the Market with Machine Learning Algorithms: An Application of NMC-BERT-LSTM-DQN-X Algorithm in Quantitative Trading","publication_year":2022,"publication_date":"2022-01-08","ids":{"openalex":"https://openalex.org/W4206454720","doi":"https://doi.org/10.1145/3488378"},"language":"en","primary_location":{"id":"doi:10.1145/3488378","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3488378","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"},"type":"article","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/A5000641603","display_name":"Chang Liu","orcid":"https://orcid.org/0000-0001-9790-9883"},"institutions":[{"id":"https://openalex.org/I204831749","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822","country_code":"CN","type":"education","lineage":["https://openalex.org/I204831749"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chang Liu","raw_affiliation_strings":["Southwestern University of Finance and Economics, Chengdu, Sichuan Province, China"],"raw_orcid":"https://orcid.org/0000-0001-9790-9883","affiliations":[{"raw_affiliation_string":"Southwestern University of Finance and Economics, Chengdu, Sichuan Province, China","institution_ids":["https://openalex.org/I204831749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101825456","display_name":"Jie Yan","orcid":"https://orcid.org/0000-0003-3930-8785"},"institutions":[{"id":"https://openalex.org/I204831749","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822","country_code":"CN","type":"education","lineage":["https://openalex.org/I204831749"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Yan","raw_affiliation_strings":["Southwestern University of Finance and Economics, Chengdu, Sichuan Province, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southwestern University of Finance and Economics, Chengdu, Sichuan Province, China","institution_ids":["https://openalex.org/I204831749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110836787","display_name":"Feiyue Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I204831749","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822","country_code":"CN","type":"education","lineage":["https://openalex.org/I204831749"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feiyue Guo","raw_affiliation_strings":["Southwestern University of Finance and Economics, Chengdu, Sichuan Province, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southwestern University of Finance and Economics, Chengdu, Sichuan Province, China","institution_ids":["https://openalex.org/I204831749"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005763661","display_name":"Min Guo","orcid":"https://orcid.org/0000-0002-2093-8771"},"institutions":[{"id":"https://openalex.org/I4210103805","display_name":"Great Wall Motors (China)","ror":"https://ror.org/019xwzn44","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103805"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Guo","raw_affiliation_strings":["China Great-Wall Asset Management Co., Ltd, Chengdu, Sichuan Province, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Great-Wall Asset Management Co., Ltd, Chengdu, Sichuan Province, China","institution_ids":["https://openalex.org/I4210103805"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5000641603"],"corresponding_institution_ids":["https://openalex.org/I204831749"],"apc_list":null,"apc_paid":null,"fwci":2.2036,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.87856178,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"16","issue":"4","first_page":"1","last_page":"22"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9969000220298767,"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9966999888420105,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.7154049873352051},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.6770700216293335},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6367683410644531},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.628611147403717},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6228502988815308},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4220000207424164},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.3294829726219177}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.7154049873352051},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.6770700216293335},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6367683410644531},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.628611147403717},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6228502988815308},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4220000207424164},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.3294829726219177},{"id":"https://openalex.org/C2780762169","wikidata":"https://www.wikidata.org/wiki/Q5905368","display_name":"Horse","level":2,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3488378","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3488378","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals"}],"awards":[{"id":"https://openalex.org/G4879763856","display_name":null,"funder_award_id":"2021YJG050","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1259090559","https://openalex.org/W1965520378","https://openalex.org/W1967719510","https://openalex.org/W1968361347","https://openalex.org/W1997042816","https://openalex.org/W2007358469","https://openalex.org/W2015262594","https://openalex.org/W2025053102","https://openalex.org/W2031505816","https://openalex.org/W2053615983","https://openalex.org/W2054969052","https://openalex.org/W2059852492","https://openalex.org/W2074250525","https://openalex.org/W2076278672","https://openalex.org/W2090818941","https://openalex.org/W2129380428","https://openalex.org/W2153851210","https://openalex.org/W2320868325","https://openalex.org/W2533443122","https://openalex.org/W2572554044","https://openalex.org/W2594142095","https://openalex.org/W2762142247","https://openalex.org/W2784381726","https://openalex.org/W2790563395","https://openalex.org/W2791299205","https://openalex.org/W2808626748","https://openalex.org/W2902087482","https://openalex.org/W2910107358","https://openalex.org/W2919195058","https://openalex.org/W2942833356","https://openalex.org/W3001770481","https://openalex.org/W3009876158","https://openalex.org/W3043420950","https://openalex.org/W3049351766","https://openalex.org/W3092910147","https://openalex.org/W3119160044","https://openalex.org/W3121238223","https://openalex.org/W3121401171","https://openalex.org/W3121479022","https://openalex.org/W3122431760","https://openalex.org/W3123451865","https://openalex.org/W3124112311","https://openalex.org/W3124520998","https://openalex.org/W3124986135","https://openalex.org/W3125591525","https://openalex.org/W4234488063"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2051487156","https://openalex.org/W2073681303","https://openalex.org/W2053286651","https://openalex.org/W2181743346","https://openalex.org/W1660242800","https://openalex.org/W2187401768","https://openalex.org/W2181413294","https://openalex.org/W2052122378","https://openalex.org/W2077211377"],"abstract_inverted_index":{"Although":[0],"machine":[1],"learning":[2],"(ML)":[3],"algorithms":[4],"have":[5,38],"been":[6],"widely":[7],"used":[8,122,166],"in":[9,176,208],"forecasting":[10,44,72,239,249,269,296],"the":[11,21,46,58,63,92,132,147,150,169,181,188,195,206,209,217,221,225,241,263,267,293],"trend":[12,43,178],"of":[13,48,65,127,149,172,183,187,197,205,216,224,251,255,260],"stock":[14,87],"market":[15,26,36,42,50,59,66,73,88,128,151,174,177,189,295,304],"indices,":[16],"they":[17],"failed":[18],"to":[19,57,84,108,123,145,167,237],"consider":[20],"following":[22],"crucial":[23],"aspects":[24],"for":[25,102,201],"forecasting:":[27],"(1)":[28],"that":[29],"investors\u2019":[30,110],"emotions":[31,111],"and":[32,61,112,156,199,203,257,284],"attitudes":[33],"toward":[34],"future":[35],"trends":[37,89],"material":[39],"impacts":[40],"on":[41,214],"(2)":[45],"length":[47,171],"past":[49,173],"data":[51,175],"should":[52,68],"be":[53,69],"dynamically":[54],"adjusted":[55],"according":[56],"status":[60],"(3)":[62],"transition":[64],"statutes":[67],"considered":[70],"when":[71],"trends.":[74],"In":[75],"this":[76],"study,":[77],"we":[78],"proposed":[79,226,242,264,294],"an":[80],"innovative":[81],"ML":[82],"method":[83],"forecast":[85,305],"China's":[86],"by":[90],"addressing":[91],"three":[93],"issues":[94],"above.":[95],"Specifically,":[96],"sentimental":[97],"factors":[98],"(see":[99],"Appendix":[100],"[1]":[101],"full":[103],"trans)":[104],"were":[105,212,228],"first":[106],"collected":[107],"measure":[109],"attitudes.":[113],"Then,":[114],"a":[115,157,231,248,291],"non-stationary":[116],"Markov":[117],"chain":[118],"(NMC)":[119],"model":[120,164,211,265,297],"was":[121,165,194],"capture":[124],"dynamic":[125],"transitions":[126],"statutes.":[129],"We":[130],"choose":[131],"state-of-the-art":[133],"(SOTA)":[134],"method,":[135],"namely,":[136],"Bidirectional":[137],"Encoder":[138],"Representations":[139],"from":[140],"Transformers":[141],"(":[142,161],"BERT":[143,198],"),":[144],"predict":[146],"state":[148,186],"at":[152,190],"time":[153,191],"t":[154,192],",":[155],"long":[158],"short-term":[159],"memory":[160],"LSTM":[162,184,210],")":[163,193],"estimate":[168],"varying":[170],"prediction,":[179],"where":[180],"input":[182],"(the":[185],"output":[196],"probabilities":[200],"opening":[202],"closing":[204],"gates":[207],"based":[213],"outputs":[215],"NMC":[218],"model.":[219],"Finally,":[220],"optimum":[222],"parameters":[223],"algorithm":[227,243],"calculated":[229],"using":[230],"reinforced":[232],"learning-based":[233],"deep":[234],"Q-Network.":[235],"Compared":[236],"existing":[238],"methods,":[240],"achieves":[244],"better":[245],"results":[246],"with":[247],"accuracy":[250],"61.77%,":[252],"annualized":[253],"return":[254],"29.25%,":[256],"maximum":[258],"losses":[259],"\u22128.29%.":[261],"Furthermore,":[262],"achieved":[266],"lowest":[268],"error:":[270],"mean":[271,276,280,285],"square":[272,277],"error":[273,278,282,288],"(0.095),":[274],"root":[275],"(0.0739),":[279],"absolute":[281,286],"(0.104),":[283],"percent":[287],"(15.1%).":[289],"As":[290],"result,":[292],"can":[298],"help":[299],"investors":[300],"obtain":[301],"more":[302],"accurate":[303],"information.":[306]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}