{"id":"https://openalex.org/W4399117092","doi":"https://doi.org/10.1145/3659211.3659333","title":"Trading Performance of an Improved PPO Algorithm in the Chinese Stock Market","display_name":"Trading Performance of an Improved PPO Algorithm in the Chinese Stock Market","publication_year":2023,"publication_date":"2023-12-08","ids":{"openalex":"https://openalex.org/W4399117092","doi":"https://doi.org/10.1145/3659211.3659333"},"language":"en","primary_location":{"id":"doi:10.1145/3659211.3659333","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3659211.3659333","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 4th International Conference on Big Data Economy and Information Management","raw_type":"proceedings-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/A5109725565","display_name":"Tao Zhang","orcid":"https://orcid.org/0009-0002-2454-7994"},"institutions":[{"id":"https://openalex.org/I4210165606","display_name":"Hubei Normal University","ror":"https://ror.org/056y3dw16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210165606"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Zhang","raw_affiliation_strings":["Hubei Normal University, China"],"raw_orcid":"https://orcid.org/0009-0002-2454-7994","affiliations":[{"raw_affiliation_string":"Hubei Normal University, China","institution_ids":["https://openalex.org/I4210165606"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068361742","display_name":"Zongwu Ke","orcid":"https://orcid.org/0009-0000-6371-6135"},"institutions":[{"id":"https://openalex.org/I4210165606","display_name":"Hubei Normal University","ror":"https://ror.org/056y3dw16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210165606"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zongwu Ke","raw_affiliation_strings":["Hubei Normal University, China"],"raw_orcid":"https://orcid.org/0009-0000-6371-6135","affiliations":[{"raw_affiliation_string":"Hubei Normal University, China","institution_ids":["https://openalex.org/I4210165606"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005132258","display_name":"L. Chen","orcid":"https://orcid.org/0009-0009-7339-5908"},"institutions":[{"id":"https://openalex.org/I4210165606","display_name":"Hubei Normal University","ror":"https://ror.org/056y3dw16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210165606"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Long Chen","raw_affiliation_strings":["Hubei Normal University, China"],"raw_orcid":"https://orcid.org/0009-0009-7339-5908","affiliations":[{"raw_affiliation_string":"Hubei Normal University, China","institution_ids":["https://openalex.org/I4210165606"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049697060","display_name":"Kai Qiao","orcid":"https://orcid.org/0009-0001-6455-338X"},"institutions":[{"id":"https://openalex.org/I4210165606","display_name":"Hubei Normal University","ror":"https://ror.org/056y3dw16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210165606"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Qiao","raw_affiliation_strings":["Hubei Normal University, China"],"raw_orcid":"https://orcid.org/0009-0001-6455-338X","affiliations":[{"raw_affiliation_string":"Hubei Normal University, China","institution_ids":["https://openalex.org/I4210165606"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103071786","display_name":"Yan Jia","orcid":"https://orcid.org/0009-0000-9471-5936"},"institutions":[{"id":"https://openalex.org/I4210165606","display_name":"Hubei Normal University","ror":"https://ror.org/056y3dw16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210165606"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Jia","raw_affiliation_strings":["Hubei Normal University, China"],"raw_orcid":"https://orcid.org/0009-0000-9471-5936","affiliations":[{"raw_affiliation_string":"Hubei Normal University, China","institution_ids":["https://openalex.org/I4210165606"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012609245","display_name":"Zeyu Zhang","orcid":"https://orcid.org/0009-0002-0565-9528"},"institutions":[{"id":"https://openalex.org/I4210165606","display_name":"Hubei Normal University","ror":"https://ror.org/056y3dw16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210165606"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zeyu Zhang","raw_affiliation_strings":["Hubei Normal University, China"],"raw_orcid":"https://orcid.org/0009-0002-0565-9528","affiliations":[{"raw_affiliation_string":"Hubei Normal University, China","institution_ids":["https://openalex.org/I4210165606"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210165606"],"apc_list":null,"apc_paid":null,"fwci":0.2233,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.61826766,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"709","last_page":"713"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9991999864578247,"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":0.9991999864578247,"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":0.982699990272522,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.949999988079071,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6858994960784912},{"id":"https://openalex.org/keywords/profitability-index","display_name":"Profitability index","score":0.5799208879470825},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.5297048091888428},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.49362218379974365},{"id":"https://openalex.org/keywords/premise","display_name":"Premise","score":0.4895413815975189},{"id":"https://openalex.org/keywords/incentive","display_name":"Incentive","score":0.4305298328399658},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.36096319556236267},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33204448223114014},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.16787055134773254},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.14557769894599915},{"id":"https://openalex.org/keywords/microeconomics","display_name":"Microeconomics","score":0.11537328362464905},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09032392501831055}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6858994960784912},{"id":"https://openalex.org/C129361004","wikidata":"https://www.wikidata.org/wiki/Q2470236","display_name":"Profitability index","level":2,"score":0.5799208879470825},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.5297048091888428},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.49362218379974365},{"id":"https://openalex.org/C2778023277","wikidata":"https://www.wikidata.org/wiki/Q321703","display_name":"Premise","level":2,"score":0.4895413815975189},{"id":"https://openalex.org/C29122968","wikidata":"https://www.wikidata.org/wiki/Q1414816","display_name":"Incentive","level":2,"score":0.4305298328399658},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.36096319556236267},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33204448223114014},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.16787055134773254},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.14557769894599915},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.11537328362464905},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09032392501831055},{"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},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3659211.3659333","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3659211.3659333","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 4th International Conference on Big Data Economy and Information Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2736601468","https://openalex.org/W2891295326","https://openalex.org/W2913655741","https://openalex.org/W2986670013","https://openalex.org/W3012333258","https://openalex.org/W3088749011","https://openalex.org/W3106281374","https://openalex.org/W3161128635","https://openalex.org/W4210259535","https://openalex.org/W4239276395"],"related_works":["https://openalex.org/W590788508","https://openalex.org/W4235873430","https://openalex.org/W2611974471","https://openalex.org/W4313233093","https://openalex.org/W2358082531","https://openalex.org/W2589976903","https://openalex.org/W2335596023","https://openalex.org/W2026719400","https://openalex.org/W2374273535","https://openalex.org/W2370669686"],"abstract_inverted_index":{"As":[0],"a":[1,12,41,45,154],"kind":[2],"of":[3,14,40,77,105,117,129,143,170,180,189,204,220],"time":[4],"series":[5],"data,":[6],"stock":[7,43,131,173],"data":[8,222],"not":[9,53],"only":[10],"has":[11],"lot":[13],"noise":[15],"and":[16,25,88,112,136,162,182,186,200],"features,":[17],"but":[18],"also":[19],"due":[20],"to":[21,35,92,108,132,157],"its":[22],"obvious":[23],"trend":[24],"seasonality,":[26],"it":[27],"is":[28,70,151,224],"difficult":[29],"for":[30,177],"conventional":[31],"machine":[32],"learning":[33],"algorithms":[34],"accurately":[36],"predict":[37],"the":[38,49,67,74,78,82,93,98,103,113,118,121,126,130,141,146,160,171,183,190,201,205,215,221],"price":[39],"certain":[42,46],"at":[44],"date":[47],"in":[48,148,167],"future,":[50],"which":[51],"will":[52],"bring":[54],"relatively":[55],"stable":[56],"returns.":[57],"In":[58],"this":[59],"paper,":[60],"an":[61],"improved":[62,206],"PPO":[63,79,191],"algorithm":[64,80,185],"based":[65,72],"on":[66,73],"Actor-Critic":[68],"structure":[69],"proposed,":[71],"A2C":[75],"implementation":[76],"as":[81,102,153,197],"main":[83],"body,":[84],"adding":[85],"LSTM":[86],"networks":[87],"fully":[89],"connected":[90],"layers":[91],"Critic":[94],"function,":[95],"effectively":[96],"solving":[97],"typical":[99],"problems":[100],"such":[101],"difficulty":[104],"intelligent":[106],"bodies":[107],"learn":[109],"effective":[110],"strategies":[111],"slow":[114],"convergence":[115],"speed":[116],"algorithm.":[119],"When":[120],"trading":[122],"strategy":[123,188],"interacts":[124],"with":[125],"quotation":[127],"information":[128],"make":[133,137],"buy-sell-hold":[134],"orders":[135,139],"buy-sell-share":[138],"under":[140],"premise":[142],"tradable":[144],"operations,":[145],"increase":[147],"cumulative":[149,202],"returns":[150,203],"used":[152,196],"positive":[155],"incentive":[156],"continuously":[158],"optimise":[159],"model":[161,207],"achieve":[163],"profitability.":[164],"Leading":[165],"stocks":[166],"popular":[168],"sectors":[169],"Chinese":[172],"market":[174],"were":[175,195,208],"selected":[176],"multiple":[178],"rounds":[179],"experiments,":[181],"baseline":[184],"buy-and-hold":[187],"given":[192],"by":[193],"Stable_Baselines":[194],"control":[198],"strategies,":[199],"generally":[209],"more":[210],"than":[211,214],"20%":[212],"higher":[213],"baseline.":[216],"The":[217],"download":[218],"address":[219],"set":[223],"http://baostock.com/baostock/index.php.":[225]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-07T14:30:12.667765","created_date":"2025-10-10T00:00:00"}