{"id":"https://openalex.org/W3023272393","doi":"https://doi.org/10.1145/3377571.3377626","title":"Inference of the Us and Chinese Stock Markets Using Statistical and Computational Methods","display_name":"Inference of the Us and Chinese Stock Markets Using Statistical and Computational Methods","publication_year":2020,"publication_date":"2020-01-10","ids":{"openalex":"https://openalex.org/W3023272393","doi":"https://doi.org/10.1145/3377571.3377626","mag":"3023272393"},"language":"en","primary_location":{"id":"doi:10.1145/3377571.3377626","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3377571.3377626","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 11th International Conference on E-Education, E-Business, E-Management, and E-Learning","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/A5070645363","display_name":"Junjin Ran","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":true,"raw_author_name":"Junjin Ran","raw_affiliation_strings":["Insurance School of Southwestern University of Finance and Economics, China"],"affiliations":[{"raw_affiliation_string":"Insurance School of Southwestern University of Finance and Economics, China","institution_ids":["https://openalex.org/I204831749"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5070645363"],"corresponding_institution_ids":["https://openalex.org/I204831749"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.07809395,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"300","last_page":"309"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.9991999864578247,"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"}},"topics":[{"id":"https://openalex.org/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.9991999864578247,"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":0.9965000152587891,"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"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9926000237464905,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6550382375717163},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6260555982589722},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.5563783645629883},{"id":"https://openalex.org/keywords/statistical-inference","display_name":"Statistical inference","score":0.48497819900512695},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4602353572845459},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3769164979457855},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3494681417942047},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18793073296546936},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.17838376760482788},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1505250334739685},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06858408451080322}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6550382375717163},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6260555982589722},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.5563783645629883},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.48497819900512695},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4602353572845459},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3769164979457855},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3494681417942047},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18793073296546936},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.17838376760482788},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1505250334739685},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06858408451080322},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3377571.3377626","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3377571.3377626","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 11th International Conference on E-Education, E-Business, E-Management, and E-Learning","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":28,"referenced_works":["https://openalex.org/W92467216","https://openalex.org/W755194005","https://openalex.org/W1549234464","https://openalex.org/W1573570773","https://openalex.org/W1618905105","https://openalex.org/W1966268097","https://openalex.org/W1978520392","https://openalex.org/W1986117370","https://openalex.org/W1999996900","https://openalex.org/W2007358469","https://openalex.org/W2058815839","https://openalex.org/W2060901752","https://openalex.org/W2115213191","https://openalex.org/W2122825543","https://openalex.org/W2138293190","https://openalex.org/W2153851210","https://openalex.org/W2159428510","https://openalex.org/W2168175751","https://openalex.org/W2169533279","https://openalex.org/W2487770199","https://openalex.org/W2501239585","https://openalex.org/W2548695521","https://openalex.org/W2606705039","https://openalex.org/W2806837779","https://openalex.org/W2940897671","https://openalex.org/W3123068586","https://openalex.org/W6622243269","https://openalex.org/W6687254837"],"related_works":["https://openalex.org/W2055243143","https://openalex.org/W4321636575","https://openalex.org/W2357796999","https://openalex.org/W2045526782","https://openalex.org/W2741131631","https://openalex.org/W137830373","https://openalex.org/W3000984192","https://openalex.org/W4286952477","https://openalex.org/W4321348134","https://openalex.org/W2103073163"],"abstract_inverted_index":{"In":[0,49],"the":[1,10,19,22,27,32,54,57,67,79,93,130,155,159,163,170,178,183,193,215,226,234,242,246,253],"contemporary":[2],"era,":[3],"people":[4],"have":[5],"strong":[6],"incentives":[7],"to":[8,42,51,77,114,137,240],"explore":[9,43],"underlying":[11],"principles":[12],"of":[13,56,144,154,162,186],"stock":[14,33,61,132,180,195,219,228,236,248],"markets":[15,34,220],"and":[16,18,44,59,65,82,109,158,212,217],"China":[17],"US":[20,58,179,194,216,235,247],"are":[21,63],"2":[23],"largest":[24],"economies":[25],"across":[26],"world.":[28],"So,":[29,222],"it":[30],"is":[31,134,166,175,230],"in":[35,46,88,124,192,203,207,245],"these":[36,70],"two":[37,71],"countries":[38],"that":[39,129,225],"we":[40,73,91,118,223],"need":[41],"study":[45,78],"this":[47,89],"paper.":[48,126],"order":[50,161],"test":[52],"whether":[53],"trends":[55,244],"Chinese":[60,131,218,227],"market":[62,133,229],"predictable":[64],"identify":[66],"difference":[68],"between":[69],"markets,":[72,209],"employed":[74],"various":[75],"models":[76,190],"S&P":[80],"500":[81],"CSI":[83,171],"300":[84,172],"indexes'":[85],"trends.":[86],"Specifically,":[87],"paper,":[90],"included":[92],"Markov":[94,97,164],"chain,":[95],"hidden":[96],"model":[98,201,258],"(HMM),":[99],"logistical":[100,148],"regression":[101,149],"with":[102,150],"lasso,":[103],"autoregressive":[104],"integrated":[105],"moving":[106],"average":[107],"(ARIMA)":[108],"support":[110],"vector":[111],"machine":[112],"(SVM)":[113],"achieve":[115],"our":[116,125],"target.Therefore,":[117],"obtained":[119],"several":[120],"interesting":[121],"key":[122],"findings":[123],"We":[127],"found":[128],"more":[135],"likely":[136],"be":[138,252],"affected":[139],"by":[140],"technical":[141,156],"indicators":[142,157],"instead":[143],"historical":[145],"information,":[146],"as":[147],"lasso":[151],"selected":[152],"most":[153],"estimated":[160],"chain":[165],"zero":[167],"when":[168],"modelling":[169],"trends,":[173],"which":[174],"different":[176],"from":[177],"market.":[181,237],"Also,":[182],"AUC":[184,205],"value":[185],"SVM":[187,250],"outperformed":[188],"other":[189],"used":[191],"market,":[196,249],"at":[197,210],"0.731,":[198],"while":[199,256],"ARIMA":[200,257],"resulted":[202],"high":[204],"values":[206],"both":[208,262],"0.606":[211],"0.622":[213],"for":[214,261],"respectively.":[221],"confirmed":[224],"less":[231],"efficient":[232],"than":[233],"What's":[238],"more,":[239],"predict":[241],"future":[243],"could":[251],"best":[254],"choice,":[255],"works":[259],"effectively":[260],"markets.":[263]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
