{"id":"https://openalex.org/W4224291882","doi":"https://doi.org/10.1145/3514262.3514346","title":"The Comparison of Return Ratio Prediction Based on Different Regression and Classifier Models","display_name":"The Comparison of Return Ratio Prediction Based on Different Regression and Classifier Models","publication_year":2022,"publication_date":"2022-01-14","ids":{"openalex":"https://openalex.org/W4224291882","doi":"https://doi.org/10.1145/3514262.3514346"},"language":"en","primary_location":{"id":"doi:10.1145/3514262.3514346","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3514262.3514346","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 13th International Conference on E-Education, E-Business, E-Management, and E-Learning (IC4E)","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/A5100319572","display_name":"Jiajun Zhang","orcid":"https://orcid.org/0000-0001-5293-7434"},"institutions":[{"id":"https://openalex.org/I905225518","display_name":"Shanghai University of International Business and Economics","ror":"https://ror.org/031t68441","country_code":"CN","type":"education","lineage":["https://openalex.org/I905225518"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiajun Zhang","raw_affiliation_strings":["Shanghai University of International Business and Economics, China"],"affiliations":[{"raw_affiliation_string":"Shanghai University of International Business and Economics, China","institution_ids":["https://openalex.org/I905225518"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026110739","display_name":"Yingshan Zhang","orcid":"https://orcid.org/0000-0001-8131-9897"},"institutions":[{"id":"https://openalex.org/I3132008252","display_name":"Nanjing Foreign Language School","ror":"https://ror.org/03wvwbe92","country_code":"CN","type":"education","lineage":["https://openalex.org/I3132008252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingshan Zhang","raw_affiliation_strings":["Guangzhou Foreign Language School, China"],"affiliations":[{"raw_affiliation_string":"Guangzhou Foreign Language School, China","institution_ids":["https://openalex.org/I3132008252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065951293","display_name":"Leyan Pang","orcid":null},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Leyan Pang","raw_affiliation_strings":["Shenzhen University, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen University, China","institution_ids":["https://openalex.org/I180726961"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100319572"],"corresponding_institution_ids":["https://openalex.org/I905225518"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.03744501,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"6","issue":null,"first_page":"533","last_page":"541"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9995999932289124,"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":0.9995999932289124,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.98580002784729,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9799000024795532,"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/gradient-boosting","display_name":"Gradient boosting","score":0.6464591026306152},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5232852101325989},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.5197553038597107},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.5171651244163513},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.48993247747421265},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.47200560569763184},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4579632878303528},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4562346935272217},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4436875283718109},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.4432591497898102},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3734091520309448},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3402734398841858},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31676027178764343},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.09382528066635132}],"concepts":[{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.6464591026306152},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5232852101325989},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.5197553038597107},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5171651244163513},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.48993247747421265},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47200560569763184},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4579632878303528},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4562346935272217},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4436875283718109},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.4432591497898102},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3734091520309448},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3402734398841858},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31676027178764343},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.09382528066635132}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3514262.3514346","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3514262.3514346","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 13th International Conference on E-Education, E-Business, E-Management, and E-Learning (IC4E)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W855508711","https://openalex.org/W1678356000","https://openalex.org/W1968361347","https://openalex.org/W1969852690","https://openalex.org/W2048452064","https://openalex.org/W2162389778","https://openalex.org/W2774513877","https://openalex.org/W2940897671","https://openalex.org/W3124047935"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W4310224730","https://openalex.org/W2766514146","https://openalex.org/W4289703016","https://openalex.org/W2885516856","https://openalex.org/W3094138326"],"abstract_inverted_index":{"Stock":[0],"prediction,":[1],"as":[2],"a":[3],"popular":[4],"event":[5],"in":[6],"quantitative":[7],"finance,":[8],"currently":[9],"attracts":[10],"lots":[11],"of":[12,25,82,129],"attentions":[13],"from":[14],"investors.":[15],"To":[16],"make":[17],"good":[18],"predictions":[19],"and":[20,28,40,55,75,85,90],"improve":[21],"the":[22,37,69,80,97,104,111,117,135],"results,":[23,98],"plenty":[24],"different":[26],"models":[27,39,64,84,100],"indicators":[29],"have":[30],"been":[31],"used.":[32,94],"In":[33],"this":[34],"paper,":[35],"both":[36],"regression":[38,63,83,99,109],"classifier":[41,86,105],"model":[42,115],"are":[43,101],"utilized":[44],"to":[45,68,78,96],"predict":[46],"three":[47],"days":[48,52,57],"return":[49,53,58,130],"ratio,":[50,54],"ten":[51],"twenty":[56],"ratio.":[59],"The":[60],"comparison":[61],"for":[62,132],"was":[65],"made":[66],"according":[67],"metric":[70],"including":[71],"RMSE,":[72],"MAPE,":[73],"MASE,":[74],"R2.":[76],"Subsequently,":[77],"compare":[79],"results":[81,123],"models,":[87,110],"precision":[88],"values":[89,92],"recall":[91],"were":[93],"According":[95],"better":[102,127],"than":[103],"model.":[106,139],"And":[107],"among":[108],"X":[112,136],"Gradient":[113,137],"Boosting":[114,138],"produced":[116],"most":[118],"satisfactory":[119],"predictions.":[120],"Overall,":[121],"these":[122],"shed":[124],"light":[125],"on":[126],"prediction":[128],"ratios":[131],"investors":[133],"using":[134]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}