{"id":"https://openalex.org/W3094166324","doi":"https://doi.org/10.5281/zenodo.18657270","title":"Kendall's Coefficient of Concordance Ranking of the Effectiveness of Single Machine Learning Models in Predicting Stock Price Movement","display_name":"Kendall's Coefficient of Concordance Ranking of the Effectiveness of Single Machine Learning Models in Predicting Stock Price Movement","publication_year":2020,"publication_date":"2020-10-28","ids":{"openalex":"https://openalex.org/W3094166324","doi":"https://doi.org/10.5281/zenodo.18657270","mag":"3094166324"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.18657270","doi":"https://doi.org/10.5281/zenodo.18657270","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.18657270","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"JournalArticle"},"volume":null,"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.18657270","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019635005","display_name":"Ernest Kwame Ampomah","orcid":"https://orcid.org/0000-0003-0796-5895","observed_orcids":["https://orcid.org/0000-0003-0796-5895"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ampomah Ernest Kwame","raw_affiliation_strings":["University of Electronic Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103776370","display_name":"Qin Zhi-guang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qin Zhiguang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042563477","display_name":"Eric Ahudey","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eric Ahudey","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Addo Prince Clement","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Addo Prince Clement","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.14425874,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"9","issue":"9","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":1.0,"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":1.0,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.0,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.0,"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/stock-price-prediction","display_name":"stock price prediction","score":0.9470000267028809},{"id":"https://openalex.org/keywords/kendall-s-coefficient-of-concordance","display_name":"Kendall's coefficient of concordance","score":0.9900000095367432},{"id":"https://openalex.org/keywords/decision-tree","display_name":"decision tree","score":0.8679999709129333},{"id":"https://openalex.org/keywords/gaussian-naive-bayes","display_name":"Gaussian naive Bayes","score":0.9399999976158142},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"logistic regression","score":0.9990000128746033},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":1.0},{"id":"https://openalex.org/keywords/k-nearest-neighbors","display_name":"k-nearest neighbors","score":0.9860000014305115},{"id":"https://openalex.org/keywords/stock-market-data","display_name":"stock market data","score":0.6010000109672546}],"concepts":[{"id":"https://openalex.org/C160798450","wikidata":"https://www.wikidata.org/wiki/Q4230870","display_name":"Concordance","level":2,"score":0.776077151298523},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6982900500297546},{"id":"https://openalex.org/C2780226923","wikidata":"https://www.wikidata.org/wiki/Q929848","display_name":"Movement (music)","level":2,"score":0.49262529611587524},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.49081525206565857},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4659401476383209},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46519172191619873},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.4244026839733124},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4152419865131378},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4130040407180786},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3276057243347168},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16457298398017883},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.10337048768997192},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.06461021304130554},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.05718410015106201},{"id":"https://openalex.org/C107038049","wikidata":"https://www.wikidata.org/wiki/Q35986","display_name":"Aesthetics","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.5281/zenodo.18657270","doi":"https://doi.org/10.5281/zenodo.18657270","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.18657270","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"JournalArticle"},{"id":"doi:10.5281/zenodo.18657271","doi":"https://doi.org/10.5281/zenodo.18657271","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.18657271","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"JournalArticle"},{"id":"mag:3094166324","doi":null,"is_oa":false,"landing_page_url":"https://www.ijert.org/research/kendalls-coefficient-of-concordance-ranking-of-the-effectiveness-of-single-machine-learning-models-in-predicting-stock-price-movement-IJERTV9IS090471.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"International Journal of Engineering Research and","raw_type":null}],"best_oa_location":{"id":"doi:10.5281/zenodo.18657270","doi":"https://doi.org/10.5281/zenodo.18657270","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.18657270","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"JournalArticle"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W298212978","https://openalex.org/W2004463884","https://openalex.org/W2012079387","https://openalex.org/W2024804972","https://openalex.org/W2024805871","https://openalex.org/W2025623395","https://openalex.org/W2030005958","https://openalex.org/W2045260557","https://openalex.org/W2113822305","https://openalex.org/W2165726487","https://openalex.org/W2510620627","https://openalex.org/W2624385633","https://openalex.org/W2935896247","https://openalex.org/W2967663220","https://openalex.org/W4239510810"],"related_works":["https://openalex.org/W3206349399","https://openalex.org/W3208240004","https://openalex.org/W2018128208","https://openalex.org/W3138877015","https://openalex.org/W2321270884","https://openalex.org/W3123303471","https://openalex.org/W1993439939","https://openalex.org/W3010759106","https://openalex.org/W2064709414","https://openalex.org/W2980325416","https://openalex.org/W2187466180","https://openalex.org/W2024690352","https://openalex.org/W2901586661","https://openalex.org/W1525405387","https://openalex.org/W1829670590","https://openalex.org/W1603116935","https://openalex.org/W2783258320","https://openalex.org/W2585575883","https://openalex.org/W2357664875","https://openalex.org/W3125153682"],"abstract_inverted_index":{"The":[0,109,135,155,165],"prediction":[1,33],"of":[2,12,27,29,34,37,51,68,75,104,106,181,205],"stock":[3,15,38,107,116,123],"price":[4,39],"behavior":[5],"is":[6,40,111,128,149],"extremely":[7],"important":[8],"in":[9,100],"the":[10,24,35,55,73,102,140,145,153,160,174,197,202],"world":[11],"finance.":[13],"Generally,":[14],"investment":[16],"decisions":[17,46],"are":[18,81,137,157],"made":[19],"by":[20,22,42],"investors":[21,43],"predicting":[23],"future":[25],"movement":[26,36,105],"prices":[28],"stocks.":[30],"An":[31],"accurate":[32],"needed":[41],"to":[44,71,151],"make":[45],"regarding":[47],"buying":[48],"and":[49,53,96,132,144,190],"selling":[50],"stocks,":[52],"minimize":[54],"risk":[56],"associated":[57],"with":[58,184,208],"such":[59],"investment.":[60],"In":[61],"this":[62],"study,":[63],"we":[64],"adopt":[65],"Kendall's":[66,179,203],"coefficient":[67,180,204],"concordance":[69,182,206],"technique":[70],"rank":[72,176,199],"effectiveness":[74],"five":[76],"single":[77],"machine":[78],"models":[79,136,156],"(which":[80],"Decision":[82],"Tree":[83],"(DT),":[84],"Gaussian":[85],"Na\u00efve":[86],"Bayes":[87],"(GNB),":[88],"Logistic":[89],"Regression":[90],"(LR),":[91],"Support":[92],"Vector":[93],"Machine":[94],"(SVM)":[95],"k-Nearest":[97],"Neighbor":[98],"(kNN))":[99],"forecasting":[101],"direction":[103],"price.":[108],"experiment":[110],"conducted":[112],"utilizing":[113],"ten":[114],"different":[115],"data":[117,126,142,147],"sets":[118],"randomly":[119],"collected":[120],"from":[121],"three":[122],"markets.":[124],"Each":[125],"set":[127,148],"split":[129],"into":[130],"training":[131,141],"test":[133,146],"sets.":[134],"trained":[138],"on":[139],"set,":[143],"used":[150],"evaluate":[152],"models.":[154],"evaluated":[158],"using":[159,178,201],"six":[161],"classical":[162],"evaluation":[163],"metrics.":[164,192],"experimental":[166],"results":[167],"recorded":[168],"illustrate":[169],"that":[170],"LR":[171],"model":[172,195],"had":[173],"highest":[175],"when":[177,200],"(W)":[183,207],"accuracy,":[185],"precision,":[186],"f1-":[187],"score,":[188],"specificity,":[189],"AUC":[191],"However,":[193],"SVM":[194],"achieved":[196],"best":[198],"recall":[209],"metric.":[210]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2020-10-29T00:00:00"}