{"id":"https://openalex.org/W2981097085","doi":"https://doi.org/10.3390/axioms8040116","title":"A Study Concerning Soft Computing Approaches for Stock Price Forecasting","display_name":"A Study Concerning Soft Computing Approaches for Stock Price Forecasting","publication_year":2019,"publication_date":"2019-10-18","ids":{"openalex":"https://openalex.org/W2981097085","doi":"https://doi.org/10.3390/axioms8040116","mag":"2981097085"},"language":"en","primary_location":{"id":"doi:10.3390/axioms8040116","is_oa":true,"landing_page_url":"https://doi.org/10.3390/axioms8040116","pdf_url":"https://www.mdpi.com/2075-1680/8/4/116/pdf?version=1571387295","source":{"id":"https://openalex.org/S4210173132","display_name":"Axioms","issn_l":"2075-1680","issn":["2075-1680"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Axioms","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2075-1680/8/4/116/pdf?version=1571387295","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045056026","display_name":"Chao Shi","orcid":"https://orcid.org/0000-0001-5051-4710"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Chao Shi","raw_affiliation_strings":["Department of Mathematics, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"last","author":{"id":null,"display_name":"Xiaosheng Zhuang","orcid":"https://orcid.org/0000-0001-5051-4710"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Xiaosheng Zhuang","raw_affiliation_strings":["Department of Mathematics, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0001-5051-4710","affiliations":[{"raw_affiliation_string":"Department of Mathematics, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I168719708"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":1.384,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.83344488,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":97},"biblio":{"volume":"8","issue":"4","first_page":"116","last_page":"116"},"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9965999722480774,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9943000078201294,"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/mean-reversion","display_name":"Mean reversion","score":0.6467125415802002},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.6103620529174805},{"id":"https://openalex.org/keywords/soft-computing","display_name":"Soft computing","score":0.6023916602134705},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5676779747009277},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5623727440834045},{"id":"https://openalex.org/keywords/volatility","display_name":"Volatility (finance)","score":0.5561363697052002},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.5260786414146423},{"id":"https://openalex.org/keywords/stock-market-index","display_name":"Stock market index","score":0.4223890006542206},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.3891071379184723},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3441796898841858},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.24214839935302734},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10700380802154541}],"concepts":[{"id":"https://openalex.org/C135205223","wikidata":"https://www.wikidata.org/wiki/Q367137","display_name":"Mean reversion","level":2,"score":0.6467125415802002},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.6103620529174805},{"id":"https://openalex.org/C140073362","wikidata":"https://www.wikidata.org/wiki/Q738759","display_name":"Soft computing","level":3,"score":0.6023916602134705},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5676779747009277},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5623727440834045},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.5561363697052002},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.5260786414146423},{"id":"https://openalex.org/C88389905","wikidata":"https://www.wikidata.org/wiki/Q223371","display_name":"Stock market index","level":4,"score":0.4223890006542206},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.3891071379184723},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3441796898841858},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.24214839935302734},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10700380802154541},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C2780762169","wikidata":"https://www.wikidata.org/wiki/Q5905368","display_name":"Horse","level":2,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/axioms8040116","is_oa":true,"landing_page_url":"https://doi.org/10.3390/axioms8040116","pdf_url":"https://www.mdpi.com/2075-1680/8/4/116/pdf?version=1571387295","source":{"id":"https://openalex.org/S4210173132","display_name":"Axioms","issn_l":"2075-1680","issn":["2075-1680"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Axioms","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:daf6469c3687456d89dc34633c6f308c","is_oa":true,"landing_page_url":"https://doaj.org/article/daf6469c3687456d89dc34633c6f308c","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Axioms, Vol 8, Iss 4, p 116 (2019)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2075-1680/8/4/116/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/axioms8040116","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Axioms; Volume 8; Issue 4; Pages: 116","raw_type":"Text"},{"id":"pmh:oai:pure.atira.dk:publications/4ac9ccba-1074-4306-b559-65d597bc058f","is_oa":true,"landing_page_url":"https://hdl.handle.net/2031/4ac9ccba-1074-4306-b559-65d597bc058f","pdf_url":null,"source":{"id":"https://openalex.org/S7407055387","display_name":"CityU Scholars","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Shi, C & Zhuang, X 2019, 'A Study Concerning Soft Computing Approaches for Stock Price Forecasting', Axioms, vol. 8, no. 4, 116. https://doi.org/10.3390/axioms8040116","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.3390/axioms8040116","is_oa":true,"landing_page_url":"https://doi.org/10.3390/axioms8040116","pdf_url":"https://www.mdpi.com/2075-1680/8/4/116/pdf?version=1571387295","source":{"id":"https://openalex.org/S4210173132","display_name":"Axioms","issn_l":"2075-1680","issn":["2075-1680"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Axioms","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7641554321","display_name":null,"funder_award_id":"CityU 11300717","funder_id":"https://openalex.org/F4320334890","funder_display_name":"University Research Committee, University of Hong Kong"}],"funders":[{"id":"https://openalex.org/F4320334890","display_name":"University Research Committee, University of Hong Kong","ror":"https://ror.org/02zhqgq86"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2981097085.pdf"},"referenced_works_count":81,"referenced_works":["https://openalex.org/W385004530","https://openalex.org/W415659269","https://openalex.org/W1520633830","https://openalex.org/W1964357740","https://openalex.org/W1966676388","https://openalex.org/W1966938154","https://openalex.org/W1978520392","https://openalex.org/W1979575715","https://openalex.org/W1980836123","https://openalex.org/W1983220355","https://openalex.org/W1983965282","https://openalex.org/W1986478348","https://openalex.org/W1988518729","https://openalex.org/W1991963339","https://openalex.org/W2004463884","https://openalex.org/W2007221293","https://openalex.org/W2012079387","https://openalex.org/W2020760721","https://openalex.org/W2021938316","https://openalex.org/W2032170121","https://openalex.org/W2039240409","https://openalex.org/W2043406008","https://openalex.org/W2049916782","https://openalex.org/W2056704409","https://openalex.org/W2065610112","https://openalex.org/W2068585979","https://openalex.org/W2070473414","https://openalex.org/W2081895431","https://openalex.org/W2083036265","https://openalex.org/W2084341220","https://openalex.org/W2086694651","https://openalex.org/W2088202114","https://openalex.org/W2091621250","https://openalex.org/W2092871037","https://openalex.org/W2093311990","https://openalex.org/W2116282447","https://openalex.org/W2116988722","https://openalex.org/W2120224355","https://openalex.org/W2121970262","https://openalex.org/W2128846462","https://openalex.org/W2131773668","https://openalex.org/W2137356002","https://openalex.org/W2151306296","https://openalex.org/W2152627601","https://openalex.org/W2157160947","https://openalex.org/W2162845233","https://openalex.org/W2163321856","https://openalex.org/W2169533279","https://openalex.org/W2183936775","https://openalex.org/W2252909801","https://openalex.org/W2268394201","https://openalex.org/W2292977547","https://openalex.org/W2344265502","https://openalex.org/W2400770063","https://openalex.org/W2489162210","https://openalex.org/W2500605637","https://openalex.org/W2520513325","https://openalex.org/W2523498403","https://openalex.org/W2524215529","https://openalex.org/W2606362422","https://openalex.org/W2737799997","https://openalex.org/W2786741175","https://openalex.org/W2799635155","https://openalex.org/W2890270470","https://openalex.org/W2897534289","https://openalex.org/W2907723841","https://openalex.org/W2936018573","https://openalex.org/W3121241862","https://openalex.org/W3121794066","https://openalex.org/W3122153541","https://openalex.org/W3123403928","https://openalex.org/W4255272544","https://openalex.org/W6631018597","https://openalex.org/W6645077983","https://openalex.org/W6668138380","https://openalex.org/W6683839367","https://openalex.org/W6683944182","https://openalex.org/W6686358049","https://openalex.org/W6697150682","https://openalex.org/W6704259782","https://openalex.org/W6981857433"],"related_works":["https://openalex.org/W2990652064","https://openalex.org/W3134659362","https://openalex.org/W3123122373","https://openalex.org/W4386639254","https://openalex.org/W2360554747","https://openalex.org/W3208509893","https://openalex.org/W2611464132","https://openalex.org/W2363401122","https://openalex.org/W2386753225","https://openalex.org/W2049127694"],"abstract_inverted_index":{"Financial":[0],"time-series":[1],"are":[2,101,109,131],"well":[3],"known":[4],"for":[5,34,46],"their":[6],"non-linearity":[7],"and":[8,36,49,54,84,94,111,120,125,163,183],"non-stationarity":[9],"nature.":[10],"The":[11,23,103,113],"application":[12],"of":[13,26,65,77,90,106,115,154,165],"conventional":[14],"econometric":[15],"models":[16,108,141],"in":[17,97],"prediction":[18,104,130,145,170],"can":[19],"incur":[20],"significant":[21],"errors.":[22],"fast":[24],"advancement":[25],"soft":[27,67],"computing":[28,42,68],"techniques":[29],"provides":[30],"an":[31],"alternative":[32],"approach":[33],"estimating":[35],"forecasting":[37],"volatile":[38],"stock":[39,96,121],"prices.":[40],"Soft":[41],"approaches":[43],"exploit":[44],"tolerance":[45],"imprecision,":[47],"uncertainty,":[48],"partial":[50],"truth":[51],"to":[52,87],"progressively":[53],"adaptively":[55],"solve":[56],"practical":[57],"problems.":[58],"In":[59],"this":[60],"study,":[61],"a":[62,75,178],"comprehensive":[63],"review":[64],"latest":[66],"tools":[69],"is":[70],"given.":[71],"Then,":[72],"examples":[73],"incorporating":[74],"series":[76],"machine":[78],"learning":[79],"models,":[80,86],"including":[81],"both":[82],"single":[83],"hybrid":[85],"predict":[88],"prices":[89],"two":[91],"representative":[92],"indexes":[93],"one":[95],"Hong":[98],"Kong\u2019s":[99],"market":[100],"undertaken.":[102],"performances":[105],"different":[107],"evaluated":[110],"compared.":[112],"effects":[114],"the":[116,143,152,161],"training":[117,156],"sample":[118,157],"size":[119,158],"patterns":[122],"(viz.":[123],"momentum":[124,184],"mean":[126,181],"reversion)":[127],"on":[128],"model":[129],"also":[132,149],"investigated.":[133],"Results":[134],"indicate":[135],"that":[136,151],"artificial":[137],"neural":[138],"network":[139],"(ANN)-based":[140],"yield":[142],"highest":[144],"accuracy.":[146],"It":[147],"was":[148],"found":[150],"determination":[153],"optimal":[155],"should":[159],"take":[160],"pattern":[162],"volatility":[164],"stocks":[166,176],"into":[167],"consideration.":[168],"Large":[169],"errors":[171],"could":[172],"be":[173],"incurred":[174],"when":[175],"exhibit":[177],"transition":[179],"between":[180],"reversion":[182],"trend.":[185]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-05-22T06:13:13.366637","created_date":"2019-10-25T00:00:00"}