{"id":"https://openalex.org/W2917928566","doi":"https://doi.org/10.1155/2019/4324878","title":"Is Deep Learning for Image Recognition Applicable to Stock Market Prediction?","display_name":"Is Deep Learning for Image Recognition Applicable to Stock Market Prediction?","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2917928566","doi":"https://doi.org/10.1155/2019/4324878","mag":"2917928566"},"language":"en","primary_location":{"id":"doi:10.1155/2019/4324878","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2019/4324878","pdf_url":"https://downloads.hindawi.com/journals/complexity/2019/4324878.pdf","source":{"id":"https://openalex.org/S207319443","display_name":"Complexity","issn_l":"1076-2787","issn":["1076-2787","1099-0526"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Complexity","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://downloads.hindawi.com/journals/complexity/2019/4324878.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5048398079","display_name":"Hyun Sik Sim","orcid":"https://orcid.org/0000-0001-5031-2289"},"institutions":[{"id":"https://openalex.org/I28615091","display_name":"Kyonggi University","ror":"https://ror.org/032xf8h46","country_code":"KR","type":"education","lineage":["https://openalex.org/I28615091"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyun Sik Sim","raw_affiliation_strings":["Department of Industrial & Management Engineering, Kyonggi University, Suwon 16227, Republic of Korea","Department of Industrial & Management Engineering, Kyonggi University, Suwon 16227"],"affiliations":[{"raw_affiliation_string":"Department of Industrial & Management Engineering, Kyonggi University, Suwon 16227, Republic of Korea","institution_ids":["https://openalex.org/I28615091"]},{"raw_affiliation_string":"Department of Industrial & Management Engineering, Kyonggi University, Suwon 16227","institution_ids":["https://openalex.org/I28615091"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035519274","display_name":"Hae In Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hae In Kim","raw_affiliation_strings":["Department of Information & Statistics, Yonsei University, Wonju 03722, Republic of Korea","Department of Information & Statistics, Yonsei University, Wonju 03722"],"affiliations":[{"raw_affiliation_string":"Department of Information & Statistics, Yonsei University, Wonju 03722, Republic of Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"Department of Information & Statistics, Yonsei University, Wonju 03722","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045345496","display_name":"Jae Joon Ahn","orcid":"https://orcid.org/0000-0001-7974-8027"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Jae Joon Ahn","raw_affiliation_strings":["Department of Information & Statistics, Yonsei University, Wonju 03722, Republic of Korea","Department of Information & Statistics, Yonsei University, Wonju 03722"],"affiliations":[{"raw_affiliation_string":"Department of Information & Statistics, Yonsei University, Wonju 03722, Republic of Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"Department of Information & Statistics, Yonsei University, Wonju 03722","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5045345496"],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":{"value":2300,"currency":"USD","value_usd":2300},"apc_paid":{"value":2300,"currency":"USD","value_usd":2300},"fwci":13.8163,"has_fulltext":true,"cited_by_count":120,"citation_normalized_percentile":{"value":0.99035257,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"2019","issue":"1","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":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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9878000020980835,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9847999811172485,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7895985841751099},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7083895206451416},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6377968788146973},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6305636167526245},{"id":"https://openalex.org/keywords/stock-market-prediction","display_name":"Stock market prediction","score":0.5873140692710876},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.5518239736557007},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5505853891372681},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.5150136947631836},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5141022801399231},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49752548336982727},{"id":"https://openalex.org/keywords/stock-exchange","display_name":"Stock exchange","score":0.4289707541465759},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3512471914291382},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3225440979003906},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.1146697998046875},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.07534188032150269}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7895985841751099},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7083895206451416},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6377968788146973},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6305636167526245},{"id":"https://openalex.org/C2776256503","wikidata":"https://www.wikidata.org/wiki/Q7617906","display_name":"Stock market prediction","level":4,"score":0.5873140692710876},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.5518239736557007},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5505853891372681},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.5150136947631836},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5141022801399231},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49752548336982727},{"id":"https://openalex.org/C200870193","wikidata":"https://www.wikidata.org/wiki/Q11691","display_name":"Stock exchange","level":2,"score":0.4289707541465759},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3512471914291382},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3225440979003906},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.1146697998046875},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.07534188032150269},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","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},{"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}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1155/2019/4324878","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2019/4324878","pdf_url":"https://downloads.hindawi.com/journals/complexity/2019/4324878.pdf","source":{"id":"https://openalex.org/S207319443","display_name":"Complexity","issn_l":"1076-2787","issn":["1076-2787","1099-0526"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Complexity","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:hin:complx:4324878","is_oa":false,"landing_page_url":"http://downloads.hindawi.com/journals/8503/2019/4324878.xml","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},{"id":"pmh:oai:doaj.org/article:78ffd35508d34dc6aa5d6c5ba5e2da18","is_oa":true,"landing_page_url":"https://doaj.org/article/78ffd35508d34dc6aa5d6c5ba5e2da18","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Complexity, Vol 2019 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1155/2019/4324878","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2019/4324878","pdf_url":"https://downloads.hindawi.com/journals/complexity/2019/4324878.pdf","source":{"id":"https://openalex.org/S207319443","display_name":"Complexity","issn_l":"1076-2787","issn":["1076-2787","1099-0526"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Complexity","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.46000000834465027,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2917928566.pdf","grobid_xml":"https://content.openalex.org/works/W2917928566.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W1508947067","https://openalex.org/W1522301498","https://openalex.org/W1928278792","https://openalex.org/W1972312461","https://openalex.org/W1980836123","https://openalex.org/W2112796928","https://openalex.org/W2126701480","https://openalex.org/W2147800946","https://openalex.org/W2154987621","https://openalex.org/W2156909104","https://openalex.org/W2161012272","https://openalex.org/W2166280719","https://openalex.org/W2247374898","https://openalex.org/W2316116400","https://openalex.org/W2473418344","https://openalex.org/W2624385633","https://openalex.org/W2785509731","https://openalex.org/W2940897671","https://openalex.org/W2963287440","https://openalex.org/W2964671472","https://openalex.org/W3033076611","https://openalex.org/W3121863257","https://openalex.org/W4237284205","https://openalex.org/W4299297331","https://openalex.org/W6631190155","https://openalex.org/W6648914341"],"related_works":["https://openalex.org/W2586556113","https://openalex.org/W3135178882","https://openalex.org/W2542516223","https://openalex.org/W2624043242","https://openalex.org/W18886619","https://openalex.org/W2110351804","https://openalex.org/W3023530306","https://openalex.org/W2086993069","https://openalex.org/W2355584621","https://openalex.org/W3209908847"],"abstract_inverted_index":{"Stock":[0],"market":[1],"prediction":[2,17,115],"is":[3],"a":[4,14,109],"challenging":[5],"issue":[6],"for":[7,70,112,146],"investors.":[8],"In":[9],"this":[10],"paper,":[11],"we":[12,102],"propose":[13],"stock":[15,34,74,114,147],"price":[16,148],"model":[18,82,91],"based":[19],"on":[20],"convolutional":[21],"neural":[22,88],"network":[23,89],"(CNN)":[24],"to":[25,56,85,151,156],"validate":[26],"the":[27,47,51,59,65,76,80,99,119,122,131,142],"applicability":[28],"of":[29,46,58,67,79,121,144],"new":[30],"learning":[31,69],"methods":[32],"in":[33,73],"markets.":[35],"When":[36],"applying":[37],"CNN,":[38],"9":[39],"technical":[40,52],"indicators":[41,53],"were":[42,54,83],"chosen":[43],"as":[44],"predictors":[45],"forecasting":[48],"model,":[49],"and":[50,92,154],"converted":[55],"images":[57],"time":[60],"series":[61],"graph.":[62],"For":[63],"verifying":[64],"usefulness":[66],"deep":[68],"image":[71],"recognition":[72],"markets,":[75],"predictive":[77],"accuracies":[78],"proposed":[81,123],"compared":[84],"typical":[86],"artificial":[87],"(ANN)":[90],"support":[93],"vector":[94],"machine":[95],"(SVM)":[96],"model.":[97],"From":[98],"experimental":[100],"results,":[101],"can":[103,107],"see":[104],"that":[105],"CNN":[106,145,153],"be":[108],"desirable":[110],"choice":[111],"building":[113],"models.":[116],"To":[117],"examine":[118],"performance":[120],"method,":[124],"an":[125],"empirical":[126],"study":[127,136],"was":[128],"performed":[129],"using":[130],"S&P":[132],"500":[133],"index.":[134],"This":[135],"addresses":[137],"two":[138],"critical":[139],"issues":[140],"regarding":[141],"use":[143,152],"prediction:":[149],"how":[150,155],"optimize":[157],"them.":[158]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":21},{"year":2022,"cited_by_count":21},{"year":2021,"cited_by_count":15},{"year":2020,"cited_by_count":27},{"year":2019,"cited_by_count":4}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}