{"id":"https://openalex.org/W3046517298","doi":"https://doi.org/10.3390/computation8030070","title":"Forecasting Economic Recession through Share Price in the Logistics Industry with Artificial Intelligence (AI)","display_name":"Forecasting Economic Recession through Share Price in the Logistics Industry with Artificial Intelligence (AI)","publication_year":2020,"publication_date":"2020-08-03","ids":{"openalex":"https://openalex.org/W3046517298","doi":"https://doi.org/10.3390/computation8030070","mag":"3046517298"},"language":"en","primary_location":{"id":"doi:10.3390/computation8030070","is_oa":true,"landing_page_url":"https://doi.org/10.3390/computation8030070","pdf_url":"https://www.mdpi.com/2079-3197/8/3/70/pdf?version=1596434071","source":{"id":"https://openalex.org/S2738402919","display_name":"Computation","issn_l":"2079-3197","issn":["2079-3197"],"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":"Computation","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2079-3197/8/3/70/pdf?version=1596434071","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5006815488","display_name":"Yuk Ming Tang","orcid":"https://orcid.org/0000-0001-8215-4190"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"YM Tang","raw_affiliation_strings":["Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China"],"raw_orcid":"https://orcid.org/0000-0001-8215-4190","affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041023060","display_name":"Ka Yin Chau","orcid":"https://orcid.org/0000-0002-0381-8401"},"institutions":[{"id":"https://openalex.org/I6469544","display_name":"City University of Macau","ror":"https://ror.org/04gpd4q15","country_code":"MO","type":"education","lineage":["https://openalex.org/I6469544"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Ka-Yin Chau","raw_affiliation_strings":["Faculty of International Tourism and Management, City University of Macau, Macau, China"],"raw_orcid":"https://orcid.org/0000-0002-0381-8401","affiliations":[{"raw_affiliation_string":"Faculty of International Tourism and Management, City University of Macau, Macau, China","institution_ids":["https://openalex.org/I6469544"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100363897","display_name":"Wenqiang Li","orcid":"https://orcid.org/0000-0001-6161-4776"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Wenqiang Li","raw_affiliation_strings":["Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China"],"raw_orcid":"https://orcid.org/0000-0001-6161-4776","affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112553629","display_name":"TW Wan","orcid":null},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"TW Wan","raw_affiliation_strings":["Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China","institution_ids":["https://openalex.org/I14243506"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100363897"],"corresponding_institution_ids":["https://openalex.org/I14243506"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":2.4159,"has_fulltext":true,"cited_by_count":27,"citation_normalized_percentile":{"value":0.89504215,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"8","issue":"3","first_page":"70","last_page":"70"},"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":0.9988999962806702,"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":0.995199978351593,"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-squared-error","display_name":"Mean squared error","score":0.7015140056610107},{"id":"https://openalex.org/keywords/recession","display_name":"Recession","score":0.6610676050186157},{"id":"https://openalex.org/keywords/mean-absolute-percentage-error","display_name":"Mean absolute percentage error","score":0.6485344171524048},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.6347191333770752},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5722426176071167},{"id":"https://openalex.org/keywords/share-price","display_name":"Share price","score":0.543251633644104},{"id":"https://openalex.org/keywords/stock-price","display_name":"Stock price","score":0.48303279280662537},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4800078272819519},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.43760380148887634},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4358372688293457},{"id":"https://openalex.org/keywords/mean-absolute-error","display_name":"Mean absolute error","score":0.4354046881198883},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.42564791440963745},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39248713850975037},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.3705211877822876},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32404613494873047},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.3057495653629303},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.28060656785964966},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.24333694577217102},{"id":"https://openalex.org/keywords/stock-exchange","display_name":"Stock exchange","score":0.20353510975837708},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.15965837240219116},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13576245307922363},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13505539298057556},{"id":"https://openalex.org/keywords/macroeconomics","display_name":"Macroeconomics","score":0.09916308522224426}],"concepts":[{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.7015140056610107},{"id":"https://openalex.org/C195742910","wikidata":"https://www.wikidata.org/wiki/Q176494","display_name":"Recession","level":2,"score":0.6610676050186157},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.6485344171524048},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6347191333770752},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5722426176071167},{"id":"https://openalex.org/C2779538965","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Share price","level":3,"score":0.543251633644104},{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.48303279280662537},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4800078272819519},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.43760380148887634},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4358372688293457},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.4354046881198883},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.42564791440963745},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39248713850975037},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.3705211877822876},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32404613494873047},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.3057495653629303},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.28060656785964966},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24333694577217102},{"id":"https://openalex.org/C200870193","wikidata":"https://www.wikidata.org/wiki/Q11691","display_name":"Stock exchange","level":2,"score":0.20353510975837708},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.15965837240219116},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13576245307922363},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13505539298057556},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.09916308522224426},{"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}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/computation8030070","is_oa":true,"landing_page_url":"https://doi.org/10.3390/computation8030070","pdf_url":"https://www.mdpi.com/2079-3197/8/3/70/pdf?version=1596434071","source":{"id":"https://openalex.org/S2738402919","display_name":"Computation","issn_l":"2079-3197","issn":["2079-3197"],"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":"Computation","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:2aed30e242fc4f97a0501f1ed0c946f3","is_oa":true,"landing_page_url":"https://doaj.org/article/2aed30e242fc4f97a0501f1ed0c946f3","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":"Computation, Vol 8, Iss 3, p 70 (2020)","raw_type":"article"},{"id":"pmh:oai:ira.lib.polyu.edu.hk:10397/88328","is_oa":true,"landing_page_url":"http://hdl.handle.net/10397/88328","pdf_url":null,"source":{"id":"https://openalex.org/S4306400205","display_name":"PolyU Institutional Research Archive (Hong Kong Polytechnic University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I14243506","host_organization_name":"Hong Kong Polytechnic University","host_organization_lineage":["https://openalex.org/I14243506"],"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":null,"raw_type":"Journal/Magazine Article"},{"id":"pmh:oai:mdpi.com:/2079-3197/8/3/70/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/computation8030070","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":"Computation; Volume 8; Issue 3; Pages: 70","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/computation8030070","is_oa":true,"landing_page_url":"https://doi.org/10.3390/computation8030070","pdf_url":"https://www.mdpi.com/2079-3197/8/3/70/pdf?version=1596434071","source":{"id":"https://openalex.org/S2738402919","display_name":"Computation","issn_l":"2079-3197","issn":["2079-3197"],"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":"Computation","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.49000000953674316,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3046517298.pdf","grobid_xml":"https://content.openalex.org/works/W3046517298.grobid-xml"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W2077610490","https://openalex.org/W2116261113","https://openalex.org/W2117014758","https://openalex.org/W2130695501","https://openalex.org/W2164480471","https://openalex.org/W2171088065","https://openalex.org/W2228533757","https://openalex.org/W2586338032","https://openalex.org/W2743196251","https://openalex.org/W2747599906","https://openalex.org/W2794778778","https://openalex.org/W2893556428","https://openalex.org/W2946975908","https://openalex.org/W3011597365","https://openalex.org/W3121231826","https://openalex.org/W4238749099","https://openalex.org/W4239444753","https://openalex.org/W6684197699","https://openalex.org/W6733184499"],"related_works":["https://openalex.org/W3121369812","https://openalex.org/W3178576217","https://openalex.org/W4316658904","https://openalex.org/W2106583216","https://openalex.org/W4304606463","https://openalex.org/W2933969434","https://openalex.org/W4291801331","https://openalex.org/W4200374151","https://openalex.org/W2625413331","https://openalex.org/W2087911819"],"abstract_inverted_index":{"Time":[0],"series":[1,72],"forecasting":[2,10,39,73],"technology":[3,44],"and":[4,67,107,137],"related":[5],"applications":[6],"for":[7,45,124,129],"stock":[8],"price":[9,47,56],"are":[11],"gradually":[12],"receiving":[13],"attention.":[14],"These":[15],"approaches":[16],"can":[17,146],"be":[18],"a":[19],"great":[20],"help":[21],"in":[22,49,62,134],"making":[23],"decisions":[24],"based":[25],"on":[26,76],"historical":[27,54],"information":[28],"to":[29,93],"predict":[30,95],"possible":[31],"future":[32],"situations.":[33],"This":[34],"research":[35],"aims":[36],"at":[37],"establishing":[38],"models":[40],"with":[41,69,104],"deep":[42],"learning":[43],"share":[46,55,96],"prediction":[48,152],"the":[50,77,91,110,125,138,151,154,157],"logistics":[51,60,132],"industry.":[52],"The":[53,98],"data":[57],"of":[58,153],"five":[59],"companies":[61],"Hong":[63,135],"Kong":[64],"were":[65],"collected":[66],"trained":[68,103],"various":[70,121],"time":[71],"algorithms.":[74],"Based":[75],"Mean":[78,112],"Absolute":[79],"Percentage":[80],"Error":[81,114],"(MAPE)":[82],"results,":[83],"we":[84,119,145],"adopted":[85],"Long":[86],"Short-Term":[87],"Memory":[88],"(LSTM)":[89],"as":[90],"methodology":[92],"further":[94],"price.":[97],"proposed":[99,126],"LSTM":[100,127,158],"model":[101,128],"was":[102,142],"different":[105,131],"hyperparameters":[106],"validated":[108],"by":[109],"Root":[111],"Square":[113],"(RMSE).":[115],"In":[116],"this":[117],"study,":[118],"found":[120],"optimal":[122],"parameters":[123],"six":[130],"stocks":[133],"Kong,":[136],"best":[139],"RMSE":[140],"result":[141],"0.43%.":[143],"Finally,":[144],"forecast":[147],"economic":[148],"recessions":[149],"through":[150],"stocks,":[155],"using":[156],"model.":[159]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}