{"id":"https://openalex.org/W4293103696","doi":"https://doi.org/10.3390/fi14090252","title":"Forecasting the Risk Factor of Frontier Markets: A Novel Stacking Ensemble of Neural Network Approach","display_name":"Forecasting the Risk Factor of Frontier Markets: A Novel Stacking Ensemble of Neural Network Approach","publication_year":2022,"publication_date":"2022-08-25","ids":{"openalex":"https://openalex.org/W4293103696","doi":"https://doi.org/10.3390/fi14090252"},"language":"en","primary_location":{"id":"doi:10.3390/fi14090252","is_oa":true,"landing_page_url":"https://doi.org/10.3390/fi14090252","pdf_url":"https://www.mdpi.com/1999-5903/14/9/252/pdf?version=1661437658","source":{"id":"https://openalex.org/S34838331","display_name":"Future Internet","issn_l":"1999-5903","issn":["1999-5903"],"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":"Future Internet","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1999-5903/14/9/252/pdf?version=1661437658","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015055011","display_name":"Mst Shapna Akter","orcid":"https://orcid.org/0000-0002-9859-6265"},"institutions":[{"id":"https://openalex.org/I172980758","display_name":"Kennesaw State University","ror":"https://ror.org/00jeqjx33","country_code":"US","type":"education","lineage":["https://openalex.org/I172980758"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mst. Shapna Akter","raw_affiliation_strings":["Department of Computer Science, Kennesaw State University, 370 Paulding Ave., Kennesaw, GA 30144, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Kennesaw State University, 370 Paulding Ave., Kennesaw, GA 30144, USA","institution_ids":["https://openalex.org/I172980758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052820694","display_name":"Hossain Shahriar","orcid":"https://orcid.org/0000-0003-1021-7986"},"institutions":[{"id":"https://openalex.org/I172980758","display_name":"Kennesaw State University","ror":"https://ror.org/00jeqjx33","country_code":"US","type":"education","lineage":["https://openalex.org/I172980758"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hossain Shahriar","raw_affiliation_strings":["Department of Information Technology, Kennesaw State University, 370 Paulding Ave., Kennesaw, GA 30144, USA"],"affiliations":[{"raw_affiliation_string":"Department of Information Technology, Kennesaw State University, 370 Paulding Ave., Kennesaw, GA 30144, USA","institution_ids":["https://openalex.org/I172980758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056299273","display_name":"Reaz A. Chowdhury","orcid":"https://orcid.org/0000-0002-8478-4222"},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Reaz Chowdhury","raw_affiliation_strings":["Department of Electrical and Engineering, University of Alberta, Edmonton, AB T6G 2P5, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Engineering, University of Alberta, Edmonton, AB T6G 2P5, Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012837694","display_name":"M. R. C. Mahdy","orcid":"https://orcid.org/0000-0003-3737-0315"},"institutions":[{"id":"https://openalex.org/I157386601","display_name":"North South University","ror":"https://ror.org/05wdbfp45","country_code":"BD","type":"education","lineage":["https://openalex.org/I157386601"]}],"countries":["BD"],"is_corresponding":true,"raw_author_name":"M. R. C. Mahdy","raw_affiliation_strings":["Department of Electrical and Computer Engineering, North South University, Dhaka 1229, Bangladesh"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, North South University, Dhaka 1229, Bangladesh","institution_ids":["https://openalex.org/I157386601"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5012837694","https://openalex.org/A5052820694"],"corresponding_institution_ids":["https://openalex.org/I157386601","https://openalex.org/I172980758"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":2.881,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.91215759,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"14","issue":"9","first_page":"252","last_page":"252"},"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/T11059","display_name":"Market Dynamics and Volatility","score":0.998199999332428,"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":0.9979000091552734,"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/computer-science","display_name":"Computer science","score":0.7855011224746704},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6761443614959717},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6441535949707031},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6120961308479309},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5972305536270142},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.5962600708007812},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5325844287872314},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5248028039932251},{"id":"https://openalex.org/keywords/volatility","display_name":"Volatility (finance)","score":0.5219944715499878},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.5081288814544678},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.46095800399780273},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4471196234226227},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.43936723470687866},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.4208317995071411},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.35527950525283813},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.333553671836853},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.09545528888702393}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7855011224746704},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6761443614959717},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6441535949707031},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6120961308479309},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5972305536270142},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.5962600708007812},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5325844287872314},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5248028039932251},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.5219944715499878},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.5081288814544678},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.46095800399780273},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4471196234226227},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.43936723470687866},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.4208317995071411},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.35527950525283813},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.333553671836853},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.09545528888702393},{"id":"https://openalex.org/C2780762169","wikidata":"https://www.wikidata.org/wiki/Q5905368","display_name":"Horse","level":2,"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/fi14090252","is_oa":true,"landing_page_url":"https://doi.org/10.3390/fi14090252","pdf_url":"https://www.mdpi.com/1999-5903/14/9/252/pdf?version=1661437658","source":{"id":"https://openalex.org/S34838331","display_name":"Future Internet","issn_l":"1999-5903","issn":["1999-5903"],"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":"Future Internet","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:gam:jftint:v:14:y:2022:i:9:p:252-:d:897255","is_oa":false,"landing_page_url":"https://www.mdpi.com/1999-5903/14/9/252/","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:8f9a3a71c2c146489f8f49dab7fb5d2c","is_oa":true,"landing_page_url":"https://doaj.org/article/8f9a3a71c2c146489f8f49dab7fb5d2c","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","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Future Internet, Vol 14, Iss 9, p 252 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1999-5903/14/9/252/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/fi14090252","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":"Future Internet; Volume 14; Issue 9; Pages: 252","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/fi14090252","is_oa":true,"landing_page_url":"https://doi.org/10.3390/fi14090252","pdf_url":"https://www.mdpi.com/1999-5903/14/9/252/pdf?version=1661437658","source":{"id":"https://openalex.org/S34838331","display_name":"Future Internet","issn_l":"1999-5903","issn":["1999-5903"],"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":"Future Internet","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4293103696.pdf","grobid_xml":"https://content.openalex.org/works/W4293103696.grobid-xml"},"referenced_works_count":71,"referenced_works":["https://openalex.org/W204924188","https://openalex.org/W1972567154","https://openalex.org/W2002014776","https://openalex.org/W2007272376","https://openalex.org/W2064675550","https://openalex.org/W2097847779","https://openalex.org/W2098178566","https://openalex.org/W2106100548","https://openalex.org/W2110056747","https://openalex.org/W2112796928","https://openalex.org/W2114471683","https://openalex.org/W2144499799","https://openalex.org/W2150755264","https://openalex.org/W2165404743","https://openalex.org/W2185657421","https://openalex.org/W2223222085","https://openalex.org/W2291961022","https://openalex.org/W2461729787","https://openalex.org/W2489292218","https://openalex.org/W2498672755","https://openalex.org/W2509065397","https://openalex.org/W2562607067","https://openalex.org/W2600563756","https://openalex.org/W2607231344","https://openalex.org/W2610314771","https://openalex.org/W2622999711","https://openalex.org/W2624385633","https://openalex.org/W2735056555","https://openalex.org/W2737799997","https://openalex.org/W2756789966","https://openalex.org/W2774513877","https://openalex.org/W2789758093","https://openalex.org/W2797279162","https://openalex.org/W2800911105","https://openalex.org/W2809317444","https://openalex.org/W2811395591","https://openalex.org/W2883588054","https://openalex.org/W2885195348","https://openalex.org/W2885567841","https://openalex.org/W2895960091","https://openalex.org/W2900293687","https://openalex.org/W2901217160","https://openalex.org/W2911688499","https://openalex.org/W2919115771","https://openalex.org/W2920574057","https://openalex.org/W2942441318","https://openalex.org/W2967732991","https://openalex.org/W2972909277","https://openalex.org/W2975788427","https://openalex.org/W2987201163","https://openalex.org/W2989818023","https://openalex.org/W3007075806","https://openalex.org/W3009716010","https://openalex.org/W3012514215","https://openalex.org/W3013265712","https://openalex.org/W3015504841","https://openalex.org/W3022367143","https://openalex.org/W3044078994","https://openalex.org/W3081125651","https://openalex.org/W3083252136","https://openalex.org/W3098357269","https://openalex.org/W3100777112","https://openalex.org/W3108703399","https://openalex.org/W3115870039","https://openalex.org/W3206052710","https://openalex.org/W4244895750","https://openalex.org/W4251708881","https://openalex.org/W6686470949","https://openalex.org/W6768464860","https://openalex.org/W6771303446","https://openalex.org/W6803034115"],"related_works":["https://openalex.org/W2327035729","https://openalex.org/W2348748958","https://openalex.org/W3039673966","https://openalex.org/W2884325279","https://openalex.org/W1570592793","https://openalex.org/W1525436954","https://openalex.org/W2385662756","https://openalex.org/W1992847598","https://openalex.org/W4296079469","https://openalex.org/W4298012357"],"abstract_inverted_index":{"Forecasting":[0],"the":[1,5,31,39,46,66,74,100,118,166,181,189,204,220,223,276,294,307,332],"risk":[2,33],"factor":[3],"of":[4,38,41,48,73,99,180,203,222,301],"financial":[6],"frontier":[7,21,63,182],"markets":[8],"has":[9,23,52,226],"always":[10],"been":[11,216,227,249],"a":[12,20,24,36,95,325],"very":[13,326],"challenging":[14],"task.":[15],"Unlike":[16],"an":[17],"emerging":[18],"market,":[19],"market":[22,183],"missing":[25,43,167],"parameter":[26,44,168],"named":[27,169],"\u201cvolatility\u201d,":[28],"which":[29,247,317],"indicates":[30],"market\u2019s":[32],"and":[34,45,71,138,158,184,198,213,243,259,271,275,279,284,297,303,313],"as":[35,131,149],"result":[37],"absence":[40],"this":[42,90,254],"lack":[47],"proper":[49],"prediction,":[50],"it":[51,78],"almost":[53],"become":[54],"difficult":[55],"for":[56,176,251],"direct":[57],"customers":[58],"to":[59,82,218],"invest":[60],"money":[61],"in":[62,253],"markets.":[64],"However,":[65],"noises,":[67],"seasonality,":[68],"random":[69],"spikes":[70],"trends":[72],"time-series":[75],"datasets":[76],"make":[77],"even":[79],"more":[80],"complicated":[81],"predict":[83],"stock":[84,172],"prices":[85],"with":[86,117,142,331],"high":[87],"accuracy.":[88],"In":[89],"work,":[91],"we":[92,262],"have":[93,112,164,215,248,263],"developed":[94],"novel":[96],"stacking":[97,201,232],"ensemble":[98,128,193,202,233,290],"neural":[101,205,234],"network":[102,206,235],"model":[103,236,268,323],"that":[104,229,320],"performs":[105],"best":[106],"on":[107],"multiple":[108],"data":[109],"patterns.":[110],"We":[111,163],"compared":[113,330],"our":[114,199,230,266,321],"model\u2019s":[115],"performance":[116,119,221],"results":[120],"obtained":[121],"by":[122],"using":[123,171,188,265],"some":[124,143],"traditional":[125,144,240,289,333],"machine":[126,191,241],"learning":[127,146,192,196,242,245,291,309],"models":[129,147,197,246,292,310],"such":[130,148],"Random":[132],"Forest,":[133],"AdaBoost,":[134],"Gradient":[135],"Boosting":[136],"Machine":[137],"Stacking":[139],"Ensemble,":[140],"along":[141],"deep":[145,195,244,308],"Convolutional":[150],"Neural":[151],"Network":[152],"(CNN),":[153],"Long":[154,160],"Short-Term":[155,161],"Memory":[156],"(LSTM)":[157],"Bidirectional":[159],"(BiLSTM).":[162],"calculated":[165],"\u201cvolatility\u201d":[170],"price":[173],"(Close":[174],"price)":[175],"20":[177],"different":[178],"companies":[179],"then":[185],"made":[186],"predictions":[187],"aforementioned":[190],"models,":[194],"proposed":[200,231,267,322],"model.":[207],"The":[208,256,288],"statistical":[209],"evaluation":[210],"metrics":[211],"RMSE":[212,258,278,296],"MAE":[214,260,280,298],"used":[217,250],"evaluate":[219],"models.":[224,334],"It":[225],"found":[228],"outperforms":[237],"all":[238],"other":[239],"comparison":[252],"paper.":[255],"lowest":[257],"values":[261,281],"received":[264],"are":[269,282],"0.3626":[270],"0.3682":[272],"percent,":[273,286,305,315],"respectively,":[274,316],"highest":[277,295],"2.5696":[283],"2.444":[285],"respectively.":[287],"give":[293,311],"error":[299,328],"rate":[300],"20.4852":[302],"20.4260":[304],"while":[306],"15.2332":[312],"15.1668":[314],"clearly":[318],"states":[319],"provides":[324],"low":[327],"value":[329]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":5}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}