{"id":"https://openalex.org/W4379932686","doi":"https://doi.org/10.1145/3590837.3590919","title":"NIFTY Banking Sector Closing Price Prediction Using Deep Learning","display_name":"NIFTY Banking Sector Closing Price Prediction Using Deep Learning","publication_year":2022,"publication_date":"2022-12-23","ids":{"openalex":"https://openalex.org/W4379932686","doi":"https://doi.org/10.1145/3590837.3590919"},"language":"en","primary_location":{"id":"doi:10.1145/3590837.3590919","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3590837.3590919","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3590837.3590919","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 4th International Conference on Information Management & Machine Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3590837.3590919","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091039207","display_name":"Aman Singh Chauhan","orcid":"https://orcid.org/0009-0003-8289-1512"},"institutions":[{"id":"https://openalex.org/I55016150","display_name":"Manav Rachna International Institute of Research and Studies","ror":"https://ror.org/02kf4r633","country_code":"IN","type":"education","lineage":["https://openalex.org/I4405253735","https://openalex.org/I55016150"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Aman Singh Chauhan","raw_affiliation_strings":["Computer Science Engineering, Manav Rachna University, India"],"raw_orcid":"https://orcid.org/0009-0003-8289-1512","affiliations":[{"raw_affiliation_string":"Computer Science Engineering, Manav Rachna University, India","institution_ids":["https://openalex.org/I55016150"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Basireddy Chinna Ankalugari Vamsidhar Reddy","orcid":"https://orcid.org/0009-0001-9420-4199"},"institutions":[{"id":"https://openalex.org/I55016150","display_name":"Manav Rachna International Institute of Research and Studies","ror":"https://ror.org/02kf4r633","country_code":"IN","type":"education","lineage":["https://openalex.org/I4405253735","https://openalex.org/I55016150"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Basireddy Chinna Ankalugari Vamsidhar Reddy","raw_affiliation_strings":["Computer Science Engineering, Manav Rachna University, India"],"raw_orcid":"https://orcid.org/0009-0001-9420-4199","affiliations":[{"raw_affiliation_string":"Computer Science Engineering, Manav Rachna University, India","institution_ids":["https://openalex.org/I55016150"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082448960","display_name":"Mamta Arora","orcid":"https://orcid.org/0000-0003-0616-8827"},"institutions":[{"id":"https://openalex.org/I55016150","display_name":"Manav Rachna International Institute of Research and Studies","ror":"https://ror.org/02kf4r633","country_code":"IN","type":"education","lineage":["https://openalex.org/I4405253735","https://openalex.org/I55016150"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Mamta Arora","raw_affiliation_strings":["Manav Rachna University, India"],"raw_orcid":"https://orcid.org/0000-0003-0616-8827","affiliations":[{"raw_affiliation_string":"Manav Rachna University, India","institution_ids":["https://openalex.org/I55016150"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075207877","display_name":"Mrinal Pandey","orcid":"https://orcid.org/0000-0001-8942-3568"},"institutions":[{"id":"https://openalex.org/I55016150","display_name":"Manav Rachna International Institute of Research and Studies","ror":"https://ror.org/02kf4r633","country_code":"IN","type":"education","lineage":["https://openalex.org/I4405253735","https://openalex.org/I55016150"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Mrinal Pandey","raw_affiliation_strings":["Manav Rachna University, India"],"raw_orcid":"https://orcid.org/0000-0001-8942-3568","affiliations":[{"raw_affiliation_string":"Manav Rachna University, India","institution_ids":["https://openalex.org/I55016150"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I55016150"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.32668329,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.9894000291824341,"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/T10047","display_name":"Financial Markets and Investment Strategies","score":0.9891999959945679,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/closing","display_name":"Closing (real estate)","score":0.874125063419342},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6836754679679871},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6578817367553711},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6468936204910278},{"id":"https://openalex.org/keywords/stock-price","display_name":"Stock price","score":0.5712277889251709},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.5643765926361084},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.5596994757652283},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5441367626190186},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4717080295085907},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.37692791223526},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33517444133758545},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.2721889019012451},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.16935878992080688},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14800605177879333},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.13550740480422974}],"concepts":[{"id":"https://openalex.org/C2778775528","wikidata":"https://www.wikidata.org/wiki/Q5135432","display_name":"Closing (real estate)","level":2,"score":0.874125063419342},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6836754679679871},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6578817367553711},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6468936204910278},{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.5712277889251709},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.5643765926361084},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.5596994757652283},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5441367626190186},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4717080295085907},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.37692791223526},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33517444133758545},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.2721889019012451},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.16935878992080688},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14800605177879333},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.13550740480422974},{"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},{"id":"https://openalex.org/C2780762169","wikidata":"https://www.wikidata.org/wiki/Q5905368","display_name":"Horse","level":2,"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":1,"locations":[{"id":"doi:10.1145/3590837.3590919","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3590837.3590919","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3590837.3590919","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 4th International Conference on Information Management & Machine Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3590837.3590919","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3590837.3590919","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3590837.3590919","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 4th International Conference on Information Management & Machine Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4379932686.pdf","grobid_xml":"https://content.openalex.org/works/W4379932686.grobid-xml"},"referenced_works_count":4,"referenced_works":["https://openalex.org/W2484997644","https://openalex.org/W2736856097","https://openalex.org/W3017193407","https://openalex.org/W4313293087"],"related_works":["https://openalex.org/W4223943233","https://openalex.org/W4312200629","https://openalex.org/W4360585206","https://openalex.org/W4364306694","https://openalex.org/W4380086463","https://openalex.org/W4309045103","https://openalex.org/W4225161397","https://openalex.org/W3014300295","https://openalex.org/W2371073704","https://openalex.org/W3125677545"],"abstract_inverted_index":{"In":[0],"this":[1],"time":[2],"of":[3,13,38,73,80,110],"data":[4,40,59,81],"science":[5],"and":[6,24,45,54,98,116,124,157,163],"machine":[7],"learning,":[8],"predicting":[9,150],"different":[10,71,94],"feature":[11],"values":[12,123],"the":[14,61,66,107,121],"stock":[15,152],"market":[16],"is":[17,158],"getting":[18],"more":[19],"popular":[20],"among":[21],"young":[22],"people":[23,25],"who":[26],"are":[27,128],"connected":[28],"to":[29,76,131],"technology.":[30],"For":[31],"our":[32,58,74,114,147],"study":[33],"we":[34,64,91,144],"used":[35],"5":[36],"years":[37],"real":[39],"from":[41,93],"Indian":[42],"Bank":[43],"Nifty":[44],"applied":[46],"artificial":[47],"neural":[48],"network":[49],"techniques":[50],"LSTM,":[51],"Bi-LSTM,":[52],"GRU,":[53],"standard":[55],"ANN.":[56],"As":[57],"contained":[60],"null":[62],"values,":[63],"preprocessed":[65],"data.":[67],"We":[68,105],"also":[69,102,118],"visualize":[70],"aspects":[72],"dataset":[75],"have":[77],"deep":[78],"knowledge":[79],"before":[82],"applying":[83],"any":[84],"algorithm":[85],"on":[86],"it.":[87],"The":[88,134],"results":[89],"which":[90,112,139],"got":[92],"algorithms":[95],"were":[96,101],"good":[97],"loss":[99],"factors":[100],"very":[103,129,159],"low.":[104],"predicted":[106],"close":[108],"price":[109,153,155],"stocks":[111],"was":[113,140,149],"target":[115],"then":[117],"compared":[119],"with":[120],"actual":[122,132],"found":[125],"that":[126,146],"they":[127],"similar":[130],"values.":[133],"system":[135],"achieves":[136],"overall":[137],"success":[138],"expected.":[141],"And":[142],"finally":[143],"observed":[145],"model":[148],"daily":[151],"closing":[154],"well":[156],"useful":[160],"for":[161],"financial":[162],"technical":[164],"industries.":[165]},"counts_by_year":[],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}