{"id":"https://openalex.org/W3115197668","doi":"https://doi.org/10.1109/tencon50793.2020.9293712","title":"Indian Stock Market Prediction using Deep Learning","display_name":"Indian Stock Market Prediction using Deep Learning","publication_year":2020,"publication_date":"2020-11-16","ids":{"openalex":"https://openalex.org/W3115197668","doi":"https://doi.org/10.1109/tencon50793.2020.9293712","mag":"3115197668"},"language":"en","primary_location":{"id":"doi:10.1109/tencon50793.2020.9293712","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon50793.2020.9293712","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5089050505","display_name":"Ayan Maiti","orcid":"https://orcid.org/0000-0002-5759-5530"},"institutions":[{"id":"https://openalex.org/I11880225","display_name":"National Institute of Technology Karnataka","ror":"https://ror.org/01hz4v948","country_code":"IN","type":"education","lineage":["https://openalex.org/I11880225"]},{"id":"https://openalex.org/I42014448","display_name":"Sardar Vallabhbhai National Institute of Technology Surat","ror":"https://ror.org/02y394t43","country_code":"IN","type":"education","lineage":["https://openalex.org/I42014448"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Ayan Maiti","raw_affiliation_strings":["Department of Mathematical and Computational Sciences, National Institute of Technology, Surathkal, Karnataka, India"],"affiliations":[{"raw_affiliation_string":"Department of Mathematical and Computational Sciences, National Institute of Technology, Surathkal, Karnataka, India","institution_ids":["https://openalex.org/I11880225","https://openalex.org/I42014448"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018213450","display_name":"D. Pushparaj Shetty","orcid":"https://orcid.org/0000-0001-5246-8631"},"institutions":[{"id":"https://openalex.org/I42014448","display_name":"Sardar Vallabhbhai National Institute of Technology Surat","ror":"https://ror.org/02y394t43","country_code":"IN","type":"education","lineage":["https://openalex.org/I42014448"]},{"id":"https://openalex.org/I11880225","display_name":"National Institute of Technology Karnataka","ror":"https://ror.org/01hz4v948","country_code":"IN","type":"education","lineage":["https://openalex.org/I11880225"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Pushparaj Shetty D","raw_affiliation_strings":["Department of Mathematical and Computational Sciences, National Institute of Technology, Surathkal, Karnataka, India"],"affiliations":[{"raw_affiliation_string":"Department of Mathematical and Computational Sciences, National Institute of Technology, Surathkal, Karnataka, India","institution_ids":["https://openalex.org/I11880225","https://openalex.org/I42014448"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5089050505"],"corresponding_institution_ids":["https://openalex.org/I11880225","https://openalex.org/I42014448"],"apc_list":null,"apc_paid":null,"fwci":0.8976,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.78500564,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1215","last_page":"1220"},"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":0.9908000230789185,"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":0.98580002784729,"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":0.6871256232261658},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.602818489074707},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5992462635040283},{"id":"https://openalex.org/keywords/stock-exchange","display_name":"Stock exchange","score":0.5361475944519043},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.5326476097106934},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.49028709530830383},{"id":"https://openalex.org/keywords/closing","display_name":"Closing (real estate)","score":0.47928208112716675},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47299036383628845},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.467193603515625},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4658200740814209},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.45536085963249207},{"id":"https://openalex.org/keywords/stock-price","display_name":"Stock price","score":0.45347338914871216},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.44721320271492004},{"id":"https://openalex.org/keywords/stock-market-prediction","display_name":"Stock market prediction","score":0.4212353527545929},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.33489611744880676},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.179711252450943},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.1640053689479828},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08823612332344055}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6871256232261658},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.602818489074707},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5992462635040283},{"id":"https://openalex.org/C200870193","wikidata":"https://www.wikidata.org/wiki/Q11691","display_name":"Stock exchange","level":2,"score":0.5361475944519043},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.5326476097106934},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.49028709530830383},{"id":"https://openalex.org/C2778775528","wikidata":"https://www.wikidata.org/wiki/Q5135432","display_name":"Closing (real estate)","level":2,"score":0.47928208112716675},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47299036383628845},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.467193603515625},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4658200740814209},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.45536085963249207},{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.45347338914871216},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.44721320271492004},{"id":"https://openalex.org/C2776256503","wikidata":"https://www.wikidata.org/wiki/Q7617906","display_name":"Stock market prediction","level":4,"score":0.4212353527545929},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.33489611744880676},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.179711252450943},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.1640053689479828},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08823612332344055},{"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},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon50793.2020.9293712","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon50793.2020.9293712","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6700000166893005}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1946238955","https://openalex.org/W2064675550","https://openalex.org/W2099471712","https://openalex.org/W2607162077","https://openalex.org/W2796929742","https://openalex.org/W2969886070","https://openalex.org/W2982662853","https://openalex.org/W4320013936"],"related_works":["https://openalex.org/W2912153778","https://openalex.org/W4387163678","https://openalex.org/W4288108708","https://openalex.org/W2973430807","https://openalex.org/W4385280324","https://openalex.org/W2890685186","https://openalex.org/W2984436043","https://openalex.org/W4390245176","https://openalex.org/W2912831041","https://openalex.org/W3173606726"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"predict":[4],"the":[5,21,29,38,47,52,62,65,69,75,85,91,100,107],"stock":[6],"prices":[7],"of":[8,64,78,87,93,102],"five":[9],"companies":[10],"listed":[11],"on":[12,106],"India's":[13],"National":[14],"Stock":[15],"Exchange":[16],"(NSE)":[17],"using":[18],"two":[19],"models-":[20],"Long":[22],"Short":[23],"Term":[24],"Memory":[25],"(LSTM)":[26],"model":[27,34],"and":[28,40,60,95],"Generative":[30],"Adversarial":[31],"Network":[32],"(GAN)":[33],"with":[35],"LSTM":[36],"as":[37,46,58],"generator":[39],"a":[41,79],"simple":[42],"dense":[43],"neural":[44],"network":[45],"discriminant.":[48],"Both":[49],"models":[50],"take":[51],"online":[53],"published":[54],"historical":[55],"stock-price":[56],"data":[57],"input":[59],"produce":[61],"prediction":[63,108],"closing":[66],"price":[67],"for":[68,90],"next":[70],"trading":[71],"day.":[72],"To":[73],"emulate":[74],"thought":[76],"process":[77],"real":[80],"trader,":[81],"our":[82],"implementation":[83],"applies":[84],"technique":[86],"rolling":[88],"segmentation":[89],"partition":[92],"training":[94],"testing":[96],"dataset":[97],"to":[98],"examine":[99],"effect":[101],"different":[103],"interval":[104],"partitions":[105],"performance.":[109]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
