{"id":"https://openalex.org/W2803203280","doi":"https://doi.org/10.36227/techrxiv.16778611","title":"A Robust Predictive Model for Stock Price Forecasting","display_name":"A Robust Predictive Model for Stock Price Forecasting","publication_year":2021,"publication_date":"2021-10-12","ids":{"openalex":"https://openalex.org/W2803203280","doi":"https://doi.org/10.36227/techrxiv.16778611","mag":"2803203280"},"language":"en","primary_location":{"id":"doi:10.36227/techrxiv.16778611","doi":"https://doi.org/10.36227/techrxiv.16778611","is_oa":true,"landing_page_url":"https://doi.org/10.36227/techrxiv.16778611","pdf_url":"https://www.techrxiv.org/articles/preprint/A_Robust_Predictive_Model_for_Stock_Price_Forecasting/16778611/1/files/31040749.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"TechRxiv","raw_type":"posted-content"},"volume":null,"type":"preprint","indexed_in":["crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.techrxiv.org/articles/preprint/A_Robust_Predictive_Model_for_Stock_Price_Forecasting/16778611/1/files/31040749.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050768547","display_name":"Jaydip Sen","orcid":"https://orcid.org/0000-0002-4120-8700","observed_orcids":["https://orcid.org/0000-0002-4120-8700"]},"institutions":[{"id":"https://openalex.org/I4405254716","display_name":"Praxis Business School","ror":"https://ror.org/0259r0013","country_code":"IN","type":"education","lineage":["https://openalex.org/I4405254716"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Jaydip Sen","raw_affiliation_strings":["Professor Praxis Business School Kolkata , INDIA","Praxis Business School"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Professor Praxis Business School Kolkata , INDIA","institution_ids":["https://openalex.org/I4405254716"]},{"raw_affiliation_string":"Praxis Business School","institution_ids":["https://openalex.org/I4405254716"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061199365","display_name":"Tamal Datta Chaudhuri","orcid":"https://orcid.org/0000-0002-5086-6019","observed_orcids":["https://orcid.org/0000-0002-5086-6019"]},"institutions":[{"id":"https://openalex.org/I106542073","display_name":"University of Calcutta","ror":"https://ror.org/01e7v7w47","country_code":"IN","type":"education","lineage":["https://openalex.org/I106542073"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Tamal Datta Chaudhuri","raw_affiliation_strings":["Calcutta Business School"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Calcutta Business School","institution_ids":["https://openalex.org/I106542073"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":33,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"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":1.0,"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":1.0,"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":0.0,"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":0.0,"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/stock-price-prediction","display_name":"stock price prediction","score":0.9779999852180481},{"id":"https://openalex.org/keywords/technical-analysis","display_name":"technical analysis","score":0.9549999833106995},{"id":"https://openalex.org/keywords/efficient-market-hypothesis","display_name":"efficient market hypothesis","score":0.8690000176429749},{"id":"https://openalex.org/keywords/regression-based-forecasting","display_name":"regression-based forecasting","score":0.7229999899864197},{"id":"https://openalex.org/keywords/national-stock-exchange-of-india","display_name":"National Stock Exchange of India","score":0.8050000071525574},{"id":"https://openalex.org/keywords/tata-steel","display_name":"Tata Steel","score":0.9879999756813049}],"concepts":[{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.679617166519165},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.6617969274520874},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.513443112373352},{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.5009462833404541},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4845939874649048},{"id":"https://openalex.org/C200870193","wikidata":"https://www.wikidata.org/wiki/Q11691","display_name":"Stock exchange","level":2,"score":0.48017755150794983},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.4533795416355133},{"id":"https://openalex.org/C106159729","wikidata":"https://www.wikidata.org/wiki/Q2294553","display_name":"Financial economics","level":1,"score":0.39715954661369324},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.1297483742237091},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09545600414276123},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.07610777020454407},{"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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":4,"locations":[{"id":"doi:10.36227/techrxiv.16778611","doi":"https://doi.org/10.36227/techrxiv.16778611","is_oa":true,"landing_page_url":"https://doi.org/10.36227/techrxiv.16778611","pdf_url":"https://www.techrxiv.org/articles/preprint/A_Robust_Predictive_Model_for_Stock_Price_Forecasting/16778611/1/files/31040749.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"TechRxiv","raw_type":"posted-content"},{"id":"doi:10.36227/techrxiv.16778611.v1","doi":"https://doi.org/10.36227/techrxiv.16778611.v1","is_oa":true,"landing_page_url":"https://doi.org/10.36227/techrxiv.16778611.v1","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},{"id":"pmh:oai:figshare.com:article/16778611","doi":null,"is_oa":true,"landing_page_url":"https://figshare.com/articles/preprint/A_Robust_Predictive_Model_for_Stock_Price_Forecasting/16778611","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"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":"","raw_type":"Text"},{"id":"doi:10.13140/rg.2.2.19130.49603/1","doi":"https://doi.org/10.13140/rg.2.2.19130.49603/1","is_oa":false,"landing_page_url":"https://doi.org/10.13140/rg.2.2.19130.49603/1","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.36227/techrxiv.16778611","doi":"https://doi.org/10.36227/techrxiv.16778611","is_oa":true,"landing_page_url":"https://doi.org/10.36227/techrxiv.16778611","pdf_url":"https://www.techrxiv.org/articles/preprint/A_Robust_Predictive_Model_for_Stock_Price_Forecasting/16778611/1/files/31040749.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"TechRxiv","raw_type":"posted-content"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.41999998688697815}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2803203280.pdf","grobid_xml":"https://content.openalex.org/works/W2803203280.grobid-xml"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W1574627286","https://openalex.org/W1969852690","https://openalex.org/W1995136661","https://openalex.org/W1996984227","https://openalex.org/W2039873584","https://openalex.org/W2101269184","https://openalex.org/W2101755808","https://openalex.org/W2103997983","https://openalex.org/W2113159968","https://openalex.org/W2149170345","https://openalex.org/W2173913116","https://openalex.org/W2243132937","https://openalex.org/W2331820705","https://openalex.org/W2400222690","https://openalex.org/W2418367812","https://openalex.org/W2724964707","https://openalex.org/W2962736218","https://openalex.org/W3122014485","https://openalex.org/W3143995862"],"related_works":["https://openalex.org/W3019808409","https://openalex.org/W4392194541","https://openalex.org/W4285379330","https://openalex.org/W7217097421","https://openalex.org/W4407221323","https://openalex.org/W3169742823","https://openalex.org/W4246773542","https://openalex.org/W3001055787","https://openalex.org/W4205482008","https://openalex.org/W4402039359"],"abstract_inverted_index":{"Prediction":[0],"of":[1,4,12,22,63,70,104,173,193],"future":[2],"movement":[3,172],"stock":[5,30,50,79,115,131,170],"prices":[6,31,51,116],"has":[7],"been":[8,94,188],"the":[9,23,37,97,118,143,191,199],"subject":[10],"matter":[11],"many":[13],"research":[14],"work.":[15],"On":[16,36],"one":[17],"hand,":[18,39],"we":[19],"have":[20,44,59,93,187],"proponents":[21],"Efficient":[24],"Market":[25],"Hypothesis":[26],"who":[27],"claim":[28],"that":[29,43,92,136],"cannot":[32],"be":[33,53,148],"predicted":[34,54],"accurately.":[35,56],"other":[38],"there":[40],"are":[41,164],"propositions":[42],"shown":[45],"that,":[46],"if":[47],"appropriately":[48],"modelled,":[49],"can":[52,141,147],"fairly":[55],"The":[57,102],"latter":[58],"focused":[60],"on":[61,99,114,190],"choice":[62],"variables,":[64],"appropriate":[65],"functional":[66],"forms":[67],"and":[68,85,125,146,160,168,182],"techniques":[69,155,196],"forecasting.":[71,152],"This":[72],"work":[73,106],"proposes":[74],"a":[75,127,138],"granular":[76,139],"approach":[77,140],"to":[78,108,166],"price":[80,171],"prediction":[81],"by":[82],"combining":[83],"statistical":[84],"machine":[86],"learning":[87],"methods":[88],"with":[89],"some":[90],"concepts":[91],"advanced":[95],"in":[96,123,177],"literature":[98],"technical":[100],"analysis.":[101],"objective":[103],"our":[105],"is":[107,135],"take":[109],"5":[110],"minute":[111],"daily":[112],"data":[113],"from":[117],"National":[119],"Stock":[120],"Exchange":[121],"(NSE)":[122],"India":[124],"develop":[126],"forecasting":[128,195],"framework":[129],"for":[130,150,197],"prices.":[132],"Our":[133],"contention":[134],"such":[137],"model":[142,167],"inherent":[144],"dynamics":[145],"fine-tuned":[149],"immediate":[151],"Six":[153],"different":[154],"including":[156],"three":[157,161],"regression-based":[158],"approaches":[159,163],"classification-based":[162],"applied":[165],"predict":[169],"two":[174],"stocks":[175],"listed":[176],"NSE":[178],"-":[179],"Tata":[180],"Steel":[181],"Hero":[183],"Moto.":[184],"Extensive":[185],"results":[186],"provided":[189],"performance":[192],"these":[194],"both":[198],"stocks.":[200]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":5},{"year":2018,"cited_by_count":1}],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2018-06-01T00:00:00"}
