{"id":"https://openalex.org/W2743043342","doi":"https://doi.org/10.1145/3106426.3106482","title":"Detecting, quantifying and accessing impact of news events on Indian stock indices","display_name":"Detecting, quantifying and accessing impact of news events on Indian stock indices","publication_year":2017,"publication_date":"2017-08-10","ids":{"openalex":"https://openalex.org/W2743043342","doi":"https://doi.org/10.1145/3106426.3106482","mag":"2743043342"},"language":"en","primary_location":{"id":"doi:10.1145/3106426.3106482","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3106426.3106482","pdf_url":null,"source":{"id":"https://openalex.org/S4306524158","display_name":"Proceedings of the International Conference on Web Intelligence","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Web Intelligence","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/A5059071467","display_name":"Ishan Verma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ishan Verma","raw_affiliation_strings":["TCS Innovation Labs, New Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TCS Innovation Labs, New Delhi, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102724695","display_name":"Lipika Dey","orcid":"https://orcid.org/0000-0003-3831-5545"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lipika Dey","raw_affiliation_strings":["TCS Innovation Labs, New Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TCS Innovation Labs, New Delhi, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028455101","display_name":"Hardik Meisheri","orcid":"https://orcid.org/0000-0002-9014-1098"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hardik Meisheri","raw_affiliation_strings":["TCS Innovation Labs, New Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TCS Innovation Labs, New Delhi, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.6147,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.9379845,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"550","last_page":"557"},"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9912999868392944,"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/T10047","display_name":"Financial Markets and Investment Strategies","score":0.9853000044822693,"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/computer-science","display_name":"Computer science","score":0.670992374420166},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.6351572275161743},{"id":"https://openalex.org/keywords/paragraph","display_name":"Paragraph","score":0.5703673362731934},{"id":"https://openalex.org/keywords/stock-market-prediction","display_name":"Stock market prediction","score":0.5225702524185181},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.5210656523704529},{"id":"https://openalex.org/keywords/granger-causality","display_name":"Granger causality","score":0.5055413246154785},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5042597055435181},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4586922526359558},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3935689330101013},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.37689679861068726},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3657311797142029},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3635593056678772},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.15774092078208923},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.11296287178993225},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09634923934936523}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.670992374420166},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.6351572275161743},{"id":"https://openalex.org/C2777206241","wikidata":"https://www.wikidata.org/wiki/Q194431","display_name":"Paragraph","level":2,"score":0.5703673362731934},{"id":"https://openalex.org/C2776256503","wikidata":"https://www.wikidata.org/wiki/Q7617906","display_name":"Stock market prediction","level":4,"score":0.5225702524185181},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.5210656523704529},{"id":"https://openalex.org/C129824826","wikidata":"https://www.wikidata.org/wiki/Q2630107","display_name":"Granger causality","level":2,"score":0.5055413246154785},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5042597055435181},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4586922526359558},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3935689330101013},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.37689679861068726},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3657311797142029},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3635593056678772},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.15774092078208923},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.11296287178993225},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09634923934936523},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3106426.3106482","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3106426.3106482","pdf_url":null,"source":{"id":"https://openalex.org/S4306524158","display_name":"Proceedings of the International Conference on Web Intelligence","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Web Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W268561219","https://openalex.org/W1947985544","https://openalex.org/W1983381518","https://openalex.org/W2001340398","https://openalex.org/W2036760811","https://openalex.org/W2042502701","https://openalex.org/W2071248588","https://openalex.org/W2110485445","https://openalex.org/W2115770250","https://openalex.org/W2126267628","https://openalex.org/W2128310653","https://openalex.org/W2131744502","https://openalex.org/W2132527043","https://openalex.org/W2134183765","https://openalex.org/W2140597371","https://openalex.org/W2150355110","https://openalex.org/W2171468534","https://openalex.org/W2178225550","https://openalex.org/W2296438605","https://openalex.org/W2325248126","https://openalex.org/W2579460845","https://openalex.org/W3121279039","https://openalex.org/W3122799874","https://openalex.org/W3123216131","https://openalex.org/W3123779227"],"related_works":["https://openalex.org/W2586556113","https://openalex.org/W2542516223","https://openalex.org/W3135178882","https://openalex.org/W2624043242","https://openalex.org/W3209908847","https://openalex.org/W3023530306","https://openalex.org/W2086993069","https://openalex.org/W2110351804","https://openalex.org/W1980850818","https://openalex.org/W2785399422"],"abstract_inverted_index":{"The":[0,168],"impact":[1,137],"of":[2,5,41,47,55,83,86,96,113,125,164,173],"different":[3,29,84,181],"types":[4,85],"events":[6,48],"reported":[7],"in":[8,67],"News":[9],"articles":[10],"on":[11,23,45,178],"stock":[12,37,106,182],"market":[13,92],"is":[14],"a":[15,53,64,94,142],"widely":[16],"accepted":[17],"phenomenon.":[18],"Market":[19],"analysts":[20],"rely":[21],"heavily":[22],"technology":[24],"to":[25,77,116,132,159],"combine":[26],"data":[27,128,179],"from":[28,93,180],"sources":[30,42],"and":[31,59,79,99],"generate":[32],"appropriate":[33],"insights":[34],"for":[35,104],"predicting":[36,105],"movements.":[38],"With":[39],"plethora":[40],"reporting":[43],"news":[44],"plentitude":[46],"happening":[49],"across":[50],"the":[51,81,91,102,111,138,161,165,174],"world,":[52],"combination":[54],"text":[56],"mining":[57],"techniques":[58],"predictive":[60],"technologies":[61],"can":[62,89],"play":[63],"significant":[65],"role":[66],"this":[68,71],"arena.":[69],"In":[70],"paper":[72],"we":[73],"have":[74,121],"presented":[75],"methodologies":[76],"identify":[78],"quantify":[80],"presence":[82],"information":[87,103,145,166],"that":[88],"affect":[90],"multitude":[95],"web":[97],"sources,":[98],"finally":[100],"use":[101,112],"movement":[107],"direction.":[108],"We":[109,120,140,148],"propose":[110,141],"PESTEL":[114],"factors":[115],"categorize":[117],"market-impacting":[118],"information.":[119],"analyzed":[122],"large":[123],"volumes":[124],"past":[126],"available":[127],"using":[129],"Granger":[130],"causality":[131],"understand":[133],"how":[134],"these":[135],"categories":[136],"market.":[139],"paragraph-vector":[143],"based":[144,156],"classification":[146],"mechanism.":[147],"also":[149],"present":[150],"Long-Short":[151],"term":[152],"memory":[153],"Network":[154],"(LSTM)":[155],"prediction":[157,162],"model":[158],"investigate":[160],"capabilities":[163],"components.":[167],"proposed":[169],"system":[170],"outperforms":[171],"state":[172],"art":[175],"linear":[176],"SVM":[177],"indices.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}