{"id":"https://openalex.org/W2129389638","doi":"https://doi.org/10.1109/ijcnn.2003.1224024","title":"Analyzing dividend events with neural network rule extraction","display_name":"Analyzing dividend events with neural network rule extraction","publication_year":2004,"publication_date":"2004-06-22","ids":{"openalex":"https://openalex.org/W2129389638","doi":"https://doi.org/10.1109/ijcnn.2003.1224024","mag":"2129389638"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2003.1224024","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2003.1224024","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","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/A5072879756","display_name":"Ming Dong","orcid":"https://orcid.org/0000-0002-7101-5555"},"institutions":[{"id":"https://openalex.org/I185443292","display_name":"Wayne State University","ror":"https://ror.org/01070mq45","country_code":"US","type":"education","lineage":["https://openalex.org/I185443292"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ming Dong","raw_affiliation_strings":["Machine Vision and Pattern Recognition Laboratory, Department of Computer Science, Wayne State University, Detroit, MI, USA","Dept. of Comput Sci., Wayne State Univ., Detroit, MI, USA"],"affiliations":[{"raw_affiliation_string":"Machine Vision and Pattern Recognition Laboratory, Department of Computer Science, Wayne State University, Detroit, MI, USA","institution_ids":["https://openalex.org/I185443292"]},{"raw_affiliation_string":"Dept. of Comput Sci., Wayne State Univ., Detroit, MI, USA","institution_ids":["https://openalex.org/I185443292"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000483166","display_name":"Xu-Shen Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I190816365","display_name":"Bloomsburg University","ror":"https://ror.org/007dga614","country_code":"US","type":"education","lineage":["https://openalex.org/I190816365"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xu-Shen Zhou","raw_affiliation_strings":["Department of Finance and Legal Studies College of Business, Bloomsburg University, Bloomsburg, PA, USA","[Bloomsburg University of Pennsylvania]"],"affiliations":[{"raw_affiliation_string":"Department of Finance and Legal Studies College of Business, Bloomsburg University, Bloomsburg, PA, USA","institution_ids":["https://openalex.org/I190816365"]},{"raw_affiliation_string":"[Bloomsburg University of Pennsylvania]","institution_ids":["https://openalex.org/I190816365"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5072879756"],"corresponding_institution_ids":["https://openalex.org/I185443292"],"apc_list":null,"apc_paid":null,"fwci":0.434,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.70083839,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"4","issue":null,"first_page":"2854","last_page":"2859"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9991999864578247,"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":0.9991999864578247,"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":0.9836999773979187,"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"}},{"id":"https://openalex.org/T10047","display_name":"Financial Markets and Investment Strategies","score":0.9728999733924866,"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/dividend","display_name":"Dividend","score":0.7768226861953735},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5976287722587585},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5917326807975769},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.5824465155601501},{"id":"https://openalex.org/keywords/stock-price","display_name":"Stock price","score":0.5150001049041748},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5018966197967529},{"id":"https://openalex.org/keywords/dividend-yield","display_name":"Dividend yield","score":0.4195585548877716},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4110007584095001},{"id":"https://openalex.org/keywords/financial-economics","display_name":"Financial economics","score":0.40861088037490845},{"id":"https://openalex.org/keywords/dividend-policy","display_name":"Dividend policy","score":0.348905086517334},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.28988754749298096},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.11050847172737122},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08872950077056885}],"concepts":[{"id":"https://openalex.org/C116168712","wikidata":"https://www.wikidata.org/wiki/Q181201","display_name":"Dividend","level":2,"score":0.7768226861953735},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5976287722587585},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5917326807975769},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.5824465155601501},{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.5150001049041748},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5018966197967529},{"id":"https://openalex.org/C184530449","wikidata":"https://www.wikidata.org/wiki/Q2706107","display_name":"Dividend yield","level":4,"score":0.4195585548877716},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4110007584095001},{"id":"https://openalex.org/C106159729","wikidata":"https://www.wikidata.org/wiki/Q2294553","display_name":"Financial economics","level":1,"score":0.40861088037490845},{"id":"https://openalex.org/C59399099","wikidata":"https://www.wikidata.org/wiki/Q5284040","display_name":"Dividend policy","level":3,"score":0.348905086517334},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.28988754749298096},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.11050847172737122},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08872950077056885},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","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/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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2003.1224024","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2003.1224024","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","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/W1554663460","https://openalex.org/W1975272859","https://openalex.org/W2025746315","https://openalex.org/W2033390701","https://openalex.org/W2063046703","https://openalex.org/W2104796460","https://openalex.org/W2111255674","https://openalex.org/W2111918129","https://openalex.org/W2119621677","https://openalex.org/W2125055259","https://openalex.org/W2125389748","https://openalex.org/W2128633294","https://openalex.org/W2136734237","https://openalex.org/W3094633476","https://openalex.org/W3122464303","https://openalex.org/W3122578644","https://openalex.org/W3122727604","https://openalex.org/W3122993843","https://openalex.org/W3125902854","https://openalex.org/W4251733784","https://openalex.org/W4285719527","https://openalex.org/W6633075811","https://openalex.org/W6659001531","https://openalex.org/W6678449394","https://openalex.org/W6678583879"],"related_works":["https://openalex.org/W1484809014","https://openalex.org/W2380198706","https://openalex.org/W2090345768","https://openalex.org/W2356514433","https://openalex.org/W3215587151","https://openalex.org/W2347893185","https://openalex.org/W3200849755","https://openalex.org/W1517418884","https://openalex.org/W2351531671","https://openalex.org/W2993425899"],"abstract_inverted_index":{"Over":[0],"the":[1,39,47,88,92,97,125,148,151,171,174,208],"last":[2],"two":[3],"decades,":[4],"artificial":[5],"neural":[6,36,61],"networks":[7,37],"(ANN)":[8],"have":[9,113],"been":[10],"applied":[11],"to":[12,31,41,82,103,109,132],"solve":[13],"a":[14,43],"variety":[15],"of":[16,46,71,99,127,164],"problems":[17],"such":[18],"as":[19,138,140],"pattern":[20],"classification":[21],"and":[22,75,96,117,155],"function":[23,59],"approximation.":[24],"In":[25,50],"many":[26],"applications,":[27],"it":[28],"is":[29,106,158,167],"desirable":[30],"extract":[32],"knowledge":[33],"from":[34,58,79,173,207],"trained":[35],"for":[38],"users":[40],"gain":[42],"better":[44],"understanding":[45],"network's":[48],"solution.":[49],"this":[51],"paper,":[52],"we":[53,85],"apply":[54],"REFANN":[55],"(rule":[56],"extraction":[57,187],"approximating":[60],"networks)":[62],"in":[63,197],"dividend":[64,73,101,115,133,176],"events":[65,78,134,177,199],"study.":[66],"Based":[67],"on":[68,193],"our":[69],"study":[70],"1530":[72],"initiations":[74],"692":[76],"resumptions":[77],"April":[80],"1965":[81],"December":[83],"2000,":[84],"find":[86],"that":[87,112,124,147,169],"positive":[89],"relation":[90],"between":[91,150],"short-term":[93,128],"price":[94,105,130,153],"reaction":[95],"ratio":[98,116],"annualized":[100],"amount":[102],"stock":[104,129,152],"primarily":[107],"limited":[108],"96":[110],"firms":[111],"high":[114],"small":[118],"firm":[119,156],"size.":[120],"The":[121,143],"results":[122,144],"suggest":[123],"degree":[126],"underreaction":[131],"may":[135,179,189],"not":[136],"be":[137,205],"dramatic":[139],"previously":[141],"believed.":[142],"also":[145,159],"show":[146],"relations":[149],"response":[154],"size":[157],"different":[160,162],"across":[161],"types":[163],"firms.":[165],"It":[166],"suggested":[168],"drawing":[170],"conclusions":[172],"whole":[175],"data":[178],"leave":[180],"some":[181,191],"important":[182],"information":[183,203],"unexamined.":[184],"Our":[185],"rule":[186],"method":[188],"shed":[190],"lights":[192],"further":[194],"empirical":[195],"research":[196],"corporate":[198],"studies":[200],"because":[201],"more":[202],"can":[204],"drawn":[206],"data.":[209]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}