{"id":"https://openalex.org/W4382284335","doi":"https://doi.org/10.1145/3584871.3584872","title":"Deep Learning techniques for stock market forecasting: Recent trends and challenges","display_name":"Deep Learning techniques for stock market forecasting: Recent trends and challenges","publication_year":2023,"publication_date":"2023-01-31","ids":{"openalex":"https://openalex.org/W4382284335","doi":"https://doi.org/10.1145/3584871.3584872"},"language":"en","primary_location":{"id":"doi:10.1145/3584871.3584872","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3584871.3584872","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 The 6th International Conference on Software Engineering and Information Management","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/A5102897809","display_name":"Manali Patel","orcid":"https://orcid.org/0000-0002-4909-1869"},"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"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Manali Patel","raw_affiliation_strings":["Department of Computer Science and Engineering, Sardar Vallabhbhai National Institute of Technology, India"],"raw_orcid":"https://orcid.org/0000-0002-4909-1869","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Sardar Vallabhbhai National Institute of Technology, India","institution_ids":["https://openalex.org/I42014448"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076659208","display_name":"Krupa Jariwala","orcid":"https://orcid.org/0000-0002-9675-0548"},"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"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Krupa Jariwala","raw_affiliation_strings":["Department of Computer Science and Engineering, Sardar Vallabhbhai National Institute of Technology, India"],"raw_orcid":"https://orcid.org/0000-0002-9675-0548","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Sardar Vallabhbhai National Institute of Technology, India","institution_ids":["https://openalex.org/I42014448"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054084830","display_name":"Chiranjoy Chattopadhyay","orcid":"https://orcid.org/0000-0002-3431-0483"},"institutions":[{"id":"https://openalex.org/I2802865875","display_name":"Flame University","ror":"https://ror.org/0252mqn49","country_code":"IN","type":"education","lineage":["https://openalex.org/I2802865875"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Chiranjoy Chattopadhyay","raw_affiliation_strings":["School of Computing and Data Science, FLAME University, India"],"raw_orcid":"https://orcid.org/0000-0002-3431-0483","affiliations":[{"raw_affiliation_string":"School of Computing and Data Science, FLAME University, India","institution_ids":["https://openalex.org/I2802865875"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8882,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.86322231,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9958000183105469,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9921000003814697,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6894738674163818},{"id":"https://openalex.org/keywords/stock-market-prediction","display_name":"Stock market prediction","score":0.5974441170692444},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.5837220549583435},{"id":"https://openalex.org/keywords/market-capitalization","display_name":"Market capitalization","score":0.5650341510772705},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5532535314559937},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5496925711631775},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5375755429267883},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49428844451904297},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.37643909454345703},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.17076590657234192}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6894738674163818},{"id":"https://openalex.org/C2776256503","wikidata":"https://www.wikidata.org/wiki/Q7617906","display_name":"Stock market prediction","level":4,"score":0.5974441170692444},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.5837220549583435},{"id":"https://openalex.org/C193445137","wikidata":"https://www.wikidata.org/wiki/Q471719","display_name":"Market capitalization","level":4,"score":0.5650341510772705},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5532535314559937},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5496925711631775},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5375755429267883},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49428844451904297},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.37643909454345703},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.17076590657234192},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3584871.3584872","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3584871.3584872","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 The 6th International Conference on Software Engineering and Information Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4099999964237213}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W1549571940","https://openalex.org/W1885849541","https://openalex.org/W1969852690","https://openalex.org/W2014341469","https://openalex.org/W2080937097","https://openalex.org/W2487275603","https://openalex.org/W2549882901","https://openalex.org/W2572501969","https://openalex.org/W2789399411","https://openalex.org/W2796929742","https://openalex.org/W2806777472","https://openalex.org/W2893230400","https://openalex.org/W2896551579","https://openalex.org/W2902087482","https://openalex.org/W2912614123","https://openalex.org/W2918597653","https://openalex.org/W2942240963","https://openalex.org/W2947836816","https://openalex.org/W2994949492","https://openalex.org/W3007101959","https://openalex.org/W3017051726","https://openalex.org/W3088934411","https://openalex.org/W3092663266","https://openalex.org/W3093690785","https://openalex.org/W3093963912","https://openalex.org/W3095571390","https://openalex.org/W3110420963","https://openalex.org/W3111134894","https://openalex.org/W3115197668","https://openalex.org/W3123329971","https://openalex.org/W3124500271","https://openalex.org/W3126479554","https://openalex.org/W3134798453","https://openalex.org/W3158669684","https://openalex.org/W3166332876","https://openalex.org/W3176497015","https://openalex.org/W3183775315","https://openalex.org/W3183904482","https://openalex.org/W3183916825","https://openalex.org/W3200703117","https://openalex.org/W3202315245","https://openalex.org/W3203924575","https://openalex.org/W3217384338","https://openalex.org/W4205156336","https://openalex.org/W4205507482","https://openalex.org/W4205627296","https://openalex.org/W4210356397","https://openalex.org/W4214708079","https://openalex.org/W4224220755","https://openalex.org/W4226270189","https://openalex.org/W4226443343","https://openalex.org/W4231546411","https://openalex.org/W4242300972","https://openalex.org/W4281251068"],"related_works":["https://openalex.org/W2072009250","https://openalex.org/W2946782651","https://openalex.org/W3125536859","https://openalex.org/W2581658593","https://openalex.org/W3121504727","https://openalex.org/W2168058661","https://openalex.org/W2586556113","https://openalex.org/W1551656699","https://openalex.org/W4249647964","https://openalex.org/W2080708425"],"abstract_inverted_index":{"Stock":[0],"market":[1,124,204,208],"forecasting":[2,205],"has":[3,58,189],"been":[4,75],"a":[5,33],"very":[6],"intensive":[7],"area":[8],"of":[9,22,43,50,137,165,211,216],"research":[10,61],"in":[11,54,228],"recent":[12,93,225],"years":[13],"due":[14],"to":[15,63,77,84,96,110,159,175,220],"the":[16,47,60,141,193,199,229],"highly":[17],"uncertain":[18],"and":[19,81,86,119,135,154,162,182,232],"volatile":[20],"nature":[21],"stock":[23,123,180,203],"data":[24,169,188],"which":[25,101],"makes":[26],"this":[27,127,217],"task":[28],"challenging.":[29],"By":[30],"accurately":[31],"predicting":[32],"particular":[34],"stock's":[35],"price":[36],"investors":[37,183],"can":[38],"gain":[39],"maximum":[40],"profit":[41],"out":[42],"their":[44],"investment.":[45],"With":[46],"great":[48,190],"success":[49],"Deep":[51,98],"Learning":[52,99],"methods":[53,73],"various":[55,97,138,145,179],"domains,":[56],"it":[57],"attracted":[59],"community":[62],"apply":[64],"these":[65],"models":[66,100,147,156],"for":[67,122,202,236],"financial":[68,166],"domain":[69,231],"also.":[70],"These":[71],"DL":[72,146],"have":[74,157],"proven":[76],"achieve":[78],"better":[79],"accuracy":[80],"predictions":[82],"compared":[83],"econometric":[85],"traditional":[87],"ML":[88],"methods.":[89,139],"This":[90],"work":[91,128,226],"reviews":[92,130],"papers":[94],"according":[95],"included:":[102],"Artificial":[103],"Neural":[104,107,117,152],"Networks,":[105,108,115],"Convolution":[106],"Sequence":[109,111],"models,":[112],"Generative":[113],"Adversarial":[114],"Graph":[116,151],"Networks":[118,153],"Transformers":[120],"applied":[121],"forecasting.":[125],"Furthermore":[126],"also":[129,197],"datasets,":[131],"features,":[132],"evaluation":[133],"parameters":[134],"results":[136],"From":[140],"analysis":[142],"done":[143,227],"on":[144,192,207],"we":[148],"found":[149],"that":[150],"Transformer":[155],"potential":[158],"interpret":[160],"dynamic":[161],"non-linear":[163],"patterns":[164],"time":[167],"series":[168],"with":[170,186],"greater":[171],"accuracy.":[172,195],"In":[173],"addition":[174],"this,":[176],"correlation":[177],"among":[178],"indices":[181],"sentiment":[184],"along":[185],"historical":[187],"influence":[191],"prediction":[194],"We":[196],"identified":[198],"benchmark":[200],"datasets":[201],"based":[206],"capitalization":[209],"value":[210],"an":[212],"economy.":[213],"The":[214],"aim":[215],"paper":[218],"is":[219],"provide":[221],"insight":[222],"into":[223],"most":[224],"finance":[230],"identify":[233],"future":[234],"directions":[235],"more":[237],"accurate":[238],"predictions.":[239]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}