{"id":"https://openalex.org/W7204269135","doi":"https://doi.org/10.20944/preprints202608.1869.v1","title":"Hybrid LSTM Model for Bitcoin Price Forecasting: Integrating Reddit Sentiment and On-Chain Data to Assess Financial Stability and Systemic Risk","display_name":"Hybrid LSTM Model for Bitcoin Price Forecasting: Integrating Reddit Sentiment and On-Chain Data to Assess Financial Stability and Systemic Risk","publication_year":2026,"publication_date":"2026-08-26","ids":{"openalex":"https://openalex.org/W7204269135","doi":"https://doi.org/10.20944/preprints202608.1869.v1"},"language":"en","primary_location":{"id":"doi:10.20944/preprints202608.1869.v1","doi":"https://doi.org/10.20944/preprints202608.1869.v1","is_oa":true,"landing_page_url":"https://doi.org/10.20944/preprints202608.1869.v1","pdf_url":null,"source":{"id":"https://openalex.org/S6309402219","display_name":"Preprints.org","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.20944/preprints202608.1869.v1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5148289986","display_name":"Sehak Chun","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Sehak Chun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148191926","display_name":"Tae Rip Kim","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tae Rip Kim","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148294758","display_name":"Ajam Atefeh","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ajam Atefeh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011246229","display_name":"Tshewang Phuntsho","orcid":"https://orcid.org/0000-0003-4095-5816","observed_orcids":["https://orcid.org/0000-0003-4095-5816"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tshewang Phuntsho","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108681918","display_name":"Joon Ho Hwang","orcid":"https://orcid.org/0009-0009-6612-3445","observed_orcids":["https://orcid.org/0009-0009-6612-3445"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Joon Ho Hwang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5019787573","display_name":"Hyoduk Shin","orcid":"https://orcid.org/0000-0003-1595-9145","observed_orcids":["https://orcid.org/0000-0003-1595-9145"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hyoduk Shin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5148289986"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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/T10270","display_name":"Blockchain Technology Applications and Security","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10047","display_name":"Financial Markets and Investment Strategies","score":0.0,"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/bitcoin-price-prediction","display_name":"Bitcoin price prediction","score":0.949999988079071},{"id":"https://openalex.org/keywords/cryptocurrency-price-prediction","display_name":"cryptocurrency price prediction","score":0.9079999923706055},{"id":"https://openalex.org/keywords/on-chain-data","display_name":"on-chain data","score":0.9789999723434448},{"id":"https://openalex.org/keywords/technical-indicators","display_name":"technical indicators","score":0.9729999899864197},{"id":"https://openalex.org/keywords/lstm","display_name":"LSTM","score":0.5860000252723694},{"id":"https://openalex.org/keywords/price-volatility","display_name":"price volatility","score":0.7329999804496765},{"id":"https://openalex.org/keywords/financial-stability","display_name":"financial stability","score":0.7519999742507935},{"id":"https://openalex.org/keywords/systemic-risk","display_name":"systemic risk","score":0.8220000267028809},{"id":"https://openalex.org/keywords/market-sentiment","display_name":"market sentiment","score":0.5389999747276306}],"concepts":[{"id":"https://openalex.org/C180706569","wikidata":"https://www.wikidata.org/wiki/Q13479982","display_name":"Cryptocurrency","level":2,"score":0.8026000261306763},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6873000264167786},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.583899974822998},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5408999919891357},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5130000114440918},{"id":"https://openalex.org/C181622380","wikidata":"https://www.wikidata.org/wiki/Q26911","display_name":"Profit (economics)","level":2,"score":0.44279998540878296},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.40639999508857727},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.3962000012397766},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.39010000228881836},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.38119998574256897},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.37290000915527344},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.32710000872612},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31610000133514404},{"id":"https://openalex.org/C166052673","wikidata":"https://www.wikidata.org/wiki/Q83021","display_name":"Empirical evidence","level":2,"score":0.31349998712539673},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31029999256134033},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26910001039505005},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C117245426","wikidata":"https://www.wikidata.org/wiki/Q235038","display_name":"Technical analysis","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.25999999046325684},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.25619998574256897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.20944/preprints202608.1869.v1","doi":"https://doi.org/10.20944/preprints202608.1869.v1","is_oa":true,"landing_page_url":"https://doi.org/10.20944/preprints202608.1869.v1","pdf_url":null,"source":{"id":"https://openalex.org/S6309402219","display_name":"Preprints.org","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.20944/preprints202608.1869.v1","doi":"https://doi.org/10.20944/preprints202608.1869.v1","is_oa":true,"landing_page_url":"https://doi.org/10.20944/preprints202608.1869.v1","pdf_url":null,"source":{"id":"https://openalex.org/S6309402219","display_name":"Preprints.org","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7163901981","https://openalex.org/W4409746385","https://openalex.org/W7037690952","https://openalex.org/W4412742276","https://openalex.org/W7161689909","https://openalex.org/W4410586653","https://openalex.org/W4414930473","https://openalex.org/W4400472950","https://openalex.org/W4411094514","https://openalex.org/W7166847754"],"abstract_inverted_index":{"This":[0,37],"study":[1,93,181],"proposes":[2],"a":[3,41,173],"Bitcoin":[4,117],"price":[5,30,118,216],"prediction":[6,217],"model":[7,124,153],"utilizing":[8],"Long":[9],"Short-Term":[10],"Memory":[11],"(LSTM)":[12],"networks,":[13],"integrating":[14,80],"technical":[15,108,160],"indicators,":[16,19,109,161],"Reddit":[17,110,166],"sentiment":[18,167],"and":[20,49,53,112,134,146,168],"on-chain":[21,113,169],"data.":[22],"The":[23],"cryptocurrency":[24,215],"market,":[25,192],"particularly":[26],"Bitcoin,":[27],"exhibits":[28],"extreme":[29],"volatility,":[31],"despite":[32],"its":[33],"high":[34],"profit":[35],"potential.":[36],"volatility":[38],"stems":[39],"from":[40],"combination":[42],"of":[43,68,79,98,164,185,190,209],"macroeconomic":[44],"factors,":[45],"market":[46],"participant":[47],"sentiment,":[48,71,111],"fluctuations":[50],"in":[51,172,176,213],"supply":[52],"demand":[54],"within":[55],"the":[56,76,96,121,151,162,183,187,191,207],"blockchain":[57],"ecosystem.":[58],"Existing":[59],"literature":[60],"typically":[61],"examines":[62],"only":[63,159],"one":[64],"or":[65,72],"two":[66],"types":[67],"data\u2014whether":[69],"technical,":[70],"on-chain\u2014without":[73],"systematically":[74],"verifying":[75],"complementary":[77],"effects":[78],"these":[81],"heterogeneous":[82,210],"data":[83,100,114,211],"sources":[84],"on":[85,116],"predictive":[86,156],"performance.":[87],"To":[88],"address":[89],"this":[90,92,180],"gap,":[91],"quantitatively":[94],"analyzes":[95],"contribution":[97],"each":[99],"type":[101],"by":[102],"constructing":[103],"four":[104],"experimental":[105,139],"settings":[106],"combining":[107],"based":[115],"movements.":[119],"Moreover,":[120],"proposed":[122],"LSTM":[123,152],"is":[125],"benchmarked":[126],"against":[127],"Linear":[128],"Regression":[129],"(LR),":[130],"Random":[131],"Forest":[132],"(RF),":[133],"XGBoost":[135],"(XGB)":[136],"under":[137],"identical":[138],"conditions.":[140],"Evaluation":[141],"metrics,":[142],"including":[143],"RMSE,":[144],"MAE,":[145],"MAPE,":[147],"indicate":[148],"that":[149],"while":[150],"demonstrates":[154],"superior":[155],"performance":[157],"using":[158],"inclusion":[163],"both":[165],"indicators":[170],"results":[171],"slight":[174],"increase":[175],"error":[177],"metrics.":[178],"Nevertheless,":[179],"emphasizes":[182],"potential":[184],"capturing":[186],"multifaceted":[188],"characteristics":[189],"which":[193],"are":[194],"often":[195],"overlooked":[196],"with":[197],"single":[198],"price-based":[199],"indicators.":[200],"It":[201],"provides":[202],"an":[203],"empirical":[204],"foundation":[205],"supporting":[206],"effectiveness":[208],"integration":[212],"future":[214],"research.":[218]},"counts_by_year":[],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2026-08-27T00:00:00"}
