{"id":"W2934606109","doi":"10.3390/jrfm12020053","title":"Sentiment-Induced Bubbles in the Cryptocurrency Market","year":2019,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Deutsche Forschungsgemeinschaft","keywords":"Cryptocurrency; Volatility (finance); Econometrics; Leverage (statistics); Index (typography); Autoregressive model; Bubble; Leverage effect; Stock market index; Heteroscedasticity; Economics; Economic bubble; Computer science; Statistical physics; Autoregressive conditional heteroskedasticity; Stock market; Physics; Monetary economics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001372251,0.0001366872,0.0003397376,0.0002982764,0.00007394603,0.00009884303,0.0002637062,0.00005617285,0.0001913153],"category_scores_gemma":[0.00005698811,0.0001066429,0.0001101455,0.000256991,0.00002907505,0.0002855089,0.0000584347,0.0002324707,0.00005112661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003570896,"about_ca_system_score_gemma":0.00001387138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006414672,"about_ca_topic_score_gemma":0.0000145433,"domain_scores_codex":[0.9987999,0.00003926923,0.0006741897,0.0001811294,0.000082625,0.0002229536],"domain_scores_gemma":[0.9991845,0.00005307336,0.0005289549,0.0001773429,0.00002112037,0.00003499788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001295521,0.0002372164,0.1988527,0.00009681734,0.00002777385,0.00004986733,0.00108034,0.00001312905,0.000004039002,0.754019,0.006821259,0.03866827],"study_design_scores_gemma":[0.0009957686,0.0002216444,0.6857796,0.00006058478,0.00001423801,0.000006517995,0.0004755065,0.00005951144,0.000002917467,0.11591,0.1963217,0.0001520515],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9101219,0.00293057,0.0006574211,0.0003215491,0.0009838776,0.0003501672,0.00001793477,0.000004623263,0.08461201],"genre_scores_gemma":[0.9939907,0.004861598,0.0004155645,0.000260865,0.0001331309,0.000007138494,8.546125e-7,0.000008094315,0.0003220561],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.638109,"threshold_uncertainty_score":0.434877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01332397806762762,"score_gpt":0.2018128050230707,"score_spread":0.1884888269554431,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}