{"id":"W4405205487","doi":"10.3390/jrfm17120554","title":"The Application of Machine Learning Techniques to Predict Stock Market Crises in Africa","year":2024,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stock market; Stock (firearms); Economics; Financial economics; Artificial intelligence; Business; Machine learning; Econometrics; Computer science; Geography; Archaeology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001263363,0.0004960669,0.0004062881,0.001867161,0.0002730794,0.0008111209,0.0002191023,0.0003410001,0.0004392925],"category_scores_gemma":[0.004977027,0.0001645578,0.000281154,0.001085808,0.0001708643,0.0008157942,0.000366494,0.0004893286,0.000154256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003083904,"about_ca_system_score_gemma":0.0005343573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00328594,"about_ca_topic_score_gemma":0.002981091,"domain_scores_codex":[0.9996833,0.0001598875,0.00003083024,0.00003144502,0.00005447611,0.00004011247],"domain_scores_gemma":[0.9988697,0.0007165031,0.0001822489,0.00003038256,0.0001669317,0.00003411654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000417287,0.0002482726,0.2735658,0.0003171445,0.0002761423,0.0008394496,0.0006041398,0.3557123,0.009423603,0.004871713,0.001722354,0.3520019],"study_design_scores_gemma":[0.00001346751,0.0001394838,0.04380961,0.00009263958,0.00003606579,0.0001663159,0.0004048703,0.9476163,0.003600278,0.002524484,0.00158065,0.00001578782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9371202,0.001769719,0.05537689,0.0008035091,0.00006215156,0.00008092112,0.0002693977,0.0001451467,0.004372104],"genre_scores_gemma":[0.9852666,0.0005331023,0.01378773,0.00002464524,0.00001751123,0.0000134205,0.0001151186,0.000004761918,0.000237118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00328594,"threshold_uncertainty_score":0.006681383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01159841353644809,"score_gpt":0.2130398842115392,"score_spread":0.2014414706750912,"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."}}