{"id":"W4380763485","doi":"10.2139/ssrn.4469996","title":"Online Appendix for \"Crash Prediction Using Fundamental Variables: Evidence From Mainland China\"","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Equity (law); Crash; Earnings yield; Mainland China; Econometrics; Earnings; Economics; China; Financial economics; Stock (firearms); Portfolio; Price–earnings ratio; Earnings per share; Geography; Finance; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000927616,0.0007036843,0.0004993727,0.003328092,0.0005320361,0.0005636915,0.001337555,0.0007766359,0.5680224],"category_scores_gemma":[0.009608281,0.0004242605,0.0004787518,0.006103209,0.0001443804,0.0007478576,0.000939926,0.0004328357,0.1594869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005655543,"about_ca_system_score_gemma":0.002273626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04320149,"about_ca_topic_score_gemma":0.05494359,"domain_scores_codex":[0.9995883,0.00005969428,0.000112783,0.00005985338,0.000117672,0.00006163548],"domain_scores_gemma":[0.990359,0.003705709,0.000952511,0.0007304757,0.003786019,0.0004662875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00005334891,0.0001060669,0.006466044,0.0004276569,0.00001762357,0.00007087568,0.00006697007,0.000424762,0.00009886327,0.0007488616,0.9727654,0.01875337],"study_design_scores_gemma":[0.001051149,0.0003201382,0.2839996,0.0006038839,0.0001100306,0.0003094664,0.001167172,0.003691678,0.0008845303,0.006175789,0.7015751,0.0001114559],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004322695,0.00008469179,0.0008461558,0.0003416066,0.0001945573,0.0003397274,0.983433,0.0005006019,0.009936872],"genre_scores_gemma":[0.02167086,0.0003388396,0.003590687,0.0004077434,0.0002065502,0.001377946,0.9260501,0.0001682161,0.04618906],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5680224,"threshold_uncertainty_score":0.6161636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04254255366671388,"score_gpt":0.2527142477092438,"score_spread":0.2101716940425299,"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."}}