{"id":"W4386012837","doi":"10.1016/j.gfj.2023.100889","title":"Firm performance & effective mitigation of adverse business scenarios","year":2023,"lang":"en","type":"article","venue":"Global Finance Journal","topic":"Risk Management in Financial Firms","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Social Sciences and Humanities Research Council","keywords":"Downside risk; Valuation (finance); Earnings; Shareholder; Enterprise value; Economics; Econometrics; Empirical evidence; Maximization; Shareholder value; Business; Financial economics; Microeconomics; Finance; Corporate governance","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006354555,0.0002252858,0.0002973776,0.0002609647,0.0003506141,0.00009049582,0.0004212447,0.00009110646,0.00006394304],"category_scores_gemma":[0.0003185424,0.0002146733,0.0001319964,0.002981464,0.0000944151,0.001866462,0.0002281361,0.0002124533,0.001477461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001345964,"about_ca_system_score_gemma":0.00004871089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001316193,"about_ca_topic_score_gemma":0.00005561327,"domain_scores_codex":[0.9982841,0.0000140033,0.0004723492,0.000257564,0.0005094165,0.0004625941],"domain_scores_gemma":[0.9985564,0.00002461921,0.0006186718,0.0002547943,0.000530959,0.00001451233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003021354,0.0001589355,0.7762147,0.0007412845,0.00007290483,0.0000932819,0.00009583077,0.02243051,0.0002412566,0.01039352,0.06789506,0.1213606],"study_design_scores_gemma":[0.0006717723,0.0000162222,0.8807432,0.0003075209,0.00004415642,0.00001418355,0.00003118716,0.002094502,0.00001652549,0.003898063,0.1119399,0.0002227587],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898758,0.00008065729,0.0006310761,0.0005371029,0.001789649,0.0003650337,0.000009303194,0.000126433,0.006584948],"genre_scores_gemma":[0.9973341,0.0003517045,0.0003641961,0.0002866494,0.001164826,0.00002036895,0.0000323988,0.00002014026,0.000425631],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1211378,"threshold_uncertainty_score":0.9993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01053360667283564,"score_gpt":0.2291615713787589,"score_spread":0.2186279647059232,"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."}}