{"id":"W2955161886","doi":"","title":"RISK AND RETURN DETERMINANTS OF US INSURERS","year":2019,"lang":"en","type":"article","venue":"˜The œinternational journal of business and finance research","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Profitability index; Risk–return spectrum; Systematic risk; Actuarial science; Leverage (statistics); Proxy (statistics); Business; Return on assets; Financial risk management; Rate of return; Incentive; Risk-adjusted return on capital; Risk management; Economics; Finance; Profit (economics); Statistics; Portfolio; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0006508327,0.0001357673,0.0001360274,0.0008589759,0.0001819095,0.0006946828,0.00019239,0.0003329726,0.002474007],"category_scores_gemma":[0.005469292,0.00009646517,0.0003753556,0.0006696548,0.0002115051,0.0002816198,0.0004142904,0.0006192488,0.0003575638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004376528,"about_ca_system_score_gemma":0.0003643795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01226488,"about_ca_topic_score_gemma":0.009774281,"domain_scores_codex":[0.9996247,0.00007160733,0.00004105699,0.00003783491,0.0001388114,0.00008585589],"domain_scores_gemma":[0.9934628,0.001490381,0.003572341,0.0002204024,0.0005189392,0.0007351386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003327555,0.0000525824,0.9958197,0.000004057435,0.00003853929,0.00006695237,0.00005400331,0.0003753919,0.0001383119,0.0002131475,0.0002753245,0.00292876],"study_design_scores_gemma":[0.000002016383,0.00002243258,0.9980804,0.000004723699,0.0000168238,0.0001062352,0.00007151238,0.001097726,0.00008384902,0.0001245664,0.0003858853,0.000003945966],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998128,0.0002160684,0.0001411771,0.0002812704,0.000003067155,0.000004226857,0.0003787003,0.00001090897,0.0008366085],"genre_scores_gemma":[0.9992971,0.00009183139,0.00004524798,0.00001865273,0.00000912965,0.000001355176,0.0002689049,0.000001609318,0.0002661752],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01226488,"threshold_uncertainty_score":0.024387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03719559261290023,"score_gpt":0.2850305868059617,"score_spread":0.2478349941930614,"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."}}