{"id":"W3166630008","doi":"10.2139/ssrn.3833200","title":"Revisit the Use of Asset Turnover and Profit Margin in Forecasting Operating Profitability: Further Evidence","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Financial Reporting and Valuation Research","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Profitability index; Asset turnover; Profit margin; Margin (machine learning); Business; Operating margin; Profit (economics); Asset (computer security); Economics; Industrial organization; Microeconomics; Monetary economics; Finance; Computer science; Return on assets","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":[0.009870433,0.001122265,0.0007426254,0.002251761,0.0004821357,0.004312583,0.001818288,0.001795224,0.002624712],"category_scores_gemma":[0.04418425,0.0003257127,0.0009954557,0.00195249,0.001015218,0.004301395,0.001019521,0.002191509,0.001729838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006072153,"about_ca_system_score_gemma":0.0007528957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01095808,"about_ca_topic_score_gemma":0.01181963,"domain_scores_codex":[0.9972579,0.00117192,0.0002717441,0.0004944961,0.0006664461,0.0001374603],"domain_scores_gemma":[0.8625386,0.1115153,0.013463,0.004743104,0.006762908,0.0009770412],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009261187,0.0004936723,0.9094598,0.000201974,0.0006156494,0.0001698368,0.0005559301,0.003988412,0.0008990746,0.001003167,0.000864872,0.08082148],"study_design_scores_gemma":[0.00009963231,0.001201043,0.9347727,0.0005686593,0.0008012574,0.000379631,0.001849919,0.04777036,0.002570112,0.005099554,0.004777168,0.0001099114],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9805711,0.007465249,0.002578258,0.001557953,0.00007619656,0.00002309254,0.0005305511,0.00004559252,0.00715212],"genre_scores_gemma":[0.995479,0.001780852,0.001322633,0.000214377,0.0001009595,0.000005867203,0.000433458,0.00001638658,0.0006464291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01095808,"threshold_uncertainty_score":0.0522005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1420889928211695,"score_gpt":0.3172105210458172,"score_spread":0.1751215282246477,"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."}}