{"id":"W4321497455","doi":"10.3390/jrfm16030140","title":"Forecasting Methods of Key Ratios and Their Impact in Company’s Value","year":2023,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Valuation (finance); Computer science; Business valuation; Discounting; Variable (mathematics); Key (lock); Selection (genetic algorithm); Process (computing); Value (mathematics); Sensitivity (control systems); Econometrics; Operations research; Economics; Finance; Engineering; Machine learning; Mathematics","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":[0.005001807,0.0009126659,0.0007075682,0.003200491,0.0004025499,0.001849384,0.001056319,0.0007145242,0.001934876],"category_scores_gemma":[0.02599507,0.0002862937,0.000759449,0.002103368,0.000724056,0.003520645,0.0006709856,0.001308391,0.0005012667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009811186,"about_ca_system_score_gemma":0.0005590857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003488075,"about_ca_topic_score_gemma":0.001601538,"domain_scores_codex":[0.9980956,0.000717598,0.000104786,0.0003219078,0.0006674125,0.0000926815],"domain_scores_gemma":[0.9925733,0.005413423,0.0006777522,0.0005545907,0.0006972178,0.00008378311],"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":[0.0001164046,0.00005514931,0.01120593,0.0001591377,0.00008674881,0.0001336164,0.000277887,0.5578573,0.002946642,0.1401523,0.001252416,0.2857565],"study_design_scores_gemma":[0.000008438818,0.00004414946,0.002831585,0.00005370107,0.00001986949,0.00009870114,0.00005713523,0.935727,0.002352099,0.05645447,0.002312978,0.00003985184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03189304,0.0009417526,0.9619243,0.0003347818,0.00009637349,0.00003386198,0.0001265217,0.0002851519,0.004364323],"genre_scores_gemma":[0.740648,0.001708373,0.2555631,0.00005066178,0.0001390648,0.00008144345,0.0002641379,0.0001145889,0.001430729],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005001807,"threshold_uncertainty_score":0.02645242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05145868647033698,"score_gpt":0.2853058372180126,"score_spread":0.2338471507476756,"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."}}