{"id":"W4414247247","doi":"10.3390/jrfm18090515","title":"Editorial: Machine Learning Applications in Finance, 2nd Edition","year":2025,"lang":"en","type":"editorial","venue":"Journal of risk and financial management","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Focus (optics); Volume (thermodynamics); Key (lock); Term (time); Training set","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":[0.007245645,0.004731503,0.005920937,0.008395541,0.003870821,0.01265267,0.004206774,0.01269829,0.03294396],"category_scores_gemma":[0.03573924,0.00150434,0.00372591,0.003507401,0.002474012,0.005328691,0.001856151,0.01690351,0.03336183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00304251,"about_ca_system_score_gemma":0.005115911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002828339,"about_ca_topic_score_gemma":0.009830603,"domain_scores_codex":[0.9936154,0.0009657235,0.0007170535,0.000735065,0.003588617,0.0003780538],"domain_scores_gemma":[0.9637619,0.01266063,0.002336603,0.0008320194,0.01583458,0.004574133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001506338,0.00000564378,0.000009859668,0.00009192769,0.000007588455,0.00002203356,0.000002068154,0.00001730668,0.00001746523,0.00009541716,0.9972988,0.002416897],"study_design_scores_gemma":[0.00007828702,0.0000246102,0.0002886931,0.0005620471,0.00004129609,0.000165903,0.00001591916,0.0002525662,0.00006717524,0.001334507,0.9971469,0.00002201771],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00001412511,0.005258767,0.0001345641,0.01890852,0.9741154,0.00002075673,0.0001084174,0.00008384386,0.001355692],"genre_scores_gemma":[0.000176637,0.004223974,0.0001048226,0.01205609,0.9744547,0.00002998805,0.0000697641,0.00004946412,0.008834577],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.03294396,"threshold_uncertainty_score":0.1102085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01612048446276786,"score_gpt":0.3344166731516066,"score_spread":0.3182961886888387,"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."}}