{"id":"W4309461041","doi":"10.3390/jrfm15110535","title":"A Bibliometric Analysis of Machine Learning Econometrics in Asset Pricing","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Econometrics; Capital asset pricing model; Computer science; Inference; Financial econometrics; Econometric model; Asset (computer security); Machine learning; Economics; Artificial intelligence; Finance; Financial analysis","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":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.009377345,0.0007144623,0.001135858,0.07537585,0.001787518,0.008147466,0.001089269,0.001511308,0.01068705],"category_scores_gemma":[0.09157746,0.0003702067,0.001379136,0.1250789,0.001670755,0.008315504,0.002613469,0.001149634,0.002784307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003430204,"about_ca_system_score_gemma":0.003188807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005265716,"about_ca_topic_score_gemma":0.00436242,"domain_scores_codex":[0.9893963,0.003242008,0.0008956731,0.0008808238,0.005172292,0.0004130094],"domain_scores_gemma":[0.9235402,0.04998132,0.007749705,0.004586572,0.01329723,0.0008450191],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001042203,0.0001983442,0.1191628,0.003280624,0.0005447683,0.0005020644,0.002604888,0.01588659,0.001097881,0.3385541,0.06118754,0.456876],"study_design_scores_gemma":[0.000049093,0.0001713514,0.2425132,0.002959688,0.0006575523,0.001707602,0.00532828,0.09669569,0.002885365,0.2909646,0.3557946,0.0002729445],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2547016,0.07590724,0.2154372,0.02639497,0.00246699,0.0006889851,0.03425541,0.002315116,0.3878324],"genre_scores_gemma":[0.8882568,0.02911892,0.04772438,0.0007864517,0.002760438,0.0005078184,0.009750596,0.000344652,0.0207499],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9906226,"threshold_uncertainty_score":0.04959273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02074567158677638,"score_gpt":0.2146089286708954,"score_spread":0.193863257084119,"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."}}