{"id":"W3134860850","doi":"10.3390/jrfm14050201","title":"Portfolio Optimization Constrained by Performance Attribution","year":2021,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sharpe ratio; Portfolio; Portfolio optimization; Econometrics; Drawdown (hydrology); Ranking (information retrieval); Post-modern portfolio theory; Selection (genetic algorithm); Benchmark (surveying); Capital asset pricing model; Asset allocation; Asset (computer security); Computer science; Economics; Financial economics; Replicating portfolio; Artificial intelligence; Engineering","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":[0.006214689,0.001829919,0.001798105,0.0009409672,0.000298167,0.002110179,0.0009521319,0.0009393225,0.001179114],"category_scores_gemma":[0.02337734,0.0005664522,0.0006260134,0.0009692047,0.001169329,0.00186976,0.001868166,0.001360989,0.0001831709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001014663,"about_ca_system_score_gemma":0.00128421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001253204,"about_ca_topic_score_gemma":0.0004867532,"domain_scores_codex":[0.9966449,0.001807519,0.0001703916,0.0003660358,0.0006115958,0.0003995576],"domain_scores_gemma":[0.989821,0.007252804,0.001320392,0.0007143019,0.0006565314,0.0002349873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005451461,0.00004429031,0.0009870571,0.00004200047,0.00004916537,0.00003649071,0.00001901454,0.976233,0.0006732188,0.01328927,0.0001612987,0.00841063],"study_design_scores_gemma":[0.0000130252,0.00008798816,0.0005532224,0.00001497782,0.00001099626,0.00001483401,0.00001128022,0.9863273,0.000783274,0.01193805,0.0002357807,0.000009325893],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2197481,0.0009557502,0.763606,0.000630982,0.00005665574,0.0001830511,0.0001654059,0.0002316463,0.01442242],"genre_scores_gemma":[0.9545355,0.0003666629,0.04240614,0.0001003839,0.00004317583,0.0001999263,0.0001710383,0.00007411983,0.002103094],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006214689,"threshold_uncertainty_score":0.03286684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01736654639651164,"score_gpt":0.2723520749742028,"score_spread":0.2549855285776912,"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."}}