{"id":"W3125706253","doi":"","title":"L-performance with an application to hedge funds","year":2009,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Sharpe ratio; Hedge fund; Econometrics; Trimming; Skewness; Ranking (information retrieval); Smoothing; Measure (data warehouse); L-moment; Economics; Mathematics; Actuarial science; Computer science; Statistics; Financial economics; Finance; Order statistic; Artificial intelligence","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":[0.004856025,0.001002221,0.0009635367,0.004089645,0.0005688282,0.002579057,0.0006674131,0.001494636,0.002328883],"category_scores_gemma":[0.03098027,0.0002411102,0.0006892128,0.003352929,0.001561123,0.00262411,0.002797622,0.002190679,0.0007046051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00139921,"about_ca_system_score_gemma":0.0009252874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001316604,"about_ca_topic_score_gemma":0.0005263875,"domain_scores_codex":[0.9965335,0.001580427,0.0001863464,0.0002632154,0.00119792,0.0002384837],"domain_scores_gemma":[0.9903758,0.005698126,0.001244179,0.0008005463,0.001516488,0.0003648486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001547784,0.0001159182,0.008370415,0.0002023396,0.00009212348,0.0006850709,0.0002904297,0.3003494,0.003421065,0.4531259,0.009352575,0.22384],"study_design_scores_gemma":[0.00002640602,0.0001576273,0.002431009,0.00007972863,0.00002452149,0.0004423706,0.0001093897,0.717171,0.003126442,0.2642709,0.01207988,0.00008069431],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.05660799,0.004540884,0.9029362,0.002861495,0.0004530116,0.0001017348,0.0003274571,0.001193333,0.03097796],"genre_scores_gemma":[0.8363265,0.002803336,0.1526809,0.0003233636,0.001218431,0.0001428956,0.0003166715,0.0003066562,0.005881258],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.004856025,"threshold_uncertainty_score":0.02568138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04074927026940148,"score_gpt":0.2812998087764073,"score_spread":0.2405505385070058,"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."}}