{"id":"W3125582787","doi":"10.1111/j.1475-6803.2010.01268.x","title":"DYNAMIC HEDGE FUND STYLE ANALYSIS WITH ERRORS‐IN‐VARIABLES","year":2010,"lang":"en","type":"article","venue":"The Journal of Financial Research","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Benchmark (surveying); Kalman filter; Econometrics; Hedge fund; Computer science; Series (stratigraphy); Identification (biology); Dynamic factor; Returns-based style analysis; Style analysis; Mathematics; Economics; Finance; Artificial intelligence; Investment management; Fund administration","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006994113,0.0001361556,0.0005089584,0.0010999,0.0002509401,0.0001094298,0.0006798371,0.0001223196,0.0003952059],"category_scores_gemma":[0.0007611297,0.00009477597,0.000138327,0.002103307,0.0003644418,0.0003581081,0.00007886811,0.001256345,0.00005624529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009757106,"about_ca_system_score_gemma":0.0002881512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008185068,"about_ca_topic_score_gemma":0.003903364,"domain_scores_codex":[0.9982675,0.00009726446,0.0007177352,0.0001883864,0.0002152945,0.0005138427],"domain_scores_gemma":[0.9985578,0.0003179422,0.0004107568,0.000378866,0.0002345716,0.0001000294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.001440388,0.0006002996,0.1631164,0.00006857613,0.0003486408,0.0001275684,0.002397266,0.0009555726,0.001450395,0.8216743,0.003514526,0.004306077],"study_design_scores_gemma":[0.0008107463,0.0006162784,0.905162,0.00003480279,0.00004303478,0.00002244301,0.0002256495,0.001517841,0.00007200903,0.06614795,0.02513376,0.0002134493],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.972996,0.001005483,0.0004294969,0.001168689,0.0002565748,0.0001575197,0.00002459917,0.000004347335,0.02395729],"genre_scores_gemma":[0.9976668,0.00053975,0.0006393131,0.00006573946,0.0001287882,0.000005640137,0.000001644584,0.00001550281,0.0009368146],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7555264,"threshold_uncertainty_score":0.5458264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06479509123389932,"score_gpt":0.3131769922171479,"score_spread":0.2483819009832486,"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."}}