{"id":"W2102457310","doi":"10.3905/jpm.2009.35.3.106","title":"Beyond the Central Tendency: <i>Quantile Regression as a Tool in Quantitative Investing</i>","year":2009,"lang":"en","type":"article","venue":"The Journal of Portfolio Management","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadian Seaplants (Canada)","funders":"","keywords":"Quantile regression; Econometrics; Ordinary least squares; Quantile; Regression; Portfolio; Population; Economics; Extension (predicate logic); Statistics; Computer science; Mathematics; Financial economics; Sociology","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":[0.01165581,0.0009254214,0.0009242873,0.004252351,0.0006834907,0.00507942,0.001564181,0.001745447,0.004959882],"category_scores_gemma":[0.0528148,0.0005117079,0.001269873,0.007725033,0.003626681,0.007401546,0.001913018,0.003615,0.00168039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001012721,"about_ca_system_score_gemma":0.000896516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002613581,"about_ca_topic_score_gemma":0.001376191,"domain_scores_codex":[0.9951036,0.002819044,0.0002439575,0.0006537347,0.001031266,0.0001484378],"domain_scores_gemma":[0.9691861,0.02228664,0.00265038,0.003291265,0.002168206,0.0004173749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001130912,0.00009434727,0.02072743,0.0004603037,0.0002182276,0.0002406548,0.001157214,0.01808567,0.001658212,0.6495083,0.03718484,0.2705517],"study_design_scores_gemma":[0.00002415524,0.0001364477,0.01768784,0.0003376893,0.00007001827,0.0003505491,0.000581548,0.1152253,0.002473305,0.8063776,0.05659356,0.0001421028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01875355,0.009175003,0.9426758,0.01139373,0.0008110249,0.00004816833,0.0004843546,0.00125035,0.01540808],"genre_scores_gemma":[0.6287362,0.01071848,0.3372723,0.005160963,0.004711587,0.0002692602,0.0007988491,0.001148948,0.01118339],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01165581,"threshold_uncertainty_score":0.06164253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02375959661402713,"score_gpt":0.2462737327478816,"score_spread":0.2225141361338544,"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."}}