{"id":"W2920377001","doi":"10.2139/ssrn.2908552","title":"Beating the Market: Dynamic Asset Allocation with a Market Portfolio Benchmark","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Capital market line; Portfolio; Asset allocation; Replicating portfolio; Financial economics; Benchmark (surveying); Black–Litterman model; Economics; Asset (computer security); Business; Portfolio optimization; Stock market; Computer science; Market depth; Context (archaeology); Computer security","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":[0.003247067,0.0008348218,0.001650829,0.000977954,0.0005964828,0.003737674,0.00171657,0.002477786,0.005601616],"category_scores_gemma":[0.0224101,0.0003550539,0.0004638132,0.001171478,0.001251525,0.004790852,0.001832479,0.00182373,0.0004000464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000988143,"about_ca_system_score_gemma":0.001030543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001944591,"about_ca_topic_score_gemma":0.0009629017,"domain_scores_codex":[0.9989952,0.0004746498,0.00004474801,0.000164594,0.0001675047,0.0001532062],"domain_scores_gemma":[0.9963213,0.001740101,0.0004868084,0.000482576,0.0004342826,0.0005349072],"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":[0.0007083136,0.0002789006,0.001392964,0.00008294537,0.0001016935,0.0002252676,0.00007797646,0.4675218,0.002083729,0.4802562,0.005769733,0.04150039],"study_design_scores_gemma":[0.0000499246,0.00006091241,0.0002935374,0.000007818657,0.00001349938,0.00002302115,0.00001419701,0.8971273,0.0002682647,0.101699,0.0004320023,0.00001056687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5327352,0.001714271,0.4003686,0.003491795,0.0006136521,0.0001311074,0.0004743482,0.0005512604,0.05991971],"genre_scores_gemma":[0.9761204,0.0002393753,0.01817582,0.000172197,0.0001596435,0.00004076998,0.0001656865,0.00006781969,0.004858248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005601616,"threshold_uncertainty_score":0.01873928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01042063578752068,"score_gpt":0.214336537016948,"score_spread":0.2039159012294273,"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."}}