{"id":"W1959115675","doi":"10.2139/ssrn.1365014","title":"Keynes Meets Markowitz: The Tradeoff Between Familiarity and Diversification","year":2009,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":75,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia Hospital; University of British Columbia; Wilfrid Laurier University; University of Waterloo","funders":"","keywords":"Diversification (marketing strategy); Portfolio; Ambiguity; Economics; Standard deviation; Econometrics; Financial economics; Asset (computer security); Portfolio optimization; Modern portfolio theory; Ambiguity aversion; Capital asset pricing model; Actuarial science; Business; Computer science; Mathematics; Statistics","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":[0.001383985,0.0001174016,0.0002027413,0.00007663319,0.0003805951,0.0001180761,0.0002216373,0.00006873595,0.00002251965],"category_scores_gemma":[0.0000476839,0.00009499757,0.00007515679,0.0001331682,0.00006806289,0.0003121695,0.00001624468,0.0006455721,0.00002337563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002010948,"about_ca_system_score_gemma":0.0001159618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009493779,"about_ca_topic_score_gemma":0.00004859096,"domain_scores_codex":[0.9985968,0.00002546548,0.0003144051,0.0001916382,0.0000433404,0.0008283065],"domain_scores_gemma":[0.9995627,0.00003473266,0.0002011114,0.000138349,0.00001658852,0.00004656487],"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":[0.00001220716,0.00002000642,0.04304811,0.000001831268,0.00003552703,3.708218e-7,0.0001243764,0.000001087449,0.000008810236,0.9438404,0.0002728289,0.01263442],"study_design_scores_gemma":[0.0002069668,0.0001527336,0.3711731,0.000003424146,0.00000861041,0.00001274115,0.0002649016,0.00005188947,0.000005214154,0.6162336,0.01179125,0.00009544925],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9420715,0.01625906,0.001380647,0.01536814,0.0001965246,0.0001878286,0.00001913907,0.00002727564,0.02448987],"genre_scores_gemma":[0.9868364,0.01204888,0.00004068861,0.0002504365,0.0002130228,0.000001707885,0.000003322956,0.000006625898,0.0005989206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.328125,"threshold_uncertainty_score":0.3873889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02121189044401507,"score_gpt":0.2054635854324841,"score_spread":0.184251694988469,"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."}}