{"id":"W4322008041","doi":"10.1007/s10203-023-00388-z","title":"Revisiting the 1/N-strategy: a neural network framework for optimal strategies","year":2023,"lang":"en","type":"article","venue":"Decisions in Economics and Finance","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Portfolio; Risk aversion (psychology); Benchmark (surveying); Portfolio optimization; Artificial neural network; Computer science; Econometrics; Expected utility hypothesis; Mathematical optimization; Function (biology); Economics; Mathematical economics; Mathematics; Actuarial science; Artificial intelligence; Financial economics","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":[0.00256308,0.001197677,0.001319932,0.0008705113,0.0005802106,0.002773054,0.002633737,0.003356646,0.00615939],"category_scores_gemma":[0.008186218,0.000647075,0.0009107105,0.001030257,0.002412171,0.006994625,0.001527734,0.003280079,0.0005043377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001827957,"about_ca_system_score_gemma":0.00150928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007902909,"about_ca_topic_score_gemma":0.005689049,"domain_scores_codex":[0.9989685,0.0005696992,0.00005592524,0.0001573866,0.0001612111,0.00008723042],"domain_scores_gemma":[0.9973843,0.002048712,0.0001458407,0.0001067799,0.0002194964,0.00009485571],"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":[0.00003120824,0.00003267518,0.0002922603,0.00009369264,0.00003095161,0.00005533457,0.00008224267,0.2878294,0.0003352365,0.6928303,0.001484385,0.01690224],"study_design_scores_gemma":[0.000007281875,0.00001095355,0.00006050238,0.00001951349,0.000005986081,0.00001049534,0.000008915237,0.6945782,0.00006400266,0.3046693,0.0005577319,0.000007132257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02958935,0.002580734,0.9384763,0.004521627,0.0001970197,0.00004137535,0.0001792132,0.00008589155,0.02432853],"genre_scores_gemma":[0.8551713,0.003165582,0.1225484,0.0008011269,0.000556545,0.0001576144,0.0001275652,0.000119338,0.01735252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007902909,"threshold_uncertainty_score":0.02060521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0521787293055006,"score_gpt":0.2692460317594215,"score_spread":0.2170673024539209,"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."}}