{"id":"W4400700956","doi":"10.4995/carma2024.2024.17554","title":"Unlocking the Potential of Machine Learning in Portfolio Selection: A Hybrid Approach with Genetic Optimization","year":2024,"lang":"en","type":"article","venue":"","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Selection (genetic algorithm); Artificial intelligence; Portfolio; Machine learning; 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":[0.001672125,0.0007718262,0.0008755378,0.001047976,0.0003238182,0.00122217,0.0008519348,0.001111382,0.0007618157],"category_scores_gemma":[0.00302872,0.0004214184,0.0005446889,0.0008335269,0.0007765397,0.001085371,0.0007613777,0.0009102139,0.0001728822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000701258,"about_ca_system_score_gemma":0.0008927083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002999573,"about_ca_topic_score_gemma":0.003168968,"domain_scores_codex":[0.9995235,0.0002105682,0.00001989139,0.00005675452,0.0001502726,0.00003905852],"domain_scores_gemma":[0.9988476,0.0008226194,0.00008710307,0.00006680106,0.0001465958,0.00002923085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005939183,0.00008610891,0.001216594,0.0000400601,0.00008705234,0.00006049545,0.0000464661,0.9174626,0.002614425,0.009380392,0.0003059232,0.06864051],"study_design_scores_gemma":[0.000006082904,0.00002337674,0.0000834638,0.000004936065,0.000006666557,0.000007678028,0.000004080404,0.9973567,0.000358504,0.001988563,0.0001568396,0.000003181218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08515044,0.000758526,0.9083673,0.0006939283,0.00006709541,0.00006136596,0.00002253794,0.0003309857,0.004547882],"genre_scores_gemma":[0.6668891,0.0004378662,0.3304459,0.0003346743,0.00008273236,0.0001132976,0.00004858902,0.00008731797,0.001560585],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002999573,"threshold_uncertainty_score":0.008843124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0429907634424886,"score_gpt":0.3286525595725973,"score_spread":0.2856617961301087,"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."}}