{"id":"W7118595361","doi":"10.70764/gdpu-sft.2025.1(2)-07","title":"A Hybrid NAKA-FA-PSO Algorithm with Nakagami Distribution for Multi-Objective Portfolio Optimization","year":2025,"lang":"","type":"article","venue":"Start-up and Financial Technology","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Particle swarm optimization; Portfolio; Portfolio optimization; Cardinality (data modeling); Multi-objective optimization; Optimization problem; Firefly algorithm; Nakagami distribution","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":[0.001105223,0.0009723572,0.001053792,0.001080924,0.0004616902,0.001210788,0.001214251,0.001460663,0.001986427],"category_scores_gemma":[0.00346488,0.0003960353,0.0009247366,0.0009925563,0.0004306156,0.001189807,0.0008837652,0.001113757,0.0004856877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007009656,"about_ca_system_score_gemma":0.001426172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004843345,"about_ca_topic_score_gemma":0.004818438,"domain_scores_codex":[0.9994844,0.0001684679,0.00003040462,0.00008852457,0.0001701257,0.00005801078],"domain_scores_gemma":[0.999047,0.0005424343,0.0001076171,0.00006181921,0.0001841693,0.00005696128],"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":[0.00008011998,0.00008526263,0.001778482,0.00009191102,0.0001045778,0.00008254045,0.00004875351,0.8788935,0.001939375,0.006410922,0.002039006,0.1084454],"study_design_scores_gemma":[0.00001098605,0.00002407197,0.0001308184,0.000006950661,0.000008265386,0.00002152722,0.000004975976,0.9978727,0.0002455884,0.001076559,0.0005934145,0.00000425187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0203775,0.0007946576,0.9741492,0.0003355229,0.00008126289,0.00007676439,0.00006466768,0.0003328297,0.00378767],"genre_scores_gemma":[0.4597329,0.0006421045,0.5335168,0.0003369497,0.0001204745,0.0003336423,0.000268804,0.0001169947,0.004931438],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004843345,"threshold_uncertainty_score":0.009630322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01958155944520113,"score_gpt":0.3063777615289546,"score_spread":0.2867962020837534,"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."}}