{"id":"W4388929300","doi":"10.1007/s43069-023-00277-6","title":"ChatGPT-Based Investment Portfolio Selection","year":2023,"lang":"en","type":"article","venue":"Operations Research Forum","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Portfolio; Portfolio optimization; Selection (genetic algorithm); Investment strategy; Black–Litterman model; Computer science; Stock (firearms); Stock market; Econometrics; Economics; Artificial intelligence; Replicating portfolio; Financial economics; Microeconomics; Engineering; Context (archaeology)","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":[0.002564793,0.0007355592,0.001708206,0.001599854,0.000525416,0.001512385,0.001878389,0.001507929,0.007778931],"category_scores_gemma":[0.009272056,0.0004590431,0.0009203429,0.001492707,0.0004655145,0.001214394,0.001687253,0.001276779,0.001190917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008050941,"about_ca_system_score_gemma":0.001741376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004647964,"about_ca_topic_score_gemma":0.004232642,"domain_scores_codex":[0.998669,0.0005650955,0.0000555033,0.000165778,0.0003948821,0.0001497785],"domain_scores_gemma":[0.9956641,0.002587162,0.0001329771,0.0002719766,0.001132325,0.0002115342],"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":[0.0008259164,0.0002199691,0.003317219,0.0001549572,0.0002085525,0.000262665,0.0000490844,0.6092607,0.002363278,0.01016389,0.01049236,0.3626814],"study_design_scores_gemma":[0.00002821332,0.00003972373,0.0002621259,0.000005702104,0.00001471523,0.00003629482,0.000004096783,0.996842,0.0004255756,0.001883354,0.0004534088,0.00000483935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05644413,0.0006703016,0.9303161,0.0005693329,0.0003046345,0.0002169497,0.000324763,0.001779138,0.009374699],"genre_scores_gemma":[0.7536424,0.0002735077,0.2340886,0.0004155074,0.0002342531,0.0002850967,0.001049072,0.0002073061,0.009804315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007778931,"threshold_uncertainty_score":0.02602315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2278071890230692,"score_gpt":0.4938378116246731,"score_spread":0.2660306226016039,"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."}}