{"id":"W3009939887","doi":"10.3390/su12052006","title":"Sustainable Portfolio Optimization with Higher-Order Moments of Risk","year":2020,"lang":"en","type":"article","venue":"Sustainability","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Kurtosis; Skewness; Econometrics; Portfolio; Diversification (marketing strategy); Economics; Stock (firearms); Financial economics; Order (exchange); Portfolio optimization; Stock market; Actuarial science; Business; Mathematics; Statistics; Engineering; Geography; Finance","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007795343,0.0001709632,0.000432912,0.00009406911,0.0001052947,0.0000382955,0.0002049065,0.00009465787,0.001193369],"category_scores_gemma":[0.001221504,0.0001757944,0.00008313318,0.0007586879,0.0001325594,0.0002750701,0.0001168711,0.0001695553,0.000003633786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003715178,"about_ca_system_score_gemma":0.0001544433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001219717,"about_ca_topic_score_gemma":0.000007383643,"domain_scores_codex":[0.9983727,0.00004640895,0.0006116047,0.0005218324,0.00006749973,0.0003799229],"domain_scores_gemma":[0.9981707,0.00005976295,0.0004792921,0.0004584843,0.0006842638,0.0001474987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002187496,0.000168449,0.8837386,0.0003895298,0.00004623793,0.000008183929,0.0005149123,0.01340354,1.209103e-7,0.1011627,0.0001432916,0.0002057692],"study_design_scores_gemma":[0.002056725,0.0005017851,0.4113406,0.000007643564,0.00003681331,0.000001046995,0.001987871,0.3437164,0.00001141522,0.2261605,0.01353349,0.0006456905],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8439317,0.0003684569,0.1187469,0.002778141,0.0001052867,0.00121659,0.0002442604,0.0001051986,0.03250346],"genre_scores_gemma":[0.9964173,0.0000302651,0.00214918,0.00008366483,0.00003256118,0.00002417945,0.00002944664,0.00002219551,0.00121114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4723979,"threshold_uncertainty_score":0.9997197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00998814171466302,"score_gpt":0.2045544689197735,"score_spread":0.1945663272051105,"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."}}