{"id":"W4385254020","doi":"10.2139/ssrn.4513666","title":"Stock Market Index Enhancement via Machine Learning","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Quest University Canada","funders":"","keywords":"Index (typography); Stock market; Business; Stock market index; Capitalization-weighted index; Stock (firearms); Financial economics; Economics; Computer science; Materials science; World Wide Web","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":[0.0006498688,0.0003428642,0.0004794513,0.0006078957,0.000136763,0.0006348802,0.0003680006,0.0003884638,0.00181716],"category_scores_gemma":[0.002301735,0.0001426719,0.0003492587,0.000534845,0.0001275708,0.0007755026,0.0003847637,0.0004447423,0.0006563081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001347769,"about_ca_system_score_gemma":0.0002634191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003872047,"about_ca_topic_score_gemma":0.0005810842,"domain_scores_codex":[0.9998059,0.00004917104,0.00001492404,0.00003912451,0.00006401259,0.00002699518],"domain_scores_gemma":[0.9992372,0.0003398627,0.0001057424,0.0001019629,0.0001907445,0.00002454393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00040775,0.0005482109,0.005148442,0.0001083776,0.00009158251,0.00008652718,0.00003984692,0.07330901,0.04610332,0.002268613,0.002888293,0.869],"study_design_scores_gemma":[0.00002167203,0.0001229671,0.002790347,0.000007986737,0.00004646923,0.00004549909,0.000008306381,0.9794337,0.01528752,0.001225033,0.001001177,0.000009331156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2989842,0.001318262,0.6830553,0.0005063294,0.0003048949,0.0001261268,0.0001954113,0.002750408,0.01275903],"genre_scores_gemma":[0.8883556,0.0003153564,0.1072622,0.0001169229,0.0002017268,0.00003942151,0.0002097259,0.00006064318,0.003438343],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00181716,"threshold_uncertainty_score":0.006079018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05166884512180222,"score_gpt":0.3723960515990673,"score_spread":0.3207272064772651,"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."}}