{"id":"W4390564860","doi":"10.54254/2754-1169/59/20231129","title":"XGBoost-LSTM for Feature Selection and Predictions for the S&P 500 Financial Sector","year":2024,"lang":"en","type":"article","venue":"Advances in Economics Management and Political Sciences","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Feature selection; Computer science; Test set; Artificial intelligence; Feature (linguistics); Selection (genetic algorithm); Set (abstract data type); Index (typography); Machine learning; Finance; Financial sector; Plot (graphics); Econometrics; Statistics; Mathematics; 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":[0.001313167,0.001082148,0.0006031938,0.001209379,0.0004407814,0.0006037678,0.0007520012,0.0008765043,0.003146827],"category_scores_gemma":[0.002163783,0.0003452996,0.0007657876,0.001146907,0.0002413752,0.0007198399,0.0004365728,0.00130107,0.00101927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007491644,"about_ca_system_score_gemma":0.001293581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0150091,"about_ca_topic_score_gemma":0.01584837,"domain_scores_codex":[0.999639,0.0001089052,0.00002969141,0.00007285397,0.00008674549,0.00006271942],"domain_scores_gemma":[0.9995943,0.0002106285,0.000044145,0.00002804478,0.0001013594,0.00002160599],"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":[0.0003286652,0.0002259563,0.003976207,0.0001073967,0.0001738874,0.0001441571,0.00005889081,0.3691635,0.005828884,0.001666458,0.01090855,0.6074173],"study_design_scores_gemma":[0.000006481216,0.00002453045,0.0004338905,0.000006601324,0.000008498372,0.000009737106,0.000008916212,0.997076,0.001289245,0.0006422992,0.0004892246,0.000004581228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1311204,0.002116316,0.8498755,0.0008393903,0.000331783,0.000186844,0.001050373,0.01128719,0.003192152],"genre_scores_gemma":[0.6669549,0.0005206844,0.3239713,0.0002758619,0.0001012027,0.0002563353,0.002049794,0.0002829193,0.005586949],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0150091,"threshold_uncertainty_score":0.02984351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07298653236017226,"score_gpt":0.4019335633726758,"score_spread":0.3289470310125035,"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."}}