{"id":"W4389891511","doi":"10.32920/24625161.v1","title":"Financial Bandits - Development of Thompson Sampling for Financial Data","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Reinforcement learning; Computer science; Class (philosophy); Artificial intelligence; Focus (optics); Bayesian probability; Thompson sampling; Machine learning; Financial market; Parametric statistics; Bayesian inference; Sampling (signal processing); Finance; Economics; Mathematics","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":[0.007482543,0.001115824,0.001512203,0.001686026,0.0006962018,0.002967721,0.002134397,0.001892513,0.003481917],"category_scores_gemma":[0.03793207,0.0009003502,0.001071634,0.002182867,0.002053817,0.002907123,0.002331882,0.003767278,0.001058981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001649582,"about_ca_system_score_gemma":0.001806364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005824658,"about_ca_topic_score_gemma":0.004605581,"domain_scores_codex":[0.9963338,0.002318387,0.0001779476,0.0003741266,0.0006583935,0.0001373618],"domain_scores_gemma":[0.9878916,0.009154739,0.0007686559,0.0009327883,0.0009242229,0.0003280837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007877787,0.00005147876,0.002221805,0.0001190608,0.0001118281,0.0001761324,0.0001445182,0.3308275,0.0006476053,0.5756076,0.003880844,0.08613285],"study_design_scores_gemma":[0.00001257513,0.00001456079,0.0001759716,0.0000347056,0.000008200858,0.00002758696,0.000009054732,0.8365675,0.0002513001,0.1599116,0.002974507,0.00001253938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003434898,0.0007031796,0.9928403,0.0004272138,0.0000966001,0.00004899387,0.0000962044,0.0001846195,0.002167912],"genre_scores_gemma":[0.3253736,0.003195794,0.6603255,0.0006380226,0.0007069581,0.0005962374,0.0007380627,0.0003883108,0.008037596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007482543,"threshold_uncertainty_score":0.03957194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7430885098891811,"score_gpt":0.5754503766900538,"score_spread":0.1676381331991272,"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."}}