{"id":"W3128970176","doi":"10.2139/ssrn.3726765","title":"Intraday Market Predictability: A Machine Learning Approach","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations; University of Guelph; Western University","funders":"","keywords":"Predictability; Machine learning; Artificial intelligence; Computer science; Econometrics; Financial economics; Economics; Statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","research_integrity"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03568978,0.0002841552,0.0005211248,0.0002346237,0.00046676,0.0003570169,0.001515548,0.0001277247,0.0008361999],"category_scores_gemma":[0.03076065,0.0002090813,0.0003032711,0.001378665,0.0001279525,0.0003807975,0.0002753526,0.005084405,0.00006891257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004937341,"about_ca_system_score_gemma":0.001796971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001936745,"about_ca_topic_score_gemma":0.00004647763,"domain_scores_codex":[0.9910174,0.002622969,0.001016588,0.0007441767,0.001974069,0.002624807],"domain_scores_gemma":[0.9963834,0.001993902,0.0005393109,0.0004009204,0.0002731924,0.000409315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0009567269,0.000143661,0.1275857,0.00001279368,0.0002631016,0.00001603776,0.001762555,0.001194251,0.0002735432,0.01332974,0.004585451,0.8498764],"study_design_scores_gemma":[0.002425701,0.002001795,0.01033531,0.00002250483,0.0001173526,0.002807566,0.0059277,0.3177913,0.00007183603,0.5684129,0.08925204,0.0008339615],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1337232,0.00324003,0.8064942,0.006537238,0.0004993829,0.0004009871,0.000006238494,0.0002375866,0.04886119],"genre_scores_gemma":[0.982223,0.000297076,0.01182674,0.0003633642,0.0007416654,0.00000807293,0.000002412144,0.00004642201,0.004491197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8490425,"threshold_uncertainty_score":0.9972109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06257727973731803,"score_gpt":0.3342176552335359,"score_spread":0.2716403754962178,"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."}}