{"id":"W3125713255","doi":"10.1002/for.2552","title":"An analysis on the predictability of CAPM beta for momentum returns","year":2018,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Capital asset pricing model; Predictability; Momentum (technical analysis); BETA (programming language); Economics; Econometrics; Financial economics; Stock (firearms); Estimator; Trading strategy; Mathematics; Statistics; Computer science; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.001758785,0.00008416246,0.0003661441,0.0002102796,0.0001181039,0.00004096793,0.0002273939,0.00004436476,0.0001381975],"category_scores_gemma":[0.0004159942,0.00006021048,0.0002509639,0.0003079843,0.0001128829,0.0002256075,0.00001416712,0.00009808248,0.000001444411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005104461,"about_ca_system_score_gemma":0.00002463391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004216363,"about_ca_topic_score_gemma":0.00003283026,"domain_scores_codex":[0.9988641,0.00001910465,0.0007796697,0.0001275659,0.00005316193,0.0001564025],"domain_scores_gemma":[0.9982685,0.0001654459,0.001152243,0.0002056843,0.0001625488,0.0000456081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0005025648,0.0004781915,0.4788146,0.0001073085,0.001149146,0.000002423658,0.003042551,0.0009491115,0.0002946493,0.5093119,0.002684931,0.002662624],"study_design_scores_gemma":[0.001389564,0.007858497,0.6403624,0.0001567288,0.0004314054,0.0000107628,0.001691311,0.1350828,0.003518102,0.2003042,0.008754573,0.0004395879],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893421,0.0001343117,0.002129557,0.0004682773,0.0002950659,0.0001166461,0.0000821867,0.000003148995,0.007428709],"genre_scores_gemma":[0.9985238,0.00001567754,0.0009698851,0.00007708788,0.0003590136,0.000003048003,0.000001985642,0.000007267429,0.00004222297],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3090077,"threshold_uncertainty_score":0.2455312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07226554214504533,"score_gpt":0.2533654612180952,"score_spread":0.1810999190730498,"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."}}