{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003910943,0.0004252603,0.0004911958,0.001628688,0.0003564793,0.001493742,0.000419772,0.0005027288,0.001365723],"category_scores_gemma":[0.0343869,0.0002996475,0.000363249,0.000940446,0.0005742772,0.001015825,0.0004542653,0.001028568,0.0002338607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005035947,"about_ca_system_score_gemma":0.0003480459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002824401,"about_ca_topic_score_gemma":0.001263551,"domain_scores_codex":[0.9993597,0.0001744552,0.00002762251,0.0001296275,0.0002042237,0.0001044058],"domain_scores_gemma":[0.9647209,0.02695617,0.003588475,0.002202489,0.001853004,0.0006789597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006510421,0.0001167002,0.7709803,0.00006233776,0.0002604069,0.00135779,0.0004377487,0.1602822,0.004753385,0.01502519,0.003321207,0.04275163],"study_design_scores_gemma":[0.0000146253,0.0001019566,0.1694178,0.00002465329,0.00004748476,0.0002641618,0.00007031835,0.821793,0.00139203,0.006319879,0.0005164738,0.00003754069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9867471,0.0001734866,0.01029907,0.0002782644,0.00002953521,0.00000917073,0.0002101431,0.000135716,0.002117495],"genre_scores_gemma":[0.9994281,0.00003130506,0.0002791845,0.000007144956,0.0000223028,0.000002200183,0.0001236103,0.00001007779,0.00009608096],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003910943,"threshold_uncertainty_score":0.02068329,"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."}}