{"id":"W3121177359","doi":"10.1080/07350015.2012.680412","title":"Components of Bull and Bear Markets: Bull Corrections and Bear Rallies","year":2012,"lang":"en","type":"preprint","venue":"Journal of Business and Economic Statistics","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations; University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada; Brock University; McMaster University","keywords":"Economics; Econometrics; Probabilistic logic; Bayesian probability; Nonmarket forces; Index (typography); Value (mathematics); Distribution (mathematics); Contrast (vision); Financial economics; Microeconomics; Factor market; Statistics; Mathematics; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005465023,0.0002523875,0.001272027,0.0003771987,0.000114309,0.0001706934,0.0001372814,0.0001585243,0.0004450766],"category_scores_gemma":[0.00007521349,0.0002717155,0.00009761091,0.00006015229,0.0001796603,0.0001735514,0.0002802766,0.0002619199,0.00001043644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007912178,"about_ca_system_score_gemma":0.0000422237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00240983,"about_ca_topic_score_gemma":0.0001355023,"domain_scores_codex":[0.9980459,0.00002162831,0.00141563,0.0002699443,0.00003792169,0.0002089457],"domain_scores_gemma":[0.9971954,0.0001502492,0.002142488,0.0002130389,0.000144093,0.0001547921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000586406,0.000463455,0.8485031,0.004696479,0.004424765,0.00004226559,0.002611989,0.00362393,0.00003746075,0.1030187,0.01558132,0.01641024],"study_design_scores_gemma":[0.001158504,0.00008781489,0.9057421,0.0003367643,0.0003259757,0.0001603344,0.0003664825,0.01250601,0.000002327021,0.01926128,0.05946827,0.0005841032],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.944988,0.02578142,0.02043136,0.000442569,0.002578137,0.0002236501,0.004452081,0.000007400954,0.001095374],"genre_scores_gemma":[0.9761285,0.01649092,0.006362347,0.00002528398,0.0003837208,0.000003479705,0.0000581449,0.00003640035,0.0005111603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08375739,"threshold_uncertainty_score":0.9999735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03457218887851925,"score_gpt":0.2196288630934154,"score_spread":0.1850566742148961,"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."}}