{"id":"W4300815108","doi":"10.1142/9789813144385_0006","title":"The Predictive Ability of the Bond Stock Earnings Yield Differential Model","year":2016,"lang":"en","type":"book-chapter","venue":"WORLD SCIENTIFIC eBooks","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Earnings yield; Stock (firearms); Bond; Earnings; Yield (engineering); Differential (mechanical device); Econometrics; Economics; Materials science; Price–earnings ratio; Earnings per share; Finance; Composite material; Physics; Thermodynamics; Metallurgy","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":["metaepi_narrow","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.01366985,0.0005652667,0.0008165279,0.0005358408,0.001939456,0.0007259757,0.004456833,0.0003004658,0.0006730917],"category_scores_gemma":[0.005639745,0.0002548564,0.0009587301,0.0002406577,0.004938046,0.0001019445,0.001914817,0.001053188,0.00009374192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001692246,"about_ca_system_score_gemma":0.0006503507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004109973,"about_ca_topic_score_gemma":0.0007338809,"domain_scores_codex":[0.9905931,0.0004712378,0.001713056,0.001744612,0.004810003,0.0006679717],"domain_scores_gemma":[0.9815776,0.01073755,0.002020504,0.004242697,0.00120795,0.0002136855],"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":[0.0008148771,0.00006857234,0.001173475,0.00005653341,0.0003326494,0.000004754822,0.003481264,0.0001777926,0.008976653,0.3051409,0.3243081,0.3554644],"study_design_scores_gemma":[0.0002977331,0.00005719063,0.001762151,0.0005270062,0.0001322364,0.000004971419,0.00003449248,0.005247379,0.001330357,0.6280542,0.3620388,0.0005134802],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.003631811,0.00007244008,0.009194321,0.0003313359,0.006202319,0.001176198,0.0002681417,0.00007045869,0.979053],"genre_scores_gemma":[0.09863403,3.314939e-7,0.000528297,0.00003046765,0.0001984029,0.00003805469,0.000001200874,0.00005487771,0.9005144],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3549509,"threshold_uncertainty_score":0.9999903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1153187465825147,"score_gpt":0.33898737396231,"score_spread":0.2236686273797954,"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."}}