{"id":"W2018046218","doi":"10.1287/mnsc.2013.1871","title":"What Do Credit Markets Tell Us About the Speed of Leverage Adjustment?","year":2014,"lang":"en","type":"article","venue":"Management Science","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Leverage (statistics); Pecking order; Market timing; Capital structure; Economics; Monetary economics; Financial economics; Econometrics; Business; Finance; Computer science; Debt; Initial public offering","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":[0.003112451,0.000268945,0.0005441625,0.002109146,0.0003502987,0.003607619,0.0005180215,0.001142063,0.00279881],"category_scores_gemma":[0.05563779,0.0003975986,0.0003650027,0.001812069,0.00100756,0.006746485,0.0005925581,0.001415838,0.0006962877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005542868,"about_ca_system_score_gemma":0.0002602442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002729035,"about_ca_topic_score_gemma":0.002113617,"domain_scores_codex":[0.9993408,0.0001973531,0.00007117427,0.0001542876,0.0001515663,0.0000848937],"domain_scores_gemma":[0.9542152,0.02402485,0.01719614,0.002157876,0.001615292,0.0007906968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007218506,0.00008815406,0.848873,0.0001076695,0.00035098,0.0002383431,0.0008530055,0.01597919,0.002183532,0.02851237,0.003565182,0.09852676],"study_design_scores_gemma":[0.00005141115,0.0001995175,0.8499305,0.0001002022,0.0001124419,0.0005906055,0.0006887138,0.05038452,0.002819262,0.09023014,0.004706363,0.0001863377],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.953604,0.002387536,0.02513936,0.003217568,0.0000962479,0.00002190627,0.001761734,0.0001730575,0.01359856],"genre_scores_gemma":[0.9975156,0.0006606631,0.00104833,0.0001173265,0.00009356796,0.000005238093,0.0002594538,0.00002059844,0.0002791554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003607619,"threshold_uncertainty_score":0.01646042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0210481896781348,"score_gpt":0.2256828576965772,"score_spread":0.2046346680184424,"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."}}