{"id":"W4200372379","doi":"10.33423/jabe.v23i7.4867","title":"IPO Underpricing and Prospectus Readability: A Machine Learning Approach","year":2021,"lang":"en","type":"article","venue":"Journal of Applied Business and Economics","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Prospectus; Initial public offering; Business; Readability; Stock exchange; Monetary economics; Accounting; Finance; Economics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002108535,0.0001172872,0.0002811729,0.00005199727,0.00004790628,0.00007754193,0.00004192657,0.00006700822,0.00000417992],"category_scores_gemma":[0.0000416871,0.0001061912,0.00002732654,0.00008945161,0.00003206165,0.00007237878,0.00003921077,0.0002439063,3.663592e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004796496,"about_ca_system_score_gemma":0.00002619127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004150698,"about_ca_topic_score_gemma":0.000004465236,"domain_scores_codex":[0.9994254,0.000003487251,0.0002888256,0.0001178936,0.00003616293,0.0001282227],"domain_scores_gemma":[0.9996446,0.00006858986,0.00006573322,0.00008779535,0.00005949168,0.00007379692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002175114,0.00005214265,0.004995338,0.0005301567,0.00009677832,0.00001030164,0.0002944832,0.976919,0.001052427,0.002130153,0.00001871371,0.01387877],"study_design_scores_gemma":[0.00188505,0.00007996089,0.06516193,0.0001639149,0.0002156612,0.0008008407,0.0007146133,0.9000527,0.003683624,0.0111363,0.01516834,0.0009370944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9877751,0.001751214,0.008146426,0.00009115026,0.0001675313,0.0000533439,0.000002269027,0.00002540883,0.001987565],"genre_scores_gemma":[0.9890601,0.004672846,0.006102331,0.00001575753,0.0001145317,0.000001777823,0.000003037596,0.00002236141,0.000007278133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07686631,"threshold_uncertainty_score":0.4330352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004972010358045508,"score_gpt":0.1565757729240755,"score_spread":0.15160376256603,"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."}}