{"id":"W4415458932","doi":"10.3390/jrfm18110593","title":"Systemic Risk Modeling with Expectile Regression Neural Network and Modified LASSO","year":2025,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi; Lembaga Pengelola Dana Pendidikan; Institut Teknologi Sepuluh Nopember; Ministrstvo za visoko šolstvo, znanost in tehnologijo","keywords":"Systemic risk; Fragility; Artificial neural network; Lasso (programming language); Stock (firearms); Regression; Risk assessment; Stock market","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":[0.000917742,0.0001319683,0.0003961965,0.0001976522,0.0002114451,0.00007616989,0.0001015471,0.00006398919,0.000005126336],"category_scores_gemma":[0.00005578087,0.0001085418,0.00006540645,0.0001899449,0.00002714748,0.0001298282,0.0000899915,0.0002482197,4.02567e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000449994,"about_ca_system_score_gemma":0.000009632325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009939134,"about_ca_topic_score_gemma":0.00003703901,"domain_scores_codex":[0.9989526,0.0000328969,0.0005686167,0.000217613,0.00004701095,0.0001812375],"domain_scores_gemma":[0.9991407,0.00004379718,0.0005663456,0.0001518076,0.00004101029,0.00005637039],"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":[0.000727693,0.00008202327,0.7788301,0.0002235816,0.0001118914,0.00003559656,0.0004073005,0.03183025,3.37555e-7,0.1056438,0.0004719727,0.08163543],"study_design_scores_gemma":[0.0014628,0.0001297811,0.1051359,0.0003134303,0.00008135983,0.00001293152,0.0001606634,0.8110945,2.094739e-7,0.07866552,0.002759055,0.0001838599],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7701166,0.006564189,0.2196822,0.0000461443,0.000359443,0.0001417974,0.00001569262,0.000006636094,0.00306732],"genre_scores_gemma":[0.9897438,0.008641763,0.001321774,0.00003569289,0.0001065249,0.000004340182,7.421311e-7,0.00000759432,0.0001377115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7792643,"threshold_uncertainty_score":0.4426206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01043122430849128,"score_gpt":0.2016256437963288,"score_spread":0.1911944194878375,"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."}}