{"id":"W2540938175","doi":"10.5539/ijef.v8n11p159","title":"Predicting Financial Distress: Multi Scenarios Modeling Using Neural Network","year":2016,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Financial distress; Market liquidity; Leverage (statistics); Sample (material); Cash flow; Distress; Cash; Economics; Business; Artificial neural network; Stock market; Finance; Financial system; Computer science; Artificial intelligence; Psychology; Geography","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":[0.0001923538,0.0001150043,0.0001714851,0.00009625834,0.000109877,0.0001669693,0.0002269332,0.00005580825,0.000008590801],"category_scores_gemma":[0.0001015286,0.00009013244,0.00008799833,0.00004537944,0.00003988855,0.001266286,0.0001092005,0.00009394607,0.00000278548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000562207,"about_ca_system_score_gemma":0.00003149657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001265692,"about_ca_topic_score_gemma":0.00009688568,"domain_scores_codex":[0.9991445,0.00000359389,0.0004577767,0.0001469262,0.00008003164,0.0001671703],"domain_scores_gemma":[0.9991281,0.00001945797,0.0005373429,0.00006209573,0.0002434331,0.000009559025],"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":[0.0003130998,0.0000874697,0.06736518,0.00001713337,0.00005682178,0.00004223829,0.00003144789,0.7913163,0.0001202824,0.04303676,0.0002237744,0.09738953],"study_design_scores_gemma":[0.0008992821,0.00001231033,0.01046628,0.0002262467,0.00001900833,0.00002691145,0.00001049516,0.9790431,0.000005561568,0.004266266,0.004897734,0.0001268617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899594,0.0002565817,0.00679133,0.0006260439,0.002200317,0.00004303169,0.00002025986,0.000007172413,0.00009589034],"genre_scores_gemma":[0.9941562,0.0006904096,0.0006808771,0.000258579,0.004180156,0.000001209858,0.000002923087,0.00001227745,0.00001739978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1877268,"threshold_uncertainty_score":0.3675494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02306846874732851,"score_gpt":0.2218447705261086,"score_spread":0.1987763017787801,"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."}}