{"id":"W3159678997","doi":"10.18280/mmep.080203","title":"A Multi-Objective Risk Return Trade off Models for Banks: Fuzzy Programming Approach","year":2021,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Kalyani; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Liquidity risk; Market liquidity; Business; Interest rate risk; Fuzzy logic; Liquidity crisis; Interest rate; Economics; Actuarial science; Computer science; Finance","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001296438,0.001215944,0.001356789,0.0008063156,0.0007081463,0.002240868,0.001542259,0.002690706,0.003806324],"category_scores_gemma":[0.001672424,0.0007139508,0.001488897,0.0009973381,0.0007684386,0.001285598,0.001067441,0.00223769,0.0003270479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001616706,"about_ca_system_score_gemma":0.001765276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01264361,"about_ca_topic_score_gemma":0.006866008,"domain_scores_codex":[0.9995229,0.0002127596,0.00001904474,0.00007434264,0.00009813491,0.00007278439],"domain_scores_gemma":[0.9992767,0.0004549501,0.00008028899,0.00001736876,0.0001297451,0.0000409459],"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":[0.00001590893,0.00002448091,0.0001742077,0.00004463731,0.00002015281,0.00006147289,0.00003514797,0.9832913,0.0002200407,0.01261459,0.0003580122,0.0031401],"study_design_scores_gemma":[0.00000332071,0.00001273545,0.00003617385,0.000008785108,0.000005769111,0.000007405857,0.000009158131,0.9956956,0.0000406117,0.003918793,0.0002577689,0.000003938934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01911388,0.001345958,0.9675509,0.0008828906,0.00007504941,0.00007441206,0.0001418493,0.00008171717,0.01073326],"genre_scores_gemma":[0.8425616,0.00272659,0.1360866,0.0003112554,0.0001513747,0.0005972744,0.00024076,0.00006918194,0.01725553],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01264361,"threshold_uncertainty_score":0.02514005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02927408131449633,"score_gpt":0.2181879187134168,"score_spread":0.1889138373989205,"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."}}