Assessment of the effect on technical efficiency of bad loans in banking industry: a principal component analysis and neuro-fuzzy system

被引：10

作者：

Hajialiakbari, Firoozaeh ^{[1
]}

Gholami, Mohamad H. ^{[2
]}

Roshandel, Jinus ^{[3
]}

Hatami-Shirkouhi, Loghman ^{[4
]}

机构：

[1] Islamic Azad Univ, Zanjan Branch, Dept Management, Zanjan, Iran

[2] Amirkabir Univ Technol, Dept Ind Engn & Management Syst, Tehran, Iran

[3] Khatam Inst Higher Educ, Dept Ind Engn, Tehran, Iran

[4] Islamic Azad Univ, Roudbar Branch, Roudbar, Iran

来源：

NEURAL COMPUTING & APPLICATIONS | 2013年 / 23卷

关键词：

Neuro-fuzzy system; Principal component analysis; Bad loans; Technical efficiency; Banking industry; DATA ENVELOPMENT ANALYSIS; PERFORMANCE;

D O I：

10.1007/s00521-013-1413-z

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents the integration of principal component analysis (PCA) and adaptive network-based fuzzy inference system (ANFIS) to assess the impact of bad loans on technical efficiency of banks. Bad loans or non-performing loans (NPLs) include past due loans, bankrupt and quasi-bankrupt assets and doubtful assets. Bad loans are considered as a bad output for calculation of the technical efficiency through PCA. ANFIS is used to model the relationship between bad loans and technical efficiency. ANFIS modeling is used to capture the non-linearity and fuzziness existed in the modeling environment. In the ANFIS model, technical efficiency is considered as the output which is modeled with respect to bad loans, profit and costs. The results of the proposed model are illustrated through a case study in Iranian governmental banks. It is evidenced that the effects of bad loans on technical efficiency of banks are not linear but a nonlinear negative impact.

引用

页码：S315 / S322

页数：8

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