Article
Predicting performance in ASEAN banks: an integrated fuzzy MCDM–neural network approach
Peter Wanke,
Md. Abul Kalam Azad,
C. P. Barros,
Abdollah Hadi-Vencheh,
Corresponding Author
Peter Wanke
COPPEAD Graduate Business School, Federal University of Rio de Janeiro, Rua Paschoal Lemme, 355, 21949–900 Rio de Janeiro, Brazil
Search for more papers by this authorMd. Abul Kalam Azad
Department of Applied Statistics, Faculty of Economics and Administration, University of Malaya, 50603 Kuala Lumpur, Malaysia
Search for more papers by this authorC. P. Barros
ULisboa and CEsA - Research Centre on African, Asian and Latin American Studies, ISEG – Lisbon School of Economics and Management, Rua Miguel Lupi, 20 1249–078, Lisbon, Portugal
Search for more papers by this authorAbdollah Hadi-Vencheh
Department of Mathematics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
Search for more papers by this authorPeter Wanke,
Md. Abul Kalam Azad,
C. P. Barros,
Abdollah Hadi-Vencheh,
Corresponding Author
Peter Wanke
COPPEAD Graduate Business School, Federal University of Rio de Janeiro, Rua Paschoal Lemme, 355, 21949–900 Rio de Janeiro, Brazil
Search for more papers by this authorMd. Abul Kalam Azad
Department of Applied Statistics, Faculty of Economics and Administration, University of Malaya, 50603 Kuala Lumpur, Malaysia
Search for more papers by this authorC. P. Barros
ULisboa and CEsA - Research Centre on African, Asian and Latin American Studies, ISEG – Lisbon School of Economics and Management, Rua Miguel Lupi, 20 1249–078, Lisbon, Portugal
Search for more papers by this authorAbdollah Hadi-Vencheh
Department of Mathematics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
Search for more papers by this authorAbstract
This paper presents a performance assessment of 88 Association of Southeast Asian Nations banks from 2010 to 2013, using an integrated three-stage approach on financial criteria that emulates the CAMELS rating system. More precisely, fuzzy analytic hierarchy process is used first to assess the relative weights of a number of criteria related to capital adequacy (C), asset quality (A), management quality (M), earnings (E), liquidity (L), and sensitivity to market risk (S) based on the opinion of 88 Association of Southeast Asian Nations experts. Then, these weights are used as technique for order of preference by similarity to ideal solution inputs to assess their relative efficiency. Lastly, neural networks are combined with technique for order of preference by similarity to ideal solution results to produce a model for banking performance with effective predictive ability. The results reveal that contextual variables have a prominent impact on efficiency. Specifically, parsimony in equity leveraging derived from Islamic finance principles may be the underlying cause in explaining higher efficiency levels.
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