Financial performance analysis of European banks using a fuzzified Self-Organizing Map

Due to the recent wave of bank failures, stress tests have been conducted on banks within the European Union. The stress tests, however, only consider the adequacy of a bank's capital ratios, whereas the general financial performance of individual banks is disregarded. In this paper, we use the Self-Organizing Map (SOM) to perform a visual multidimensional and temporal financial performance analysis of European banks. We address several different problems concerning financial performance analysis. We deal with the problem of selecting suitable financial ratios by performing dimensionality reduction using PCA. We also deal with difficult data using outlier trimming and normalization techniques, and use the SOM for imputing missing values. We use a decision-framework for choosing the final model, based upon a set of map and clustering quality measures. Finally, we implement a second-level fuzzified Ward clustering for visualization purposes and for assessing the crispness of the solution. The result is a visual SOM model for financial performance analysis of European banks.

[1]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[2]  Peter Sarlin.  Visual monitoring of financial stability with a self-organizing neural network , 2010, 2010 10th International Conference on Intelligent Systems Design and Applications.

[3]  Tomas Eklund,et al.  Fuzzy Clustering of the Self-Organizing Map: Some Applications on Financial Time Series , 2011, WSOM.

[4]  Juha Vesanto,et al.  On the Decomposition of the Self-Organizing Map Distortion Measure , 2003 .

[5]  Carlos Serrano-Cinca,et al.  Self-organizing neural networks for the analysis and representation of data: Some financial cases , 1993, Neural Computing & Applications.

[6]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[7]  Marie Cottrell,et al.  Missing values : processing with the Kohonen algorithm , 2007, ArXiv.

[8]  Juha Vesanto,et al.  SOM-based data visualization methods , 1999, Intell. Data Anal..

[9]  William L. Megginson,et al.  The Financial and Operating Performance of Newly Privatized Firms: an International Empirical Analysis , 1994, Estudios de Administración.

[10]  Cengiz Kahraman,et al.  Fuzzy performance evaluation in Turkish Banking Sector using Analytic Hierarchy Process and TOPSIS , 2009, Expert Syst. Appl..

[11]  Christopher Nobes,et al.  The survival of international differences under IFRS: towards a research agenda , 2006 .

[12]  Quey-Jen Yeh.  The Application of Data Envelopment Analysis in Conjunction with Financial Ratios for Bank Performance Evaluation , 1996 .

[13]  Zahirul Hoque,et al.  Linking Balanced Scorecard Measures to Size and Market Factors: Impact on Organizational Performance , 2000 .

[14]  C. Kao,et al.  Predicting bank performance with financial forecasts: A case of Taiwan commercial banks , 2004 .

[15]  George Foster,et al.  Financial Statement Analysis. , 1980 .

[16]  Jorma Laaksonen,et al.  SOM_PAK: The Self-Organizing Map Program Package , 1996 .

[17]  Marina Resta,et al.  Early Warning Systems: an approach via Self Organizing Maps with applications to emergent markets , 2009, WIRN.

[18]  George Halkos,et al.  Efficiency measurement of the Greek commercial banks with the use of financial ratios: a data envelopment analysis approach , 2004 .

[19]  Baruch Lev,et al.  Financial Statement Analysis: A New Approach , 1974 .

[20]  Hannu Vanharanta,et al.  A Face Validation of a SOM‐Based Financial Benchmarking Model , 2008 .

[21]  John W. Sammon,et al.  A Nonlinear Mapping for Data Structure Analysis , 1969, IEEE Transactions on Computers.

[22]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[23]  Hannu Vanharanta,et al.  Using the Self-Organizing Map as a Visualization Tool in Financial Benchmarking , 2003, Inf. Vis..

[24]  Ann Gaeremynck,et al.  Attribute Differences between Us Gaap and IFRS Earnings: An Exploratory Study , 2006 .

[25]  Ömer Kaan Baykan,et al.  Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey , 2009, Expert Syst. Appl..

[26]  Aapo Länsiluoto,et al.  Suitability of self-organising maps for analysing a macro-environment an empirical field survey , 2007, Int. J. Bus. Inf. Syst..

[27]  Kaisa Sere,et al.  Managing Complexity in Large Data Bases Using Self-Organizing Maps , 1996 .

[28]  Tariq Samad,et al.  Self–organization with partial data , 1992 .

[29]  Kun-Li Wen,et al.  Applying Grey Relational Analysis and Grey Decision-Making to evaluate the relationship between company attributes and its financial performance - A case study of venture capital enterprises in Taiwan , 2007, Decis. Support Syst..

[30]  Jarkko Venna,et al.  Neighborhood Preservation in Nonlinear Projection Methods: An Experimental Study , 2001, ICANN.

[31]  C. Feng,et al.  Performance evaluation for airlines including the consideration of financial ratios , 2000 .