PERFORMANCE BENCHMARKING OF NON-BANKING FINANCIAL INSTITUTIONS BY MEANS OF SELF-ORGANISING MAP ALGORITHM

We construct a benchmarking model in the form of a two- dimensional self-organising map (SOM) to compare the performance of non- banking financial institutions (NFIs) in Romania. The NFIs are characterized by a number of performance dimensions such as capital adequacy, assets' quality and profitability. First, we apply Kohonen' SOM algorithm (an unsupervised neural network algorithm) to group the NFIs in clusters with similar characteristics. Then, we use the U-matrix method to build maps that facilitate the visualization of SOM results and select the best map in terms of quantisation error and ease of readability. The best map is used to analyze the companies over time by studying the cluster where each company was positioned for each period. We conclude that there are benefits in using SOM for interpreting large and complex financial data by identifying and visualizing clusters.

[1]  Hannu Vanharanta,et al.  Financial benchmarking using self-organizing maps studying the international pulp and paper industry , 2003 .

[2]  Carlos Serrano-Cinca,et al.  Self organizing neural networks for financial diagnosis , 1996, Decision Support Systems.

[3]  C. Serrano-Cinca.  From financial information to strategic groups: a self‐organizing neural network approach , 1998 .

[4]  Hannu Vanharanta,et al.  Contents Matching Defined by Prototypes: Methodology Verification with Books of the Bible , 2002, J. Manag. Inf. Syst..

[5]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[6]  Constantin Zopounidis,et al.  A Multicriteria Bank Rating System , 2009 .

[7]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[8]  Alfred Ultsch,et al.  Self Organized Feature Maps for Monitoring and Knowledge Aquisition of a Chemical Process , 1993 .

[9]  John O'Keefe,et al.  The Scor System of Off-Site Monitoring: Its Objectives, Functioning, and Performance , 2006 .

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

[11]  Constantin Zopounidis,et al.  Multiple criteria decision aiding , 2010 .

[12]  Ronald J. Brachman,et al.  The Process of Knowledge Discovery in Databases , 1996, Advances in Knowledge Discovery and Data Mining.

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

[14]  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..

[15]  C. S. Cinca,et al.  From Financial Information to Strategic Groups: A Self- Organizing Neural Network Approach , 1996 .

[16]  Hannu Vanharanta,et al.  Comparing numerical data and text information from annual reports using self-organizing maps , 2001, Int. J. Account. Inf. Syst..

[17]  Olli Simula,et al.  Process Monitoring and Modeling Using the Self-Organizing Map , 1999, Integr. Comput. Aided Eng..

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

[19]  Antonina Kloptchenko,et al.  Text Mining Based on the Prototype Matching Method , 2003 .

[20]  Martin Bichler,et al.  Design science in information systems research , 2006, Wirtschaftsinf..

[21]  Jorge Luis Romeu.  OPERATIONS RESEARCH / STATISTICS TECHNIQUES : A KEY TO QUANTITATIVE DATA MINING , 2001 .

[22]  Hannu Vanharanta,et al.  Combining data and text mining techniques for analysing financial reports , 2004, Intell. Syst. Account. Finance Manag..

[23]  Hannu Vanharanta,et al.  Financial Benchmarking of Telecommunications Companies , 2001 .

[24]  Jeffrey A. Clark,et al.  Off-site monitoring systems for predicting bank underperformance: a comparison of neural networks, discriminant analysis, and professional human judgment , 2001, Intell. Syst. Account. Finance Manag..

[25]  Hannu Vanharanta,et al.  Validation of Text Clustering Based on Document Contents , 2001, MLDM.