Classifying non-banking financial institutions based on their financial performance

Abstract

In this paper we evaluate comparatively the performance of non-banking financial institutions in Romania by the means of unsupervised neural networks in terms of Kohonen’ Self-Organizing Maps algorithm. We create a benchmarking model in the form of a two-dimensional map (a self-organizing map) that can be used to assess visually the performance of non-banking financial institutions based on different performance dimensions, such as capital adequacy, assets’ quality and profitability. We use the following indicators: Equity ratio (Leverage) for the capital adequacy dimension, Loans granted to clients (net value) / total assets (net value) for the assets’ quality dimension and Return on assets (ROA) for the profitability dimension. We have excluded from our analysis the other three dimensions used in evaluating the performance of banks, due to lack of data (for the two qualitative dimensions: quality of ownership and management) and irrelevance with the NFIs’ sector (liquidity). The proposed model is based on the Self-Organising Map algorithm which creates a two-dimensional map (e.g. 6x4 = 24 neurons) from p-dimensional input data. The data were collected for eleven non-banking financial institutions for four years 2007-2010, in total 44 observations. Using the visualization capabilities of the Self-Organising Map model and the trajectories we show the movements of the three non-banking financial institutions with the worst performance: the largest underperformer denoted with X, the second largest underperformer denoted with Y and the third largest underperformer denoted with Z between 2007 and 2010.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Alhoniemi, E., Hollmen, J., Simula, O., & Vesanto, J. (1999). Process Monitoring and Modeling Using the Self-Organising Map. Integrated Computer-Aided Engineering, 6(1), 3-14.

  • Back, B., Sere, K., & Vanharanta, H. (1996). Data Mining Accounting Numbers Using Self Organising Maps. In J. Alander, T. Honkela, M. Jakobsson (eds.), Proceedings of Finnish Artificial Intelligence Society Conference, 35-47. Vaasa, Finland.

  • Back, B., Sere, K., & Vanharanta, H. (1998). Managing Complexity in Large Databases Using Self-Organizing Maps. Accounting Management and Information Technologies, 8(4), 191-210.

  • Cerna, S., Donath, L., Seulean, V., Herbei, M., Bărglăzan, D., Albulescu, C., & Boldea, B. (2008). Financial Stability. Timişoara: West University Publishing House.

  • Costea, A. (2005). Computational Intelligence Methods for Quantitative Data Mining. TUCS PhD dissertation No. 67, Åbo Akademi University, Turku, Finland.

  • Costea, A. (2006). The Analysis of the Telecommunications Sector by the Means of Data Mining Techniques. Journal of Applied Quantitative Methods (JAQM), 1(2), 144-150.

  • Costea, A. (2013). Performance benchmarking of non-banking financial institutions by means of Self-Organising Map algorithm. East-West Journal of Economics and Business, XVI(1), 37-58.

  • Costea, A., & Bleotu, V. (2012). A new fuzzy clustering algorithm for evaluating the performance of non-banking financial institutions in Romania. Economic Computation and Economic Cybernetics Studies and Research, 46(4), 179-199.

  • Costea, A., & Eklund, T. (2003). A Two-Level Approach to Making Class Predictions. In RH. Sprague Jr. (ed.), Proceedings of 36th Annual Hawaii International Conference on System Sciences (HICSS 2003) (9 pages). IEEE Computer Society, Hawaii, USA, January 6-9, 2003, Track: Decision Technologies for Management, Minitrack: Intelligent Systems and Soft Computing, ISBN: 0-7695-1874-5.

  • Eklund, T., Back B., Vanharanta, H., & Visa, A. (2003). Financial Benchmarking Using Self-Organising Maps – Studying the International Pulp and Paper Industry. In J. Wang J. (ed.), Data Mining - Opportunities and Challenges (Chapter 14 – pp. 323-349). Hershey, PA: Idea Group Publishing.

  • Karlsson, J., Back, B., Vanharanta, H., & Visa, A. (2001). Financial Benchmarking of Telecommunications Companies. TUCS Technical Report, No. 395.

  • Kohonen, T. (1997). Self-Organising Maps (2nd ed.). Heidelberg: Springer-Verlag.

  • Kohonen, T., Hynninen, J., Kangas, J., & Laaksonen, J. (1996). SOM_PAK: The Self-Organising Map Program Package. Helsinki University of Technology, Report A31, Otaniemi.

  • Lehtinen, J. (1996). Financial Ratios in an International Comparison. Validity and Reliability. Acta Wasaensia 49, Vaasa, Finland.

  • Martín-del-Brío, B., & Serrano Cinca, C. (1993). Self Organizing Neural Networks for the Analysis and Representation of Data: some Financial Cases. Neural Computing & Applications, Springer Verlag (ed.), 1(2), 193-206.

  • Moinescu, B., & Costea, A. (2014). Towards an early-warning system of distressed non-banking financial institutions. Economic Computation and Economic Cybernetics Studies and Research, 48(2), 75-90.

  • Serrano Cinca, C. (1996). Self Organising Neural Networks for Financial Diagnosis. Decision Support Systems, 17, 227-238.

  • Serrano Cinca, C. (1998a). SOM as a tool for Initial Data Analysis (Let Financial Data Speak for Themselves). In G. Deboeck, T. Kohonen (eds.), Visual Intelligence in Finance: withg Self-organising Maps (pp. 4-17). Berlin: Springer Verlag.

  • Serrano Cinca, C. (1998b). From Financial Information to Strategic Groups - a Self Organising Neural Network Approach. Journal of Forecasting, 17, 415-428.

  • Ultsch, A. (1993). Self organized feature maps for monitoring and knowledge aquisition of a chemical process. In S. Gielen, B. Kappen (eds.), Proceedings of the International Conference on Artificial Neural Networks (ICANN93) (pp. 864-867). London: Springer-Verlag.

OPEN ACCESS

Journal + Issues

Search