The corporate governance quality has always been a decision criterion for investments, many recent studies trying to define metrics in order to help investors in their decision process. In this paper we investigate whether the clustering of companies’ information concerning their corporate governance politics and financial information could be mapped with the help of clustering. Our approach is to build clusters using machine learning techniques, based on corporate governance and financial variables from a number of 1400 listed companies. We evaluate the obtained clusters by matching them with the classes of two well-known indicators (Tobin’s Q and Altman Z-score), used to estimate the companies’ performance. We obtain partial matches of the benchmark variables and we compare the performances of the used algorithms.
If the inline PDF is not rendering correctly, you can download the PDF file here.
 Tobin J: A General Equilibrium Approach To Monetary Theory. Journal of Money Credit and Banking (1) pp.15–29 (1969)
 Altman E. I. Saunders A.: Credit risk measurement: Developments over the last 20 years. Journal of banking & finance 21(11) pp. 1721-1742 (1997).
 Weimin Chen Guocheng Xiang Youjin Liu Kexi Wang Credit risk Evaluation by hybrid data mining technique Systems Engineering Procedia 3 (2012)
 Kambal E.; Osman I.; Taha M.; Mohammed N.; Mohammed S. Credit scoring using data mining techniques Computing Electrical and Electronics Engineering (ICCEEE) IEEE (2013)
 Kirkos Efstathios Charalambos Spathis and Yannis Manolopoulos. “Data mining techniques for the detection of fraudulent financial statements.” Expert Systems with Applications 32(4) pp.995-1003 (2007)
 Moldovan D. and Mutu S. Learning the Relationship between Corporate Governance and Company Performance using Data Mining Proceedings of the 11th International Conference on Machine Learning and Data Mining (MLDM’15) Hamburg Germany July 2015 In press.
 Arthur David and Sergei Vassilvitskii. “k-means++: The advantages of careful seeding.” Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics 2007.
 Moon Todd K. “The expectation-maximization algorithm.” Signal processing magazine IEEE 13.6 (1996): 47-60.
 Ester Martin et al. “A density-based algorithm for discovering clusters in large spatial databases with noise.” Kdd. Vol. 96. No. 34. 1996.
 Ankerst Mihael et al. “OPTICS: ordering points to identify the clustering structure.” ACM Sigmod Record. Vol. 28. No. 2. ACM 1999.
 Aitken Michael et al. “Price clustering on the Australian stock exchange.” Pacific-Basin Finance Journal 4.2 (1996): 297-314.
 Lux Thomas and Michele Marchesi. “Volatility clustering in financial markets: a microsimulation of interacting agents.” International Journal of Theoretical and Applied Finance 3.04 (2000): 675-702.
 Kumar Rohini. “Risk indifference price of options under fast mean-reverting stochastic volatility.” Conference on Stochastic Asymptotics and Applications. 2014.
 Narayan Paresh Kumar and Russell Smyth. “Has political instability contributed to price clustering on Fiji's stock market?.” Journal of Asian Economics 28 (2013): 125-130.
 Bastos João A. and Jorge Caiado. “Clustering financial time series with variance ratio statistics.” Quantitative Finance 14.12 (2014): 2121-2133.
 Cameron A. Colin Jonah B. Gelbach and Douglas L. Miller. “Robust inference with multiway clustering.” Journal of Business & Economic Statistics 29.2 (2011).
 Aghabozorgi Saeed and Ying Wah Teh. “Stock market co-movement assessment using a three-phase clustering method.” Expert Systems with Applications 41.4 (2014): 1301-1314.
 Enke David and Suraphan Thawornwong. “The use of data mining and neural networks for forecasting stock market returns.” Expert Systems with applications 29.4 (2005): 927-940.
 Cai Fan Nhien-An Le-Khac and M-Tahar Kechadi. “Clustering approaches for financial data analysis: a survey.” Proceedings of the 8th International Conference on Data Mining(DM’12) Las Vegas Nevada USA. 2012.
 Vilalta Ricardo and Irina Rish. “A decomposition of classes via clustering to explain and improve naive Bayes.” Machine Learning: ECML 2003. Springer Berlin Heidelberg 2003. 444-455.
 Lopez Manuel Ignacio et al. “Classification via Clustering for Predicting Final Marks Based on Student Participation in Forums.” International Educational Data Mining Society (2012).
 Kohonen Teuvo. “The self-organizing map.” Proceedings of the IEEE 78.9 (1990): 1464-1480.