A Clustering Of Listed Companies Considering Corporate Governance And Financial Variables

[1] Tobin, J: A General Equilibrium Approach To Monetary Theory. Journal of Money, Credit and Banking (1) pp.15–29 (1969)10.2307/1991374Search in Google Scholar

[2] Altman, E. I., Saunders, A.: Credit risk measurement: Developments over the last 20 years. Journal of banking & finance, 21(11), pp. 1721-1742 (1997).Search in Google Scholar

[3] Weimin Chen, Guocheng Xiang, Youjin Liu, Kexi Wang, Credit risk Evaluation by hybrid data mining technique, Systems Engineering Procedia, 3 (2012)10.1016/j.sepro.2011.10.029Search in Google Scholar

[4] Kambal, E.; Osman, I.; Taha, M.; Mohammed, N.; Mohammed, S. Credit scoring using data mining techniques, Computing, Electrical and Electronics Engineering (ICCEEE), IEEE (2013)Search in Google Scholar

[5] 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)10.1016/j.eswa.2006.02.016Search in Google Scholar

[6] Moldovan, D., and Mutu, S., Learning the Relationship between Corporate Governance and Company Performance using Data Mining, Proceedings of the 11^th International Conference on Machine Learning and Data Mining (MLDM’15), Hamburg, Germany, July 2015, In press.10.1007/978-3-319-21024-7_25Search in Google Scholar

[7] 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.Search in Google Scholar

[8] Moon, Todd K. “The expectation-maximization algorithm.” Signal processing magazine, IEEE 13.6 (1996): 47-60.10.1109/79.543975Search in Google Scholar

[9] Ester, Martin, et al. “A density-based algorithm for discovering clusters in large spatial databases with noise.” Kdd. Vol. 96. No. 34. 1996.Search in Google Scholar

[10] Ankerst, Mihael, et al. “OPTICS: ordering points to identify the clustering structure.” ACM Sigmod Record. Vol. 28. No. 2. ACM, 1999.10.1145/304181.304187Search in Google Scholar

[11] Aitken, Michael, et al. “Price clustering on the Australian stock exchange.” Pacific-Basin Finance Journal 4.2 (1996): 297-314.10.1016/0927-538X(96)00016-9Search in Google Scholar

[12] 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): 675-702.10.1142/S0219024900000826Search in Google Scholar

[13] Kumar, Rohini. “Risk indifference price of options under fast mean-reverting stochastic volatility.” Conference on Stochastic Asymptotics and Applications. 2014.Search in Google Scholar

[14] Narayan, Paresh Kumar, and Russell Smyth. “Has political instability contributed to price clustering on Fiji's stock market?.” Journal of Asian Economics 28 (2013): 125-130.10.1016/j.asieco.2013.07.002Search in Google Scholar

[15] Bastos, João A., and Jorge Caiado. “Clustering financial time series with variance ratio statistics.” Quantitative Finance 14.12 (2014): 2121-2133.10.1080/14697688.2012.726736Search in Google Scholar

[16] Cameron, A. Colin, Jonah B. Gelbach, and Douglas L. Miller. “Robust inference with multiway clustering.” Journal of Business & Economic Statistics 29.2 (2011).10.1198/jbes.2010.07136Search in Google Scholar

[17] Aghabozorgi, Saeed, and Ying Wah Teh. “Stock market co-movement assessment using a three-phase clustering method.” Expert Systems with Applications 41.4 (2014): 1301-1314.10.1016/j.eswa.2013.08.028Search in Google Scholar

[18] 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): 927-940.10.1016/j.eswa.2005.06.024Search in Google Scholar

[19] 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.Search in Google Scholar

[20] 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. 444-455.10.1007/978-3-540-39857-8_40Search in Google Scholar

[21] Lopez, Manuel Ignacio, et al. “Classification via Clustering for Predicting Final Marks Based on Student Participation in Forums.” International Educational Data Mining Society (2012).Search in Google Scholar

[22] Kohonen, Teuvo. “The self-organizing map.” Proceedings of the IEEE 78.9 (1990): 1464-1480.10.1109/5.58325Search in Google Scholar