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A Clustering Of Listed Companies Considering Corporate Governance And Financial Variables Cover

A Clustering Of Listed Companies Considering Corporate Governance And Financial Variables

By: Darie Moldovan and  Mircea Moca  
Open Access
|Nov 2015

References

  1. [1] Tobin, J: A General Equilibrium Approach To Monetary Theory. Journal of Money, Credit and Banking (1) pp.15–29 (1969)10.2307/1991374
  2. [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).
  3. [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.2011.10.029
  4. [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)
  5. [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.2006.02.016
  6. [6] 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.10.1007/978-3-319-21024-7_25
  7. [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.
  8. [8] Moon, Todd K. “The expectation-maximization algorithm.” Signal processing magazine, IEEE 13.6 (1996): 47-60.10.1109/79.543975
  9. [9] Ester, Martin, et al. “A density-based algorithm for discovering clusters in large spatial databases with noise.” Kdd. Vol. 96. No. 34. 1996.
  10. [10] Ankerst, Mihael, et al. “OPTICS: ordering points to identify the clustering structure.” ACM Sigmod Record. Vol. 28. No. 2. ACM, 1999.10.1145/304181.304187
  11. [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)00016-9
  12. [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/S0219024900000826
  13. [13] Kumar, Rohini. “Risk indifference price of options under fast mean-reverting stochastic volatility.” Conference on Stochastic Asymptotics and Applications. 2014.
  14. [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.2013.07.002
  15. [15] Bastos, João A., and Jorge Caiado. “Clustering financial time series with variance ratio statistics.” Quantitative Finance 14.12 (2014): 2121-2133.10.1080/14697688.2012.726736
  16. [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.2010.07136
  17. [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.2013.08.028
  18. [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.2005.06.024
  19. [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.
  20. [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_40
  21. [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).
  22. [22] Kohonen, Teuvo. “The self-organizing map.” Proceedings of the IEEE 78.9 (1990): 1464-1480.10.1109/5.58325
Language: English
Page range: 338 - 343
Published on: Nov 24, 2015
In partnership with: Paradigm Publishing Services
Publication frequency: 3 issues per year

© 2015 Darie Moldovan, Mircea Moca, published by Nicolae Balcescu Land Forces Academy
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.