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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

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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)
  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)
  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)
  6. [6] Moldovan, D., and Mutu, S., Learning the Relationship between Corporate Governance and Company Performance using Data Mining, Proceedings of the 11International Conference on Machine Learning and Data Mining (MLDM’15), Hamburg, Germany, July 2015, In press.
  7. [7] Arthur, David, and Sergei Vassilvitskii. “k-means++: The advantages of careful seeding.”. Society for Industrial and Applied Mathematics, 2007.
  8. [8] Moon, Todd K. “The expectation-maximization algorithm.”13.6 (1996): 47-60.
  9. [9] Ester, Martin, et al. “A density-based algorithm for discovering clusters in large spatial databases with noise.”. Vol. 96. No. 34. 1996.
  10. [10] Ankerst, Mihael, et al. “OPTICS: ordering points to identify the clustering structure.”. Vol. 28. No. 2. ACM, 1999.
  11. [11] Aitken, Michael, et al. “Price clustering on the Australian stock exchange.”4.2 (1996): 297-314.
  12. [12] Lux, Thomas, and Michele Marchesi. “Volatility clustering in financial markets: a microsimulation of interacting agents.”3.04 (2000): 675-702.
  13. [13] Kumar, Rohini. “Risk indifference price of options under fast mean-reverting stochastic volatility.”. 2014.
  14. [14] Narayan, Paresh Kumar, and Russell Smyth. “Has political instability contributed to price clustering on Fiji's stock market?.”28 (2013): 125-130.
  15. [15] Bastos, João A., and Jorge Caiado. “Clustering financial time series with variance ratio statistics.”14.12 (2014): 2121-2133.
  16. [16] Cameron, A. Colin, Jonah B. Gelbach, and Douglas L. Miller. “Robust inference with multiway clustering.”29.2 (2011).
  17. [17] Aghabozorgi, Saeed, and Ying Wah Teh. “Stock market co-movement assessment using a three-phase clustering method.”41.4 (2014): 1301-1314.
  18. [18] Enke, David, and Suraphan Thawornwong. “The use of data mining and neural networks for forecasting stock market returns.”29.4 (2005): 927-940.
  19. [19] Cai, Fan, Nhien-An Le-Khac, and M-Tahar Kechadi. “Clustering approaches for financial data analysis: a survey.”. 2012.
  20. [20] Vilalta, Ricardo, and Irina Rish. “A decomposition of classes via clustering to explain and improve naive Bayes.”. Springer Berlin Heidelberg, 2003. 444-455.
  21. [21] Lopez, Manuel Ignacio, et al. “Classification via Clustering for Predicting Final Marks Based on Student Participation in Forums.”(2012).
  22. [22] Kohonen, Teuvo. “The self-organizing map.”78.9 (1990): 1464-1480.
DOI: https://doi.org/10.1515/kbo-2015-0056 | Journal eISSN: 2451-3113 (formerly 1843-6722) | Journal ISSN: 1843-6722
Language: English
Page range: 338 - 343
Published on: Nov 24, 2015
Published by: Nicolae Balcescu Land Forces Academy
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.