Abstract
The analysis of stock markets has become relevant mainly because of its financial implications. In this paper, we propose a novel methodology for performing a structured cluster analysis of stock market data. Our proposed method uses a tree-based neural network called the TTOSOM. The TTOSOM performs self-organization to construct tree-based clusters of vector data in the multi-dimensional space. The resultant tree possesses interesting mathematical properties such as a succinct representation of the original data distribution, and a preservation of the underlying topology. In order to demonstrate the capabilities of our method, we analyze 206 assets of the Italian stock market. We were able to establish topological relationships between various companies traded on the Italian stock market and visually inspect the resultant taxonomy. The results that we obtained, briefly reported here (but more elaborately in [10]), were amazingly accurate and reflected the real-life relationships between the stocks.
C.A. Astudillo—Assistant Professor. IEEE Member. The work of this author is partially supported by the FONDECYT grant 11121350, Chile.
B.J. Oommen—Chancellor’s Professor; Fellow: IEEE and Fellow: IAPR. This author is also an Adjunct Professor with the University of Agder in Grimstad, Norway. The work of this author was partially supported by NSERC, the Natural Sciences and Engineering Research Council of Canada.
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Notes
- 1.
A paper, written by two of these present authors, which reported the preliminary results of a dynamic Tree SOM, won the Best Paper Award in a well-known international AI conference [1].
- 2.
Other examples of applying the TTOSOM are found in [2].
- 3.
The results presented here are brief in the interest of space. Additional results can be found in [10], and this paper can be sent to the Referees is required.
- 4.
In this table, the SR values have been multiplied by 100 to make the results more readable.
References
Astudillo, C.A., Oommen, B.J. On using adaptive binary search trees to enhance self organizing maps. In: Nicholson, A., Li, X. (eds.) 22nd Australasian Joint Conference on Artificial Intelligence (AI 2009), pp. 199–209 (2009)
Astudillo, C.A., Oommen, B.J.: Imposing tree-based topologies onto self organizing maps. Inform. Sci. 181(18), 3798–3815 (2011)
Astudillo, C.A., Oommen, B.J.: On achieving semi-supervised pattern recognition by utilizing tree-based SOMs. Pattern Recogn. 46(1), 293–304 (2013)
Astudillo, C.A., Oommen, B.J.: Topology-oriented self-organizing maps: a survey. Pattern Anal. Appl. (2014). http://dx.doi.org/10.1007/s10044-014-0367-9
Astudillo, C.A., Oommen, B.J.: Fast BMU search in SOMs using random hyperplane trees. In: Pham, D.-N., Park, S.-B. (eds.) PRICAI 2014. LNCS (LNAI), vol. 8862, pp. 39–51. Springer, Heidelberg (2014). doi:10.1007/978-3-319-13560-1_4
Gámez, J.A., Rumí, R., Salmerón, A.: Unsupervised Naive Bayes for data clustering with mixtures of truncated exponentials. In: Studený, M., Vomlel, J. (eds.) Third European Workshop on Probabilistic Graphical Models, 12-15 September 2006, Prague, Czech Republic, Electronic Proceedings, pp. 123–130 (2006)
Guan, L.: Self-organizing trees and forests: a powerful tool in pattern clustering and recognition. In: Campilho, A., Kamel, M.S. (eds.) ICIAR 2006. LNCS, vol. 4141, pp. 1–14. Springer, Heidelberg (2006). doi:10.1007/11867586_1
Kohonen, T.: Self-Organizing Maps. Springer-Verlag New York Inc., Secaucus (1995)
Pakkanen, J., Iivarinen, J., Oja, E.: The evolving tree – a novel self-organizing network for data analysis. Neural Process. Lett. 20(3), 199–211 (2004)
Poblete, J.A., Resta, M., Oommen, B.J., Astudillo, C.A.: On representing the stock market topologically and inferring its economic indicators using tree-based SOMs (2016, to be submitted)
Resta, M.: The shape of crisis lessons from self organizing maps. In: Kahraman, C. (ed.) Computational Intelligence Systems in Industrial Engineering. Atlantis Computational Intelligence Systems, vol. 6, pp. 535–555. Atlantis Press, Amsterdam (2012)
Sharpe, W.F.: The sharpe ratio. J. Portfolio Manage. 21(1), 4958 (1994)
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Astudillo, C.A., Poblete, J., Resta, M., Oommen, B.J. (2016). A Cluster Analysis of Stock Market Data Using Hierarchical SOMs. In: Kang, B.H., Bai, Q. (eds) AI 2016: Advances in Artificial Intelligence. AI 2016. Lecture Notes in Computer Science(), vol 9992. Springer, Cham. https://doi.org/10.1007/978-3-319-50127-7_8
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