Abstract
Ensemble classifiers comprised of neural networks trained using particle swarm optimisation are used to identify characteristic factors of companies that have experienced high share price volatility either by an objective measure or relative to their peers, or whose calculated firm-specific equity risk exceeds a comparison value appropriate to their industry and region. Use is made of a novel training metric, the Matthews correlation coefficient, that is shown to better handle numerically unbalanced data sets. A comparison is made with results from input-output correlation analysis and it is noted that the factors derived from ensemble weightings appear to be more predictive.
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References
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: IEEE International Conference Symposium on Neural Networks, pp. 1942–1948. IEEE Press, New York (1995)
Poli, R.: An Analysis of Publications on Particle Swarm Optimisation Application. Technical report, Department of Computer Science, University of Essex (2007)
Banks, A., Vincent, J., Anyakoha, C.: An Review of Particle Swarm Optimization. Part II: Hybridisation, Combinatorial, Multicriteria and Constrained Optimization, and Indicative Applications. Natural Computing 7, 109–124 (2008)
Gudise, V.G., Venayagamoorthy, G.K.: Comparison of Particle Swarm Optimization and Backpropagation as Training Algorithms for Neural Networks. In: Swarm Intelligence Symposium, pp. 110–117. IEEE Press, New York (2003)
Lee, J.-s., Lee, S., Chang, S., Ahn, B.-H.: A Comparison of GA and PSO for Excess Return Evaluation in Stock Markets. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 221–230. Springer, Heidelberg (2005)
Matthews, B.W.: Comparison of the Predicted and Observed Secondary Structure of T4 Phage Lysozyme. Biochim. Biophys. Acta 405, 442–451 (1975)
Ang, A., Hodrick, R.J., Xing, Y., Zhang, X.: The Cross-Section of Volatility and Expected Returns. Journal of Finance 57, 259–299 (2006)
Fabozzi, F.J., Markowitz, H.M.: Multifactor Equity Risk Models. Wiley, NJ (2011)
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Khoury, P., Gorse, D. (2012). Identification of Factors Characterising Volatility and Firm-Specific Risk Using Ensemble Classifiers. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34478-7_55
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DOI: https://doi.org/10.1007/978-3-642-34478-7_55
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