Using heavy-tailed distributions to stress-test kernel methods for segregating the firms that are likely to survive

Authors:
Pouyan Hosseinizadeh;Aziz Guergachi
Affiliations:
Mechanical & Industrial Engineering Department, Ryerson University, Toronto, Canada;Ted Rogers School of Information Technology Management, Ryerson University, Toronto, Canada
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

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Abstract

while kernel-based learning methods have emerged during the last two decades as major tools to effectively manage uncertainty, heavy-tailed distributions remain a major challenge for modelers who aim to predict the future behavior of complex systems. In this article, Weibull distribution has been used to stress-test kernel-based methods and study more specifically the impact of heavy-tailed distributions on the performance of Fisher kernels in identifying the potential for collapse of an enterprise based on its stock price.