Identifying the potential for failure of businesses in the technology, pharmaceutical and banking sectors using kernel-based machine learning methods

Authors:
Yashodhan Athavale;Pouyan Hosseinizadeh;Sridhar Krishnan;Aziz Guergachi
Affiliations:
Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada;Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, Canada;Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada;Ted Rogers School of 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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SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics

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Abstract

The objective of this paper is to analyze the performance of a kernel-based method in identifying the potential for collapse (or survival) of a firm operating in three different sectors of the economy - Technology, Pharmaceutical and Banking. The analysis uses the actual stock market data, collected on a weekly basis in a common time-series interval for the active and dead companies in each of the three sectors. The basic idea is to apply the concept of Fisher kernels and visualization to reduce the data from a time-series format to two-dimensional plots that can be visually inspected and potentially segregate the 'collapse' class from the 'survival' one. From our experiments we observe that our method fits well for the Technology and Banking sectors, but is not able to provide a visually clear classification for the Pharmaceuticals sector. Depending on the range of data we use as input, and its distribution, the classification pattern varies from an ideally separable case to a non separable one, in a two dimensional feature space.