Kernel methods for short-term portfolio management

Authors:
Huseyin Ince;Theodore B. Trafalis
Affiliations:
School of Business Administration Studies, Gebze Institute of Technology, Çayirova Fab., Yolu, No: 101 P.K:141 41400, Gebze/Kocaeli, Turkey;School of Industrial Engineering, The University of Oklahoma, 202 West Boyd, Ste 124, Norman, OK 73019, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2006

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Abstract

Portfolio optimization problem has been studied extensively. In this paper, we look at this problem from a different perspective. Several researchers argue that the USA equity market is efficient. Some of the studies show that the stock market is not efficient around the earning season. Based on these findings, we formulate the problem as a classification problem by using state of the art machine learning techniques such as minimax probability machine (MPM) and support vector machines (SVM). The MPM method finds a bound on the misclassification probabilities. On the other hand, SVM finds a hyperplane that maximizes the distance between two classes. Both methods prove similar results for short-term portfolio management.