Stock selection using support vector machines
Fan, A.
Palaniswami, M.
Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic.;
This paper appears in: Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Publication Date: 2001
Volume: 3,
On page(s): 1793-1798 vol.3
Meeting Date: 07/15/2001 - 07/19/2001
Location: Washington, DC, USA
ISBN: 0-7803-7044-9
References Cited: 12
INSPEC Accession Number: 7036651
Digital Object Identifier: 10.1109/IJCNN.2001.938434
Current Version Published: 2002-08-07
Abstract
We used the support vector machines (SVM) in a classification
approach to `beat the market'. Given the fundamental accounting and
price information of stocks trading on the Australian Stock Exchange, we
attempt to use SVM to identify stocks that are likely to outperform the
market by having exceptional returns. The equally weighted portfolio
formed by the stocks selected by SVM has a total return of 208% over a
five years period, significantly outperformed the benchmark of 71%. We
also give a new perspective with a class sensitivity tradeoff, whereby
the output of SVM is interpreted as a probability measure and ranked,
such that the stocks selected can be fixed to the top 25%
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
