Stock selection using support vector machines

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%.

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