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A SVM APPROACH TO STOCK TRADING

Statistical arbitrage is the application of modern statistics, significant computing power, and large data sets to the discovery of financial market mispricing and then exploiting those inefficiencies for profit. Fundamental analysis is the process of analyzing all public company disclosures including financial statements in the attempt to uncover indicators of future company performance. In this paper, we endeavor to combine both approaches to investing in pursuit of superior risk adjusted returns. There are a large number of market participants with different views on the correct valuation of varying securities and these participants constantly update their own market outlook and security views. The information a market participant may consider relevant on one day may be considered insignificant by the same individual one month later. The markets are in a constant state of flux of what is critical for security valuation and the monetary value to ascribe to this information. Support Vector Machine (SVM) is currently one of the most popular learning algorithms and has successfully been applied to numerous fields. Its popularity can be explained both by a high generalization performance and a mathematically well posed training method. In the context of financial data modeling, SVM is particularly appealing for multiple reasons. Firstly, it makes no strong assumptions on the data. Secondly, since it is not an empirical error minimization method, therefore it should not over-fit the data. These two reasons explain why SVM compares well to other methods commonly used for financial forecasting such as ARIMA of Artificial Neural Networks (See [CaoTay01]). Further, in our context of incorporating several features, we need an algorithm that handles high dimensional data well, which is the case for SVM.