Machine Learning in Statistical Arbitrage
نویسندگان
چکیده
We apply machine learning methods to obtain an index arbitrage strategy. In particular, we employ linear regression and support vector regression (SVR) onto the prices of an exchange-traded fund and a stream of stocks. By using principal component analysis (PCA) in reducing the dimension of feature space, we observe the benefit and note the issues in application of SVR. To generate trading signals, we model the residuals from the previous regression as a mean reverting process. At the end, we show how our trading strategies beat the market.
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تاریخ انتشار 2009