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Multiple Kernel Learning (MKL) is used to replicate the signal combinationprocess that trading rules embody when they aggregate multiple sources offinancial information when predicting an asset's price movements. A set offinancially motivated kernels is constructed for the EURUSD currency pair andis used to predict the direction of price movement for the currency overmultiple time horizons. MKL is shown to outperform each of the kernelsindividually in terms of predictive accuracy. Furthermore, the kernelweightings selected by MKL highlights which of the financial featuresrepresented by the kernels are the most informative for predictive tasks.

Currency Forecasting using Multiple Kernel Learning with Financially Motivated Features