AI has long been applied to the problem of predicting financial markets.

The student will investigate how artificial neural networks can be trained to forecast developments of financial time series. He will first need to establish whether any similar research has been conducted previously, and if so to review the various approaches to the problem suggested therein. Following this prestudy, the student should decide on an approach and make the necessary implementations to train and test the neural networks. The attainable forecasting performance should be evaluated emprically. Simulations will be done using historical intraday trade data which has been procured for a selection of stocks from the Oslo Stock Exchange.

Multiple Kernel Learning (MKL) is used to replicate the signal combination process that trading rules embody when they aggregate multiple sources of financial information when predicting an asset’s price movements. A set of financially motivated kernels is constructed for the EURUSD currency pair and is used to predict the direction of price movement for the currency over multiple time horizons. MKL is shown to outperform each of the kernels individually in terms of predictive accuracy. Furthermore, the kernel weightings selected by MKL highlights which of the financial features represented by the kernels are the most informative for predictive tasks. 1 1

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