| Summary: | The usage of machine learning techniques for the prediction of financial time series is investigated. Both discriminative and generative methods are considered and compared to more standard financial prediction techniques. Generative methods such as Switching Autoregressive Hidden Markov and changepoint models are found to be unsuccessful at predicting daily and minutely prices from a wide range of asset classes. Committees of discriminative techniques (Support Vector Machines (SVM), Relevance Vector Machines and Neural Networks) are found to perform well when incorporating sophisticated exogenous financial information in order to predict daily FX carry basket returns. The higher dimensionality that Electronic Communication Networks make available through order book data is transformed into simple features. These volume-based features, along with other price-based ones motivated by common trading rules, are used by Multiple Kernel Learning (MKL) to classify the direction of price movement for a currency over a range of time horizons. Outperformance relative to both individual SVM and benchmarks is found, along with an indication of which features are the most informative for financial prediction tasks. Fisher kernels based on three popular market microstructural models are added to the MKL set. Two subsets of this full set, constructed from the most frequently selected and highest performing individual kernels are also investigated. Furthermore, kernel learning is employed - optimising hyperparameter and Fisher feature parameters with the aim of improving predictive performance. Significant improvements in out-of-sample predictive accuracy relative to both individual SVM and standard MKL is found using these various novel enhancements to the MKL algorithm.
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