Abstract
A common theme in Computational Finance is that the predictability of financial time series is to some extent a function of the data-representation adopted (see Burgess (1999) for a review). In particular a number of authors use multivariate techniques to create combinations of time-series, generally as a preliminary step to performing predictive modelling itself. Such methods include factor models (Jacobs and Levy, 1988), canonical correlation (Lo and MacKinley, 1995), relative prices (Bentz et al., 1996), principal component analysis (Burgess, 1996), cointegration (Burgess and Refenes, 1995, 1996; Steurer and Hann, 1996) and independent component analysis (Back and Weigend, 1998).
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© 2002 Springer-Verlag London
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Burgess, A.N. (2002). Cointegration. In: Shadbolt, J., Taylor, J.G. (eds) Neural Networks and the Financial Markets. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0151-2_21
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DOI: https://doi.org/10.1007/978-1-4471-0151-2_21
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