Titlebook: Neural Networks and the Financial Markets; Predicting, Combinin Jimmy Shadbolt,John G. Taylor Book 2002 Springer-Verlag London 2002 Arbitra

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Introduction to the Financial Markets get a better view of how some asset values will change? Even more preliminarily, what do these quantities such as “yield curve” and “basis points” mean for investors and those, like ourselves, who are trying to project the values of certain financial assets? If we can get a better view of asset val

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General Form of Models of Financial Marketse time step in the future, . + 1, will have the form .where the time series vectors .. are economic variables evaluated at earlier times t, t − 1, t − 2,…, t − 23 (where our cut-off at 24 lags is somewhat arbitrary but cannot be expected to be larger with any assurance). In this chapter we will disc

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Linear Models returns from the random walk model would provide a theoretical basis for a more general form for the expectations of asset returns. Under this assumption the naïve estimate, based purely on statistics of historical data, is replaced by a general estimate conditioned on the most recent past returns

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Input Selectionchniques, especially neural networks, can only use a limited number of inputs because of the parameterisation of the model and the limited number of data points available. We note that a number of techniques (e.g. adaptive lag and linear RVM) have their own selection techniques, and so avoid some of

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