Empirical evaluation of a dimension-reduction method for time-series prediction
Stock price prediction is one of the most challenging problems in finance. The multivariate conditional mean is a point estimator to minimize the mean square error of prediction giver past data. However, the calculation of the condition mean and covariance involves the numerical inverse of a typically ill-conditioned matrix, leading to numerical issues. To overcome this problem, we develop a method based on filtering the data using principle components. Principal component analysis (PCA) identifies a small number of principle components that explain most of the variation in a data set. This ...
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