The use of Support Vector Machines (SVMs) is studied in financial
forecasting by comparing it with a multi-layer perceptron trained
by the Back Propagation (BP) algorithm. SVMs forecast better than
BP based on the criteria of Normalised Mean Square Error (NMSE).
Mean Absolute Error (MAE), Directional Symmetry (DS) Correct Up (CP)
trend and Correct Down (CD) trend S&P 500 daily price index is used
as the data set. Since there is no structured way to choose the free
parameters of SVMs, the generalisation error with respect to the
free parameters of SVMs is investigated in this experiment. As illustrated
in the experiment, they have little impact on the solution. Analysis
of the experimental results demonstrates that it is advantageous
to apply SVMs to forecast the financial rime series.
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