Financial Forecasting Using Support Vector Machines |
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| Authors: | Lijuan Cao Francis EH Tay |
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| Affiliation: | (1) Department of Mechanical and Production Engineering, National University of Singapore, Singapore, SG |
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| Abstract: | 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 time series. |
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| Keywords: | :Back propagation algorithm Financial time series forecasting Generalisation Multi-layer perception Support vector machines |
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