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Zbl 1002.68689
Cao, Lijuan; Tay, Francis E.H.
Financial forecasting using support vector machines. (English)
[J] Neural Comput. Appl. 10, No.2, 184-192 (2001). ISSN 0941-0643; ISSN 1433-3058/e

Summary: 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.

MSC 2000:: *68U99 Computing methodologies
68T05 Learning and adaptive systems
91B28 Finance etc.

Keywords: Back propagation algorithm; Financial time series forecasting; Generalisation; Multi-layer perception; Support vector machines

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