Abstract
An admissible manifold wavelet kernel is proposed to construct manifold wavelet support vector machine (MWSVM) for stock returns forecasting. The manifold wavelet kernel is obtained by incorporating manifold theory into wavelet technique in support vector machine (SVM). Since manifold wavelet function can yield features that describe of the stock time series both at various locations and at varying time granularities, the MWSVM can approximate arbitrary nonlinear functions and forecast stock returns accurately. The applicability and validity of MWSVM for stock returns forecasting is confirmed through experiments on real-world stock data.
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Foundation item: the Hunan Natural Science Foundation (No. 09JJ3129), the Hunan Key Social Science Foundation (No. 09ZDB04), and the Hunan Social Science Foundation (No. 08JD28)
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Tang, Lb., Sheng, Hy. & Tang, Lx. Forecasting of stock returns by using manifold wavelet support vector machine. J. Shanghai Jiaotong Univ. (Sci.) 15, 49–53 (2010). https://doi.org/10.1007/s12204-010-9707-0
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DOI: https://doi.org/10.1007/s12204-010-9707-0