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Forecasting of Stock Returns by Using Manifold Wavelet Support Vector Machine

Forecasting of Stock Returns by Using Manifold Wavelet Support Vector Machine
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摘要 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. 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.
出处 《Journal of Shanghai Jiaotong university(Science)》 EI 2010年第1期49-53,共5页 上海交通大学学报(英文版)
基金 the Hunan Natural Science Foundation(No. 09JJ3129) the Hunan Key Social Science Foundation (No. 09ZDB04) the Hunan Social Science Foundation (No. 08JD28)
关键词 股票时间序列 支持向量机 流形 预测 收益率 非线性函数 小波核 小波函数 stock returns forecasting, kernel, manifold wavelet support vector machine (MWSVM)
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  • 1YASER S A M, ATIYA A F. Introduction to financial forecasting [J]. Applied Intelligence, 1996, 6: 205-213.
  • 2CAO L, TAY F E H. Financial forecasting using support vector machines [J]. Neural Computing and Applications, 2001, 10(2): 184-192.
  • 3MULLER K R, SMOLA A, RATSCH G, et al. Predicting time series with support vector machines [C]// Proceedings of the International Conference on Artificial Neural Networks. Lausanne: Springer, 1997: 999-1004.
  • 4SMOLA A J, SCHOLKOPF B. A tutorial on support vector regression [J]. Statistics and Computing, 2004, 14(3): 199-222.
  • 5VAPNIK V N. The nature of statistical learning theory [M]. New York: Springer, 2000.
  • 6SCHOLKOPF B, BURGES C J C, SMOLA A J, et al. Advances in kernel methods: Support vector Learning [M]. London: The MIT Press, 1999.
  • 7DAUBECHIES I. The wavelet transform, time-frequency localization and signalanalysis [J]. Information Theory, IEEE Transactions on, 1990, 36(5): 961-1005.
  • 8DAUBECHIES I. Ten lectures on wavelets [M]. Philadelphia: SIAM, 1992.
  • 9MALLAT S G. A theory for multiresolution signal decomposition: The waveletrepresentation [J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1989, 11(7): 674-693.
  • 10MALLAT S. A wavelet tour of signal processing [M]. Boston: Academic Press, 1999.

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