1. Bessler, D.A., Brandt, J.A.: Forecasting livestock prices with individual and composite methods. Applied Economics 13, 513–522 (1981)

   Article  Google Scholar 

2. Harris, K.S., Leuthold, R.M.: A comparison of alternative forecasting techniques for live stock prices: A case study. North Central J. Agricultural Economics 7, 40–50 (1985)

   Article  Google Scholar 

3. Dorfman, J.H., Mcintosh, C.S.: Results of a price forecasting competition. American J. Agricultural Economics 72, 804–808 (1990)

   Article  Google Scholar 

4. Blank, S.C.: Chaos in future markets? A nonlinear dynamic analysis. J. Futures Markets 11, 711–728 (1991)

   Article  Google Scholar 

5. Chavas, J., Holt, M.T.: Market instability and nonlinear dynamics. American J. Agricultural Economics 75, 113–120 (1993)

   Article  Google Scholar 

6. Hall, J.W.: Adaptive selection of U.S. stocks with neural nets. In: Deboeck, G.J. (ed.) Trading on the edge: neural, genetic, and fuzzy systems for chaotic financial markets, Wiley, New York (1994)

   Google Scholar 

7. Yaser, S.A.M., Atiya, A.F.: Introduction to financial forecasting. Applied Intelligence 6, 205–213 (1996)

   Article  Google Scholar 

8. Grudnitski, G., Osburn, L.: Forecasting S&P and gold futures prices: an application of neural networks. The Journal of Futures Markets 13(6), 631–643 (1993)

   Article  Google Scholar 

9. Kim, S., Chun, S.: Graded forecasting using an array of bipolar predictions: application of probabilistic neural networks to a stock market index. International Journal of Forecasting 14(3), 323–337 (1998)

   Article  Google Scholar 

10. Saad, E., Prokhorov, D., Wunsch, D.: Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Transactions on Neural Networks 9(6), 1456–1470 (1998)

    Article  Google Scholar 

11. Cheng, W., Wanger, L., Lin, C.: Forecasting the 30-year US treasury bond witha system of neural networks. Journal of Computational Intelligence in Finance 4, 10–16 (1996)

    Google Scholar 

12. Sharda, R., Patil, R.B.: A connectionist approach to time series prediction: an empirical test. In: Trippi, R.R., Turban, E. (eds.) Neural Networks in Finance and Investing, pp. 451–464. Probus Publishing Co., Chicago (1994)

    Google Scholar 

13. Haykin, S.: Neural networks: a comprehensive foundation. Prentice Hall, Englewood CliKs (1999)

    MATH  Google Scholar 

14. Zhang, G.Q., Michael, Y.H.: Neural network forecasting of the British Pound=US Dollar exchange rate. Omega 26(4), 495–506 (1998)

    Article  Google Scholar 

15. Kaastra, I., Milton, S.B.: Forecasting futures trading volume using neural networks. The Journal of Futures Markets 15(8), 853–970 (1995)

    Article  Google Scholar 

16. Vapnik, V.N.: The nature of statistical learning theory. Springer, New York (1995)

    MATH  Google Scholar 

17. Schmidt, M.: Identifying speaker with support vector networks. In: Interface 1996 Proceedings, Sydney (1996)

    Google Scholar 

18. Muller, K.R., Smola, A., Scholkopf, B.: Prediction time series with support vector machines. In: Proceedings of International Conference on Artificial Neural Networks, Lausanne, Switzerland, p. 999 (1997)

    Google Scholar 

19. Vapnik, V.N., Golowich, S.E., Smola, A.J.: Support vector method for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems 9, 281–287 (1996)

    Google Scholar 

20. Thomason, M.: The practitioner methods and tool. Journal of Computational Intelligence in Finance 7(3), 36–45 (1999)

    Google Scholar 

21. Keerthi, S.S., Lin, C.-.J.: Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation 15(7), 1667–1689

    Google Scholar 

22. Lin, H.-T., Lin, C.-J.: A study on sigmoid kernels for SVM and the training of non- PSD kernels by SMO-type methods. Technical report, Department of Computer Science and Information Engineering, National Taiwan University, Available athttp://www.csie.ntu.edu.tw/~cjlin/papers

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