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Haiqin Yang, Irwin King and Laiwan Chan Department of Computer Science and Engineering

Non-fixed and Asymmetrical Margin Approach to Stock Market Prediction using Support Vector Regression. Haiqin Yang, Irwin King and Laiwan Chan Department of Computer Science and Engineering The Chinese University of Hong Kong November 18-22, 2002 ICONIP ’ 02. Index. Motivation.

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Haiqin Yang, Irwin King and Laiwan Chan Department of Computer Science and Engineering

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  1. Non-fixed and Asymmetrical Margin Approach to Stock Market Prediction using Support Vector Regression Haiqin Yang, Irwin King and Laiwan Chan Department of Computer Science and Engineering The Chinese University of Hong Kong November 18-22, 2002 ICONIP’02

  2. Index • Motivation • SVR Introduction • Approach • Experiments & Results • Conclusion

  3. Motivation • Predictive accuracy only? • Downside risk! • Two characteristics: fixed and symmetrical • Combine them: Non-fixed and Asymmetrical margin

  4. Support Vector Regreesion (SVR) introduction • Developed by Vapnik (1995) • Model: train data: estimate objective function: minimize

  5. SVR Introduction (Cont’d) • Loss function: • The objective function f is represented by the dotted points.

  6. Related Applications • Support Vector Method for Function Approximation, Regression Estimation and Signal Processing (Vapnik et al., 1996) • Predicting time series with support vector machine (Muller et al., 1997) • Application of support vector machines to financial time series forecasting (E.H.Tay and L.J.Cao. 2001)

  7. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Approach • Two characteristics: • 4 kinds of margins fixed, symmetrical. FASM FAAM NASM NAAM

  8. Previous setting • Previous others’ method • In our previous work:Support Vector Machines Regression for volatile stock market prediction (IDEAL’02)

  9. New Approach • Two characteristics of the margin in – insensitive loss function: fixed and symmetrical. Symmetrical Asymmetrical Fixed Non-fixed

  10. Formulas • A general type of –Insensitive loss function • Fixed and Symmetrical Margin (FASM): • Fixed and Asymmetrical Margin (FAAM): • Non-fixed and Symmetrical Margin (NASM): • Non-fixed and Asymmetrical Margin (NAAM): up margin down margin

  11. Formulas • QP problem: s.t. • Objective function: • Kernel function: e.g. RBF

  12. Margin width: Up margin: Down margin: How to set margin?

  13. Experiment • Accuracy Metrics • MAE: • UMAE: • DMAE: • actual value, • predictive value • number of testing data Total error Upside risk Downside risk

  14. Experiment Description • Model: • Data: Hang Seng Index (HSI), Dow Jones Industrial Average (DJIA). • Time periods: Jan. 2, 1998 ~ Dec. 29, 2000(3 years) • Ratio of training data and testing data: 5:1. • Procedures: one day ahead prediction. • Environments • CPU: Pentium 4, 1.4 G • Memory: RAM 512M • OS: Windows2000 • Time: few hours.

  15. Experiment Description • Three kinds of experiments • Test the effect of parameters in NAAM to obtain a better result. • Compare the result of NAAM with NASM, AR(4), RBF network (also test the effect of the number of hidden units). • Compare the results of NAAM, NASM with FASM and FAAM.

  16. Actual Parameter Setting

  17. Effect of Length of EMA in NAAM • HSI • DJIA Error Error

  18. Graphes • HSI • DJIA

  19. Effect of in NAAM • HSI • DJIA Error Error

  20. Effect of k in NAAM • HSI • DJIA Error Error

  21. Comparison Results • HSI Error

  22. Results • DJIA Error

  23. NAAM, NASM vs. FASM, FAAM • Fixed Margin: • HSI Step: Error

  24. NAAM, NASM vs. FASM, FAAM • Fixed Margin: • DJIA Step: Error

  25. Conclusion • Propose non-fixed and asymmetrical margin (NAAM) approach in SVR to predict stock market. • Compare this method to non-fixed symmetrical margin (NASM) approach, AR(4), RBF network. • NAAM, NASM outperform AR(4), RBF network. • NAAM can reduce the downside risk. • NAAM, NASM outperform FASM, FAAM.

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