Non-fixed and Asymmetrical Margin Approach to Stock Market Prediction using Support Vector Regressio...
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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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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

  • SVR Introduction

  • Approach

  • Experiments & Results

  • Conclusion


Motivation

  • Predictive accuracy only?

  • Downside risk!

  • Two characteristics: fixed and symmetrical

  • Combine them:

    Non-fixed and Asymmetrical margin


Support Vector Regreesion (SVR) introduction

  • Developed by Vapnik (1995)

  • Model:

train data:

estimate objective function:

minimize


SVR Introduction (Cont’d)

  • Loss function:

  • The objective function f is represented by the dotted points.


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)


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Approach

  • Two characteristics:

  • 4 kinds of margins

fixed,

symmetrical.

FASM

FAAM

NASM

NAAM


Previous setting

  • Previous others’ method

  • In our previous work:Support Vector Machines Regression for volatile stock market prediction (IDEAL’02)


New Approach

  • Two characteristics of the margin in

    – insensitive loss function: fixed and symmetrical.

Symmetrical

Asymmetrical

Fixed

Non-fixed


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


Formulas

  • QP problem:

    s.t.

  • Objective function:

  • Kernel function:

    e.g. RBF


Margin width:

Up margin:

Down margin:

How to set margin?


Experiment

  • Accuracy Metrics

    • MAE:

    • UMAE:

    • DMAE:

    • actual value,

    • predictive value

    • number of testing data

Total error

Upside risk

Downside risk


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.


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.


Actual Parameter Setting


Effect of Length of EMA in NAAM

  • HSI

  • DJIA

Error

Error


Graphes

  • HSI

  • DJIA


Effect of in NAAM

  • HSI

  • DJIA

Error

Error


Effect of k in NAAM

  • HSI

  • DJIA

Error

Error


Comparison Results

  • HSI

Error


Results

  • DJIA

Error


NAAM, NASM vs. FASM, FAAM

  • Fixed Margin:

  • HSI

Step:

Error


NAAM, NASM vs. FASM, FAAM

  • Fixed Margin:

  • DJIA

Step:

Error


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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