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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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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
Index Prediction using Support Vector Regression

  • Motivation

  • SVR Introduction

  • Approach

  • Experiments & Results

  • Conclusion


Motivation
Motivation Prediction using Support Vector Regression

  • Predictive accuracy only?

  • Downside risk!

  • Two characteristics: fixed and symmetrical

  • Combine them:

    Non-fixed and Asymmetrical margin


Support vector regreesion svr introduction
Support Vector Regreesion (SVR) introduction Prediction using Support Vector Regression

  • Developed by Vapnik (1995)

  • Model:

train data:

estimate objective function:

minimize


Svr introduction cont d
SVR Introduction (Cont Prediction using Support Vector Regression’d)

  • Loss function:

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


Related applications
Related Applications Prediction using Support Vector Regression

  • 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)


Approach

+ + Prediction using Support Vector Regression

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Approach

  • Two characteristics:

  • 4 kinds of margins

fixed,

symmetrical.

FASM

FAAM

NASM

NAAM


Previous setting
Previous setting Prediction using Support Vector Regression

  • Previous others’ method

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


New approach
New Approach Prediction using Support Vector Regression

  • Two characteristics of the margin in

    – insensitive loss function: fixed and symmetrical.

Symmetrical

Asymmetrical

Fixed

Non-fixed


Formulas
Formulas Prediction using Support Vector Regression

  • 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


Formulas1
Formulas Prediction using Support Vector Regression

  • QP problem:

    s.t.

  • Objective function:

  • Kernel function:

    e.g. RBF


How to set margin

Margin width: Prediction using Support Vector Regression

Up margin:

Down margin:

How to set margin?


Experiment
Experiment Prediction using Support Vector Regression

  • Accuracy Metrics

    • MAE:

    • UMAE:

    • DMAE:

    • actual value,

    • predictive value

    • number of testing data

Total error

Upside risk

Downside risk


Experiment description
Experiment Description Prediction using Support Vector Regression

  • 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 description1
Experiment Description Prediction using Support Vector Regression

  • 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
Actual Parameter Setting Prediction using Support Vector Regression


Effect of length of ema in naam
Effect of Length of EMA in NAAM Prediction using Support Vector Regression

  • HSI

  • DJIA

Error

Error


Graphes
Graphes Prediction using Support Vector Regression

  • HSI

  • DJIA


Effect of in naam
Effect of in NAAM Prediction using Support Vector Regression

  • HSI

  • DJIA

Error

Error


Effect of k in naam
Effect of Prediction using Support Vector Regressionk in NAAM

  • HSI

  • DJIA

Error

Error


Comparison results
Comparison Results Prediction using Support Vector Regression

  • HSI

Error


Results
Results Prediction using Support Vector Regression

  • DJIA

Error


Naam nasm vs fasm faam
NAAM, NASM vs. FASM, FAAM Prediction using Support Vector Regression

  • Fixed Margin:

  • HSI

Step:

Error


Naam nasm vs fasm faam1
NAAM, NASM vs. FASM, FAAM Prediction using Support Vector Regression

  • Fixed Margin:

  • DJIA

Step:

Error


Conclusion
Conclusion Prediction using Support Vector Regression

  • 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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