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Integration of Neural Network and Fuzzy system for Stock Price Prediction

Student : Dah-Sheng Lee

Professor: Hahn-Ming Lee

Date:5 December 2003

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Outline

Original Network Architecture (1992)[1] GA based Fuzzy Neural Network (2001)[2][3]

Quantitative model (artificial neural network) Qualitative model (GA fuzzy neural network) Decision integration (artificial neural network)

Computation results and comparison[3] Reference

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Original Network Architecture

The Neural Network has 2 hidden layer, 15 input unit and 1 output unit (15 - ? - ? - 1)

input unit :

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Original Network Architecture(cont…)

Output unit : a number between 0 to 1

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Original Network Architecture(cont…)

Learning Algorithm:

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GA based Fuzzy Neural Network (2001)

The System consist of factors identification (technical indexes) qualitative model (GA fuzzy neural network) decision integration (artificial neural network)

Index of Taiwan Stock market Training samples are from 1/1/1994 to 12/31/1995 Testing samples are from 1/1/1996 to 4/30/1997

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GA based Fuzzy Neural Network (2001) (cont…)

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GA based Fuzzy Neural Network (2001)---factors identification

This part collect 42 kinds of technical indexes and non-quantitative information

The 42 kinds of technical indexes are

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GA based Fuzzy Neural Network (2001)---factors identification (cont…)

The non-quantitative information include related economics journals, government technical reports and newspaper from 1991 to 1997

The experienced experts eliminated the unnecessary events and then divided the useful events into six dimensions (political,financial,economic,message,technical, and international)

The questionnaire for each event has the following format: IF event A occurs, THEN it’s effect on the stock market is from

to .

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GA based Fuzzy Neural Network (2001)---qualitative model

The fuzzy method is employed to capture the stock experts’ knowledge

GA used in this model with parameters below Fitness function

Where N denotes the number of the population and value is set to be 50

Ti represents the i-th desired output Yi represents the i-th actual output format of Chromosome is 8-digit value on the basis of 2

21( )N

i ii

NF

T Y

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GA based Fuzzy Neural Network (2001)---qualitative model (cont…)

The “Dimensional GFNN” combines all events of specific dimension occurred and Integrated by using an “Integrated GFNN”

The GA parameters in “Dimensional GFNN” is Generations : 1000 Crossover rate: 0.2 Crossover type: two-point crossover Mutation rate: 0.8

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GA based Fuzzy Neural Network (2001)---qualitative model (cont…)

The GA parameters in “Integration Dimensional GFNN” is Generations : 1000 Crossover rate: 0.2 Crossover type: two-point crossover Mutation rate: 0.8

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GA based Fuzzy Neural Network (2001)---decision integration (cont…)

Both the quantitative and qualitative factors are inputs of ANN, and should normalized in [0,1]

The ANN including “time effect” input node In this system,two different out-puts, O1 and O2,

are verified

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GA based Fuzzy Neural Network (2001)---decision integration (cont…)

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Computation results and comparison

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Computation results and comparison (cont…)

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Computation results and comparison (cont…)

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Computation results and comparison (cont…)

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Computation results and comparison (cont…)

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Reference [1] “An intelligent forecasting system of stock price using neural networks” Baba, N.; Kozaki, M.;

Neural Networks, 1992. IJCNN., International Joint Conference on , Volume: 1 , 7-11 June 1992 Page(s): 371 -377 vol.1

[2] “Integration of artificial neural networks and fuzzy Delphi for stock market forecasting” Kuo, R.J.; Lee, L.C.; Lee, C.F.; Systems, Man, and Cybernetics, 1996., IEEE International Conference on , Volume: 2 , 14-17 Oct. 1996 Page(s): 1073 -1078 vol.2

[3]”An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network”

Kuo, R.J.; Chen, C.H.; Hwang, Y.C. Fuzzy Sets and Systems Volume: 118, Issue: 1, February 16, 2001, Page(s): 21-45