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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. Outline. Original Network Architecture (1992)[1] GA based Fuzzy Neural Network (2001)[2][3] Quantitative model (artificial neural network)
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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
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
Original Network Architecture • The Neural Network has 2 hidden layer, 15 input unit and 1 output unit (15 - ? - ? - 1) • input unit :
Original Network Architecture(cont…) • Output unit : a number between 0 to 1
Original Network Architecture(cont…) • Learning Algorithm:
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
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
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 .
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
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
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
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
GA based Fuzzy Neural Network (2001)---decision integration (cont…)
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