1. 1 Training Neural Network with Genetic Algorithm for Stock Prediction Student : Dah-Sheng Lee Professor: Hahn-Ming Lee Date:11 October 2003

2. 2 Outline Original Network Architecture (1992) Modified Neural Network 1 (1997) Modified Neural Network 2 (2000) Reference

3. 3 Original Network Architecture The Neural Network has 2 hidden layer, 15 input unit and 1 output unit (15 - ? - ? - 1) input unit :

4. 4 Original Network Architecture(cont…) Output unit : a number between 0 to 1

5. 5 Original Network Architecture(cont…) Learning Algorithm:

6. 6 Modified Neural Network 1 (1997) The Neural Network has 1 hidden layer, 5 input unit and 1 output unit (5 - 6 - 1) Share comprehension index in shanghais share Exchange from 3/28/1994 to 8/1/1994 Input unit : 1. Industry Share(IS) 2. Commerce Share(CS) 3. Real Estate Share(RES) 4. Utility Share(US) 5. Comprehensive Share(CS)

7. 7 Modified Neural Network 1 (1997)(cont…) Output unit: Share Price Index Learning Algorithm Wi - the value of No I Node, L - the width of weigh space, initial data is 20 W jo - the media value of the No I node space, initial data is 0 VS - the value of the 8-bytes binary string,

8. 8 Modified Neural Network 1 (1997)(cont…) Fitness function: F = 1/(1+SE) SE – the square sum of error Yi – the output of NN Di – the expect output of NN Single-point break cross Cross probability is 0.8 Mutation probability is 0.02

9. 9 Modified Neural Network 1 (1997)(cont…) The input data is matrix B, Let matrix

10. 10 Modified Neural Network 1 (1997)(cont…) Let matrix Let Output is

11. 11 Modified Neural Network 1 (1997)(cont…) Simulation Result

12. 12 Modified Neural Network 2 (2000) The Neural Network has 1 hidden layer, 8 input unit and 2 output unit (8 - 15 - 2) Tokyo Stock Exchange Price Indexes (TOPIX) from 11/1995 to 10/1997 Input unit : 1. Changes of TOPIX 2. PBR 3. Changes of the turnover by foreign traders 4. Changes of the currency rate (Yen - Dollar) 5. Changes of the turnover Tokyo Stock Market 6. etc.

13. 13 Output unit: highest and lowest value of TOPIX till 4 weeks in the future Modified Neural Network 2 (2000)(cont…)

14. 14 Modified Neural Network 2 (2000)(cont…) Learning Algorithm

15. 15 Modified Neural Network 2 (2000)(cont…) Simulation Result Utilize the above chromosome for the dealings of TOPIX during the period from November 1996 to October 1997. In this period, TOPIX changed from 1556 yen to 1360 yen. This means that we would have lost about 13% of our investments if we had not executed dealings. However, we have succeeded in keeping our investments by following the dealing rule obtained by Gas. (The total amount of money has been changed as follows: 10.00 billion yen -> 10.41 billion yen.)

16. 16 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] “Training neural network with genetic algorithms for forecasting the stock price index” Fu Kai; Xu Wenhua; Intelligent Processing Systems, 1997. ICIPS '97. 1997 IEEE International Conference on, Volume: 1, 28-31 Oct. 1997 Page(s): 401 -403 vol.1 [3] “Utilization of neural networks and GAs for constructing reliable decision support systems to deal stocks” Baba, N.; Inoue, N.; Asakawa, H.; Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on, Volume: 5, 24-27 July 2000 Page(s): 111 -116 vol.5