1. Amnesic Neural Network for Classification: Application on Stock Trend Prediction* Author: Qiang Ye, Bing Liang, Yijun Li Publication: ICSSSM 2005 Presenter: Yu-Hsiang Huang 2011.9.23 1

2. Introduction & Literature review Methodology BP Neural Network Model Amnesic Neural Network Model Training Algorithm Experiment Data Classification algorithm Experiment Result Outline 2

4. The ANN learns from experience

6. By analyzing patterns and trends shown in price and volume chartIntroduction & Literature review 3

8. In real world: customer behavior change greatly

9. Training data set may be time-variant

11. Select only the latest data: lose useful information hidden in data of early timeData selectionin stock markets prediction will influence the training result Introduction & Literature review 4

13. Effectiveness data depends on time

14. Present data is more useful than old data

15. Old data has less effect on training result, like gradually forgetting5 Introduction (cont.)

16. Introduction & Literature review Methodology BP Neural Network Model Amnesic Neural Network Model Training Algorithm Experiment Data Classification algorithm Experiment Result 6

18. Capable of performing massively parallel computations for data processing and knowledge representation

20. Each iteration:

21. Forwardactivation to produce a solution

22. Backwardpropagation of computed error to modify the weight7 Methodology

23. Introduction & Literature review Methodology BP Neural Network Model Amnesic Neural Network Model Training Algorithm Experiment Data Classification algorithm Experiment Result 8

25. Output:

26. Weight: connects the node j in previous layer to the node k

27. Activation function:

28. Error signal:

29. I(n) is a set of input

30. Y(n) is a set of corresponding output

34. Present data is more useful than old data.

35. Just like forgetting as time goes on.

36. Assign different weight to data of different times ( new>old ).

37. Original square error:

39. is forgetting coefficient

40. is the benchmark time (current time or the time of newest data)

41. is time of I(n)

43. Becomes to14 Methodology (cont.)

44. Introduction & Literature review Methodology BP Neural Network Model Amnesic Neural Network Model Training Algorithm Classification algorithm Experiment Data Experiment Result 15

46. Initialize weights to connections with random weight.

47. Subject to Uniform Distribution(0,1)

48. Step2

49. Input I(n) = , d(n) is expected output of example n.

50. Step3

51. Compute output according to Equation 1, compute the output of neuron in each layer . = is initial input.

52. ...... (1)

55. ηis learning step.

56. Step5

58. Introduction & Literature review Methodology BP Neural Network Model Amnesic Neural Network Model Training Algorithm Experiment Data Classification algorithm Experiment Result 19

60. 14 factors were considered

61. Stock price, major profit ratio

62. EPS, interest cover

63. ROE, receivables turnover

64. Liability/asset ratio, asset turn-over

65. Liquidity ratio, Liquid market value

66. PE, tax profit growth

68. 1000 records were selected for training the ANN*

69. Other 500 records formed a testing sample21 Methodology (cont.)

70. Introduction & Literature review Methodology BP Neural Network Model Amnesic Neural Network Model Training Algorithm Experiment Data Classification algorithm Experiment Result 22

74. Introduction & Literature review Methodology BP Neural Network Model Amnesic Neural Network Model Training Algorithm Classification algorithm Experiment Data Experiment Result 25

76. Neural nodes in the hidden layer, 25

77. Neural nodes in the output layer, 3

78. Training step (η), 0.01

79. Threshold of maximum training cycles, 10000

80. Threshold of minimum error rate, 0.005

81. Forgetting coefficient, test 10 different forgetting coefficients from 0~1 )26 Experiment Result

83. When forgetting coefficient is 0, the ANN* would be ordinary BP

84. The ANN* could do better than ordinary BP with careful selection of forgetting coefficient27 Experiment Result (cont.) BP ANN Amnesic ANN better

86. The aim of Amnesic ANN is to address the stochastic time variation problem in customer’s behaviors.

87. With carefully selected forgetting coefficients, the Amnesic ANN could perform better than the common ANN (BP) in stock price prediction

88. Further research should be done for the ratio of the right classified stocks is still very low in this experiment.28 Conclusions

89. Thanks for your listening. Q & A 29