1. The document presents an Amnesic Neural Network model for stock trend prediction that incorporates "forgetting" to address time variation in customer behavior data. 2. It applies the model to data on 900 stocks from 2001-2004 to classify price changes, training the network on 1000 records and testing it on 500. 3. The model achieved the highest classification accuracy of the test data with a forgetting coefficient of 0.1, outperforming a standard BP neural network model without forgetting.

1. Amnesic Neural Network for Classification: Application on Stock Trend Prediction* Author: Qiang Ye, Bing Liang, Yijun Li Publication: ICSSSM 2005 Presenter: Yu-Hsiang Huang2011.9.231

2. Introduction & Literature reviewMethodologyBP Neural Network Model Amnesic Neural Network Model Training AlgorithmExperiment Data Classification algorithm Experiment ResultOutline2

3. Artificial Neural Network models (ANN)Based on the neural structure of the brain

4. The ANN learns from experience

5. Critical step: network trainingTwo classes of predict stock market methodFundamental analysisMacroeconomic data and basic financial status of companyTechnical analysisHistory will repeat itselfThe correlation between price and volume reveals market behaviorPredictionBy exploiting implications hidden in past trading activities

6. By analyzing patterns and trends shown in price and volume chartIntroduction & Literature review3

7. Stock price predictionTraditional assumption: customer behavior is consistent

8. In real world: customer behavior change greatly

9. Training data set may be time-variant

10. Difficult to predict customer behavior from old dataTwo strategiesSelect all data from different time: can’t represent current knowledge

11. Select only the latest data: lose useful information hidden in data of early timeData selectionin stock markets prediction will influence the training resultIntroduction & Literature review4

12. Amnesic Neural network (ANN*) modelIntroducing psychological notion of forgetting into Back Propagation (BP) neural networkSolve the problem of cross-temporal data classification

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 forgetting5Introduction (cont.)

16. Introduction & Literature reviewMethodologyBP Neural Network Model Amnesic Neural Network Model Training Algorithm Experiment Data Classification algorithmExperiment Result6

17. Artificial Neural NetworkComputational modeling tools for Modeling complex real-world problems

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

19. The feed-forward-error-back-propagation learning algorithm is the most famous procedure for training ANNBack Propagation Neural NetworkBased on searching an error surface using gradient descent for points with minimum errors

20. Each iteration:

21. Forwardactivation to produce a solution

22. Backwardpropagation of computed error to modify the weight7Methodology

23. Introduction & Literature reviewMethodologyBP Neural Network Model Amnesic Neural Network Model Training Algorithm Experiment Data Classification algorithmExperiment Result8

24. 9Methodology (cont.) BP network ModelInput:

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

31. D(n) is the expected output (given by training example)10Methodology (cont.)InputhiddenoutputInput vectoroutput vectorweightbias

32. 11Methodology (cont.)Square error:Summation of square error:Summation of square error for entire training data set: Objective of learningFind W to minimize , which is Introduction & Literature reviewMethodologyBP Neural Network ModelAmnesic Neural Network Model Training Algorithm Experiment Data Classification algorithmExperiment Result12

33. 13Methodology (cont.)Amnesic Neural Network ModelThe effectiveness of data depends on the time.

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:

38. Square error: is forgettingfunction

39. is forgetting coefficient

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

41. is time of I(n)

42. Summation of square error for entire training data set:Objective of learning Find W to minimize , which is

43. Becomes to14Methodology (cont.)

44. Introduction & Literature reviewMethodologyBP Neural Network ModelAmnesic Neural Network Model Training Algorithm Classification algorithm Experiment DataExperiment Result15

45. 16Methodology (cont.) Training Algorithm in ANN*Step1

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)

53. is output of neuron j in the ith layer in nth round learning17Methodology (cont.)Step4

54. Modify weight Calculateδbackward For the node in output layer (1)For the node in hidden layer (2)Modify the weight from output layer to previous layer(3)

55. ηis learning step.

56. Step5

57. n=n+1 go to Step2 until the training converges and system error decrease below an acceptable threshold.18Methodology (cont.)Step4Modify weightStep2Input I(n) Step3Compute outputStep1Initialize weights

58. Introduction & Literature reviewMethodologyBP Neural Network ModelAmnesic Neural Network Model Training Algorithm Experiment Data Classification algorithmExperiment Result19

59. 20Methodology (cont.)Experiment dataAbout 900 stocks are selected with the data from 2001 to 2004

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

67. PBPercentage figure of price change for each stock

68. 1000 records were selected for training the ANN*

69. Other 500 records formed a testing sample21Methodology (cont.)

70. Introduction & Literature reviewMethodologyBP Neural Network ModelAmnesic Neural Network Model Training Algorithm Experiment Data Classification algorithmExperiment Result22

71. 23Methodology (cont.)Classification algorithmInputPatterns to be classified ;Neural Network Weight matrix W;Output

72. Class of XAlgorithm

73. Step1Input as an output of layer 0Step2Computes output in hidden layer Step3Computes output in output layer Step4Assigns output to , satisfying Step5Return class label , satisfying 24Methodology (cont.)

74. Introduction & Literature reviewMethodologyBP Neural Network ModelAmnesic Neural Network Model Training Algorithm Classification algorithm Experiment DataExperiment Result25

75. Parameters of ANN*Neural nodes in the input layer, 21

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 )26Experiment Result

82. Result2.1. 3.The highestcorrect classification ratio on the test sample is achieved when forgetting coefficient is set to 0.1

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 coefficient27Experiment Result (cont.)BP ANNAmnesic ANNbetter

85. This paper introduced the psychological notion of ‘forgetting’ into the BP neural network model established the Amnesic ANN model.

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.28Conclusions

89. Thanks for your listening.Q & A29