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Integrating a Piecewise Linear Representation   Method and a Neural Network Model for       Stock Trading Points Predictio...
Outline• Introduction• IPLR Model  – Piecewise Linear Representation  – Stepwise Regression Algorithm  – Genetic Algorithm...
Introduction• Stock market  – Highly nonlinear dynamic system      • Interest rates, inflation rate, economic environments...
IPLR Model                                    Candidate Stocks Screening                                                  ...
Genetic AlgorithmInitialization                                Randomly generate initial population                       ...
IPLR Model      Candidate Stocks Screening                      Selected stock                 GAPLR         Turning point...
Piecewise Linear RepresentationStock                          Turningprice                           point              Tu...
Piecewise Linear Representation
Piecewise Linear Representation                      Get trend of time series data  Calculate trend Only in turning point
Piecewise Linear Representation                       Derive the trading signal      TraditionUp  Down : 1 [sell]Down  U...
Piecewise Linear Representation                          Derive the trading signalRedefine the trading signals
IPLR Model               Candidate Stocks Screening                               Selected stock                          ...
Stepwise Regression Algorithm
Stepwise Regression Algorithm                                           Apply by SPSS (Statistic Package for Social Scienc...
IPLR Model               Candidate Stocks Screening                               Selected stock                          ...
Back-propagation Network
IPLR Model               Candidate Stocks Screening                                Selected stock                         ...
Back-propagation Network                                              Trading decision            Test data input to BPCha...
IPLR Model                                    Candidate Stocks Screening                                                  ...
Experimental resultsHistoric data : from 2004/01/02 to 2006/04/12Training data : 2004/01/02 to 2005/09/30Testing data : 20...
Experimental results UpSteadyDown
Experimental resultsS&P500 : four years data [2000-2003]
Conclusion• Trading decision > determine stock price itself• IPLR  – PLR : find turning point  – GA : improve the threshol...
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Integrating a piecewise linear representation method and a neural network model for stock trading points prediction

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Integrating a piecewise linear representation method and a neural network model for stock trading points prediction

  1. 1. Integrating a Piecewise Linear Representation Method and a Neural Network Model for Stock Trading Points Prediction Pei-Chann Chang, Chin-Yuan Fan, and Chen-Hao Liu TSMCC.2008 Presenter: Yu Hsiang Huang Date: 2011-12-30
  2. 2. Outline• Introduction• IPLR Model – Piecewise Linear Representation – Stepwise Regression Algorithm – Genetic Algorithm – Back-propagation Network• Experimental results• Conclusion
  3. 3. Introduction• Stock market – Highly nonlinear dynamic system • Interest rates, inflation rate, economic environments, political issues…• Most resent research – Derive accurate models – Predict the future price of stock movement• In this paper – Trading decision • Buy/Sell points – Critical role to make a profit
  4. 4. IPLR Model Candidate Stocks Screening Selected stock GA Technical indexes SRA Related input variableno PLR Turning point Trading signal Reach Expect output input number of generation ? Calculate profit Test BP(train) yes End Trading decision Related input variables BP Buy/sell Related input variables
  5. 5. Genetic AlgorithmInitialization Randomly generate initial population 50 1 0 … 0 1 10 0.8 0.1 Selection Fitness function roulette-wheel selection Tournament selectionReproduction Crossover Two-point Mutation genetic diversityTermination # of generation , reach the best fitness value , …
  6. 6. IPLR Model Candidate Stocks Screening Selected stock GAPLR Turning point Trading signal
  7. 7. Piecewise Linear RepresentationStock Turningprice point Turning point t1 t2 t3 t4 t5 date segment1
  8. 8. Piecewise Linear Representation
  9. 9. Piecewise Linear Representation Get trend of time series data Calculate trend Only in turning point
  10. 10. Piecewise Linear Representation Derive the trading signal TraditionUp  Down : 1 [sell]Down  UP : 0 [buy] Not quite related to the price variation
  11. 11. Piecewise Linear Representation Derive the trading signalRedefine the trading signals
  12. 12. IPLR Model Candidate Stocks Screening Selected stock GATechnical indexes SRA Related input variable PLR Turning point Trading signal
  13. 13. Stepwise Regression Algorithm
  14. 14. Stepwise Regression Algorithm Apply by SPSS (Statistic Package for Social Science) Calculate the significant value S yes noX1X2 YX3 X4 no Last X ? X5 Xp yes Output
  15. 15. IPLR Model Candidate Stocks Screening Selected stock GATechnical indexes SRA Related input variable PLR Turning point Trading signal Expect output input BP(train)
  16. 16. Back-propagation Network
  17. 17. IPLR Model Candidate Stocks Screening Selected stock GATechnical indexes SRA Related input variable PLR Turning point Trading signal Expect output input Test BP(train) Trading decision Related input variables BP Buy/sell
  18. 18. Back-propagation Network Trading decision Test data input to BPChange of the trading signal pass through the boundary value:Change is upward  sellChange is downward  buy Boundary value : 0.508
  19. 19. IPLR Model Candidate Stocks Screening Selected stock GA Technical indexes SRA Related input variableno PLR Turning point Trading signal Reach Expect output input number of generation ? Calculate profit Test BP(train) yes End Trading decision Related input variables BP Buy/sell Related input variables
  20. 20. Experimental resultsHistoric data : from 2004/01/02 to 2006/04/12Training data : 2004/01/02 to 2005/09/30Testing data : 2005/10/1 to 2006/04/12Up-trend : 30-day moving average cross over 90-day moving averageDown-trend : 30-day moving average cross down 90-day moving averageSteady : no major tendency of 30-day moving average with 90-day moving average
  21. 21. Experimental results UpSteadyDown
  22. 22. Experimental resultsS&P500 : four years data [2000-2003]
  23. 23. Conclusion• Trading decision > determine stock price itself• IPLR – PLR : find turning point – GA : improve the threshold value for PLR – BPN : train the connection of the model – Significant amount of profit• Clustering of financial time series data• A different forecasting model – SVM , FNN,…• A similar training pattern

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