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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
Outline
• Introduction
• IPLR Model
  – Piecewise Linear Representation
  – Stepwise Regression Algorithm
  – Genetic Algorithm
  – Back-propagation Network
• Experimental results
• Conclusion
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
IPLR Model
                                    Candidate Stocks Screening
                                                       Selected stock
                                                 GA

                     Technical indexes          SRA                  Related input variable

no                        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
Genetic Algorithm

Initialization                                Randomly generate initial population

                                                                                                    50
                                          1    0    … 0       1                                     10
                                                                                                    0.8
                                                                                                    0.1


 Selection                       Fitness function                   roulette-wheel selection

                                                                     Tournament selection



Reproduction                     Crossover              Two-point                        Mutation



             genetic diversity




Termination                         # of generation , reach the best fitness value , …
IPLR Model
      Candidate Stocks Screening
                      Selected stock
                 GA



PLR         Turning point              Trading signal
Piecewise Linear Representation



Stock                          Turning
price                           point




              Turning
               point


         t1             t2               t3   t4   t5
                                                        date
                    segment1
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


      Tradition

Up  Down : 1 [sell]
Down  UP : 0 [buy]

                                              Not quite related to the
                                                  price variation
Piecewise Linear Representation
                          Derive the trading signal


Redefine the trading signals
IPLR Model
               Candidate Stocks Screening
                               Selected stock
                          GA

Technical indexes         SRA               Related input variable

   PLR               Turning point               Trading signal
Stepwise Regression Algorithm
Stepwise Regression Algorithm

                                           Apply by SPSS (Statistic Package for Social Science)




                          Calculate the significant value S

                    yes                                       no
X1
X2              Y
X3
 X4                                                       no
                                      Last X ?
      X5
           Xp                        yes
                                      Output
IPLR Model
               Candidate Stocks Screening
                               Selected stock
                          GA

Technical indexes         SRA               Related input variable

   PLR               Turning point                Trading signal

                                            Expect output            input


                                                    BP(train)
Back-propagation Network
IPLR Model
               Candidate Stocks Screening
                                Selected stock
                           GA

Technical indexes         SRA                 Related input variable

   PLR               Turning point                  Trading signal

                                              Expect output                input


                                       Test             BP(train)


                                        Trading decision

                    Related input variables        BP               Buy/sell
Back-propagation Network
                                              Trading decision

            Test data input to BP
Change of the trading signal pass through the boundary value:
Change is upward  sell
Change is downward  buy




                                                                 Boundary value : 0.508
IPLR Model
                                    Candidate Stocks Screening
                                                       Selected stock
                                                 GA

                     Technical indexes          SRA                  Related input variable

no                        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
Experimental results
Historic data : from 2004/01/02 to 2006/04/12
Training data : 2004/01/02 to 2005/09/30
Testing data : 2005/10/1 to 2006/04/12

Up-trend : 30-day moving average cross over 90-day moving average
Down-trend : 30-day moving average cross down 90-day moving average
Steady : no major tendency of 30-day moving average with 90-day moving average
Experimental results


 Up




Steady



Down
Experimental results
S&P500 : four years data [2000-2003]
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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Integrating a piecewise linear representation method and a neural network model for stock trading points prediction

  • 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. Outline • Introduction • IPLR Model – Piecewise Linear Representation – Stepwise Regression Algorithm – Genetic Algorithm – Back-propagation Network • Experimental results • Conclusion
  • 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. IPLR Model Candidate Stocks Screening Selected stock GA Technical indexes SRA Related input variable no 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. Genetic Algorithm Initialization Randomly generate initial population 50 1 0 … 0 1 10 0.8 0.1 Selection Fitness function roulette-wheel selection Tournament selection Reproduction Crossover Two-point Mutation genetic diversity Termination # of generation , reach the best fitness value , …
  • 6. IPLR Model Candidate Stocks Screening Selected stock GA PLR Turning point Trading signal
  • 7. Piecewise Linear Representation Stock Turning price point Turning point t1 t2 t3 t4 t5 date segment1
  • 9. Piecewise Linear Representation Get trend of time series data Calculate trend Only in turning point
  • 10. Piecewise Linear Representation Derive the trading signal Tradition Up  Down : 1 [sell] Down  UP : 0 [buy] Not quite related to the price variation
  • 11. Piecewise Linear Representation Derive the trading signal Redefine the trading signals
  • 12. IPLR Model Candidate Stocks Screening Selected stock GA Technical indexes SRA Related input variable PLR Turning point Trading signal
  • 14. Stepwise Regression Algorithm Apply by SPSS (Statistic Package for Social Science) Calculate the significant value S yes no X1 X2 Y X3 X4 no Last X ? X5 Xp yes Output
  • 15. IPLR Model Candidate Stocks Screening Selected stock GA Technical indexes SRA Related input variable PLR Turning point Trading signal Expect output input BP(train)
  • 17. IPLR Model Candidate Stocks Screening Selected stock GA Technical 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. Back-propagation Network Trading decision Test data input to BP Change of the trading signal pass through the boundary value: Change is upward  sell Change is downward  buy Boundary value : 0.508
  • 19. IPLR Model Candidate Stocks Screening Selected stock GA Technical indexes SRA Related input variable no 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. Experimental results Historic data : from 2004/01/02 to 2006/04/12 Training data : 2004/01/02 to 2005/09/30 Testing data : 2005/10/1 to 2006/04/12 Up-trend : 30-day moving average cross over 90-day moving average Down-trend : 30-day moving average cross down 90-day moving average Steady : no major tendency of 30-day moving average with 90-day moving average
  • 22. Experimental results S&P500 : four years data [2000-2003]
  • 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