integrating a piecewise linear representation method and a neural network model for stock trading...
TRANSCRIPT
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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
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Outline
• Introduction
• IPLR Model
– Piecewise Linear Representation
– Stepwise Regression Algorithm
– Genetic Algorithm
– Back-propagation Network
• Experimental results
• Conclusion
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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
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Expect output input
no
yes
IPLR ModelCandidate Stocks Screening
GA
SRA
PLR
BP(train)
Related input variable
Turning point Trading signal
Trading decision
Selected stock
Reach number of
generation ? Test Calculate
profit
BP Buy/sell
End
Related input variables
Related input variables
Technical indexes
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Genetic Algorithm
Initialization
Selection
Reproduction
Termination
1 0 … 0 1
Randomly generate initial population
50
10
0.8
0.1
Fitness function roulette-wheel selection
Tournament selection
Crossover MutationTwo-point
genetic diversity
# of generation , reach the best fitness value , …
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IPLR ModelCandidate Stocks Screening
GA
PLR Turning point Trading signal
Selected stock
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Piecewise Linear Representation
Stock price
datesegment1
Turning point
Turning point
t1 t2 t3 t4 t5
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Piecewise Linear Representation
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Get trend of time series data
Calculate trend Only in turning point
Piecewise Linear Representation
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Derive the trading signal
Tradition
Up Down : 1 [sell]Down UP : 0 [buy]
Not quite related to the price variation
Piecewise Linear Representation
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Derive the trading signal
Redefine the trading signals
Piecewise Linear Representation
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IPLR ModelCandidate Stocks Screening
GA
SRA
PLR
Related input variable
Turning point Trading signal
Selected stock
Technical indexes
![Page 13: Integrating a piecewise linear representation method and a neural network model for stock trading points prediction](https://reader034.vdocuments.us/reader034/viewer/2022042817/55a0b3c91a28ab816b8b4699/html5/thumbnails/13.jpg)
Stepwise Regression Algorithm
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Stepwise Regression Algorithm
X2X3
X4
YX1
X5Xp
Calculate the significant value S
Last X ?
Output
yes no
no
yes
Apply by SPSS (Statistic Package for Social Science)
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IPLR Model
Expect output input
Candidate Stocks Screening
GA
SRA
PLR
BP(train)
Related input variable
Turning point Trading signal
Selected stock
Technical indexes
![Page 16: Integrating a piecewise linear representation method and a neural network model for stock trading points prediction](https://reader034.vdocuments.us/reader034/viewer/2022042817/55a0b3c91a28ab816b8b4699/html5/thumbnails/16.jpg)
Back-propagation Network
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IPLR Model
Expect output input
Candidate Stocks Screening
GA
SRA
PLR
BP(train)
Related input variable
Turning point Trading signal
Trading decision
Selected stock
Test
BP Buy/sellRelated input variables
Technical indexes
![Page 18: Integrating a piecewise linear representation method and a neural network model for stock trading points prediction](https://reader034.vdocuments.us/reader034/viewer/2022042817/55a0b3c91a28ab816b8b4699/html5/thumbnails/18.jpg)
Back-propagation NetworkTrading decision
Change of the trading signal pass through the boundary value:Change is upward sellChange is downward buy
Boundary value : 0.508
Test data input to BP
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IPLR Model
Expect output input
no
yes
Candidate Stocks Screening
GA
SRA
PLR
BP(train)
Related input variable
Turning point Trading signal
Trading decision
Selected stock
Reach number of
generation ? Test Calculate
profit
BP Buy/sell
End
Related input variables
Related input variables
Technical indexes
![Page 20: Integrating a piecewise linear representation method and a neural network model for stock trading points prediction](https://reader034.vdocuments.us/reader034/viewer/2022042817/55a0b3c91a28ab816b8b4699/html5/thumbnails/20.jpg)
Experimental results
Historic data : from 2004/01/02 to 2006/04/12Training data : 2004/01/02 to 2005/09/30Testing data : 2005/10/1 to 2006/04/12
Up-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
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Experimental results
Up
Steady
Down
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Experimental resultsS&P500 : four years data [2000-2003]
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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