Post on 11-Jul-2015
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
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
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 , …
IPLR ModelCandidate Stocks Screening
GA
PLR Turning point Trading signal
Selected stock
Piecewise Linear Representation
Stock price
datesegment1
Turning point
Turning point
t1 t2 t3 t4 t5
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
Piecewise Linear Representation
IPLR ModelCandidate Stocks Screening
GA
SRA
PLR
Related input variable
Turning point Trading signal
Selected stock
Technical indexes
Stepwise Regression Algorithm
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)
IPLR Model
Expect output input
Candidate Stocks Screening
GA
SRA
PLR
BP(train)
Related input variable
Turning point Trading signal
Selected stock
Technical indexes
Back-propagation Network
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
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
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
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
Experimental results
Up
Steady
Down
Experimental resultsS&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