9th International Conference on Computing & Information Technology. Held in Bangkok. Spinger Proceedings.

1. The 9th International Conference on Computing and Information
Technology (IC2IT 2013)
KMUTNB
Dhaka Stock Exchange Trend Analysis Using Support
Vector Regression
Authors:
Phayung Meesad & Risul Islam Rasel
Faculty of Information Technology
King Mongkut’s University of Technology North Bangkok
Email: pym@kmutnb.ac.th; rasel.kmutnb@gmail.com

2. Outline
Introduction
Related work
Motivation & Goal
Experiment design
Result analysis
Conclusion
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3. 1. Introduction
• Stock exchange :
is an emerging business sector has become more popular among the people.
many people, organizations are related to this business.
gaining insight about the market trend has become an important factor
• Stock trend or price prediction is regarding as challenging task because
Essentially a non-liner, non parametric
Noisy
Deterministically chaotic system
• Why deterministically chaotic system?
Liquid money and Stock adequacy
Human behavior and News related to the stock market
Share Gambling
Money exchange rate, etc.
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4. 2. Related Work
2.1 Support Vector regression (SVR)
• Support vector machine (SVM), a novel artificial intelligence-based method
developed from statistical learning theory
• SVM has two major futures: classification (SVC) & regression (SVR).
• In SVM regression, the input is first mapped onto a m-dimensional feature space
using some fixed (nonlinear) mapping, and then a linear model is constructed in this
feature space.
• a margin of tolerance (epsilon) is set in approximation.
• This type of function is often called – epsilon intensive – loss function.
• Usage of slack variables to overcome noise in the data and non - separability
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5. Related Work (cont..)
•
Support vector regression (SVR)
model with parameters w and b can be
expressed as
f(x) = w.z (x) + b
•
where y is the model output and input
x is mapped into a feature space by a
nonlinear function (x).
Image courtesy: Pao-Shan Yu*, Shien-Tsung Chen and I-Fan Chang
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6. Related Work (cont..)
•
The regression problem of SVM can be expressed as the following optimization
problem.
l
min
1 | | w | | 2 + c / (p + p* )
* 2
w, b, p, p
i=1
subject to yi - (w.z (xi) + b) # f + p
(w.z (xi) + b) - yi # f + p*
p, p* $ 0, i = 1, 2, ......, l
•
•
*
Where pand p are slack variables that specify the upper and the lower training errors
subject to an error tolerance ε.
C is a positive constant that determines the degree of penalized loss when a training
error occurs
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7. Related Work (cont..)
2.2 Windowing operator
•
•
•
•
The problem of forecasting the class attribute x, N steps ahead of time, as learning a
target function which uses a fixed number of M past values.
x(t+N) = f([x(t-0), x(t-1), …, x(t-M)]) ……………(1)
This equation can be written as:
x-0 = f([x-0, x-1, ..., x-M] – [x-0, x-1, …, x-N])
or as:
x-0= f([x-0, x-N, ..., x-M]) …………………(2)
or in the multivariate case as:
x-0 = f([x-0, y-0, x-1, y-1 ..., x-M, y-M] – [x-0, x-1, y-1, …, x-N, y-N]) …. (3)
Since Windowed Examples are of the form: [x-0, x-N, y-N, ..., x-M, y-M], we have
to remove all horizon attributes: [x-1, y-1, …, x-N, y-N].
The result is a dataset with Windowed Examples which can be fed to any machine
learning algorithm.
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8. Notations:
•0 timestep 0,the timestep we wish to predict.
•N the number of timesteps between now and
0
•M the size of the window
•attribute-[0-9] a Windowed Attribute,
measured at timestep [0-9]
•x-0 attribute x measured at timestep 0
•x-0 equivalent to x-0
•x(t+N) equivalent to x(0), if t+N is the
timestep we wish to predict
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9. Related Work (cont..)
2.3 Windowing operator:
transform the time series data into
a generic data set
convert the last row of a window
within the time series into a label
or target variable
Fed the cross sectional values as
inputs to the machine learning
technique such as liner regression,
Neural Network, Support vector
machine and so on.
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• Parameters:
Horizon (h)
Window size
Step size
Training window width
Testing window width
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10. Related Work (cont..)
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11. Related Work (cont..)
2.4 Some recent research works
1. “Stock Forecasting Using Support Vector Machine,”
•
•
•
•
Authors: Lucas, K. C. Lai, James, N. K. Liu
Applied technique: SVM and NN
Data preprocess technique: Exponential Moving Average (EMA15) and relative
difference in percentage of price (RDP)
Domain: Hong Kong Stock Exchange
2. “Stock Index Prediction: A Comparison of MARS, BPN and SVR in an
Emerging Market,”
•
•
•
Authors: Lu, Chi-Jie, Chang, Chih-Hsiang, Chen, Chien-Yu, Chiu, Chih-Chou, Lee,
Tian-Shyug,
Applied technique: Multivariate adaptive regression splines (MARS), Back
propagation neural network (BPN), support vector regression (SVR), and multiple
linear regression (MLR).
Domain: Shanghai B-share stock index
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12. Related Work (cont..)
3. “An Improved Support Vector Regression Modeling for Taiwan Stock
Exchange Market Weighted Index Forecasting,”
•
•
•
Authors: Kuan-Yu. Chen, Chia-Hui. Ho
Applied technique: SVR, GA, Auto regression (AR)
Domain: Taiwan Stock Exchange
• So, many research have been done using support vector machine (SVM) in
order predict stock market trend.
• GA, EMA, RDP and some other techniques have been used as input
selection technique or optimization technique. So, Still there are some
scope to apply different input selection or optimization technique to fed
input to the machine learning algorithm like support vector machine and
Neural network.
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13. 3. Motivation & Goal
• Motivation:
SVR is a powerful machine learning technique for pattern recognition
Introducing of using different kinds of windowing function as data preprocess is a
new idea
Combining windowing function and support vector regression can make good
model for time series prediction.
• Goal:
Propose a good Win-SVR model to predict stock price
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14. 4. Experiment Design
4.1 Data collection
Experiment dataset had been collected from Dhaka stock exchange (DSE),
Bangladesh.
4 year’s (January 2009-June 2012)historical data were collected.
Almost 522 company are listed in DSE. But for the convenient of the experiment
we only select one well known company data.
Dataset had 6 attributes. Date, open price, high price, low price, close price,
volumes.
5 attributes were used in experiment except volumes.
Total 822 days data. 700 data were used as training dataset, and 122 data were used
as testing dataset.
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15. Experiment Design (cont..)
4.2 Model Work Flow
Training phase
• Step 1: Read the training dataset from local repository.
• Step 2: Apply windowing operator to transform the time
series data into a generic dataset. This step will convert
the last row of a window within the time series into a
label or target variable. Last variable is treated as label.
• Step 3: Accomplish a cross validation process of the
produced label from windowing operator in order to feed
them as inputs into SVR model.
• Step 4: Select kernel types and select special parameters
of SVR (C, ε , g etc).
• Step 5: Run the model and observe the performance
(accuracy).
• Step 6: If performance accuracy is good than go to step 6,
otherwise go to step 4.
• Step 7: Exit from the training phase & apply trained
model to the testing dataset.
Testing phase
• Step 1: Read the testing dataset from local repository.
• Step 2: Apply the training model to test the out of sample
dataset for price prediction.
• Step 3: Produce the predicted trends and stock price
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16. Experiment Design (cont..)
4.3 Optimal Window settings
•
Three types of Windowing operator were used as data preprocess.
Normal rectangular window
Flatten window
De- flatten window
•
Optimal settings of windowing components for SVR models are given below:
Table 1: Window settings
Flatten window
De-Flatten window
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Step
size
Training
window
width
Test
window
width
All
3
1
30
30
3
1
30
30
5 days
8
1
30
30
22 days
Rectangular
Model
Window
size
1 day
Windowing
operator
25
1
30
30
All
5
1
30
30
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17. Experiment Design (cont..)
4.4 SVR kernel Parameters settings
•
•
•
•
Model 1: 1 day a-head prediction model
Model 2: 5 days a-head prediction model
Model 3: 22 days a-head prediction model
Kernel function: Radial basis function (RBF)
Table 2: SVR kernel parameters setting
SVR Model
Kernel
C
g
ε
ε+
ε-
Model-1
RBF
10000
1
2
1
1
Model-2
RBF
10000
1
2
1
1
Model-3
RBF
10000
1
2
1
1
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18. Experiment Design (cont..)
4.5 Proposed Win-SVR Models
•
•
•
•
•
1 day & 5 days a-head model
Window type: Flatten Window
Window size : 3 (1 day model), 8 (5 days model)
Attribute selection : All
Step size : 1
T.W.W : 30, t.s.w : 30, Kernel type : RBF
Table 3: SVR model for Flatten window
Model
SV
Bias (b)
Weight (w)
w[open-2]
687
5 days
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3.335
-4.658
w[Close-2]
-746.516
-1074.989
-1087.763
-546.558
w[open-6]
w[High-7]
w[High-6]
1792.63
1716.616
2231.12
2447.79
w[Low-7]
696
w[Low-2]
w[open-7]
1 day
w[High-2]
w[Low-6]
w[Close-7]
w[Close-6]
2587.202
2219.727
2762.02
2187.662
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19. Experiment Design (cont..)
•
•
•
•
•
22 days a-head model
Window type: Normal Rectangular window
Window size : 3
Attribute selection : single attribute (close)
Step size : 1
T.W.W : 30, t.s.w : 30, Kernel type : RBF
Table 4: SVR model for normal rectangular window
Model
Support
Vector
Bias
(offset)
Weight (w)
SV
22 days
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b
w [close-2]
w [close-1]
w [close-0]
675
421.3
1719.6
1631.5
805.1
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20. 5. Result Analysis
Result evolution technique:
Error calculation: Used MAPE
MAPE: Mean Average Percentage Error (MAPE) was used to calculate the error rate
between actual and predicted price.
Here,
n
MAPE = 100
/| A - P
A
|
i=1
n
A = Actual price
P = Predicted price
n = number of data to be counted
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21. Result Analysis (cont..)
Fig:2
Actual vs Predicted price
1 day a-head model
300
Close price (BDT)
Close Price (BDT)
Fig:1
250
200
150
100
50
0
300
250
200
150
100
50
0
1
8
15 22 29 36 43 50 57 64 71 78 85 92 99 106 113 120
Days (Jan-Jun'2012)
Actual
Fig:3
1
7
13
19 25 31 37
43 49 55 61
67 73 79 85
91 97 103 109
Days (Jan-jun'2012)
Predicted
Actual
Actual vs Predicted close price
22 days a-head model
Predicted
•Fig 1: 1 day a-head model result from flatten
window (MAPE : 0.04 )
300
Close Price (BDT)
Actual vs Predicted price
5 days a-head model
250
200
•Fig 2: 5 days a-head model result from flatten
window (MAPE : 0.15 )
150
100
50
0
1
6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96
Days (Jan-May'2012)
Actual
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•Fig 3: 22 days a-head model result from
rectangular window (MAPE : 0.22 )
Predicted
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22. Result Analysis (cont..)
Fig:4
Fig:5
Error rate
Normal Rectangular window
1
MAPE
MAPE
1.5
0.5
0
Jan
Feb
Mar
Apr
Error rate
Flatten window
0.50
0.40
0.30
0.20
0.10
0.00
Jan
May
Feb
Fig:6
5 days a-head
1 day a-head
22 days a-head
Error rate
De-flatten window
MAPE
Apr
May
Month
Month
1 day a-head
Mar
5 days a-head
22 days a-head
Table :Average MAPE (error) for test data (From Jan’12 to May’12)
Horizon
Rectangul
ar window
Flatten
window
Deflatten
window
1 Day a-head
16
14
12
10
8
6
4
2
0
1
0.42
0.04
7.79
5 days a-head
5
0.26
0.15
7.16
22 days
head
22
0.22
0.22
7.61
Model
Jan
Feb
Mar
Apr
May
Months
1 day a-head
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23. 6. Conclusion
6.1 Discussions :
Different windowing function can produce different prediction results.
We used 3 types of windowing operators. Normal rectangular window, Flatten
window, De-flatten window.
Rectangular and flatten windows are able to produce good prediction result for time
series data.
De-flatten window can not produce good prediction results.
6.2 Limitations & Future works:
Here we only use 3 types of windowing operators.
Only one stock exchange data set were used to undertake the experiments.
Do not compare with other machine learning techniques.
In future, we will apply our model to other stock market data set and will also
compare our research result with other types of data mining techniques.
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24. The 9th International Conference on Computing and Information
Technology (IC2IT 2013)
KMUTNB
THANK YOU
FOR YOUR ATTENTION
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