D.E Allen, R. Powell and A. K. Singh
Edith Cowan University
Reading questions
1.
What is short selling and why is it controversial?
2.
What are Support Vector Machines (SVM) and why are they
a useful technique?
3.
Explain what kernel estimation is.
4.
Why are different kernel estimators available?
5.
Explain what logistic regression is.
6.
What does Beta Measure?
7.
Why are Sharpe ratios a useful investment metric?
8.
How does Beta differ from Sharpe ratios.
9.
How do we measure mean absolute error?
10.
Why is out of sample forecasting important?
2
Introduction
Forecasting future stock price movement using financial
indicators.
Evidence from past for predictability power of financial
factors e.g. Beta, E/P, B/M, past returns etc.
Support Vector Machines (SVM), capable of handling
large amount of unstructured, noisy or nonlinear data.
SVM classification useful in prediction of future price
direction (+1,

1).
3
SVM in Classification
SVM are characterized by
Mapping input vectors into higher dimensional feature
space.
Structural risk minimization
Non linear modelling with Kernel Functions
Kernel density estimators are non

parametric density estimators
with no fixed structure. They depend on all the data points to
obtain an estimate.
Classification of classes using optimal separating
hyperplane.
4
SVM
Optimal Separating Hyperplane.
5
SVM
SVM use following kernel functions
Linear:
Polynomial:
Radial Basis Function (RBF):
Sigmoid:
Here and d are kernel parameters.
Study Uses RBF kernel for its robustness on
non linear data.
6
Data
Dow Jones Industrial Average sample Stocks’ daily data
for a period of 5 years (1/03/2005

9/03/2010).
Factors Used for forecasting
Factors
Underlying
rationale
Previous
2
days
daily
log
returns
.
Indicator
of
the
historical
performance,
which
is
widely
used
in
time
series
analysis
.
Beta
(six
months
rolling
window)
Return
dependence
on
the
market
return
in
the
long
run
.
Price
to
Earnings
Ratio
Indicator
of
the
current
company
value
which
effects
the
price
movement
.
Book
to
Market
Ratio
Fama

French
(
1992
,
1993
)
Traded
Volume
Indicator
of
the
performance
of
the
stock
in
the
market
.
Dividend
Yield
Indicator
of
company
performance
.
Blume
(
1980
)
7
Methodology
Standardization of Data
Direction of price change classified into binary

1 and 1 using
Testing sample is created using last 130 days data.
Kernel parameters, cost and gamma are optimized using grid
search. A systematic way of seeking optima.
The model is built on training data and is used for forecasting
which is tested on out sample data (130 days) SVM results are
compared with Logistic Regression results (with same training
and testing data).
Simple investment strategy used to check the predicted
directions
8
Forecasting Results
Stocks
Results
SVM
Logistic Regression
Stock 1
Correctly Classified
Instances
77 (59.2308 %)
67 (51.5385%)
C
Gamma
Incorrectly Classified
Instance
53 (40.7692%)
63 (48.4615 %)
724
0.1
Mean Absolute Error
0.4077
0.5015
Stock 2
Correctly Classified
Instances
112 (86.1538%)
109 (83.8462 %)
C
Gamma
Incorrectly Classified
Instance
18 (13.8462%)
21 (16.1538 %)
1024
0.12
Mean Absolute Error
0.1385
0.316
Stock 3
Correctly Classified
Instances
76 (58.4615%)
67 (51.5385 %)
C
Gamma
Incorrectly Classified
Instance
54 (41.5385 %)
63 (48.4615 %)
1448
0.003162
Mean Absolute Error
0.4154
0.4962
Stock 4
Correctly Classified
Instances
76 (58.4615%)
69 (53.0769 %)
C
Gamma
Incorrectly Classified
Instance
54 (41.5385 %)
61 (46.9231 %)
724
3
Mean Absolute Error
0.4154
0.4963
Stock 5
Correctly Classified
Instances
80 (61.5385%)
59 (45.3846 %)
C
Gamma
Incorrectly Classified
Instance
50 (38.4615 %)
71 (54.6154 %)
1448
0.56
Mean Absolute Error
0.3846
0.5091
9
Investment Strategy Results
Final Return
Sharpe Ratio
SVM
LOGISTIC
SVM
LOGISTIC
Stock1
20.10167056

12.0362
17.42748

13.0499
Stock2
7.246199093
6.009645
4.356055
3.369538
Stock3
16.33556329
15.30477
14.78509
13.72405
Stock4
14.33568424
5.611437
14.83901
4.495077
Stock5
18.27861273

5.49125
14.62362

6.39905
DJIA
10.12379524
8.10426878
The final net returns of the stocks are
compared using the Sharpe Ratio.
10
Conclusion
SVM classification outperforms logistic
regression in classifying price direction.
Simple stock trading strategy also reveals the
efficiency of SVM in stock trading.
Further applications can include prediction of
other financial time series.
SVM regression can be further tested for similar
work
11
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