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MARKET PREDICTION USING SOFT
AND EVOLUTIONARY COMPUTING
TECHNIQUES
Dr. RITANJALI MAJHI
Asst. Professor, NIT Warangal

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IntroductionForecasting is an important area of research in the financial world.
Financial data present a challenging and complex problem to understand
and forecast.
Forecasting is a key element of financial and managerial decision making.
Forecasting reduces the risk of decision making in financial organization,firm and private investors.
Common financial Time series that need forecasting are
(i) Stock prices
(ii) Interest rates(iii)Price indices
(iv)Currency exchange rates etc.

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Disadvantage of Statistical Approach
Several methods have been proposed in the literature.
They differ in goals of forecasting, nature of information used and the
mathematical model employed.
Classical statistical approach employs regression and correlation methods.
These methods have some constraints these are:
Autocorrelation within the data, stationarity, linear structure and Gaussian
nature of disturbance.
In real life financial series these conditions are not satisfied.
Hence statistical approach leads to poor prediction capability.
So, Banks and financial institutions are investing heavily in development of
neural network models and have started to deploy it in the financial trading.

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Motivation
Many latest soft computing tools have been applied for accurate exchange
rate and stock indices prediction.
Out of these the MLP involves more computation.
Second issue is the accuracy of the prediction using the existingtechniques.
Thus there is a need to develop simple but efficient nonlinear adaptive
structure which involves less computation and better prediction.

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Functional Link Artificial Neural Network
(FLANN)
FLANN :
Developed by Pao.
Single layer and single neuron.
Each input value is nonlinearly mapped using trigonometric or power
series or Chebyshev expansions.
Employs simple Least Mean Square (LMS) algorithm for updating the
weights.
Computational complexity is low.
Computation time is less.

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The basic block diagram of the FLANN model is shown below :
It consists of three processes.

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Design of Input DataFrom the basic raw financial data features are extracted.
The features may be mean, variance, log(data), tanh(data), max(data) and
min(data).
Input features are normalized to lie between 0 to 1.
A set of input features (corresponding to a day, week or month) is calledinput pattern.
Patterns are computed from the past financial data.
A majority of the patterns is used for training the model.
The remaining patterns are used for testing the performance.
The developed model is used for forecasting.

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Training of the modelThe initial weights of the model are set to zero.
The first pattern is applied.
The output is computed.
The output is compared with the desired output.
The difference between the two is computed to produce error.
The change in weight in each path is calculated using LMS algorithm.
The second pattern is applied and using the above steps the change in
weight in each path is obtained.
Sequentially all the training patterns are applied. In each case the change in
weight of each path of the model is computed.
The average change in weights in each branch is obtained.

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The weights are updated by adding the average change in weight of each
path.
The above steps constitutes one experiment.
The above experiment is repeated many times.
In each experiment the mean square error (MSE) is obtained.
A plot is made between the number of experiment and the corresponding
MSE.
This plot indicates the Learning/Training characteristics of the model.
When the MSE settles to a minimum value, the learning process is
stopped.
The weights are then frozen to the final values which represent the model
parameters.
The adaptive forecasting model is thus designed.

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Testing of the model
The testing of the model is done with the remaining known patterns.
The known pattern is applied to the model.
The output is obtained.
This output directly represent the predicted output.
It is compared with known target output.
Prediction performance is evaluated by % of error defined as
Percentage of error (PER) = ((True value
Predicted value)/True value)*100.

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Simulation is carried out for following
Exchange Rate Prediction
1. US Dollar to Rupees, Pound and Yen
2. Prediction Duration : 1month, 3months, 6months and 12months

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Exchange rates available for training
and testing the proposed models
Currency
conversion
Date Range Total nos. of
data available
Total nos. of
patterns
generated
No. of patterns
used for
training
No. of patterns
used for testing
1US$ to IR 730101
to
051001
393 382 365 17
1US$ to BP 710101
to
051001
418 407 390 17
1US$ to JY 710101 to051001
418 407 390 17
Table  1

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Learning characteristic of the FLANN
model for prediction of rupees at different
months
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
x 104
104
103
102
101
100
No. of Experiments
MeanSquareE
rror
12 months
6 months
3 months
1month
Observation: As the no. of days ahead increases the MSE value
decreases.

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Learning characteristic of the FLANN
model for prediction of conversion rate of
Yen at different months
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
x 104
104
103
102
101
100
No. of Experiments
MeanSquareError
12month
6month
3 month
1month
Observation: As the no. of days ahead increases the MSE value
decreases.

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Comparison of actual and predicted value
with training data set using FLANN
0 50 100 150 200 250 300 350
5
10
15
20
25
30
35
40
45
50
No of Months
Actual Value
Predicted Value
0 50 100 150 200 250 300 350 400
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
Nos. of patterns
Normalizedconversionrate
Actual
Predicted
(Equivalent rupees for 1US$ )for 1month ahead
(Equivalent pound for 1US$ )for 6months ahead
Observation : Performance degrades as the no. of months increases.

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Comparison of actual and predicted value
with test data set using FLANN
350 355 360 365 370 375 380
41
42
43
44
45
46
47
48
49
No. of Months
Rupees
Actual Value
Predicted Value
50 100 150 200 250 300 350
1.5
2
2.5
3
3.5
No. of Months
P
ound
(Equivalent rupees for 1US$ )for 1month ahead
(Equivalent pound for 1US$ )for 1month ahead
Observation : Better matching with the actual value