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7/28/2019 Presentation kk

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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 73-01-01

to

05-10-01

393 382 365 17

1US$ to BP 71-01-01

to

05-10-01

418 407 390 17

1US$ to JY 71-01-01 to05-10-01

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

10-4

10-3

10-2

10-1

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

10-4

10-3

10-2

10-1

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

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