Prediction of the Foreign Exchange Market Using Classifying Neural Network

Doug Moll

Chad Zeman

The Problem

Using neural networks, can we predict future

foreign exchange rates to profit from short-

term fluctuations?

Outline

Project History Project Proposal Data Set MPL Results PNN Results

Project History

Senior Seminar

Used Trajan for regression networks

Attempted to predict direction & movement size

Less than desirable results

Proposal

Classification of Up or Down movement

Continue to use Trajan

Maintain same biases to compare to previous

research

Minimize time to learn new tool

Data Set

Inputs (1994 – 2003)

Percent change of CA/US exchange rate

Interest differential of short term interest rates

(CA – US)

Lagged Values

Exchange Rate data 94-03

94-03 CA/US rate

1.25

1.3

1.35

1.4

1.45

1.5

1.55

1.6

1.65

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Ex

ch

an

ge

Ra

te

CA/US Exchange Rate

Percent Change Model

CA/US Percent Weekly Change

-4%

0%

4%

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

% C

han

ge

CA/US %Change

Data Set

Equalize training cases

Same number of Up examples as Down for

each of the three data periods

Training

Verification

Testing

Trajan Algorithm

User Defined Settings

Inputs & Outputs

Training, Verification, Test data splits

Network Type

Trajan Algorithm

Automatically Determined Settings

Network Complexity (# of hidden nodes)

Trajan Algorithm

Randomly builds networks

Trains using backpropagation

Utilize cross-verification techniques

Evaluate networks based on verification error

Cross-reference with out-of-sample test data

Results – MLP - Daily

16 inputs

20 hidden nodes

1 output

0.3 momentum

0.1 learning rate 50 epochs

Results – MLP - Daily

Data Set Performance

Training 58.41%

Verification 53.59%

Testing 53.59%

Results – MLP - Weekly

16 inputs

22 hidden nodes

1 output

0.3 momentum

0.1 learning rate 4 epochs

Results – MLP - Weekly

Data Set Performance

Training 52.73%

Verification 62.50%

Testing 55.47%

Probabilistic Neural Networks

Finite deterministic network Three layers

PNN Example – Training Example A

Exchange Rate

Interest Rate

1.35

2.5%

Input Layer

A

Target Output = Up

PatternLayer

OutputLayer

Up

Down

Pattern Layer

Receives input vector Calibrates Gaussian bell

PNN Example – Training Example A

Exchange Rate

Interest Rate

1.35

2.5%

Input Layer

A 100%

Target Output = Up

PatternLayer

OutputLayer

Up

Down

A

1.352.5%

1

PNN Example – Training Example B

Exchange Rate

Interest Rate

1.40

2.7%

Input Layer

A

100%

Target Output = Down

PatternLayer

OutputLayer

Up

Down

A

1.352.5%

B

1.402.7%

1

PNN Example – Out-of-Sample

Exchange Rate

Interest Rate

1.39

2.7%

Input Layer

A

80%

PatternLayer

OutputLayer

Up

Down

A

1.352.5%

B

1.402.7%

.40

.10 20%

Results – PNN - Daily

16 inputs

1224 hidden nodes

2 outputs

Results – PNN - Daily

Data Set Performance

Training 54.82%

Verification 51.14%

Testing 51.96%

Results – PNN - Weekly

16 inputs

256 hidden nodes

2 outputs

Results – PNN - Weekly

Data Set Performance

Training 77.73%

Verification 54.69%

Testing 57.03%

Conclusions

Predicting foreign exchange market is a tough problem

PNN vs. MLP

Weekly vs. Daily data