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

Percent Change Model

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 SetPerformance Training58.41% Verification53.59% Testing53.59%

Results – MLP - Weekly 16 inputs 22 hidden nodes 1 output 0.3 momentum 0.1 learning rate 4 epochs

Results – MLP - Weekly Data SetPerformance Training52.73% Verification62.50% Testing55.47%

Probabilistic Neural Networks Finite deterministic network Three layers

PNN Example – Training Example A Exchange Rate Interest Rate % Input Layer A Target Output = Up Pattern Layer Output Layer Up Down

Pattern Layer Receives input vector Calibrates Gaussian bell

PNN Example – Training Example A Exchange Rate Interest Rate % Input Layer A 100% Target Output = Up Pattern Layer Output Layer Up Down A % 1

PNN Example – Training Example B Exchange Rate Interest Rate % Input Layer A 100% Target Output = Down Pattern Layer Output Layer Up Down A % B % 1

PNN Example – Out-of-Sample Exchange Rate Interest Rate % Input Layer A 80% Pattern Layer Output Layer Up Down A % B % %

Results – PNN - Daily 16 inputs 1224 hidden nodes 2 outputs

Results – PNN - Daily Data SetPerformance Training54.82% Verification51.14% Testing51.96%

Results – PNN - Weekly 16 inputs 256 hidden nodes 2 outputs

Results – PNN - Weekly Data SetPerformance Training77.73% Verification54.69% Testing57.03%

Conclusions Predicting foreign exchange market is a tough problem PNN vs. MLP Weekly vs. Daily data