C++ Neural Networks and Fuzzy Logic: Preface

•  Moving averages

•  Exponential moving averages

•  Stochastic indicators

For the network that produces a predicted High value, the average error ranged between 7.04% and 7.65% for

various financial markets over the test period, including Treasury Bonds, Eurodollar, Japanese Yen, and S&P

500 futures contracts.

The Walk−Forward Methodology of Market Prediction

A methodology that is sometimes used in neural network design is walk−forward training and testing. This

means that you choose an interval of time (e.g., six months) over which you train a neural network and test the

network over a subsequent interval. You then move the training window and testing window forward one

month, for example, and repeat the exercise. You do this for the time period of interest to see your forecasting

results. The advantage of this approach is that you maximize the network’s ability to model the recent past in

making a prediction. The disadvantage is that the network forget characteristics of the market that happened

prior to the training window.

Takashi Kimoto et al. used the walk forward methodology in designing a trading system for Fujitsu and Nikko

Securities. They also, like Mendelsohn, use a hierarchical neural network composed of individual feedforward

neural networks. Prediction of the TOPIX, which is the Japanese equivalent of the Dow Jones Industrial

Average, was performed for 33 months from January 1987 to September 1980. Four networks were used in

the first level of the hierarchy trained on price data and economic data. The results were fed to a final network

that generated buy and sell signals. The performance of the trading system achieved a result that is 20% better

than a buy and hold strategy for the TOPIX.