C++ Neural Networks and Fuzzy Logic: Preface

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Summary

This chapter presented a neural network application in financial forecasting. As an example of the steps

needed to develop a neural network forecasting model, the change in the Standard & Poor’s 500 stock index

was predicted 10 weeks out based on weekly data for five indicators. Some examples of preprocessing of data

for the network were shown as well as issues in training.

At the end of the training period, it was seen that memorization was taking place, since the error in the test

data degraded, whereas the error in the training set improved. It is important to monitor the error in the test

data (without weight changes) while you are training to ensure that generalization ability is maintained. The

final network resulted in average RMS error of 6.9 % over the training set and 13.9% error over the test set.

This chapter’s example in forecasting highlights the ease of use and wide applicability of the backpropagation

algorithm for large, complex problems and data sets. Several examples of research in financial forecasting

were presented with a number of ideas and real−life methodologies presented.

Technical Analysis was briefly discussed with examples of studies that can be useful in preprocessing data for

neural networks.

C++ Neural Networks and Fuzzy Logic:Preface

Summary

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A Resource guide was presented for further information on financial applications of neural networks.

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C++ Neural Networks and Fuzzy Logic:Preface

Summary

331

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