C++ Neural Networks and Fuzzy Logic: Preface

A Critique of Neural Network Time−Series Forecasting for Trading

Michael de la Maza and Deniz Yuret, managers for the Redfire Capital Management Group, suggest that

risk−adjusted return, and not mean−squared error should be the metric to optimize in a neural network

application for trading. They also point out that with neural networks, like with statistical methods such as

linear regression, data facts that seem unexplainable can’t be ignored even if you want them to be. There is no

equivalent for a “don’t care,” condition for the output of a neural network. This type of condition may be an

important option for trading environments that have no “discoverable regularity” as the authors put it, and

therefore are really not tradable. Some solutions to the two problems posed are given as follows:

•  Use an algorithm other than backpropagation, which allows for maximization of risk−adjusted

return, such as simulated annealing or genetic algorithms.

•  Transform the data input to the network so that minimizing mean−squared error becomes

equivalent to maximizing risk−adjusted return.

•  Use a hierarchy (see hierarchical neural network earlier in this section) of neural networks, with

each network responsible for detecting features or regularities from one component of the data.

C++ Neural Networks and Fuzzy Logic:Preface

The S&P 500 and Sunspot Predictions

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