C++ Neural Networks and Fuzzy Logic: Preface

What Others Have Reported

In this final section of the chapter, we outline some case studies documented in periodicals and books, to give

you an idea of the successes or failures to date with neural networks in financial forecasting. Keep in mind

that the very best (= most profitable) results are usually never reported (so as not to lose a competitive edge) !

Also, remember that the market inefficiencies exploited yesterday may no longer be the same to exploit today.

Can a Three−Year−Old Trade Commodities?

Well, Hillary Clinton can certainly trade commodities, but a three−year−old, too? In his paper, “Commodity

Trading with a Three Year Old,” J. E. Collard describes a neural network with the supposed intelligence of a

three−year−old. The application used a feedforward backpropagation network with a 37−30−1 architecture.

The network was trained to buy (“go long”) or sell (“go short”) in the live cattle commodity futures market.

The training set consisted of 789 facts for trading days in 1988, 1989, 1990, and 1991. Each input vector

consisted of 18 fundamental indicators and six market technical variables (Open, High, Low, Close, Open

Interest, Volume). The network could be trained for the correct output on all but 11 of the 789 facts.

The fully trained network was used on 178 subsequent trading days in 1991. The cumulative profit increased

from $0 to $1547.50 over this period by trading one live cattle contract. The largest loss in a trade was

$601.74 and the largest gain in a trade was $648.30.

C++ Neural Networks and Fuzzy Logic:Preface

What Others Have Reported

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