C++ Neural Networks and Fuzzy Logic: Preface

Forecasting Treasury Bill and Treasury Note Yields

Milam Aiken designed a feedforward backpropagation network that predicted Treasury Bill Rates and

compared the forecast he obtained with forecasts made by top U.S. economists. The results showed the neural

network, given the same data, made better predictions (.18 versus .71 absolute error). Aiken used 250

economic data series to see correlation to T−Bills and used only the series that showed leading correlation:

Dept. of Commerce Index of Leading Economic Indicators, the Center for International Business Cycle

Research (CIBCR) Short Leading Composite Index, and the CIBCR Long Leading Composite Index. Prior

data for these three indicators for the past four years (total 12 inputs) was used to predict the average annual

T−Bill rate (one output) for the current year.

Guido Deboeck and Masud Cader designed profitable trading systems for two−year and 10−year treasury

securities. They used feedforward neural networks with a learning algorithm called

extended−delta−bar−delta (EDBD), which is a variant of backpropagation. Training samples composed of

100 facts were selected from 1120 trading days spanning from July 1 1989 to June 30, 1992. The test period

consisted of more than 150 trading days from July 1, 1992 to December 30, 1992. Performance on the test set

was monitored every N thousand training cycles, and the training procedure was stopped when performance

degraded on the test set. (This is the same procedure we used when developing a model for the S&P 500.)

A criterion used to judge model performance was the ratio of the average profit divided by the maximum

drawdown, which is the largest unrealized loss seen during the trading period. A portfolio of separate

designed trading systems for two−year and 10−year securities gave the following performance: Over a period

of 4.5 years, the portfolio had 133 total trades with 65% profitable trades and the maximum drawdown of 64

basis points, or thousands of units for bond yields. The total gain was 677 basis points over that period with a

maximum gain in one trade of 52 basis points and maximum loss in one trade of 47 basis points.

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C++ Neural Networks and Fuzzy Logic:Preface

Forecasting Treasury Bill and Treasury Note Yields

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