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"Forecasting the 30-year U.S. Treasury Bond with a System of Neural Networks"

A forecasting model based on a system of artificial neural networks (ANN) is used to predict the direction of the 30-Year U.S. Treasury Bond on a weekly basis. At the close of Friday's market, the 23-variable database is updated with the latest information and the data pre-processed for input to the prediction system. Thirty-two feed-forward neural networks are trained on the new data and then individually recalled to predict the following Friday's market direction. These results are then used as input to a decision model that ultimately determines the final prediction. Forecasting began in 1989, with live trades commencing in 1990. The average accuracy of the buy prediction was 67% over a five-year period, with an average annualized return on investment (ROI) of 17.3%. This compares with an ROI of 13.9% for the Lehman Brothers T-Bond Index. This paper describes the methods used for data selection, training and testing, the basic system architecture, and how the decision model improved the total system accuracy as compared to individual networks.