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

Wei Cheng · Chien-Hua Mike Lin

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Stock Market Forecasting Methods Neural Networks and Applications Forecasting Techniques and Applications

Abstract

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...

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1990Stock market prediction system with modular neural networks 1988Bond rating: A non-conservative application of neural networks 1990Stock price pattern recognition-a recurrent neural network approach 2012Reminiscences of a Stock Operator

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