The neural network designer

C++ Neural Networks and Fuzzy Logic:Application to Financial Forecasting

The input and output layers are fixed by the number of inputs and outputs we are using. In our case, the output is a single number, the expected change in the S&P 500 index 10 weeks from now. The input layer size will be dictated by the number of inputs we have after preprocessing. You will see more on this soon. The middle layers can be either 1 or 2. It is best to choose the smallest number of neurons possible for a given problem to allow for generalization. If there are too many neurons, you will tend to get memorization of patterns. We will use one hidden layer. The size of the first hidden layer is generally recommended as between one-half to three times the size of the input layer. If a second hidden layer is present, you may have between three and ten times the number of output neurons. The best way to determine optimum size is by trial and error.

A View of the Raw Data

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1. Three-Month Treasury Bill Yield
2. 30-Year Treasury Bond Yield
3. NYSE Advancing/Declining issues
4. NYSE New Highs/New Lows
5. S&P 500 closing price

Raw data for the period from January 4, 1980 to October 28, 1983 is taken as the training period, for a total of 200 weeks of data. The following 50 weeks are kept on reserve for a test period to see if the predictions are valid outside of the training interval. The last date of this period is October 19, 1984. Let’s look at the raw data now. (You get data on the disk available with this book that covers the period from January, 1980 to December, 1992.) In Figures 14.3 through 14.5, you will see a number of these indicators plotted over the training plus test intervals:

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