C++ Neural Networks and Fuzzy Logic: Preface

Forecasting the S&P 500

The S&P 500 index is a widely followed stock average, like the Dow Jones Industrial Average (DJIA). It has

a broader representation of the stock market since this average is based on 500 stocks, whereas the DJIA is

based on only 30. The problem to be approached in this chapter is to predict the S&P 500 index, given a

variety of indicators and data for prior weeks.

Choosing the Right Outputs and Objective

Our objective is to forecast the S&P 500 ten weeks from now. Whereas the objective may be to predict the

level of the S&P 500, it is important to simplify the job of the network by asking for a change in the level

rather than for the absolute level of the index. What you want to do is give the network the ability to fit the

C++ Neural Networks and Fuzzy Logic:Preface

Forecasting the S&P 500

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problem at hand conveniently in the output space of the output layer. Practically speaking, you know that the

output from the network cannot be outside of the 0 to 1 range, since we have used a sigmoid activation

function. You could take the S&P 500 index and scale this absolute price level to this range, for example.

However, you will likely end up with very small numbers that have a small range of variability. The

difference from week to week, on the other hand, has a much smaller overall range, and when these

differences are scaled to the 0 to 1 range, you have much more variability.

The output we choose is the change in the S&P 500 from the current week to 10 weeks from now as a

percentage of the current week’s value.