C++ Neural Networks and Fuzzy Logic: Preface

The Target and the Timeframe

What should the output of your neural network forecast? Let’s say you want to predict the stock market. Do

you want to predict the S&P 500? Or, do you want to predict the direction of the S&P 500 perhaps? You

could predict the volatility of the S&P 500 too (maybe if you’re an options player). Further, like Mr.

Mandelman, you could only want to predict tops and bottoms, say, for the Dow Jones Industrial Average. You

need to decide on the market or markets and also on your specific objectives.

Another crucial decision is the timeframe you want to predict forward. It is easier to create neural network

models for longer term predictions than it is for shorter term predictions. You can see a lot of market noise, or

seemingly random, chaotic variations at smaller and smaller timescale resolutions that might explain this.

Another reason is that the macroeconomic forces that fundamentally move market over long periods, move

slowly. The U.S. dollar makes multiyear trends, shaped by economic policy of governments around the world.

For a given error tolerance, a one−year forecast, or one−month forecast will take less effort with a neural

network than a one−day forecast will.