Why use a neural net instead of simple screening rules?
Lets say you have three indicators that are predictive of future price trends, A, B, and C. Empirically you find that when A is up and B is down and C is up, that price tends to rise for several weeks. One could set a threshold or make a rule using A, B, and C, but by doing this you might miss some signals. For instance, what if each time A is up 50% more than B, the reading of C may need to be down to get good signals. These trade-offs and higher order combinations are very hard to ferret out of the data. Neural networks are designed to do this. If a network were trained on many examples of A, B, and C, then only one rule would ever be necessary, such as buy when the neural net reading is greater than 0.95. All possible trade-offs between A, B, and C that result in an up forecast are being considered within the trained net.
