Artificial Intelligence  |  NOV 1997

Adding The Human Element To Neural Nets by Jeffrey Owen Katz, Ph.D. with Donna L. McCormick

Adding The Human Element To Neural Nets by Jeffrey Owen Katz, Ph.D., with Donna L. McCormick Is it possible to train a neural network to ""see"" the way you and I do? These Contributing Writers walk you through their research in selecting chart-based trading points by hand and then training the neural network to repeat the process. In the November 1996 article ""On developing trading systems,"" I demonstrated that neural network technology no longer works well when used in a manner employing brute force. To demonstrate, I trained a neural network to directly predict price change over the next several bars and achieved poor results, as expected. I speculated that the reason neural network technology no longer works well when applied in a crude fashion is because too many traders took the neural net approach and made the markets efficient relative to such techniques. However, if applied in a manner consistent with its capabilities and targeted at unique problems, neural network technology can be highly effective. Let us, then, explore a different approach to training a neural network. This time, I will train neural networks to emulate my subjective recognition of chart patterns. When looking at charts, I often feel that I can intuitively identify good entry points based on visually recognizable patterns. One pattern I look for is a pullback in a trend, in which the market is clearly moving in a given direction before it undergoes a small correction - that is, a pullback - before continuing. Could a neural net be trained to automatically perform the same process of recognition that I believe I can accomplish by eye? Such an application of the technology might be a more appropriate use of neural networks than trying to train a neural net to directly predict the market.

by Jeffrey Owen Katz, Ph.D., and Donna L. McCormick