Neural networks, if used properly, can provide the framework for a plethora of market analysis tools that can supplement an existing trading program or suggest new directions for future research. While the history of these tools dates back much further, their modern application took root in the late 1980s and came of age in 1993 when patent no. 5241620 was awarded to this author for the concept of embedding a neural network into a common spreadsheet. Suddenly, neural networks were not just part of the professional mainstream, but the average trading populace could access them.
The analytical foundation for this leap is built on an algorithm called back propagation. In layman’s terms, this is a method that allows a network to learn to discriminate between classes that can’t be distinguished based on linear properties. Rumelhart, Hinton and Williams presented a well-received paper on what they called “Backward propagation of errors” in 1985. Others who did research into this approach include David Parker and Paul Werbos. Werbos arguably invented these techniques and presented them in “Introduction to Pattern Analysis,” his 1974 Ph.D. dissertation at Harvard.
The back propagation algorithm consists of a multi-layer perception that uses non-linear activation functions (see “Simple net,” below). The most commonly used functions are the sigmoid, which ranges from 0 to 1, and the hyperbolic tangent function, which ranges from -1 to 1. All inputs and target outputs must be mapped into these ranges when used in these types of networks.