Probabilistic neural networks:
First of all, an idea originated from the work found in C.T.Lee and Y.P. Chen in Da-Yeh university in Taiwan (2007). They have used a back-propagation network to test the usefulness of stochastic indicators %K and %D.
Their mechanism of defining uptrend/neutral/downtrend regions certainly deserves more interest. Defining regions is always one of the hardest tasks one could accomplish, and it is always important to teach the network before it can generalize on other data.
This model can be used with a PNN, (or a self-organizing map) that is better at classification than a usual FFNN with backprop.
Another paper that might contribute to this idea is the work by Schumann and Lohrbach. It was a next-day price forecasting paper, yet they use a similarly useful classification system, for up days and down days, and what would be called a neutral day. It is similar to the threshold function used in my paper.
Left to decide, should the region classification be done manually or automatically? The first has the idea that we are using human cognition to divide regions. The disadvantage is that this kind of classification might slightly differ between one person or another, or might not have the greatest examples for a network to learn from.