What are some of the inputs used in the Marque Millennium T Neural Net?
The MM T neural net uses eight indicators that are quantitative in nature. They measure behavioral patterns such as trend persistency, unusual volume activity, volume trends, price geometry, relative volatility, and non-linear cycles. These indicators are proprietary, and were built using the principles of Chaos Theory. The goal of our behavioral studies is to find early telltale signs of persistent positive investment behavior. The most important manifestation of positive behavior is trend persistence, and the associated expansion of investor interest as price increases. We use the Hurst exponent to measure trend persistency, and volume accumulation to measure investor interest. Of course, the persistence of a trend is always threatened by the tendency for investors to become overly exuberant. Every trend tends to go to an extreme, and evoke its own reversal. Over indulgence and exuberance are shown by extreme accelerating trend persistency, with associated increases in volatility, an
