Yet, the framework should be one providing a disciplined, unemotional, mechanical basis for decision-making. To date, this could only be done piecemeal by looking first at fundamentals to see whether a market is under priced, overpriced or in line, then examining technical factors for divergence or conformation. Neural networks can take qualitative data and classify it into discrete categories, helping to find relationships and patterns in the data that are not otherwise identifiable. Seasonal factors, even those based on the day of the week, can also be ascertained. In forecasting Treasury bond prices, for instance, statistics on the Federal Reserve and banking industry, including Fed funds rates and money supply, can be used, not to mention other available figures such as the structure of interest rates.
Neural networks bring objectivity and discipline to bear on previously ignored information.
They sift through disparate fundamental and technical data with the same mechanical objectivity traditionally found in technical analysis software, manipulating daily price information and leading to more accurate forecasts and profitable trading signals. As neural computing becomes better understood and more widely adopted it will be relied upon increasingly to forecast major price moves ahead of traditional technical signals. Traders using this new methodology will gain a marked advantage over competitors still relying on rather limited means of analysis. Code Copyright 2001 Market Technologies Corporation. All Rights Reserved.