Training Neural Networks by Lou Mendelsohn
The application of neural networks to financial forecasting has quickly become a hot topic in today's globalized trading environment. With extensive technical, intermarket and fundamental data available for analysis, neural networks are well suited to pattern recognition and quantifying relationships between interrelated markets. However, neural networks are not easy to develop. Here, S&C contributor Lou Mendelsohn examines the best ways to train and test neural networks for maximum performance.
Successfully developing neural networks to implement synergistic market analysis for financial
forecasting in today's global markets requires both knowledge of the financial markets and expertise in
the design and application of artificial intelligence technologies. Now let us examine the process of
training and testing neural networks for synergistic analysis in which technical, fundamental and
intermarket data are used to find hidden patterns and relationships within the data.
To accomplish this, a set of data facts must be selected for presentation to the network. In addition,
various training parameters must be optimized during the training process, and a protocol that automates
the training and testing process to assure proper training of the network must be devised. If done
properly, superior performance and more accurate forecasts can be achieved over rule-based technical
analysis methods that rely on single market linear modeling of market dynamics.