One measure might be the difference between the market value and the network's output. This difference would be calculated for each fact in the test set, summed and divided by the number of facts in the test set. This is a standard error measure called average error. Other measures can be used to calculate the accuracy of network predictions of market direction, or how closely the network's predictions correspond to the shape of the price movement over a specific time period. We have found that real-time trading performance can be substantially improved through the implementation of custom error functions, in both training and testing, that are specifically related to financial market analysis.
Information systems offer predictive information on the target market of interest.
This information might include price predictions, market direction, or forecasts of impending turning points. In this type of system, the trader incorporates the predictive information into his overall trading strategy, using this information in conjunction with other analytic methods. Information systems can be designed that use a single network or multiple networks. In a multiple network configuration, each network is trained to forecast an independent output, such as the next day's high, while other networks predict related market information such as the next day's low or trend direction. These independently derived outputs can be used to confirm one another.