In today's global trading environment, none of these individual methods of analysis, when used alone, are sufficient. Synergistic analysis amalgamates these three approaches through the use of neural networks to synthesize global technical, fundamental, and intermarket data for precise market forecasting. Knowledge of the financial markets, coupled with the use of statistical analysis tools to find correlations between global data related to the target market, is mandatory to successful data selection for a neural network application. By reducing the number of inputs to the network, preprocessing helps it learn more easily. Two widely used preprocessing methods are known as transformation and normalization.
Transformation manipulates raw data inputs to create a single input to a net.
Normalization transforms a single data input to distribute the data evenly and scale it to match the range of the input neurons. In most neural network applications transformations involve simple algebraic or statistical manipulation of the input data. In financial forecasting applications, transforms can also include a wide variety of technical indicators, commonly used by traders to interpret market behavior. For a particular target market, such as T-bonds, preprocessed raw inputs might include differences, ratios, and moving averages of the open, high, low close, volume, and open interest data, as well as transforms on related fundamental and intermarket data. Each neuron in the input layer represents one of the preprocessed raw inputs.