The latest buzz in the Forex world is neural networks, a term taken from the artificial intelligence community. In technical terms, neural networks are data analysis methods that consist of a large number of processing units that are linked together by weighted probabilities. In more simple terms, neural networks are a model loosely resembling the way that the human brain works and learns. For several decades now, those in the artificial intelligence community have used the neural network model in creating computers that 'think' and 'learn' based on the outcomes of their actions.
Unlike the traditional data structure, neural networks take in multiple streams of data and output one result. If there's a way to quantify the data, there's a way to add it to the factors being considered in making a prediction. They're often used in Forex market prediction software because the network can be trained to interpret data and draw a conclusion from it.
Before they can be of any use in making Forex predictions, neural networks have to be 'trained' to recognize and adjust for patterns that arise between input and output. The training and testing can be time consuming, but is what gives neural networks their ability to predict future outcomes based on past data. The basic idea is that when presented with examples of pairs of input and output data, the network can 'learn' the dependencies, and apply those dependencies when presented with new data. From there, the network can compare its own output to see how close to correct the prediction was, and go back and adjust the weight of the various dependencies until it reaches the correct answer.
This requires that the network be trained with two separate data sets — the training and the testing set. One of the strengths of neural networks is that it can continue to learn by comparing its own predictions with the data that is continually fed to it. Neural networks are also very good at combining both technical and fundamental data, thus making a best of both worlds scenario. Their very power allows them to find patterns that may not have been considered, and apply those patterns to prediction to come up with uncannily accurate results.
Unfortunately, this strength can also be a weakness in the use of neural networks for trading predictions. Ultimately, the output is only as good as the input. They are very good at correlating data even when you feed them enormous amounts of it. They are very good at extracting patterns from widely disparate types of information — even when no pattern or relationship exists. Its other major strength — the ability to apply intelligence without emotion — after all, a computer doesn't have an ego — can also become a weakness when dealing with a volatile market. When an unknown factor is introduced, the artificial neural network has no way of assigning an emotional weight to that factor.
There are currently dozens of Forex trading platforms on the market that incorporate neural network theory and technology to 'teach' the network your system and let it make predictions and generate buy/sell orders based on it. The important thing to keep in mind is that the most basic rule of Forex trading applies when you set out to build your neural network — educate yourself and know what you're doing. Whether you're dealing with technical analysis, fundamentals, neural networks or your own emotions, the single most important thing you can do to ensure your success in Forex trading is to learn all you can.