Neural networks and financial prediction
Neural networks have been touted as all-powerful tools in stock-market prediction. Companies such as MJ Futures claim amazing 199.2% returns over a 2-year period using their neural network prediction methods. They also claim great ease of use; as technical editor John Sweeney said in a 1995 issue of "Technical Analysis of Stocks and Commodities," "you can skip developing complex rules (and redeveloping them as their effectiveness fades) . . . just define the price series and indicators you want to use, and the neural network does the rest."
These may be exaggerated claims, and, indeed, neural networks may be easy to use once the network is set up, but the setup and training of the network requires skill, experience, and patience. It's not all hype, though; neural networks have shown success at prediction of market trends.
The idea of stock market prediction is not new, of course. Business
people often attempt to anticipate the market by interpreting external
parameters, such as economic indicators, public opinion, and current political
climate. The question is, though, if neural networks can discover
trends in data that humans might not notice, and successfully use these
trends in their predictions.
This is a simple back-propagation network of three layers, and it is trained and tested on a high volume of historical market data. The challenge here is not in the network architecture itself, but instead in the choice of variables and the information used for training. I could not find the accuracy rates for this network, but my source claimed it achieved "remarkable success" (this source was a textbook, not a NN-prediction-selling website!).
Even better results have been achieved with a back-propagated neural network with 2 hidden layers and many more than 6 variables. I have not been able to find more details on these network architectures, however; the companies that work with them seem to want to keep their details secret.
Additional Neural Network Applications in the financial world: