It accomplishes this using neural networks to find the market synergy in today's global interrelated markets by quantifying hidden patterns and relationships in technical, fundamental, and intermarket data that current, single-market methods of analysis can not handle. Other AI technologies such as genetic algorithms are also useful in implementing synergistic analysis. These algorithms, which mimic the characteristics associated with evolution, have proven to be robust search mechanisms. They are well-suited to optimization problems, such as optimizing neural network training parameters. The same technology incorporated into genetic algorithms has also been used in classifier systems and genetic programming.
Classifier systems perform a type of machine learning that generates rules from examples.
Genetic programming goes even further by automatically generating a program from a set of primitive constructs. Even fuzzy logic, wavelets and chaos are finding applications in financial forecasting. No doubt, other technologies will continue to emerge. It should be understood that none of these tools, individually, will provide the whole answer. Instead, each tool has its own role to play in putting together the pieces of the puzzle needed to implement a synergistic approach to today's globalized markets.