Artificial Neural Networks in Financial Trading

IntroductIon Soft computing represents that area of computing adapted from the physical sciences. Artificial intelligence (AI) techniques within this realm attempt to solve problems by applying physical laws and processes. This style of computing is particularly tolerant of imprecision and uncertainty , making the approach attractive to those researching within " noisy " realms, where the signal-to-noise ratio is low. Soft computing is normally accepted to include the three key areas of fuzzy logic, artificial neural networks, and probabilistic reasoning (which includes genetic algorithms, chaos theory, etc.). The arena of investment trading is one such field where there is an abundance of noisy data. It is in this area that traditional computing typically gives way to soft computing, as the rigid conditions applied by traditional computing cannot be met. This is particularly evident where the same sets of input conditions may appear to invoke different outcomes, or there is an abundance of missing or poor-quality data. Artificial neural networks (ANNs) are a particularly promising branch on the tree of soft computing, as they possess the ability to determine nonlinear relationships and are particularly adept at dealing with noisy data sets. From an investment point of view, ANNs are particularly attractive, as they offer the possibility of achieving higher investment returns for two distinct reasons. First, with the advent of cheaper computing power, many mathematical techniques have come to be in common use, effectively minimizing any advantage they had introduced (Samuel & Malakkal, 1990). Second, in order to attempt to address the first issue, many techniques have become more complex. There is a real risk that the signal-to-noise ratio associated with such techniques may be becoming lower, particularly in the area of pattern recognition (Blakey, 2002). Investment and financial trading is normally divided into two major disciplines: fundamental analysis and technical analysis. Papers concerned with applying ANNs to these two disciplines are reviewed.