  For a number of reasons, computational intelligence and machine learning
methods have been largely dismissed by the professional community. The reasons
for this are numerous and varied, but inevitably amongst the reasons given is
that the systems designed often do not perform as expected by their designers.
The reasons for this lack of performance is a direct result of mistakes that
are commonly seen in market-prediction systems. This paper examines some of the
more common mistakes, namely dataset insufficiency; inappropriate scaling;
time-series tracking; inappropriate target quantification and inappropriate
measures of performance. The rationale that leads to each of these mistakes is
examined, as well as the nature of the errors they introduce to the analysis /
design. Alternative ways of performing each task are also recommended in order
to avoid perpetuating these mistakes, and hopefully to aid in clearing the way
for the use of these powerful techniques in industry.
