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For a number of reasons, computational intelligence and machine learningmethods have been largely dismissed by the professional community. The reasonsfor this are numerous and varied, but inevitably amongst the reasons given isthat 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 thatare commonly seen in market-prediction systems. This paper examines some of themore common mistakes, namely dataset insufficiency; inappropriate scaling;time-series tracking; inappropriate target quantification and inappropriatemeasures of performance. The rationale that leads to each of these mistakes isexamined, as well as the nature of the errors they introduce to the analysis /design. Alternative ways of performing each task are also recommended in orderto avoid perpetuating these mistakes, and hopefully to aid in clearing the wayfor the use of these powerful techniques in industry.

Common Mistakes when Applying Computational Intelligence and Machine Learning to Stock Market modelling