Financial Applications of Wavelets and Self-organizing Maps

A methodology on how to combine wavelets with Self-organizing Maps (SOM) for financial time-series visualisation and interpretation is presented. For the denoising of the stock time-series wavelet packets are used because of their optimal signal compression and denoising capabilities. The visualisation of transient shocks like crashes, in higher order wavelet coefficients is presented. The Self-organising Map Neural Network is introduced to aid the visualisation of the behaviour of the daily closing value of S&P 500 and the daily closing value of two example stocks. The features that are used for the visualisation are based on the wavelet coefficients of 32-day trading periods with daily sampling of the closing value. The trajectory formed on the U-matrix of SOM shows the evolution of the individual stock and indicator data and aids the detection of abrupt changes in the behaviour of the time-series.