Stock Fluctuations Anomaly Detection Based on Wavelet Modulus Maxima

Abstract

Stock fluctuations anomaly increase the uncertainty and investment risk in the stock market, is an important element in financial research. In this paper, wavelet modulus maxima method is used in the detection of abnormal stock analysis. It is obtained based on the irregular sampling in the multi-scale wavelet transform. It overcomes the localized limitation about traditional Fourier analysis in time and frequency domains. Experimental results show that the wavelet modulus maxima method can not only depict the position of the point mutation in the signals but also capture the singular points of the stock unusual fluctuations quickly and accurately.