Wavelet Analysis for Stock Market Forcasting

Wavelet Analysis for Stock Market Forcasting

Paper in proceedings outside WoS and Scopus

This paper deals with wavelet analysis and its application on the stock market. The time series of financial and economic data are usually non-linear and non-stationary. It has been shown that using decomposition models improves the prediction accuracy of these time series. These techniques include wavelet analysis, which decomposes data not only in the time domain but also in the frequency domain, and can predict non-periodic or non-stationary time series more accurately than Fourier transform. Given the decomposition of the time series using wavelet analysis, and last but not least attention is drawn to the advantages and disadvantages resulting from the use of the method in the financial markets.

This paper deals with wavelet analysis and its application on the stock market. The time series of financial and economic data are usually non-linear and non-stationary. It has been shown that using decomposition models improves the prediction accuracy of these time series. These techniques include wavelet analysis, which decomposes data not only in the time domain but also in the frequency domain, and can predict non-periodic or non-stationary time series more accurately than Fourier transform. Given the decomposition of the time series using wavelet analysis, and last but not least attention is drawn to the advantages and disadvantages resulting from the use of the method in the financial markets.

forecasting method; stock market; wavelet analysis; wavelet decomposition; wavelet transform

forecasting method; stock market; wavelet analysis; wavelet decomposition; wavelet transform

JANKOVÁ, Z.

2020

28.06.2019

Magnanimitas

Hradec Králové, Czech Republic

978-80-87952-30-6

Interdisciplinární mezinárodní vědecká konference doktorandů a odborných asistentů QUAERE 2019

149

153

1229