Frequency-Division Combination Forecasting of Stock Market Based on Wavelet Multiresolution Analysis

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ID: 70048
2018
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Abstract
Put forward a novel combination forecasting method (M-ARIMA-BP) that could make a more accurate and concise prediction of stock market based on wavelet multiresolution analysis. This innovative method operated by parsing of the low-frequency trend series and the high-frequency volatility series of stock market and gives an insight into the price series. Using the daily closing price data of SSE (Shanghai Stock Exchange) Composite Index and Shenzhen Component Index as samples, compared with conventional wavelet prediction model, ARIMA model, and BP neural network model, the empirical results show that the new algorithm M-ARIMA-BP can improve the accuracy of volatility forecasting and perform better in predicting prices rising and falling.
Reference Key
luo2018frequencydivisiondiscrete Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Luo, Shihua;Huo, Jiangyou;Dai, Zian;
Journal discrete dynamics in nature and society
Year 2018
DOI
DOI not found
URL
http://dx.doi.org/10.1155/2018/1259156
Keywords
Engineering (General). Civil engineering (General) Information technology Computer applications to medicine. Medical informatics neurosciences. biological psychiatry. neuropsychiatry business economics as a science finance computer engineering. computer hardware mathematics bibliography. library science. information resources accounting. bookkeeping

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