Frequency-Division Combination Forecasting of Stock Market Based on Wavelet Multiresolution Analysis
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 s...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2018/1259156 |
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| _version_ | 1832550282793320448 |
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| author | Shihua Luo Jiangyou Huo Zian Dai |
| author_facet | Shihua Luo Jiangyou Huo Zian Dai |
| author_sort | Shihua Luo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7318e0ed85fd488b86262d207021815d |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-7318e0ed85fd488b86262d207021815d2025-02-03T06:07:16ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2018-01-01201810.1155/2018/12591561259156Frequency-Division Combination Forecasting of Stock Market Based on Wavelet Multiresolution AnalysisShihua Luo0Jiangyou Huo1Zian Dai2School of Statistics, Jiangxi University of Finance and Economics, Nanchang 330013, ChinaSchool of Statistics, Jiangxi University of Finance and Economics, Nanchang 330013, ChinaSchool of Statistics, Jiangxi University of Finance and Economics, Nanchang 330013, ChinaPut 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.http://dx.doi.org/10.1155/2018/1259156 |
| spellingShingle | Shihua Luo Jiangyou Huo Zian Dai Frequency-Division Combination Forecasting of Stock Market Based on Wavelet Multiresolution Analysis Discrete Dynamics in Nature and Society |
| title | Frequency-Division Combination Forecasting of Stock Market Based on Wavelet Multiresolution Analysis |
| title_full | Frequency-Division Combination Forecasting of Stock Market Based on Wavelet Multiresolution Analysis |
| title_fullStr | Frequency-Division Combination Forecasting of Stock Market Based on Wavelet Multiresolution Analysis |
| title_full_unstemmed | Frequency-Division Combination Forecasting of Stock Market Based on Wavelet Multiresolution Analysis |
| title_short | Frequency-Division Combination Forecasting of Stock Market Based on Wavelet Multiresolution Analysis |
| title_sort | frequency division combination forecasting of stock market based on wavelet multiresolution analysis |
| url | http://dx.doi.org/10.1155/2018/1259156 |
| work_keys_str_mv | AT shihualuo frequencydivisioncombinationforecastingofstockmarketbasedonwaveletmultiresolutionanalysis AT jiangyouhuo frequencydivisioncombinationforecastingofstockmarketbasedonwaveletmultiresolutionanalysis AT ziandai frequencydivisioncombinationforecastingofstockmarketbasedonwaveletmultiresolutionanalysis |