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논문검색

Using ARIMA Model to Fit and Predict Index of Stock Price Based on Wavelet De-Noising

원문정보

Shihua Luo, Fang Yan, Dejian Lai, Wenyi Wu, Fucai Lu

보안공학연구지원센터(IJUNESST) International Journal of u- and e- Service, Science and Technology Vol.9 No.12 2016.12 pp.317-326
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초록

영어

To accommodate non-stationarity and strong noise in the SPI data, the research used wavelet method for de-noising and autoregressive integrated moving average model(ARIMA) for prediction. Seven-day moving averages of closing time SPI data in four Asian stock marketswereanalyzed.Empiricalresults show that after de-noising more accurate forecasting results can be obtained in developed markets. More developed market indexes seem more significant improvement; while for less developed market indexes, the improvement of de-noising is less significant. This is in accordance with current situation of market.

목차

Abstract
 1. Introduction
 2. Wavelet de-Noising Method
 1. Autoregressive Integrated Moving Average Model（ARIMA）
 4. Predictive Algorithm Framework
 5. Modeling of Four Asian Stock Markets
 6. Conclusion
 References

키워드

저자정보

  • Shihua Luo School of Statistics, Center of Applied Statistics, Jiangxi University of Finance & Economics, Nanchang, China
  • Fang Yan School of Statistics, Center of Applied Statistics, Jiangxi University of Finance & Economics, Nanchang, China
  • Dejian Lai The University of Texas School of Public Health, Houston, TX, USA
  • Wenyi Wu School of Statistics, Center of Applied Statistics, Jiangxi University of Finance & Economics, Nanchang, China
  • Fucai Lu School of Statistics, Center of Applied Statistics, Jiangxi University of Finance & Economics, Nanchang, China

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