Predicting financial extremes based on weighted visual graph of major stock indices
- Chen, Dong-Rui Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hanghzou 311121, China
- Liu, Chuang Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hanghzou 311121, China
- Zhang, Yi-Cheng Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hanghzou 311121, China - Department of Physics, University of Fribourg, Fribourg 1700, Switzerland
- Zhang, Zi-Ke Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hanghzou 311121, China - College of Media and International Culture, Zhejiang University, Hangzhou 310028, China
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31.10.2019
Published in:
- Complexity. - 2019, vol. 2019, p. 1–17
English
Understanding and predicting extreme turning points in the financial market, such as financial bubbles and crashes, has attracted much attention in recent years. Experimental observations of the superexponential increase of prices before crashes indicate the predictability of financial extremes. In this study, we aim to forecast extreme events in the stock market using 19-year time-series data (January 2000– December 2018) of the financial market, covering 12 kinds of worldwide stock indices. In addition, we propose an extremes indicator through the network, which is constructed from the price time series using a weighted visual graph algorithm. Experimental results on 12 stock indices show that the proposed indicators can predict financial extremes very well.
- Faculty
- Faculté des sciences et de médecine
- Department
- Département de Physique
- Language
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- English
- Classification
- Physics
- License
-
License undefined
- Identifiers
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- RERO DOC 327747
- DOI 10.1155/2019/5320686
- Persistent URL
- https://folia.unifr.ch/unifr/documents/308147
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