Predicting Financial Extremes Based on Weighted Visual Graph of Major Stock Indices

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...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2019, Vol.2019 (2019), p.1-17
Main Authors: Zhang, Zi-Ke, Zhang, Yi-Cheng, Liu, Chuang, Chen, Dong-Rui
Format: Article
Language:English
Subjects:
Algorithms
Economic forecasting
Graphs
International finance
Investments
Physics
Securities markets
Statistical mechanics
Stock exchanges
Stock markets
Stock price indexes
Stocks
Time series
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Summary: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.
ISSN:1076-2787
1099-0526
DOI:10.1155/2019/5320686