Complexity 2019:1-17 (2019)
AbstractUnderstanding 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 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.
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References found in this work
Price Linkage Rumors in the Stock Market and Investor Risk Contagion on Bilayer-Coupled Networks.Yue Dong, Jiepeng Wang & Tingqiang Chen - 2019 - Complexity 2019:1-21.
Citations of this work
A Numerical Study on the Regularity of D-Primes Via Informational Entropy and Visibility Algorithms.B. L. Mayer & L. H. A. Monteiro - 2020 - Complexity 2020:1-5.
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