Predicting Financial Extremes Based on Weighted Visual Graph of Major Stock Indices. (31st October 2019)

• Record Type: Journal Article
• Authors: Chen, Dong-Rui; Liu, Chuang; Zhang, Yi-Cheng; Zhang, Zi-Ke
• Other Names: Gambuzza Lucia Valentina Academic Editor.
• Is Part Of: Complexity. Volume 2019(2019)
• Publication Date: 2019-10-31
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2019/5320686
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : 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.