Early warning on stock market bubbles via methods of optimization, clustering and inverse problems

Date

2018-01-01

Author

Kurum, Efsun
Weber, Gerhard-Wilhelm
İyigün, Cem

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In order to avoid destructive results of financial bubbles which affect the entire economy, it is important to develop an early-warning signalling. By using optimization-supported tools, we introduce a new method for an early-warning signalling which approaches the bubble concept geometrically by determining and evaluating ellipsoids. We generate a volume-based index via minimum-volume covering ellipsoid clustering method, and to visualize these ellipsoids, we utilize Radon transform from the theory of the Inverse Problems. The analyses were conducted for US, Japan and China stock markets. In our study, we observe that when the bubble-burst time approaches, the volumes of the ellipsoids gradually decrease and, correspondingly, the figures obtained by Radon transform become more "brilliant", i.e., more strongly warning.

Subject Keywords

Optimization, Ellipsoid, Radon transform, Clustering, Early-warning, Financial bubbles

URI

https://hdl.handle.net/11511/37660

Collections

Department of Industrial Engineering, Article