Early warning on stock market bubbles via methods of optimization, clustering and inverse problems
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.
KeywordsOptimization Financial bubbles Early-warning Clustering Radon transform Ellipsoid
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