A New Attempt to Identify Long-term Precursors for Endogenous Financial Crises in the Market Correlation Structures

Abstract

Prediction of events in financial markets is every investor's dream and, usually, wishful thinking. From a more general, economic and societal viewpoint, the identification of indicators for large events is highly desirable to assess systemic risks. Unfortunately, the very nature of financial markets, particularly the predominantly non-Markovian character as well as non-stationarity, make this challenge a formidable one, leaving little hope for fully fledged answers. Nevertheless, it is called for to collect pieces of evidence in a variety of observables to be assembled like the pieces of a puzzle that eventually might help to catch a glimpse of long-term indicators or precursors for large events - if at all in a statistical sense. Here, we present a new piece for this puzzle. We use the quasi-stationary market states which exist in the time evolution of the correlation structure in financial markets. Recently, we identified such market states relative to the collective motion of the market as a whole. We study their precursor properties in the US stock markets over 16 years, including two endogenous crises, the dot-com bubble burst and the pre-phase of the Lehman Brothers crash. We identify certain interesting features and critically discuss their suitability as indicators.

23 pages, 10 figures, 4 tables

References in corpus (4)

• Kullback-Leibler distance as a measure of the information filtered from multivariate data
• Dissecting cross-impact on stock markets: An empirical analysis
• Zooming into market states
• The endogenous dynamics of markets: price impact and feedback loops

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• Multivariate Distributions in Non-Stationary Complex Systems II: Empirical Results for Correlated Stock Markets