Financial Stress Through Complexity Science

Abstract

Abstract-Financial markets typically undergo periods of prosperity followed by periods of stagnation, and this undulation makes it challenging to maintain market efficiency. The efficient market hypothesis (EMH) states that there exist differences in structural complexity of the security prices between regular conditions and abnormal situations. Yet, despite a clear link between market acceleration (cf. recession) and stress in systems, indices of financial stress still have significant scope for further development. The overarching aim of this work is therefore to determine those characteristics of financial indices which are related to their stress levels, and to establish a robust metric for the extent of 'stress' of the financial system. This is achieved based on systematic intrinsic multiscale analysis which enables us to test the so called complexity-

Complexity modeling and analysis of chaos and other fluctuating phenomena

“…Beyond this point, the sample-entropy curves, in general, increase at a much faster rate with respect to the bifurcation parameter. This increase in the complex behavior of Equation 13, as R ranges from 3.56995 to ~ 3.82843, is characterized by a periodic phase interrupted by bursts of the aperiodic behavior [46]. Moreover, there is a sudden decrease in the complexity at R = 3.85, which is most likely associated with the island of stability at R = 3.82843 [47], which shows the non-chaotic behavior.…”

“…In addition, the MSE algorithm has been found to be useful in analyzing and modeling temporal data, such as serrated flow, during mechanical deformation, in different alloy systems [7][8][9], physiological-time series [4,6,10], bearing vibration data [11], and financial time series [12,13]. However, the MSE technique does have issues, such as problems in accuracy and validity at large scale factors [5].…”

Complexity, Design and Culture: convergence for digital experiences

“…Complexity science helps to explain some of the financial market behaviors. In [70] Hemakon et al present a computational and algorithmic approach to analyze, and eventually predict, financial stress: "we examined the financial market from the point of view of complexity science [...] This work has conclusively demonstrated the utility of posterior complexity science approaches in the assessment of financial stress".…”