Abstract
We introduce an indicator that aims to detect the emergence of market instabilities by quantifying the intensity of self-organizing processes arising from stock returns’ co-movements. In financial markets, phenomena like imitation, herding and positive feedbacks characterize the emergence of endogenous instabilities, which can modify the qualitative and quantitative behavior of the underlying system. The impossibility to formalize ex-ante the dynamic laws that rule the evolution of financial systems motivates the use of a parsimonious synthetic indicator to detect the disruption of an existing equilibrium configuration. Here we show that the emergence of an interconnected sub-graph of stock returns co-movements from a broader market index is a signal of an out-of-equilibrium transition of the underlying system. To test the validity of our approach, we propose a model-free application that builds on the identification of up and down market phases.
Highlights
We introduce an indicator that aims to detect the emergence of market instabilities by quantifying the intensity of self-organizing processes arising from stock returns’ comovements
Herding behaviors spread when the knowledge about other investors’ allocation decisions influences personal strategies, meaning that investors tend to use similar investment practices to those applied by other market participants even when this is not justified by their own information set[8,9,10], while positive feedbacks can induce the underlying system to accumulate instabilities that lead to new configurations as a selffulfilling mechanism[11,12,13,14]
When the system is approaching a change in its equilibrium configuration, we observe that the absolute value of the average Pearson’s correlation coefficient (PCC) is increasing within the set of stocks composing the leading temporal module (LTM) sub-graph, but decreasing between stocks belonging to the LTM and stocks outside the LTM group, while the average AC of stocks within the LTM is increasing
Summary
We introduce an indicator that aims to detect the emergence of market instabilities by quantifying the intensity of self-organizing processes arising from stock returns’ comovements. Inspired by H.A. Simon’s near decomposability condition[20] to represent a stable system configuration[21,22,23], we hypothesize that, during instability phases, a sub-graph of stocks displays increasing co-movements and self-similarity patterns, which we propose to quantify by means of the Pearson’s correlation coefficient (PCC) and the autocovariance (AC) of stock returns (see Supplementary information, Section 3.1). Simon’s near decomposability condition[20] to represent a stable system configuration[21,22,23], we hypothesize that, during instability phases, a sub-graph of stocks displays increasing co-movements and self-similarity patterns, which we propose to quantify by means of the Pearson’s correlation coefficient (PCC) and the autocovariance (AC) of stock returns (see Supplementary information, Section 3.1) We refer to this sub-graph of stocks as the leading temporal module (LTM) of the system, whose dynamics is anticipatory for the whole underlying system. The corresponding synthetic indicator is defined as: ILt TM
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