Estimating Tipping Points in Feedback-Driven Financial Networks

TL;DR

This paper models financial market dynamics using a feedback-driven network of bargaining agents, identifying tipping points and market bubbles through hysteresis behavior and bimodal distributions, validated with S&P 500 data.

Contribution

It introduces a novel network model with an external variable to detect market tipping points and bubbles, supported by empirical analysis of real financial data.

Findings

Identification of hysteresis in market behavior

Bimodal distribution of the market overpricing variable

Empirical validation with S&P 500 index data

Abstract

Much research has been conducted arguing that tipping points at which complex systems experience phase transitions are difficult to identify. To test the existence of tipping points in financial markets, based on the alternating offer strategic model we propose a network of bargaining agents who mutually either cooperate or where the feedback mechanism between trading and price dynamics is driven by an external "hidden" variable R that quantifies the degree of market overpricing. Due to the feedback mechanism, R fluctuates and oscillates over time, and thus periods when the market is underpriced and overpriced occur repeatedly. As the market becomes overpriced, bubbles are created that ultimately burst in a market crash. The probability that the index will drop in the next year exhibits a strong hysteresis behavior from which we calculate the tipping point. The probability distribution function of R has a bimodal shape characteristic of small systems near the tipping point. By examining the S&P500 index we illustrate the applicability of the model and demonstate that the financial data exhibits a hysteresis and a tipping point that agree with the model predictions. We report a cointegration between the returns of the S&P 500 index and its intrinsic value.