Herding interactions as an opportunity to prevent extreme events in financial markets

TL;DR

This paper explores how herding interactions in agent-based financial market models can be managed to prevent extreme events like crashes, by introducing fundamentalist traders and testing control strategies.

Contribution

It demonstrates that adding fundamentalist agents reduces extreme price fluctuations and evaluates the effects of random trading strategies on market stability.

Findings

Fundamentalist agents significantly decrease the probability of extreme events.

Random trading strategies have ambiguous effects on market stability.

Herding interactions can be managed to prevent financial crises.

Abstract

A characteristic feature of complex systems in general is a tight coupling between their constituent parts. In complex socio-economic systems this kind of behavior leads to self-organization, which may be both desirable (e.g. social cooperation) and undesirable (e.g. mass panic, financial "bubbles" or "crashes"). Abundance of the empirical data as well as general insights into the trading behavior enables the creation of simple agent-based models reproducing sophisticated statistical features of the financial markets. In this contribution we consider a possibility to prevent self-organized extreme events in artificial financial market setup built upon a simple agent-based herding model. We show that introduction of agents with predefined fundamentalist trading behavior helps to significantly reduce the probability of the extreme price fluctuations events. We also test random trading control strategy, which was previously found to be promising, and find that its impact on the market is rather ambiguous. Though some of the results indicate that it might actually stabilize financial fluctuations.