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

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 financial market modeling its behavior using agent-based herding model, which reproduces main stylized facts of the financial markets. 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 investigate random trading, which was previously found to be promising extreme event prevention strategy, and find that its impact on the market has to be considered among other opportunities to stabilize the markets.