A Price Crash Alerting Strategy for Agent-based Artificial Financial Markets

Alexandru Stan
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Alexandru Stan: Babes-Bolyai University, Romania

from University of Primorska, Faculty of Management Koper

Abstract: This paper explores the consistency of a dynamic network analysis strategy for detecting flash crash market events. Price crashes are reproduced through a specific experimental set-up placed within an artificial agent-based stock exchange populated with zero-intelligence traders (ZITs) and, some- what more sophisticated, informed traders (IT). Specifically, we verify whether the emergence of anomalies and structural breaks in the dynamics of the exchange topology, can be used to detect the overall level of informed trading and the imminence of a price crash. By controlling the level of information asymmetry and the number of agents within the IT and ZIT classes, we induce different equilibria levels in the price and have the system alternate between price mono- and bi-stability. Thus, we motivate the crash occurrence by the advent of a sequence of phase transitions in the complex system. We assess the expected effectiveness of a network based crash detection approach through Monte Carlo simulation performed within the artificial market framework. We employ several topologies of private information diffusion within the IT population. We conclude our undertaking by providing a number of arguments which explain the observed market dynamics.

Keywords: Flash crash detection; complex networks; artificial markets; multi-agent systems (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:mgt:micp15:99-116

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