The purpose of this paper is to advance the understanding of the conditions that give rise to flash crash contagion, particularly with respect to overlapping asset portfolio crowding. To this end, we designed, implemented, and assessed a hybrid micro-macro agent-based model, where price impact arises endogenously through the limit order placement activity of algorithmic traders. Our novel hybrid microscopic and macroscopic model allows us to quantify systemic risk not just in terms of system stability, but also in terms of the speed of financial distress propagation over intraday timescales. We find that systemic risk is strongly dependent on the behaviour of algorithmic traders, on leverage management practices, and on network topology. Our results demonstrate that, for high-crowding regimes, contagion speed is a non-monotone function of portfolio diversification. We also find the surprising result that, in certain circumstances, increased portfolio crowding is beneficial to systemic stability. We are not aware of previous studies that have exhibited this phenomenon, and our results establish the importance of considering non-uniform asset allocations in future studies. Finally, we characterise the time window available for regulatory interventions during the propagation of flash crash distress, with results suggesting ex ante precautions may have higher efficacy than ex post reactions.

The continuing progression of globalization, coupled with the continuing adoption of new computing and network technology, means that today's global financial marketplace is best understood as a complex network of interacting market systems, in which events of world-wide significance unfold on timescales that are barely within the ability of humans to comprehend. Classic economic theories (such as, most famously, General Equilibrium Theory) have proven to be of limited value for understanding and modeling such networks of markets. Many researchers have concluded that the dynamics of networked market systems are better understood as complex adaptive systems [1], in which independent software components interact without centralised control or oversight [2]. A key characteristic of complex adaptive systems is that, even if the individual micro behaviors of system components are all perfectly understood, it remains extremely difficult to predict the overall macro behaviors that the system might exhibit. One reason for this is that such systems exhibit emergent properties, in which a behavior is produced from the overall system which would not be produced by any individual components or subsets of components.Global financial systems contain thousands of interacting, heterogeneous participants, which operate according to their own private values and incentives. One example of an emergent property in such financial systems is the price series of financial assets that arise from enormous numbers of individual orders to buy and sell placed by participants operating over a wide range of time-scales. Another prominent and extremely worrying type of emergent phenomenon is the Flash Crash. A Flash Crash is a sudden, catastrophic drop in asset value, which typically take place over a period of minutes. The first and most famous Flash Crash (and the event that gave its name to the phenomenon) took place over 36 minutes on 6 May 2010, during which time the Dow Jones Industrial Average suffered its largest ever intraday point loss: trillions of dollars were briefly wiped off the world's markets [3]. Since 2010, a number of other Flash Crashes have been reported (see sidebar 1).Events such as the Flash Crash, and the earlier global financial crisis of 2007-2009, highlight the inadequacy of the established models offered by classical economics. Such models are based on strong assumptions of perfect rationality, perfect information, and market equilibrium, and take no account of the complex web of interconnections that characterise modern financial systems. At the height of the sub-prime mortgage crisis, traditional models proved spectacularly inaccurate in their assessment of the stability of the investment bank Lehman Brothers. The subsequent collapse of Lehman Brothers sent shockwaves through the global economic establishment. Lehman Brothers, apparently financially sound when considered in isolation, had proved vulnerable to distress arising elsewhere in the financial network. When credit dried up in the interbank lending networ...

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