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Erschienen in:
On Complex Economic Dynamics: Agent-Based Computational Modeling and Beyond Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Modelling Complex Financial Markets Using Real-Time Human–Agent Trading Experiments

verfasst von : John Cartlidge, Dave Cliff

Erschienen in: Complex Systems Modeling and Simulation in Economics and Finance

Verlag: Springer International Publishing

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Abstract

To understand the impact of high-frequency trading (HFT) systems on financial-market dynamics, a series of controlled real-time experiments involving humans and automated trading agents were performed. These experiments fall at the interdisciplinary boundary between the more traditional fields of behavioural economics (human-only experiments) and agent-based computational economics (agent-only simulations). Experimental results demonstrate that: (a) faster financial trading agents can reduce market efficiency—a worrying result given the race towards zero-latency (ever faster trading) observed in real markets; and (b) faster agents can lead to market fragmentation, such that markets transition from a regime where humans and agents freely interact to a regime where agents are more likely to trade between themselves—a result that has also been observed in real financial markets. It is also shown that (c) realism in experimental design can significantly alter market dynamics—suggesting that, if we want to understand complexity in real financial markets, it is finally time to move away from the simple experimental economics models first introduced in the 1960s.

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Vorheriges Kapitel Dark Pool Usage and Equity Market Volatility
Nächstes Kapitel Does High-Frequency Trading Matter?
Fußnoten
1
Flash crashes are now so commonplace that during the writing of this chapter, a flash crash occurred in the FX rate of the British Pound (GBP). On 7 Oct 2016, GBP experienced a 6% drop in 2 min, before recovering most of the losses [53]—a typical flash crash characteristic.
 
2
The primary reason for no human involvement on these timescales is not because of granularity in decision making—i.e., limitations in human abilities to process information, e.g., [12]—but rather that humans are simply too slow to react to events happening, quite literally, in the blink of an eye.
 
3
For a more thorough background and literature review, refer to [19, pp. 6–25].
 
4
The micro-economic supply and demand model presented only considers a single commodity, ceteris paribus, and is therefore a partial equilibrium model. The market is considered independently from other markets, so this is not a general equilibrium model.
 
6
Some of the variation in α between results presented in Sects. 5.1.2 and 5.2.1 may be explained by the different permit schedules used for the two experiments (compare Tables 1 and 2). However, previous results from a direct comparison using an identical permit schedule to Table 2 show that MaxSpread = 15% results in higher α than MaxSpread = 1% [9, Appendix B]. Although, a more recent study [16] suggests the opposite result, so there is some uncertainty around this effect.
 
7
ExPo: the exchange portal: www.​theexchangeporta​l.​org.
 
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Metadaten
Titel
Modelling Complex Financial Markets Using Real-Time Human–Agent Trading Experiments
verfasst von
John Cartlidge
Dave Cliff
Copyright-Jahr
2018
Buch
Complex Systems Modeling and Simulation in Economics and Finance

Print ISBN: 978-3-319-99622-6

Electronic ISBN: 978-3-319-99624-0

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-99624-0_3

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