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

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Complex Systems Modeling and Simulation in Economics and Finance (CEF 2015)

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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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Notes

  1. 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. 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. 3.

    For a more thorough background and literature review, refer to [19, pp. 6–25].

  4. 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.

  5. 5.

    OpEx download available at: www.sourceforge.net/projects/open-exchange.

  6. 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. 7.

    ExPo: the exchange portal: www.theexchangeportal.org.

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Acknowledgements

The experimental research presented in this chapter was conducted in 2011–2012 at the University of Bristol, UK, in collaboration with colleagues Marco De Luca (the developer of OpEx) and Charlotte Szostek. They both deserve a special thanks. Thanks also to all the undergraduate students and summer interns (now graduated) that helped support related work, in particular Steve Stotter and Tomas Gražys for work on the original ExPo platform. Finally, thanks to Paul Dempster and the summer interns at UNNC for work on developing the ExPo2 platform, and the pilot studies run during July 2016. Financial support for the studies at Bristol was provided by EPSRC grants EP/H042644/1 and EP/F001096/1, and funding from the UK Government Office for Science (Go-Science) Foresight Project on The Future of Computer Trading in Financial Markets. Financial support for ExPo2 development at UNNC was provided by FoSE summer internship funding.

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Authors and Affiliations

  1. Department of Computer Science, University of Bristol, Bristol, UK

    John Cartlidge & Dave Cliff

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  1. John Cartlidge
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Correspondence to John Cartlidge .

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Editors and Affiliations

  1. AI-ECON Research Center, Department of Economics, National Chengchi University, Taipei, Taiwan

    Shu-Heng Chen

  2. AI-ECON Research Center, Department of Economics, National Chengchi University, Taipei, Taiwan

    Ying-Fang Kao

  3. Institute of Management Studies, Goldsmiths, University of London, London, UK

    Ragupathy Venkatachalam

  4. Regional Development Research Center, Taiwan Institute of Economic Research, Taipei, Taiwan

    Ye-Rong Du

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Cartlidge, J., Cliff, D. (2018). Modelling Complex Financial Markets Using Real-Time Human–Agent Trading Experiments. In: Chen, SH., Kao, YF., Venkatachalam, R., Du, YR. (eds) Complex Systems Modeling and Simulation in Economics and Finance. CEF 2015. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-99624-0_3

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