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Effects of limit order book information level on market stability metrics

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Abstract

Using an agent-based model of the limit order book, we explore how the levels of information available to participants, exchanges, and regulators can be used to improve our understanding of the stability and resiliency of a market. Ultimately, we want to know if electronic market data contains previously undetected information that could allow us to better assess market stability. Using data produced in the controlled environment of an agent-based model’s limit order book, we examine various resiliency indicators to determine their predictive capabilities. Most of the types of data created have traditionally been available either publicly or on a restricted basis to regulators and exchanges, but other types have never been collected. We confirmed our findings using actual order flow data with user identifications included from the CME (Chicago Mercantile Exchange) and New York Mercantile Exchange. Our findings strongly suggest that high-fidelity microstructure data in combination with price data can be used to define stability indicators capable of reliably signaling a high likelihood for an imminent flash crash event about one minute before it occurs.

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Notes

  1. The data and the agent distributions used in the construction of the limit order book ABM cannot be made public out of concerns of providing detailed trading strategy secrets on order size and placement.

  2. Agents only manage a single order at a time. If an agent has an old order that is still in the order book at the time it is scheduled to place a new order, it cancels the old order before adding the new order to the market.

  3. See Appendix A for exact numbers\(_{.}\)

  4. The code for the ABM will be made available upon request by emailing ...

  5. The simulation is initialized to begin running at the same price as the market started trading at on 1270. The model is run at 1 of 1/24 scale as the original number of market participants so as to decrease model run time. As a result the trading volume and resting order totals are multiplied by the 24.

    Fig. 4
    figure 4

    Real versus simulated E-Mini: volume, moving average price, and order book depth

    Full size image

  6. It is important to note that some of these metrics contain parameters that require tuning. In our work, we will select similar time scales for all the parameters so they produce new measurements every 30 s on average. We do not spend time calibrating these parameters to better fit the data, which has been an area of concern in previous research (Andersen and Bondarenko 2014).

    Fig. 5
    figure 5

    Market state diagram

    Full size image

  7. The parameters were selected based on the average volume bucket, V, would be equal to 30  s during a normal period so the model would produce new values every 30 s. The number of buckets was selected during based on the (Easley et al. 2010) value.

  8. We will simply refer to this as the price impact measure for simplicity throughout the rest of the paper.

  9. The selling algorithm was believed to have been set at a PTV value of 9 % on May 6th 2010 accord the CTFC and SEC Report (2010).

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Acknowledgments

The authors are very grateful to Philip Maymin, Michael Wellman, Nathan Palmar, Greg Feldberg and Wendy Wagner-Smith for their help and guidance with this paper. Special acknowledgement is due Andrew Todd for his help in the development of the agent-based simulation used in this paper. The research presented in this paper was authored by Mark Paddrik, Roy Hayes, William Scherer, Peter Beling, former contractors for the Commodity Futures Trading Commission (CFTC) who worked under CFTC OCE contract. The Office of the Chief Economist and CFTC economists produce original research on a broad range of topics relevant to the CFTC’s mandate to regulate commodity futures markets, commodity options markets, and the expanded mandate to regulate the swaps markets under the Dodd-Frank Wall Street Reform and Consumer Protection Act. These papers are often presented at conferences and many of these papers are later published by peer-review and other scholarly outlets. The analyses and conclusions expressed in this paper are those of the authors and do not reflect the views of other members of the Office of the Chief Economist, other Commission staff, or the Commission itself.

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Authors and Affiliations

  1. Office of Financial Research, Washington, DC, USA

    Mark Paddrik

  2. University of Virginia, Charlottesville, VA, USA

    Roy Hayes, William Scherer & Peter Beling

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  1. Mark Paddrik
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  2. Roy Hayes
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Correspondence to Mark Paddrik.

Appendix

Appendix

See Table 5.

Table 5 Simulated S&P 500 E-Mini market participation
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Paddrik, M., Hayes, R., Scherer, W. et al. Effects of limit order book information level on market stability metrics. J Econ Interact Coord 12, 221–247 (2017). https://doi.org/10.1007/s11403-015-0164-6

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