Abstract, Acknowledgements, and Statements and Declarations

  1. Introduction

  2. Background and Related Work

    2.1 Agent-based Financial Market simulation

    2.2 Flash Crash Episodes

  3. Model Structure and 3.1 Model Set-up

    3.2 Common Trader Behaviours

    3.3 Fundamental Trader (FT)

    3.4 Momentum Trader (MT)

    3.5 Noise Trader (NT)

    3.6 Market Maker (MM)

    3.7 Simulation Dynamics

  4. Model Calibration and Validation and 4.1 Calibration Target: Data and Stylised Facts for Realistic Simulation

    4.2 Calibration Workflow and Results

    4.3 Model Validation

  5. 2010 Flash Crash Scenarios and 5.1 Simulating Historical Flash Crash

    5.2 Flash Crash Under Different Conditions

  6. Mini Flash Crash Scenarios and 6.1 Introduction of Spiking Trader (ST)

    6.2 Mini Flash Crash Analysis

    6.3 Conditions for Mini Flash Crash Scenarios

  7. Conclusion and Future Work

    7.1 Summary of Achievements

    7.2 Future Works

References and Appendices

3.4 Momentum Trader (MT)

Momentum traders are also called "Chartists". This group of traders buy and sell financial assets after being influenced by recent price trends. The assumption is to take advantage of an upward or downward trend in the stock prices until the trend starts to fade. Instead of looking at the fundamental value of the stock, momentum traders focus more on recent price action and price movement. If the stock price has been recently rising, a long position is established; otherwise, momentum traders will enter a short position. In the proposed model, momentum traders can submit both limit orders and market orders. For momentum traders, the ratio between the number of limit orders and market orders is fixed, which is denoted as ρ.

3.4.1 Long-term Momentum Trader (LMT)

3.4.2 Short-term Momentum Trader (SMT)

Authors:

(1) Kang Gao, Department of Computing, Imperial College London, London SW7 2AZ, UK and Simudyne Limited, London EC3V 9DS, UK ([email protected]);

(2) Perukrishnen Vytelingum, Simudyne Limited, London EC3V 9DS, UK;

(3) Stephen Weston, Department of Computing, Imperial College London, London SW7 2AZ, UK;

(4) Wayne Luk, Department of Computing, Imperial College London, London SW7 2AZ, UK;

(5) Ce Guo, Department of Computing, Imperial College London, London SW7 2AZ, UK.

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.

[5] We also run simulation experiments with α having value 0.95 and 0.99. Similar experimental results are obtained in these settings, showing that minor changes in α value will not significantly affect the results.