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The prediction of fluctuation in the order-driven financial market

Author

Listed:
  • Fabin Shi
  • Xiao-Qian Sun
  • Jinhua Gao
  • Zidong Wang
  • Hua-Wei Shen
  • Xue-Qi Cheng

Abstract

Risk prediction is one of the important issues that draws much attention from academia and industry. And the fluctuation—absolute value of the change of price, is one of the indexes of risk. In this paper, we focus on the relationship between fluctuation and order volume. Based on the observation that the price would move when the volume of order changes, the prediction of price fluctuation can be converted into the prediction of order volume. Modelling the trader’s behaviours—order placement and order cancellation, we propose an order-based fluctuation prediction model. And our model outperforms better than baseline in OKCoin and BTC-e datasets.

Suggested Citation

  • Fabin Shi & Xiao-Qian Sun & Jinhua Gao & Zidong Wang & Hua-Wei Shen & Xue-Qi Cheng, 2021. "The prediction of fluctuation in the order-driven financial market," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-15, November.
    • Handle: RePEc:plo:pone00:0259598
    DOI: 10.1371/journal.pone.0259598
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    References listed on IDEAS

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    1. Petra Posedel, 2006. "Analysis of the Exchange Rate and Pricing Foreign Currency Options on the Croatian Market: the NGARCH Model as an Alternative to the Black-Scholes Model," Financial Theory and Practice, Institute of Public Finance, vol. 30(4), pages 347-368.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Fernandez-Rodriguez, Fernando & Gonzalez-Martel, Christian & Sosvilla-Rivero, Simon, 2000. "On the profitability of technical trading rules based on artificial neural networks:: Evidence from the Madrid stock market," Economics Letters, Elsevier, vol. 69(1), pages 89-94, October.
    4. Jiahua Wang & Hongliang Zhu & Dongxin Li, 2018. "Price Dynamics in an Order-Driven Market with Bayesian Learning," Complexity, Hindawi, vol. 2018, pages 1-15, November.
    5. Mike, Szabolcs & Farmer, J. Doyne, 2008. "An empirical behavioral model of liquidity and volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 200-234, January.
    6. Maslov, Sergei, 2000. "Simple model of a limit order-driven market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 278(3), pages 571-578.
    7. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    8. Yoshihiro Yura & Hideki Takayasu & Didier Sornette & Misako Takayasu, 2014. "Financial Brownian Particle in the Layered Order Book Fluid and Fluctuation-Dissipation Relations," Swiss Finance Institute Research Paper Series 14-06, Swiss Finance Institute.
    9. Ghiassi, M. & Saidane, H. & Zimbra, D.K., 2005. "A dynamic artificial neural network model for forecasting time series events," International Journal of Forecasting, Elsevier, vol. 21(2), pages 341-362.
    10. Fabin Shi & Nathan Aden & Shengda Huang & Neil Johnson & Xiaoqian Sun & Jinhua Gao & Li Xu & Huawei Shen & Xueqi Cheng & Chaoming Song, 2021. "Modelling Universal Order Book Dynamics in Bitcoin Market," Papers 2101.06236, arXiv.org.
    11. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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