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Adaptive order flow forecasting with multiplicative error models

Author

Listed:
  • Andrija Mihoci

    (PREA Group)

  • Christopher Hian-Ann Ting

    (Singapore Management University)

  • Meng-Jou Lu

    (Asia University)

  • Kainat Khowaja

    (Humboldt University of Berlin)

Abstract

A flexible statistical approach for the analysis of time-varying dynamics of transaction data on financial markets is here applied to intra-day trading strategies. A local adaptive technique is used to successfully predict financial time series, i.e. the buyer- and the seller-initiated trading volumes and the order flow dynamics. Analysing order flow series and its information content of mini Nikkei 225 index futures traded at the Osaka Securities Exchange in 2012 and 2013, a data-driven optimal length of local windows up to approximately 1–2 h is reasonable to capture parameter variations and is suitable for short-term prediction. Our proposed trading strategies achieve statistical arbitrage opportunities and are, therefore, beneficial for quantitative finance practice.

Suggested Citation

  • Andrija Mihoci & Christopher Hian-Ann Ting & Meng-Jou Lu & Kainat Khowaja, 2022. "Adaptive order flow forecasting with multiplicative error models," Digital Finance, Springer, vol. 4(1), pages 89-108, March.
    • Handle: RePEc:spr:digfin:v:4:y:2022:i:1:d:10.1007_s42521-021-00047-1
    DOI: 10.1007/s42521-021-00047-1
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    References listed on IDEAS

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