Adaptive order flow forecasting with multiplicative error models

Andrija Mihoci (), Christopher Hian-Ann Ting (), Meng-Jou Lu () and Kainat Khowaja ()
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Andrija Mihoci: PREA Group
Christopher Hian-Ann Ting: Singapore Management University
Meng-Jou Lu: Asia University
Kainat Khowaja: Humboldt University of Berlin

Digital Finance, 2022, vol. 4, issue 1, No 4, 89-108

Abstract: 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.

Keywords: Multiplicative error models; Trading volume; Order flow; Forecasting; B23; G11; G12 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s42521-021-00047-1

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