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Review of Statistical Approaches for Modeling High-Frequency Trading Data

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Abstract

Due to technological advancements over the last two decades, algorithmic trading strategies are now widely used in financial markets. In turn, these strategies have generated high-frequency (HF) data sets, which provide information at an extremely fine scale and are useful for understanding market behaviors, dynamics, and microstructures. In this paper, we discuss how information flow impacts the behavior of high-frequency (HF) traders and how certain high-frequency trading (HFT) strategies significantly impact market dynamics (e.g., asset prices). The paper also reviews several statistical modeling approaches for analyzing HFT data. We discuss four popular approaches for handling HFT data: (i) aggregating data into regularly spaced bins and then applying regular time series models, (ii) modeling jumps in price processes, (iii) point process approaches for modeling the occurrence of events of interest, and (iv) modeling sequences of inter-event durations. We discuss two methods for defining events, one based on the asset price, and the other based on both price and volume of the asset. We construct durations based on these two definitions, and apply models to tick-by-tick data for assets traded on the New York Stock Exchange (NYSE). We discuss some open challenges arising in HFT data analysis including some empirical analysis, and also review applications of HFT data in finance and economics, outlining several research directions.

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Notes

  1. Indeed this is one of the criticisms of HFT. The May 6, 2010 “Flash Crash,” in which the Dow Jones Industrial Average dropped by almost 1,000 points in 30 min, was the result of an execution algorithm that considered only volume, not time. As a result, $4.1 billion of E-Mini S&P 500 futures contracts were sold on the Chicago Mercantile Exchange in a mere 20 min interval (Goldstein et al. (2014)).

  2. A number of approaches can be used to classify trades as buyer- or seller-initiated, including the Lee-Ready algorithm, the tick rule, and bulk volume classification (see Easley et al. 2016 and references therein).

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Acknowledgements

The authors are very grateful to the reviewers and editors for their helpful suggestions for improving the paper.

Funding

This paper was based upon work partially supported by the National Science Foundation under Grant DMS-1638521 to the Statistical and Applied Mathematical Sciences Institute. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. In addition, the work of SB was supported in part by an NSF award (DMS-1812128).

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  1. Department of Statistics, University of Connecticut, Storrs, CT, USA

    Chiranjit Dutta & Nalini Ravishanker

  2. Department of Mathematics, Middlebury College, Middlebury, VT, USA

    Kara Karpman

  3. Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA

    Sumanta Basu

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Dutta, C., Karpman, K., Basu, S. et al. Review of Statistical Approaches for Modeling High-Frequency Trading Data. Sankhya B 85 (Suppl 1), 1–48 (2023). https://doi.org/10.1007/s13571-022-00280-7

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