Extracting information from the limit order book: New measures to evaluate equity data flow

Ye, Ziwen; Florescu, Ionuţ

doi:10.1002/hf2.10029

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…Therefore, the LOB contains valuable information on the instantaneous demand and supply for a particular financial asset (e.g., a stock, a commodity, a derivative, etc.). For this reason, LOB data has been used for many and various studies, including exploration of the price formation mechanism [20], market anomaly detection [26], and testing of trading algorithms [1].…”

Section: Introductionmentioning

confidence: 99%

The Limit Order Book Recreation Model (LOBRM): An Extended Analysis

Shi¹,

Cartlidge²

2021

Preprint

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The limit order book (LOB) depicts the fine-grained demand and supply relationship for financial assets and is widely used in market microstructure studies. Nevertheless, the availability and high cost of LOB data restrict its wider application. The LOB recreation model (LOBRM) was recently proposed to bridge this gap by synthesizing the LOB from trades and quotes (TAQ) data. However, in the original LO-BRM study, there were two limitations: (1) experiments were conducted on a relatively small dataset containing only one day of LOB data; and(2) the training and testing were performed in a non-chronological fashion, which essentially re-frames the task as interpolation and potentially introduces lookahead bias. In this study, we extend the research on LO-BRM and further validate its use in real-world application scenarios. We first advance the workflow of LOBRM by (1) adding a time-weighted z-score standardization for the LOB and (2) substituting the ordinary differential equation kernel with an exponential decay kernel to lower computation complexity. Experiments are conducted on the extended LOBSTER dataset in a chronological fashion, as it would be used in a real-world application. We find that (1) LOBRM with decay kernel is superior to traditional non-linear models, and module ensembling is effective; (2) prediction accuracy is negatively related to the volatility of order volumes resting in the LOB; (3) the proposed sparse encoding method for TAQ exhibits good generalization ability and can facilitate manifold tasks; and (4) the influence of stochastic drift on prediction accuracy can be alleviated by increasing historical samples.

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Section: Introductionmentioning

confidence: 99%