  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 LOBRM 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 LOBRM 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.
