  In an order-driven financial market, the price of a financial asset is
discovered through the interaction of orders - requests to buy or sell at a
particular price - that are posted to the public limit order book (LOB).
Therefore, LOB data is extremely valuable for modelling market dynamics.
However, LOB data is not freely accessible, which poses a challenge to market
participants and researchers wishing to exploit this information. Fortunately,
trades and quotes (TAQ) data - orders arriving at the top of the LOB, and
trades executing in the market - are more readily available. In this paper, we
present the LOB recreation model, a first attempt from a deep learning
perspective to recreate the top five price levels of the LOB for small-tick
stocks using only TAQ data. Volumes of orders sitting deep in the LOB are
predicted by combining outputs from: (1) a history compiler that uses a Gated
Recurrent Unit (GRU) module to selectively compile prediction relevant quote
history; (2) a market events simulator, which uses an Ordinary Differential
Equation Recurrent Neural Network (ODE-RNN) to simulate the accumulation of net
order arrivals; and (3) a weighting scheme to adaptively combine the
predictions generated by (1) and (2). By the paradigm of transfer learning, the
source model trained on one stock can be fine-tuned to enable application to
other financial assets of the same class with much lower demand on additional
data. Comprehensive experiments conducted on two real world intraday LOB
datasets demonstrate that the proposed model can efficiently recreate the LOB
with high accuracy using only TAQ data as input.
