The LOB Recreation Model: Predicting the Limit Order Book from TAQ History Using an Ordinary Differential Equation Recurrent Neural Network

03/02/2021
by   Zijian Shi, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/01/2021

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

The limit order book (LOB) depicts the fine-grained demand and supply re...
research
03/19/2018

Universal features of price formation in financial markets: perspectives from Deep Learning

Using a large-scale Deep Learning approach applied to a high-frequency d...
research
11/07/2018

Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market

We report successful results from using deep learning neural networks (D...
research
07/23/2022

Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification

Deep Learning models have become dominant in tackling financial time-ser...
research
06/07/2020

Generating Realistic Stock Market Order Streams

We propose an approach to generate realistic and high-fidelity stock mar...
research
02/14/2017

Regularities and Irregularities in Order Flow Data

We identify and analyze statistical regularities and irregularities in t...
research
06/17/2022

Accelerating Machine Learning Training Time for Limit Order Book Prediction

Financial firms are interested in simulation to discover whether a given...

Please sign up or login with your details

Forgot password? Click here to reset