Deep learning models for high-frequency financial data

The limit order book of a financial instrument represents its supply and demand at each point in time. The limit order book data can be used to predict the future price of the financial instrument. We develop deep learning models to capture the high dimensional data distributions (on R^d) of the limit order data. These models exploit the underlying structure of this complex data. We develop a uniform data grid model for limit order book data to achieve state-of-the-art accuracy for predicting price changes in a stock. We also develop a novel way to use non-uniform events from the limit order book data to train a non-uniform grid data model. This model substantially and consistently outperforms our uniform data grid model. Both the models have been trained and tested over a wide range of periods spanning multiple years for many stocks. The out-of-sample predictions are stable across time for both the models as shown by tests for multiple stocks. Given the huge size of the dataset we use a cluster of CPUs and GPUs to perform our experiments.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2019

Author dc:creator

• Abhinav, -

Contributors dc:contributor

• Peng, Jian
• Sirignano, Justin

• Deep learning
• finance
• limit order book
• high frequency data
• time series
• lstm
• non uniform time series
• state of the art

Statement dc:rights

• Copyright 2019 Abhinav Kohar

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/105959

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/105959