Convolutional neural networks applied to high-frequency market microstructure forecasting

By J. Doering, Michael Fairbank, Sheri M. Markose. 2017

33 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering, Model Architectures, Time-Series & Forecasting

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Stephen J. Roberts
2. A. Ozbayoglu
3. Omer Berat Sezer
4. M. Ugur Gudelek

In Mechanical Engineering

1. Stephen J. Roberts
2. A. Ozbayoglu
3. Omer Berat Sezer
4. M. Ugur Gudelek

In Model Architectures

1. M. Ugur Gudelek
2. A. Ozbayoglu
3. Omer Berat Sezer
4. Stephen J. Roberts
5. Zihao Zhang
6. Stefan Zohren
7. Weiwei Jiang
8. Longbing Cao

In Time-Series & Forecasting

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Stefan Zohren
5. Stephen J. Roberts
6. Zihao Zhang
7. Weiwei Jiang
8. Longbing Cao

Citations in the corpus

• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “[159] London Stock Exchange 2007-2008 Limit order book state, trades,”
• Deep Learning for Financial Applications : A Survey (2020, methodology): “[65] London Stock Exchange 2007-2008 Limit order book state, trades, buy/sell orders, order deletions CNN Accuracy, kappa Caffe”
• DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (2018, background): “However, there have been but a few published works that adopt CNNs to analyse finan-cial microstructure data [34, 35, 26] and the existing CNN architectures are rather unsophisticated and lack of thorough investigation.”