Built on

Dai, Zihang, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V Le, and Ruslan Salakhutdinov. 2019. “Transformer-xl: Attentive language models beyond a fixed-length context.” arXiv:1901.02860
Original
1901
Earlier work this paper cites.
Byrd, David, Maria Hybinette, and Tucker Hybinette Balch. 2019. “Abides: Towards high-fidelity market simulation for AI research.” arXiv:1904.12066
Original
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1991
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Bengio, Yoshua, Patrice Simard, and Paolo Frasconi. 1994. “Learning long-term dependencies with gradient descent is difficult.” IEEE transactions on neural networks 5 (2): 157–166
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Hochreiter, Sepp, and Jürgen Schmidhuber. 1997. “Long short-term memory.” Neural computation 9 (8): 1735–1780
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Harris, Larry. 2003. Trading and exchanges: Market microstructure for practitioners . OUP USA
2003
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Wallbridge, James. 2020. “Transformers for limit order books.” arXiv:2003.00130
Original
2003
Earlier work this paper cites.
Lim, Bryan, and Stefan Zohren. 2020. “Time series forecasting with deep learning: A survey.” arXiv:2004.13408
Original
2004
Earlier work this paper cites.

Similar

Belcak, Peter, Jan-Peter Calliess, and Stefan Zohren. 2020. “Fast Agent-Based Simulation Framework of Limit Order Books with Applications to Pro-Rata Markets and the Study of Latency Effects.” arXiv:2008.07871
Original
2008
Cited alongside, same era.
Gould, Martin D, Mason A Porter, Stacy Williams, Mark McDonald, Daniel J Fenn, and Sam D Howison. 2013. “Limit order books.” Quantitative Finance 13 (11): 1709–1742
2013
Cited alongside, same era.
Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. 2014. “Neural machine translation by jointly learning to align and translate.” arXiv:1409.0473
Original
2014
Cited alongside, same era.
Kingma, Diederik P, and Jimmy Ba. 2015. “Adam: A method for stochastic optimization.” Proceedings of the International Conference on Learning Representations
2015
Cited alongside, same era.

Then

Lim, Bryan, Stefan Zohren, and Stephen Roberts. 2019. “Enhancing Time-Series Momentum Strategies Using Deep Neural Networks.” The Journal of Financial Data Science https://jfds.pm-research.com/content/early/2019/09/09/jfds.2019.1.015
2019
Later among the works it cites.
Sirignano, Justin, and Rama Cont. 2019. “Universal features of price formation in financial markets: Perspectives from deep learning.” Quantitative Finance 19 (9): 1449–1459
2019
Later among the works it cites.
Zhang, Zihao, Stefan Zohren, and Stephen Roberts. 2019b. “Extending Deep Learning Models for Limit Order Books to Quantile Regression.” Proceedings of Time Series Workshop of the 36 th International Conference on Machine Learning, Long Beach, California, PMLR 97, 2019
2019
Later among the works it cites.
Briola, Antonio, Jeremy Turiel, and Tomaso Aste. 2020. “Deep Learning Modeling of the Limit Order Book: A Comparative Perspective.” Available at SSRN 3714230
2020
Later among the works it cites.

Beyond the bibliography

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Reading the bibliography…

2021

Deep Learning for Market by Order Data

Zhang, Zihao, Lim, Bryan, Zohren, Stefan

Understand

Market by order (MBO) data - a detailed feed of individual trade instructions for a given stock on an exchange - is arguably one of the most granular sources of microstructure information.

While limit order books (LOBs) are implicitly derived from it, MBO data is largely neglected by current academic literature which focuses primarily on LOB modelling.
In this paper, we demonstrate the utility of MBO data for forecasting high-frequency price movements, providing an orthogonal source of information to LOB snapshots and expanding the universe of alpha discovery.
We provide the first predictive analysis on MBO data by carefully introducing the data structure and presenting a specific normalisation scheme to consider level information in order books and to allow model training with multiple instruments.

Built on

Dai, Zihang, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V Le, and Ruslan Salakhutdinov. 2019. “Transformer-xl: Attentive language models beyond a fixed-length context.” arXiv:1901.02860
Original
1901
Earlier work this paper cites.
Byrd, David, Maria Hybinette, and Tucker Hybinette Balch. 2019. “Abides: Towards high-fidelity market simulation for AI research.” arXiv:1904.12066
Original
1904
Earlier work this paper cites.
Massey Jr, Frank J. 1951. “The Kolmogorov-Smirnov test for goodness of fit.” Journal of the American statistical Association 46 (253): 68–78
1951
Earlier work this paper cites.
Mills, Terence C, and Terence C Mills. 1991. Time series techniques for economists . Cambridge University Press
1991
Earlier work this paper cites.
Bengio, Yoshua, Patrice Simard, and Paolo Frasconi. 1994. “Learning long-term dependencies with gradient descent is difficult.” IEEE transactions on neural networks 5 (2): 157–166
1994
Earlier work this paper cites.
O’Hara, Maureen. 1995. Market microstructure theory . Vol. 108. Blackwell Publishers Cambridge, MA
1995
Earlier work this paper cites.
Hochreiter, Sepp, and Jürgen Schmidhuber. 1997. “Long short-term memory.” Neural computation 9 (8): 1735–1780
1997
Earlier work this paper cites.
Harris, Larry. 2003. Trading and exchanges: Market microstructure for practitioners . OUP USA
2003
Earlier work this paper cites.
Wallbridge, James. 2020. “Transformers for limit order books.” arXiv:2003.00130
Original
2003
Earlier work this paper cites.
Lim, Bryan, and Stefan Zohren. 2020. “Time series forecasting with deep learning: A survey.” arXiv:2004.13408
Original
2004
Earlier work this paper cites.

Similar

Belcak, Peter, Jan-Peter Calliess, and Stefan Zohren. 2020. “Fast Agent-Based Simulation Framework of Limit Order Books with Applications to Pro-Rata Markets and the Study of Latency Effects.” arXiv:2008.07871
Original
2008
Cited alongside, same era.
Gould, Martin D, Mason A Porter, Stacy Williams, Mark McDonald, Daniel J Fenn, and Sam D Howison. 2013. “Limit order books.” Quantitative Finance 13 (11): 1709–1742
2013
Cited alongside, same era.
Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. 2014. “Neural machine translation by jointly learning to align and translate.” arXiv:1409.0473
Original
2014
Cited alongside, same era.
Kingma, Diederik P, and Jimmy Ba. 2015. “Adam: A method for stochastic optimization.” Proceedings of the International Conference on Learning Representations
2015
Cited alongside, same era.

Then

Lim, Bryan, Stefan Zohren, and Stephen Roberts. 2019. “Enhancing Time-Series Momentum Strategies Using Deep Neural Networks.” The Journal of Financial Data Science https://jfds.pm-research.com/content/early/2019/09/09/jfds.2019.1.015
2019
Later among the works it cites.
Sirignano, Justin, and Rama Cont. 2019. “Universal features of price formation in financial markets: Perspectives from deep learning.” Quantitative Finance 19 (9): 1449–1459
2019
Later among the works it cites.
Zhang, Zihao, Stefan Zohren, and Stephen Roberts. 2019b. “Extending Deep Learning Models for Limit Order Books to Quantile Regression.” Proceedings of Time Series Workshop of the 36 th International Conference on Machine Learning, Long Beach, California, PMLR 97, 2019
2019
Later among the works it cites.
Briola, Antonio, Jeremy Turiel, and Tomaso Aste. 2020. “Deep Learning Modeling of the Limit Order Book: A Comparative Perspective.” Available at SSRN 3714230
2020
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Girija, Sanjay Surendranath. 2016. “TensorFlow: Large-scale machine learning on heterogeneous distributed systems.” Software available from tensorflow. org 39 (9)

2016

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Goodfellow, Ian, Yoshua Bengio, Aaron Courville, and Yoshua Bengio. 2016. Deep learning . Vol. 1. MIT press Cambridge

2016

Cited alongside, same era.

Tsantekidis, Avraam, Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, and Alexandros Iosifidis. 2017. “Forecasting stock prices from the limit order book using convolutional neural networks.” In 2017 IEEE 19th Conference on Business Informatics (CBI) , Vol. 1, 7–12. IEEE

2017

Cited alongside, same era.

Ntakaris, Adamantios, Martin Magris, Juho Kanniainen, Moncef Gabbouj, and Alexandros Iosifidis. 2018. “Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods.” Journal of Forecasting 37 (8): 852–866

2018

Cited alongside, same era.

Zhang, Zihao, Stefan Zohren, and Stephen Roberts. 2018. “BDLOB: Bayesian deep convolutional neural networks for limit order books.” Third workshop on Bayesian Deep Learning (NeurIPS 2018), MontrÃ©al, Canada

2018

Cited alongside, same era.

Zhang, Zihao, Stefan Zohren, and Stephen Roberts. 2019a. “DeepLOB: Deep convolutional neural networks for limit order books.” IEEE Transactions on Signal Processing 67 (11): 3001–3012

Cited in the paper.

Zhang, Zihao, Stefan Zohren, and Stephen Roberts. 2020a. “Deep learning for portfolio optimization.” The Journal of Financial Data Science 2 (4): 8–20

Cited in the paper.

Zhang, Zihao, Stefan Zohren, and Stephen Roberts. 2020b. “Deep reinforcement learning for trading.” The Journal of Financial Data Science 2 (2): 25–40

Cited in the paper.

CME. 2020. “Market by Order (MBO).” https://www.cmegroup.com/education/market-by-order-mbo.html

2020

Later among the works it cites.

Hamilton, James Douglas. 2020. Time series analysis . Princeton university press

2020

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Islam, Mohammad Rafiqul, and Nguyet Nguyen. 2020. “Comparison of Financial Models for Stock Price Prediction.” Journal of Risk and Financial Management 13 (8): 181

2020

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OUCH. 2020. “NASDAQ, OUCH.” http://www.nasdaqtrader.com/content/technicalsupport/specifications/TradingProducts/OUCH4.2.pdf

2020

Later among the works it cites.

Briola, Antonio, Jeremy Turiel, Riccardo Marcaccioli, and Tomaso Aste. 2021. “Deep Reinforcement Learning for Active High Frequency Trading.” arXiv:2101.07107

Original

2021

Closest in time.