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The success of deep learning-based limit order book forecasting models is highly dependent on the quality and the robustness of the input data representation.
On the importance of opponent modeling in auction markets
Mahfouz, M., A. Filos, C. Chtourou, J. Lockhart, S. Assefa, M. Veloso, D. Mandic, and T. Balch (2019) · 1911
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Lobster: Limit order book reconstruction system
Huang, R. and T. Polak (2011) · 2011
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Representation learning: A review and new perspectives
Bengio, Y., A. Courville, and P. Vincent (2013) · 2013
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Modelling high-frequency limit order book dynamics with support vector machines
Kercheval, A. N. and Y. Zhang (2015) · 2015
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Limit order books
Abergel, F., M. Anane, A. Chakraborti, A. Jedidi, and I. M. Toke (2016) · 2016
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Forecasting stock prices from the limit order book using convolutional neural networks
Tsantekidis, A., N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosifidis (2017a) · 2017
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Using deep learning to detect price change indications in financial markets
Tsantekidis, A., N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosifidis (2017b) · 2017
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Bai, S., J. Z. Kolter, and V. Koltun (2018) · 2018
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Market microstructure in practice
Lehalle, C.-A. and S. Laruelle (2018) · 2018
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Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods
Ntakaris, A., M. Magris, J. Kanniainen, M. Gabbouj, and A. Iosifidis (2018) · 2018
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Temporal attention-augmented bilinear network for financial time-series data analysis
Tran, D. T., A. Iosifidis, J. Kanniainen, and M. Gabbouj (2018) · 2018
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Bdlob: Bayesian deep convolutional neural networks for limit order books
Zhang, Z., S. Zohren, and S. Roberts (2018) · 2018
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Deep hedging
Deeplob: Deep convolutional neural networks for limit order books
Zhang, Z., S. Zohren, and S. Roberts (2019) · 2019
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Algorithmic trading in a microstructural limit order book model
Abergel, F., C. Huré, and H. Pham (2020) · 2020
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Using deep learning for price prediction by exploiting stationary limit order book features
Tsantekidis, A., N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosifidis (2020) · 2020
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Quant gans: Deep generation of financial time series
Wiese, M., R. Knobloch, R. Korn, and P. Kretschmer (2020) · 2020
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Temporal fusion transformers for interpretable multi-horizon time series forecasting
Lim, B., S. Ö. Arık, N. Loeff, and T. Pfister (2021) · 2021
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Time-series forecasting with deep learning: a survey
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Buehler, H., L. Gonon, J. Teichmann, and B. Wood (2019) · 2019
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Universal features of price formation in financial markets: perspectives from deep learning
Sirignano, J. and R. Cont (2019) · 2019
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Deep learning for limit order books
Sirignano, J. A. (2019) · 2019
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Lim, B. and S. Zohren (2021) · 2021
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Zhang, Z. and S. Zohren (2021) · 2021
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Informer: Beyond efficient transformer for long sequence time-series forecasting
Zhou, H., S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, and W. Zhang (2021) · 2021
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