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Previous attempts to predict stock price from limit order book (LOB) data are mostly based on deep convolutional neural networks.
1912
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Z. Eisler, J.P. Bouchaud, and J. Kockelkoren, “The price impact of order book events: market orders, limit orders and cancellations,” Quantitative Finance, 12(9), pp.1395-1419, 2012
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I. Sutskever, J. Martens, G. Dahl, and G. Hinton, “On the importance of initialization and momentum in deep learning,” In International Conference on Machine Learning (pp. 1139-1147). PMLR, 2013
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A.A. Ariyo, A.O. Adewumi, and C.K. Ayo, “Stock price prediction using the ARIMA model,” In 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation (pp. 106-112). IEEE
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A. Tsantekidis, N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosifidis, “Using deep learning to detect price change indications in financial markets,” In 2017 25th European Signal Processing Conference (EUSIPCO) (pp. 2511-2515). IEEE, 2017
2017
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A. Tsantekidis, N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosifidis, “Forecasting stock prices from the limit order book using convolutional neural networks,” In IEEE 19th Conference on Business Informatics (CBI) (Vol. 1, pp. 7-12). IEEE, 2017
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D.T. Tran, A. Iosifidis, J. Kanniainen, and M. Gabbouj, “Temporal attention-augmented bilinear network for financial time-series data analysis,” IEEE Transactions on Neural Networks and Learning Systems, 30(5), pp.1407-1418, 2018
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A. Ntakaris, M. Magris, J. Kanniainen, M. Gabbouj, and A. Iosifidis, “Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods,” Journal of Forecasting, vol. 37, no. 8, pp. 852–866, 2018
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H. Hu, Z. Zhang, Z. Xie, and S. Lin, “Local relation networks for image recognition,” In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 3464-3473), 2019
2019
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A. Tsantekidis, N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosifidis, “Using deep learning for price prediction by exploiting stationary limit order book features,” Applied Soft Computing, 93, p.106401, 2020
2020
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H. Wang, Y. Zhu, B. Green, H. Adam, A. Yuille, and L.C. Chen, “Axial-deeplab: stand-alone axial-attention for panoptic segmentation,” In European Conference on Computer Vision (pp. 108-126). Springer, 2020
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D. Kisiel, and D. Gorse, “A meta-method for portfolio management using machine learning for adaptive strategy selection,” In 2021 The 4th International Conference on Computational Intelligence and Intelligent Systems (pp. 67-71), 2021
2021
Later among the works it cites.
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Z. Zhang, S. Zohren, and S. Roberts, “DeepLOB: Deep convolutional neural networks for limit order books,” IEEE Transactions on Signal Processing 67(11), 3001–3012, 2019
2019
Cited alongside, same era.
P. Ramachandran, N. Parmar, A. Vaswani, I. Bello, A. Levskaya, and J. Shlens, “Stand-alone self-attention in vision models,” Advances in Neural Information Processing Systems, 32, 2019
2019
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J.M.J. Valanarasu, P. Oza, I. Hacihaliloglu, and V.M. Patel, “Medical transformer: gated axial-attention for medical image segmentation,” In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 36-46). Springer, 2021
2021
Later among the works it cites.