Kisiel, Damian;
Gorse, Denise;
(2022)
Axial-LOB: High-Frequency Trading with Axial Attention.
In: Ishibuchi, H and Kwoh, CK and Tan, AH and Srinivasan, D and Miao, C and Trivedi, A and Crockett, K, (eds.)
2022 IEEE Symposium Series on Computational Intelligence (SSCI).
(pp. pp. 1327-1333).
IEEE
(In press).
Preview |
Text
2212.01807.pdf - Accepted Version Download (444kB) | Preview |
Abstract
Previous attempts to predict stock price from limit order book (LOB) data are mostly based on deep convolutional neural networks. Although convolutions offer efficiency by restricting their operations to local interactions, it is at the cost of potentially missing out on the detection of long-range dependencies. Recent studies address this problem by employing additional recurrent or attention layers that increase computational complexity. In this work, we propose Axial-LOB, a novel fully-attentional deep learning architecture for predicting price movements of stocks from LOB data. By utilizing gated position-sensitive axial attention layers our architecture is able to construct feature maps that incorporate global interactions, while significantly reducing the size of the parameter space. Unlike previous works, Axial-LOB does not rely on hand-crafted convolutional kernels and hence has stable performance under input permutations and the capacity to incorporate additional LOB features. The effectiveness of Axial-LOB is demonstrated on a large benchmark dataset, containing time series representations of millions of high-frequency trading events, where our model establishes a new state of the art, achieving an excellent directional classification performance at all tested prediction horizons.
| Type: | Proceedings paper |
|---|---|
| Title: | Axial-LOB: High-Frequency Trading with Axial Attention |
| Event: | 2022 IEEE Symposium Series on Computational Intelligence (SSCI) |
| Location: | Singapore, SINGAPORE |
| Dates: | 4 Dec 2022 - 7 Dec 2022 |
| ISBN-13: | 9781665487689 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/SSCI51031.2022.10022284 |
| Language: | English |
| Additional information: | This version is the author-accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Computational modeling, Time series analysis, Computer architecture, Predictive models, Logic gates, Benchmark testing, Prediction algorithms |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10172667 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
