Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts

Cited 40 time in webofscience Cited 0 time in scopus
  • Hit : 52
  • Download : 0
How can we efficiently correlate multiple stocks for accurate stock movement prediction? Stock movement prediction has received growing interest in data mining and machine learning communities due to its substantial impact on financial markets. One way to improve the prediction accuracy is to utilize the correlations between multiple stocks, getting a reliable evidence regardless of the random noises of individual prices. However, it has been challenging to acquire accurate correlations between stocks because of their asymmetric and dynamic nature which is also influenced by the global movement of a market. In this work, we propose DTML (Data-axis Transformer with Multi-Level contexts), a novel approach for stock movement prediction that learns the correlations between stocks in an end-to-end way. DTML makes asymmetric and dynamic correlations by a) learning temporal correlations within each stock, b) generating multi-level contexts based on a global market context, and c) utilizing a transformer encoder for learning inter-stock correlations. DTML achieves the state-of-the-art accuracy on six datasets collected from various stock markets from US, China, Japan, and UK, making up to 13.8%p higher profits than the best competitors and the annualized return of 44.4% on investment simulation.
Publisher
Association for Computing Machinery
Issue Date
2021-08-16
Language
English
Citation

27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021, pp.2037 - 2045

DOI
10.1145/3447548.3467297
URI
http://hdl.handle.net/10203/311555
Appears in Collection
EE-Conference Papers(학술회의논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 40 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0