research-article

Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction

Authors:

Thanh Trung Huynh,

Minh Hieu Nguyen,

Thanh Tam Nguyen,

Matthias Weidlich,

Quoc Viet Hung Nguyen,

Karl AbererAuthors Info & Claims

WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining

Pages 850 - 858

https://doi.org/10.1145/3539597.3570427

Published: 27 February 2023 Publication History

Abstract

Advances in deep neural network (DNN) architectures have enabled new prediction techniques for stock market data. Unlike other multivariate time-series data, stock markets show two unique characteristics: (i) multi-order dynamics, as stock prices are affected by strong non-pairwise correlations (e.g., within the same industry); and (ii) internal dynamics, as each individual stock shows some particular behaviour. Recent DNN-based methods capture multi-order dynamics using hypergraphs, but rely on the Fourier basis in the convolution, which is both inefficient and ineffective. In addition, they largely ignore internal dynamics by adopting the same model for each stock, which implies a severe information loss.

In this paper, we propose a framework for stock movement prediction to overcome the above issues. Specifically, the framework includes temporal generative filters that implement a memory-based mechanism onto an LSTM network in an attempt to learn individual patterns per stock. Moreover, we employ hypergraph attentions to capture the non-pairwise correlations. Here, using the wavelet basis instead of the Fourier basis, enables us to simplify the message passing and focus on the localized convolution. Experiments with US market data over six years show that our framework outperforms state-of-the-art methods in terms of profit and stability. Our source code and data are available at https://github.com/thanhtrunghuynh93/estimate.

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Index Terms

Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Conferences

WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining

February 2023

1345 pages

ISBN:9781450394079

DOI:10.1145/3539597

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Hady Lauw
Singapore Management University
,
Program Chairs:
Luo Si
Salesforce
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Evimaria Terzi
Boston University
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Panayiotis Tsaparas
University of Ioannina

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Published: 27 February 2023

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February 27 - March 3, 2023

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Chen WLi SYu XWang HChen WWang TLarson K(2024)Automatic de-biased temporal-relational modeling for stock investment recommendationProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/221(1999-2008)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/221
Deng SSprangers OLi MSchelter Sde Rijke M(2024)Domain Generalization in Time Series ForecastingACM Transactions on Knowledge Discovery from Data10.1145/364303518:5(1-24)Online publication date: 31-Jan-2024
https://dl.acm.org/doi/10.1145/3643035
Cao ZXu JDong CYu PBai TSerra ESpezzano F(2024)MATCC: A Novel Approach for Robust Stock Price Prediction Incorporating Market Trends and Cross-time CorrelationsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679715(187-196)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679715
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