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

TL;DR

This paper introduces a novel stock prediction framework that combines memory-based individual stock patterns with hypergraph attention for multi-order dynamics, using wavelet-based convolutions for efficiency, outperforming existing methods.

Contribution

The proposed framework uniquely integrates temporal generative filters with hypergraph attention and wavelet convolutions to better model internal and multi-order dynamics in stock data.

Findings

Outperforms state-of-the-art methods in profit and stability

Effectively captures non-pairwise correlations with hypergraph attention

Utilizes wavelet basis for efficient localized convolution

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) \emph{multi-order dynamics}, as stock prices are affected by strong non-pairwise correlations (e.g., within the same industry); and (ii) \emph{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 \url{https://github.com/thanhtrunghuynh93/estimate}.