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}.Comment: Technical report for accepted paper at WSDM 202