  Stock selection attempts to rank a list of stocks for optimizing investment
decision making, aiming at minimizing investment risks while maximizing profit
returns. Recently, researchers have developed various (recurrent) neural
network-based methods to tackle this problem. Without exceptions, they
primarily leverage historical market volatility to enhance the selection
performance. However, these approaches greatly rely on discrete sampled market
observations, which either fail to consider the uncertainty of stock
fluctuations or predict continuous stock dynamics in the future. Besides, some
studies have considered the explicit stock interdependence derived from
multiple domains (e.g., industry and shareholder). Nevertheless, the implicit
cross-dependencies among different domains are under-explored. To address such
limitations, we present a novel stock selection solution -- StockODE, a latent
variable model with Gaussian prior. Specifically, we devise a Movement Trend
Correlation module to expose the time-varying relationships regarding stock
movements. We design Neural Recursive Ordinary Differential Equation Networks
(NRODEs) to capture the temporal evolution of stock volatility in a continuous
dynamic manner. Moreover, we build a hierarchical hypergraph to incorporate the
domain-aware dependencies among the stocks. Experiments conducted on two
real-world stock market datasets demonstrate that StockODE significantly
outperforms several baselines, such as up to 18.57% average improvement
regarding Sharpe Ratio.
