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Stock selection attempts to rank a list of stocks for optimizing investmentdecision making, aiming at minimizing investment risks while maximizing profitreturns. Recently, researchers have developed various (recurrent) neuralnetwork-based methods to tackle this problem. Without exceptions, theyprimarily leverage historical market volatility to enhance the selectionperformance. However, these approaches greatly rely on discrete sampled marketobservations, which either fail to consider the uncertainty of stockfluctuations or predict continuous stock dynamics in the future. Besides, somestudies have considered the explicit stock interdependence derived frommultiple domains (e.g., industry and shareholder). Nevertheless, the implicitcross-dependencies among different domains are under-explored. To address suchlimitations, we present a novel stock selection solution -- StockODE, a latentvariable model with Gaussian prior. Specifically, we devise a Movement TrendCorrelation module to expose the time-varying relationships regarding stockmovements. We design Neural Recursive Ordinary Differential Equation Networks(NRODEs) to capture the temporal evolution of stock volatility in a continuousdynamic manner. Moreover, we build a hierarchical hypergraph to incorporate thedomain-aware dependencies among the stocks. Experiments conducted on tworeal-world stock market datasets demonstrate that StockODE significantlyoutperforms several baselines, such as up to 18.57% average improvementregarding Sharpe Ratio.

Incorporating Interactive Facts for Stock Selection via Neural Recursive ODEs