  In the stock market, a successful investment requires a good balance between
profits and risks. Based on the learning to rank paradigm, stock recommendation
has been widely studied in quantitative finance to recommend stocks with higher
return ratios for investors. Despite the efforts to make profits, many existing
recommendation approaches still have some limitations in risk control, which
may lead to intolerable paper losses in practical stock investing. To
effectively reduce risks, we draw inspiration from adversarial learning and
propose a novel Split Variational Adversarial Training (SVAT) method for
risk-aware stock recommendation. Essentially, SVAT encourages the stock model
to be sensitive to adversarial perturbations of risky stock examples and
enhances the model's risk awareness by learning from perturbations. To generate
representative adversarial examples as risk indicators, we devise a variational
perturbation generator to model diverse risk factors. Particularly, the
variational architecture enables our method to provide a rough risk
quantification for investors, showing an additional advantage of
interpretability. Experiments on several real-world stock market datasets
demonstrate the superiority of our SVAT method. By lowering the volatility of
the stock recommendation model, SVAT effectively reduces investment risks and
outperforms state-of-the-art baselines by more than 30% in terms of
risk-adjusted profits. All the experimental data and source code are available
at
https://drive.google.com/drive/folders/14AdM7WENEvIp5x5bV3zV_i4Aev21C9g6?usp=sharing.
