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.

在股票市场上，成功的投资需要在利润和风险之间取得良好的平衡。基于学习排序范式，股票推荐在量化金融领域得到了广泛的研究，为投资者推荐回报率较高的股票。尽管努力盈利，但现有的许多推荐方法在风险控制方面仍然存在一定的局限性，这可能会导致实际股票投资中难以忍受的账面损失。为了有效降低风险，我们从对抗性学习中汲取灵感，提出了一种新颖的分割变分对抗训练（SVAT）方法，用于风险意识股票推荐。从本质上讲，SVAT 鼓励股票模型对风险股票示例的对抗性扰动敏感，并通过从扰动中学习来增强模型的风险意识。为了生成具有代表性的对抗性示例作为风险指标，我们设计了一种变分扰动生成器来对不同的风险因素进行建模。特别是，变分架构使我们的方法能够为投资者提供粗略的风险量化，显示出可解释性的额外优势。对几个真实世界股票市场数据集的实验证明了我们的 SVAT 方法的优越性。通过降低推荐模型的波动性，SVAT有效降低了投资风险，在风险调整后的利润方面比最先进的基线高出30%以上。所有实验数据和源代码均可在 https://drive.google.com/drive/folders/14AdM7WENEvIp5x5bV3zV_i4Aev21C9g6?usp=sharing 获取。