In complex financial markets, recommender systems can play a crucial role in
empowering individuals to make informed decisions. Existing studies
predominantly focus on price prediction, but even the most sophisticated models
cannot accurately predict stock prices. Also, many studies show that most
individual investors do not follow established investment theories because they
have their own preferences. Hence, the tricky point in stock recommendation is
that recommendations should give good investment performance but also should
not ignore individual preferences. To develop effective stock recommender
systems, it is essential to consider three key aspects: 1) individual
preferences, 2) portfolio diversification, and 3) temporal aspect of both stock
features and individual preferences. In response, we develop the portfolio
temporal graph network recommender PfoTGNRec, which can handle time-varying
collaborative signals and incorporates diversification-enhancing contrastive
learning. As a result, our model demonstrated superior performance compared to
various baselines, including cutting-edge dynamic embedding models and existing
stock recommendation models, in a sense that our model exhibited good
investment performance while maintaining competitive in capturing individual
preferences. The source code and data are available at
this https URL

在复杂的金融市场中，为了使个人能够做出明智的决策，推荐系统可以起到关键作用。现有的研究主要侧重于价格预测，但即使是最复杂的模型也不能准确预测股票价格。另外，许多研究表明，大多数个体投资者不遵循已建立的投资理论，因为他们有自己的偏好。因此，股票推荐的关键是推荐应该具有良好的投资表现，同时也不应忽视个体偏好。为了开发有效的股票推荐系统，必须考虑三个关键方面：1）个体偏好，2）投资组合多样化，以及 3）股票特征和个体偏好的时间特性。因此，我们开发了一种名为 PfoTGNRec 的投资组合时间图网络推荐系统，它可以处理时变的协同信号，并结合增强对比学习来增加多样性。结果表明，与包括尖端动态嵌入模型和现有股票推荐模型在内的各种基线相比，我们的模型表现出优越的性能，既在保持捕捉个体偏好的竞争力的同时，也展现出良好的投资表现。该文的源代码和数据可在此网址获得。

个人投资者的股票推荐：一种基于时间图网络和多样性增强对比学习的方法

Stock Recommendations for Individual Investors: A Temporal Graph Network  Approach with Diversification-Enhancing Contrastive Learning

In the stock market, a successful investment requires a good balance between
profits and risks. Recently, stock recommendation has been widely studied in
quantitative investment to select stocks with higher return ratios for
investors. Despite the success in making profits, most existing recommendation
approaches are still weak in risk control, which may lead to intolerable paper
losses in practical stock investing. To effectively reduce risks, we draw
inspiration from adversarial perturbations and propose a novel Split
Variational Adversarial Training (SVAT) framework for risk-aware stock
recommendation. Essentially, SVAT encourages the 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
three real-world stock market datasets show that SVAT effectively reduces the
volatility of the stock recommendation model and outperforms state-of-the-art
baseline methods by more than 30% in terms of risk-adjusted profits.

该研究提出了一种针对股票推荐的风险敏感框架 SVAT，以有效降低推荐模型的波动性并提高其风险意识，该方法通过对风险因素的变分扰动生成器进行建模，能够提供代表性的风险指示，并显示出可解释性的额外优势。实验表明，SVAT 在股票推荐模型中取得了显著的风险调整利润改进，超过同类基准方法 30%。