投资组合优化的大规模推荐
Large-scale Recommendation for Portfolio Optimization
摘要：
个人投资者现在大量使用在线经纪人交易股票，界面方便，费用低廉，尽管失去了传统上由全方位服务经纪人提供的建议和个性化服务。我们框架的问题所面临的在线经纪人复制这种水平的服务，以低成本和自动化的方式为大量的用户。由于在推荐金融产品时需要谨慎，我们专注于针对每个用户的投资组合和风险状况量身定制的风险管理方法。我们表明，我们的混合方法，现代投资组合理论和协同过滤的基础上，提供了一个健全和有效的解决方案。该方法不仅适用于股票，也适用于其他金融资产，而且可以很容易地与各种财务预测模型相结合。我们验证我们的建议，通过比较它与几个基线在一个领域的专家为基础的研究。
英文摘要：
Individual investors are now massively using online brokers to trade stocks with convenient interfaces and low fees, albeit losing the advice and personalization traditionally provided by full-service brokers. We frame the problem faced by online brokers of replicating this level of service in a low-cost and automated manner for a very large number of users. Because of the care required in recommending financial products, we focus on a risk-management approach tailored to each user's portfolio and risk profile. We show that our hybrid approach, based on Modern Portfolio Theory and Collaborative Filtering, provides a sound and effective solution. The method is applicable to stocks as well as other financial assets, and can be easily combined with various financial forecasting models. We validate our proposal by comparing it with several baselines in a domain expert-based study.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
