PreFER：具有评分机制的交互式智能投顾

中文摘要 AI 辅助

我们提出了一种交互式智能投顾框架，该框架从客户提供的评分中学习个性化的风险偏好。由此产生的偏好学习问题与逆强化学习（IRL）密切相关，因为智能投顾从反馈中推断客户的潜在奖励规范。智能投顾按如下方式与客户进行迭代交互。在每个交互时刻，投顾根据从推断出的个性化风险偏好中得出的最优策略分布生成投资建议。客户对建议进行评分。投顾根据反馈更新其对客户风险偏好的评估。这一学习过程促使我们研究离散时间的可预测前向探索奖励（PreFER）过程，并推导出一种探索性投资策略。通过将评分解释为一条建议的接受概率，我们的逆学习过程使用由冯·诺依曼首创的接受-拒绝方法学习客户的探索性投资分布。在CARA偏好下，我们证明，即使评分包含噪声，智能投顾在足够多的交互次数后也能识别客户当前的风险厌恶程度。随后，PreFER过程将学习到的偏好向前传递，并在更新的市场条件下生成未来的建议。

英文摘要

We propose an interactive robo-advising framework that learns personalized risk preferences from scores provided by clients. The resulting preference-learning problem is closely related to inverse reinforcement learning (IRL), as the robo-advisor infers the client's latent reward specification from feedback. The robo-advisor interacts with clients iteratively as follows. At each interaction time, the advisor generates investment advice based on the optimal policy distribution derived from an inferred personalized risk preference. The client scores the advice. The advisor updates its assessment of the client's risk preference based on the feedback. This learning procedure motivates us to investigate discrete-time Predictable Forward Exploratory Reward (PreFER) processes and derive an exploratory investment strategy. By interpreting the score as the acceptance probability of a piece of advice, our inverse learning procedure learns the client's exploratory investment distribution using the acceptance-rejection method pioneered by von Neumann. Under CARA preferences, we show that, even though the scores contain noise, the robo-advisor can identify the client's current risk aversion after a sufficiently large number of interactions. The PreFER process then carries the learned preference forward and generates future recommendations under updated market conditions.

发表机构

• Shanghai University of Finance and Economics（上海财经大学）
• The Chinese University of Hong Kong（香港中文大学）

机构由 AI 辅助整理，请以论文原文为准。