【RIBAF‌】Learning risk preferences from investment portfolios using inverse optimization 利用逆优化从投资组合中学习风险偏好——张恺轩分享

科研基地组织前沿文献研读交流会，张恺轩围绕2023年发表于Elsevier 旗下SSCI Q1期刊Research in International Business and Finance的论文Learning risk preferences from investment portfolios using inverse optimization作主题汇报。

投资者能够承受的风险水平，即风险偏好，是一种与决策中的心理学和行为科学密切相关的主观选择。文章基于均值-方差投资组合配置框架，构建一套逆优化新方法，通过现有投资组合来衡量风险偏好。该方法可依托同步观测得到的组合持仓与市场价格时序数据，持续估计实时风险偏好。文章采用智能投顾生成模拟投资组合和真实市场数据开展实证检验，数据集包含20年资产价格序列与10年公募基金持仓数据。同时，通过与该领域当前应用的两种主流风险度量指标进行对比，完成了对量化所得风险偏好参数的有效性检验。所提出的方法有望为智能投顾等自动化/个性化投资组合管理带来切实且富有成效的创新，助力提升投资顾问在长期投资周期中的智能决策水平。

ABSTRACT

The level of risk an investor can endure, known as risk-preference, is a subjective choice that is tightly related to psychology and behavioral science in decision making. This paper presents a novel approach of measuring risk preference from existing portfolios using inverse optimization on mean–variance portfolio allocation framework. Our approach allows the learner to continuously estimate real-time risk preferences using concurrent observed portfolios and market price data. We demonstrate our methods on robotic investment portfolios and real market data that consists of 20 years of asset pricing and 10 years of mutual fund portfolio holdings. Moreover, the quantified risk preference parameters are validated with two well-known risk measurements currently applied in the field. The proposed methods could lead to practical and fruitful innovations in automated/personalized portfolio management, such as Robo-advising, to augment financial advisors’ decision intelligence in a long-term investment horizon.