主讲人：
刘岩（清华大学经济管理学院金融系讲席教授）
主持老师：
（北大经院）王一鸣、王熙
参与老师：
（北大经院）刘蕴霆、巩爱博、王法、李少然
（北大国发院）黄卓、沈艳、张俊妮
时间：
2026年3月20日（周五）
10：00-11:30
地点（线下）：
8455新葡萄娱乐场特色107会议室
主讲人简介：
刘岩，清华大学经济管理学院金融系讲席教授，经管深圳院区讲席教授，教育部长江学者讲席教授，清华大学经济管理深圳研究院副院长，清华大学深圳国际研究生院创新管理研究院副院长，清华大学经济管理深圳研究院计算金融研究中心主任。他的主要研究领域为实践和理论资产定价，基金（公募和私募）业绩评估，计量经济学，大数据金融建模，机器学习建模与应用，金融另类数据构建与应用，数据要素，企业模式创新。他的多篇论文在《金融期刊》(Journal of Finance)，《金融经济学》(Journal of Financial Economics)，《金融研究评论》（Review of Financial Studies），《管理科学》(Management Science)等国际顶级学术期刊发表。此外，刘岩还担任《银行与金融》（Journal of Banking and Finance）副主编，粤港澳大湾区数字经济研究院(IDEA)客座讲席经济学家。清华大学经济管理深研院计算金融研究中心扎根深圳，深入探索金融科技和量化金融前沿，涉及课题包括（但不限于）金融文本挖掘，金融大数据和市场微观结构，金融风险模型，机器学习（时序神经网络，图神经网络模型等）指导市场预测，强化学习在市场博弈中的应用等。
报告摘要：
The standard approach to portfolio selection involves two stages: forecast the asset returns and then plug them into an optimizer. We argue that this separation is deeply problematic. The first stage treats cross-sectional prediction errors as equally important across all securities. However, given that final portfolios might differ given distinct risk preferences and investment restrictions, the standard approach fails to recognize that the investor is not just concerned with the average forecast error -but the precision of the forecasts for the specific assets that are most important for their portfolio. Hence, it is crucial to integrate the two stages. We propose a novel implementation utilizing machine learning tools that unifies the expected return generation process and the final optimized portfolio. Our empirical example provides convincing evidence that our end-to-end method outperforms the traditional two-stage approach. In our framework, each investor has their own,endogenously determined, efficient frontier that depends on risk preferences,investor-specific constraints, as well as exposure to market frictions.
