SPEAKER: 林少冲 助理教授, 香港大学 数据与系统工程系

TIME/DATE: 2026.4.28  10：00

CLASSROOM: A308

ABSTRACT: 

We study a data-driven inventory and financial hedging problem for a new product, where the demand depends on product features and returns of related financial assets. A firm makes joint decisions on ordering quantity and financial hedging to maximize the expected profit while ensuring the profit risk is under control. We employ financial hedging to manage profit risk by trading product demand-related derivatives (e.g., futures contracts). However, critical information on derivative returns to guide demand prediction and financial hedging decision-making is not observable before launching the new product. To address this challenge, we leverage proxy return factors to infer derivative returns and demand–return relationships for new products. We develop a nested expectation formulation that decouples the statistical dependence between two random variables: product demand and derivative returns, which inspired the development of a semiparametric decision-making framework named SURPASS. Specifically, SURPASS constructs a data-driven joint distribution of derivatives’ returns from historical product and financial market data by leveraging a seemingly unrelated regression structure estimated via feasible generalized least squares. The resulting residual-based representation admits a weighted sample average approximation formulation, enabling principled risk-constrained decision-making based on data. Our SURPASS theoretically and numerically surpasses conventional data-driven decision-making approaches. We characterize the sample complexity required to ensure the feasibility of data-driven solutions and establish finite-sample regret bounds. Our analysis reveals a fundamental approximation–feasibility tradeoff in data-driven optimization under the value-at-risk constraint. Experiments using real-world and synthetic data demonstrate the strong performance of the SURPASS framework and illustrate the influence of financial hedging.

GUEST BIO:

林少冲，香港大学数据与系统工程系助理教授。在同济大学经管学院获得硕士学位，在香港城市大学商学院决策分析与运营管理系获得博士学位，加州大学伯克利分校访问学者，于2023年在香港大学开始教职工作，目前的研究兴趣包括基于数据驱动的运营管理、人工智能与运筹学的交叉，以及在智慧物流、供应链、交通与调度等实际问题中的应用。林博士积极与京东、永旺、TCL等实际企业合作，用模型和算法解决实践中激发的研究问题。已发表了19个实践研究成果，包括在Production and Operations Management、IEEE Transactions on Automation Science and Engineering、Scientific Reports、International Journal of Medical Informatics、Omega、Transportation Research Part E等领域国际期刊及计算机顶会上。曾获得多个学术会议最佳论文奖和数据分析竞赛奖项。担任Management Science和Operations Research等顶级期刊的审稿人，担任第27届AAAI人工智能大会的程序委员会成员，服务型制造管理专委会常务委员。相关研究获得香港优配研究金（GRF）和国家自然科学（NSFC）青年基金的资助。更多关于他的研究信息可参考他的个人网站https://sites.google.com/view/shaochong