The classical risk-neutral newsvendor problem is to decide the order quantity that maximises the expected profit. Some recent works have proposed an alternative model, in which the goal is to minimise the conditional value-at-risk (CVaR), a different but very much important risk measure in financial risk management. In this paper, we propose a feature-based non-parametric approach to Newsvendor CVaR minimisation under adaptive data selection (NPC). The NPC method is simple and general. It can handle minimisation with both linear and nonlinear profits, and requires no prior knowledge of the demand distribution. Our main contribution is two-fold. Firstly, NPC uses a feature-based approach. The estimated parameters of NPC can be easily applied to prescriptive analytic to provide additional operational insights. Secondly, unlike common non-parametric methods, our NPC method uses an adaptive data selection criterion and requires only a small proportion of data (only data from two tails), significantly reducing the computational effort. Results from both numerical and real-life experiments confirm that NPC is robust with regard to difficult and large data structures. Using fewer data points, the computed order quantities from NPC lead to equal or less downside loss in extreme cases than competing methods.

翻译：经典的风险中性报童问题旨在确定使期望利润最大化的订货量。近期一些研究提出了替代模型，其目标是最小化条件风险价值（CVaR）——金融风险管理中重要且截然不同的风险度量。本文提出一种基于特征的非参数方法，用于自适应数据选择下的报童CVaR最小化（NPC）。NPC方法简洁且通用，能同时处理线性和非线性利润的最小化问题，且无需预先了解需求分布。我们的主要贡献体现在两方面：首先，NPC采用基于特征的方法，其估计参数可便捷地应用于规范分析，提供额外的运营洞察；其次，与常见的非参数方法不同，NPC采用自适应数据选择准则，仅需少量数据（仅来自两尾的数据），显著降低了计算负担。数值实验和实际案例结果均证实，NPC对复杂大规模数据结构具有鲁棒性。在极端情况下，使用更少数据点，NPC计算出的订货量在尾部损失方面与竞争方法持平或更优。