  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.
