Deep Learning for Commodity Procurement: Nonlinear Data-Driven Optimization of Hedging Decisions

As the number of exchange-traded commodity contracts and their volatility increase, risk management through financial hedging gains importance for commodity-purchasing firms. Existing data-driven optimization approaches for hedging decisions include linear regression-based techniques. As such, they assume linear price–feature relationships and, thus, do not automatically detect nonlinear feature effects. We propose an alternative, nonlinear data-driven approach to commodity procurement based on deep learning. The prescriptive algorithm uses artificial neural networks to allow for universal approximation and requires no a priori knowledge regarding underlying price processes. We reformulate the periodic review procurement problem as a multilabel time series classification problem as the optimal bang-bang type procurement policy allows us to treat the hedging decision for each demand period as an individual subproblem that is independent of the other periods. Thereby, we are differentiating between optimal and suboptimal hedging decisions in each period and introduce a novel opportunity cost–sensitive loss function. We train maximum likelihood classifiers based on different deep learning architectures and test their performance in numerical experiments and case studies for natural gas, crude oil, nickel, and copper procurement. We show comparable performance to the state of the art for linear price–feature relationships and considerable advantages in the nonlinear case. Funding: Financial support received through the DFG as part of the AdONE GRK2201 [Grant 277991500] is gratefully acknowledged.