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

Abstract

As the number of exchange-traded commodity contracts and their volatility increases, 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 non-linear feature effects. We propose an alternative, non-linear 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 multi-label time series classification problem, differentiating between optimal and sub-optimal 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 non-linear case.

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