A deep learning-based financial hedging approach for the effective management of commodity risks

Abstract

The development of deep learning technique has granted firms with new opportunities to substantially improve their risk management strategies for sustainable growth. This paper introduces a novel deep learning-based financial hedging (DL-HE) strategy to leverage the salient ability of deep learning in extracting nonlinear features from complex high dimensional data, thus boosting the management of inventory risks arising from erratic commodity prices. Using real-world data, we find that the average annualized economic benefit of the proposed strategy is at least 1.21 million CNY for a typical aluminum firm carrying an average level of inventory in China, as compared with those of the traditional hedging strategies. Further analysis reveals that such an economic benefit can largely be explained by the efficacy of the proposed DL-HE strategy in terms of significantly improving return while still effectively controlling risk. Moreover, the superior of this strategy remains robust when extending to copper and zinc.