Data-driven financial and operational risk management: Empirical evidence from the global tramp shipping industry

Xiwen Bai, Liangqi Cheng and Çağatay Iris

Transportation Research Part E: Logistics and Transportation Review, 2022, vol. 158, issue C

Abstract: The global shipping industry has long suffered from high volatilities in freight rates and bunker fuel prices that lead to significant earnings risks. This paper aims to investigate the effectiveness of financial hedging and operational risk management strategies of 31 world leading tramp shipping companies through a Bayesian Belief Network (BBN) model using various data sources. Operational risk management strategies are categorized into long-term (e.g., fleet diversity and fleet age) and short-to-medium-term (e.g., relative trip distance, fleet repositioning flexibility, and trading diversity) strategies. We innovatively quantify the short-to-medium-term operational risk management strategies using Automatic Identification System (AIS) data. The results show that financial hedging can effectively reduce bunker fuel price risk exposure but cannot reduce freight rate risk exposure. Meanwhile, companies can use operational risk management strategies to effectively reduce both risk exposures. This study provides significant implications for shipping risk management.

Keywords: Big data in shipping; Shipping risk management; Financial hedging; Operational risk management; Data analytics; Bayesian belief network (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (9)

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DOI: 10.1016/j.tre.2022.102617

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