Data-driven financial and operational risk management: Empirical evidence from the global tramp shipping industry. (February 2022) - Bai, Xiwen; Cheng, Liangqi; Iris, Çağatay
Highlights: An analysis for data-driven financial and operational risk management is conducted for the global shipping market. Short-to-medium-term operational risk management strategies are obtained using Automatic Identification System (AIS) data. A Bayesian Belief Network model is built to test the effectiveness of risk management strategies. Financial hedging can effectively reduce bunker fuel price exposure, but cannot reduce freight rate exposure. Trading diversity can reduce bunker fuel price risk exposure, whereas trading diversity and relative trip distance can reduce freight rate risk exposure. 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.