2025/12/23 by Michael Tsatsaronis, Hercules Haralambides, Theodore Syriopoulos +1
Engineering · Environmental Science · Social Sciences · #Cruise Tourism Development and Management #Maritime Ports and Logistics #Maritime Transport Emissions and Efficiency
paper · doi:10.1016/j.retrec.2025.101699
openalex created_date 2025/12/23 · openalex publication_date 2025/12/23 · openalex updated_date 2026/07/28
Ships are heavily traded assets and their prices fluctuate wildly in paralell with the underlying freight rates. This confronts investors with sizable market risks but also, for some, with attractive profit opportunities from sale and purchase (S&P) transactions. The paper explores the complex pricing dynamics in dry bulk shipping. We employ modern machine learning techniques, such as k-means clustering, to explore a range of investment behaviors, with the hope of offering investors, asset players and new investor archetypes novel insights on vessel pricing and investment decisions. Through cluster analysis, we classify investors in the S&P market, unveiling investor profiles and behaviours. Chinese shipping investors, for instance, focus on achieving vertical and horizontal integration within the global supply chains, whereas, Greek shipowners are reputed for investing in ship acquisitions even amid economic downturns, demonstrating an agile and adaptive business strategy (McKinsey & Company, 2024). Our results carry potent implications regarding tailored market strategies, risk management, policy formulation, market transparency, investor education, and technology adoption.