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This paper uses the container shipping capacities of 11 major trade lanes, obtained from automatic identification system (AIS), to construct a common factor based on gray incidence analysis (GIA) in the aim of improving the predictability of containerized freight index. Our results show that the common factor generated by GIA consistently exhibits better out‐of‐sample prediction performances than...