As the secondary ship market becomes increasingly active, achieving precise ship valuation while fully accounting for market fluctuations has grown increasingly important. This paper establishes a price assessment framework integrating static valuation with dynamic market adjustments: the static component employs an ACO–FA-optimized BP neural network to derive benchmark prices based on individual vessel characteristics; the dynamic component constructs a price index combined with oil prices and freight rates into a multivariate time series. A GRU model captures market adjustment factors, which are then fused with static valuations via Kalman filtering to generate final transaction prices. Empirical results based on Chongqing's 2020–2025 dry bulk carrier transaction data demonstrate that this model significantly enhances second-hand vessel price assessment while simultaneously delivering static valuations, market adjustments, and composite prices. It provides buyers and sellers with a well-explained quantitative basis for pricing, negotiation, and investment decisions across varying market conditions.
Tong et al. (Wed,) studied this question.