Pricing the second-hand dry bulk vessel through stacking ensemble with add-on plain feedforward neural networks

PurposeThe accurate valuation of second-hand vessels has become a prominent subject of interest among investors, necessitating regular impairment tests. Previous literature has predominantly concentrated on inferring a vessel's price through parameter estimation but has overlooked the prediction accuracy. With the increasing adoption of machine learning for pricing physical assets, this paper aims to quantify potential factors in a non-parametric manner. Furthermore, it seeks to evaluate whether the devised method can serve as an efficient means of valuation.Design/methodology/approachThis paper proposes a stacking ensemble approach with add-on feedforward neural networks, taking four tree-driven models as base learners. The proposed method is applied to a training dataset collected from public sources. Then, the performance is assessed on the test dataset and compared with a benchmark model, commonly used in previous studies.FindingsThe results on the test dataset indicate that the designed method not only outperforms base learners under statistical metrics but also surpasses the benchmark GAM in terms of accuracy. Notably, 73% of the testing points fall within the less-than-10% error range. The designed method can leverage the predictive power of base learners by incrementally adding a small amount of target value through residuals and harnessing feature engineering capability from neural networks.Originality/valueThis paper marks the pioneering use of the stacking ensemble in vessel pricing within the literature. The impressive performance positions it as an efficient desktop valuation tool for market users.

目的是对二手船的准确估值已成为投资者中重要的兴趣主题，需要定期进行损害测试。以前的文献主要集中在通过参数估计来推断容器的价格，但忽略了预测准确性。随着计算物理资产定价的机器学习的越来越多，本文旨在以非参数方式量化潜在因素。此外，它试图评估设计的方法是否可以作为估值的有效手段。提出的方法应用于从公共来源收集的培训数据集。然后，在测试数据集上评估了性能，并与先前研究中常用的基准模型进行了比较。调查测试数据集的结果表明，该设计的方法不仅在统计指标下超过了基础学习者，而且还超过了基准GAM准确性。值得注意的是，测试点的73％属于不到10％的误差范围。该设计的方法可以通过通过残留物和利用神经网络的特征工程能力来逐步添加少量目标价值来利用基础学习者的预测能力。原始/ValueThis纸张标志着文献中的船舶定价中的堆叠集合的开创性使用。令人印象深刻的性能将其定位为市场用户的有效台式估值工具。