This study presents a novel approach to forecast freight rates in container shipping by integrating soft facts in the form of measures originating from surveys among practitioners asked about their sentiment, confidence or perception about present and future market development. As a base case, an autoregressive integrated moving average (ARIMA) model was used and compared the results with multivariate modelling frameworks that could integrate exogenous variables, that is, ARIMAX and Vector Autoregressive (VAR). We find that incorporating the Logistics Confidence Index (LCI) provided by Transport Intelligence into the ARIMAX model improves forecast performance greatly. Hence, a sampling of sentiments, perceptions and/or confidence from a panel of practitioners active in the maritime shipping market contributes to an improved predictive power, even when compared to models that integrate hard facts in the sense of factual data collected by official statistical sources. While investigating the Far East to Northern Europe trade route only, we believe that the proposed approach of integrating such judgements by practitioners can improve forecast performance for other trade routes and shipping markets, too, and probably allows detection of market changes and/or economic development notably earlier than factual data available at that time.

本研究提出了一种预测集装箱运输运费的新方法，该方法以源自从业者调查的测量形式整合软事实，询问他们对当前和未来市场发展的情绪、信心或看法。作为基本情况，使用自回归综合移动平均 (ARIMA) 模型并将结果与​​可以集成外生变量的多元建模框架，即 ARIMAX 和向量自回归 (VAR) 进行比较。我们发现将运输智能提供的物流信心指数 (LCI) 纳入 ARIMAX 模型可以极大地提高预测性能。因此，来自活跃于海运市场的一组从业者的情绪、看法和/或信心的抽样有助于提高预测能力，即使与在官方统计来源收集的事实数据意义上整合确凿事实的模型相比也是如此。虽然仅调查远东至北欧的贸易航线，但我们认为，整合从业者的此类判断的提议方法也可以提高对其他贸易航线和航运市场的预测性能，并且可能允许检测市场变化和/或经济发展明显早于当时可用的事实数据。