Tolga TUZCUOĞLU, Hüseyin GENCER

A Model on Charter Rate Prediction in Container Shipping

The maritime industry has witnessed numerous challenges in recent years after the global pandemic, primarily characterized by sharp fluctuations in the daily charter rates. Considering an unpredictable business environment, this study aims to suggest a financial forecasting model on charter rates, creating added value for the stakeholders of the maritime trade business. The empirical analysis utilized the data from the Clarksons Research Portal, which encompassed 7,409 charter rental transactions of container ships from 01.01.2018 to 10.03.2023. After examining seven different linear and ensemble regressions, it was revealed that the XGBoost regressor resulted in the least RMSE value of 0.1833 with an R2 of 0.9015. The selected predictors were the TEU, container fixture type, charter time, charter time multiplied by TEU, ship age, laycan year, and laycan month, respectively. In addition to coping with the limitations of linear regression, the model revealed that the laycan years, charter time, and charter time multiplied with TEU were the essential variables in charter rate prediction. As a result, the model developed in the study can be used as an important decision support tool for stakeholders in the container shipping industry.

Keywords:

Forecasting, Charter market, Container shipping Ensemble regression, Ship financing strategy,

PDF

___

Chen, Y., Liu, B., Wang, T. (2021). Analysing and forecasting China containerized freight index with a hybrid decomposition–ensemble method based on EMD, grey wave and ARMA, Grey Systems: Theory and Application, 11(3), 358-371.
Clarksons Research (2023). Shipping Intelligence Network.
Hoffman, J. P. (2022). Linear regression models: Applications in R. CRC Press.
Fan, L. and Yin, J. (2015). Analysis of structural changes in container shipping, Maritime Economics & Logistics, 18(2), 174–191.
Hirata, E., Matsuda, T. (2022). Forecasting Shanghai Container Freight Index: A Deep-Learning-Based Model Experiment”, Journal of Marine Science and Engineering, 10(593).
Hoffman, J. P. (2022). Linear regression models: Applications in R. CRC Press.
Hübner, J.H. (2016). Shipping Markets and Their Economic Drivers, In: Kavussanos, M. G., Visvikis, I. D. (Eds), The International Handbook of Shipping Finance : 6-7, Palgrave Macmillan.
Jeon, J., Duru, O., Yeo, G. (2019). Modelling cyclic container freight index using system dynamics, Maritime Policy and Management,, 47(3), 287–303.
Koyuncu, K., Tavacıoğlu, L., Gökmen, N., Arıcan, U.Ç. (2021). Forecasting COVID-19 impact on RWI/ISL container throughput index by using SARIMA models, Maritime Policy and Management, 48(8), 1–13.
Kunapuli, G. (2023). Ensemble Methods for Machine Learning. Manning Publications. Lemper, B. (2008). Volkswirtschaftliche Aspekte der Schifffahrt, In: Winter, H., Hennig, C., Gerhard, M. (Eds), Grundlagen der Schiffsfinanzierung (113-162), Frankfurt School Verlag.
Luo, M., Fan, L., Liu, L. (2009). An econometric analysis for container shipping market, Maritime Policy and Management, 36(6), 507–523.
Ma, S. (2021). Economics of Maritime Business, Routledge.
Matloff, N. (2017). Statistical Regression and Classification: From Linear Models to Machine Learning. CRC Press.
Munim, Z.H. (2022). State-space TBATS model for container freight rate forecasting with improved accuracy, Maritime Transport Research, 3.
Otani, S. and Matsuda, T. (2023). Unified container shipping industry data from 1966: Freight rate, shipping quantity, newbuilding, secondhand, and scrap price, Transportation Research Part E, 176.
Petersen, H.B. (2016). Asset Risk Assessment, Analysis and Forecasting in Asset Backed Finance, In:
Kavussanos, M. G., Visvikis, I. D. (Eds), The International Handbook of Shipping Finance, 53-59, Palgrave Macmillan.
Saeed, N., Nguyen, S., Cullinane, K., Gekara, V., Chhetri, P. (2023). Forecasting container freight rates using the Prophet forecasting method, Transport Policy, 133, 86-107.
Schramm, H.S., Munim, Z.H. (2021). Container freight rate forecasting with improved accuracy by integrating soft facts from practitioners, Research in Transportation Business & Management, 41.
UNCTAD. (2021). Review of Maritime Transport 2021. Geneva.