Browse > Article
http://dx.doi.org/10.5394/KINPR.2018.42.6.539

An Analysis on Determinants of the Capesize Freight Rate and Forecasting Models  

Lim, Sang-Seop (Division of Shipping Management, Korea Maritime and Ocean University)
Yun, Hee-Sung (Centre for Shipping Big Data Analytics, Korea Maritime Institute)
Publication Information
Journal of Navigation and Port Research / v.42, no.6, 2018 , pp. 539-545 More about this Journal
Abstract
In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.
Keywords
Capesize Market; Freight Determinants; Stepwise Regression; Random Forest;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Bakshi, G., Panayotov, G. and Skoulakis, G.(2011), "The Baltic Dry Index as a Predictor of Global Stock Returns, Commodity Returns, and Global Economic Activity," SSRN. http://dx.doi.org/10.2139/ssrn.1787757.   DOI
2 Bao, J., Pan, L. and Xie, Y.(2016), "A new BDI forecasting model based on support vector machine", in 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference, pp. 65-69.
3 Batchelor, R., Alizadeh, A. and Visvikis, I.(2007), "Forecasting spot and forward prices in the international freight market," International Journal of Forecasting, Vol. 23, No. 1, pp. 101-114.   DOI
4 Breiman, L.(2001), "Random Forests," Machine Learning, Vol. 45, No. 1, pp. 5-32.   DOI
5 Cai, J. et al.(2018), "Feature selection in machine learning: A new perspective," Neurocomputing, 300, pp. 70-79.   DOI
6 Celik, M. et al.(2009), "An integrated fuzzy QFD model proposal on routing of shipping investment decisions in crude oil tanker market," Expert Systems with Applications, Vol. 36, No. 2, pp. 6227-6235.   DOI
7 Cutler, A., Cutler, D. R. and Stevens, J. R.(2012), "Random Forest," in Zhang, C. and Ma, Y. (eds) Ensemble Machine Learning-Methods and Applications. New York: Springer.
8 Goulelmos, A. M. and Goulelmos, M. A.(2009), "The problem of timing in decisions to buy or to charter a vessel," International Journal of Transport Economics, Vol. 36, No. 2, pp. 261-286.
9 Han, Q. et al.(2014), "Forecasting dry bulk freight index with improved SVM," Mathematical Problems in Engineering, Vol. 2014(ID: 460684), pp. 1-12.
10 Jovic, A., Brkic, K. and Bogunovic, N.(2015), "A review of feature selection methods with applications," in 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1200-1205.
11 Li, J. and Parsons, M. G.(1997), "Forecasting tanker freight rate using neural networks," Maritime Policy and Management, Vol. 24, No. 1, pp. 9-30.   DOI
12 Lyridis, D. et al.(2004), "Forecasting Tanker Market Using Artificial Neural Networks," Maritime Economics & Logistics, Vol. 6, pp. 93-108.   DOI
13 Paliwal, M. and Kumar, U. A.(2009), "Neural networks and statistical techniques: A review of applications," Expert Systems with Applications, Vol. 36, No. 1, pp. 2-17.   DOI
14 Shepperd, M. and MacDonell, S.(2012), "Evaluating prediction systems in software project estimation," Information and Software Technology, Vol. 54, No. 8, pp. 820-827.   DOI
15 Shin, C. H. and Jeong, S. H,(2011), "A Study on Application of ARIMA and Neural Networks for Time Series Forecasting of Port Traffic," Journal of Korean Navigation and Port Research, Vol. 35, No. 1, pp. 83-91.   DOI
16 Shin, C. H., Kang, J. S., Park, S. N. and Lee, J. H.(2008), "A Study on the Forecast of Port Traffic using Hybrid ARIMA-Neural Network Model," Journal of Korean Navigation and Port Research, Vol. 32, No. 1, pp. 81-88.   DOI
17 Veenstra, A. W. and Franses, P. H.(1997), "A co-integration approach to forecasting freight rates in the dry bulk shipping sector," Transportation Research Part A: Policy and Practice, Vol. 31, No. 6, pp. 447-458.   DOI
18 Silhavy, R., Silhavy, P. and Prokopova, Z.(2017), "Analysis and selection of a regression model for the Use Case Points method using a stepwise approach," Journal of Systems and Software, Vol. 125, pp. 1-14.   DOI
19 Stopford, M.(2009), "Maritime Economics," Routledge.
20 Talavera, L.(2005), "An Evaluation of Filter and Wrapper Methods for Feature Selection in Categorical Clustering," in Famili A.F., Kok J.N., Peña J.M., Siebes A., F. A. (ed.) Advances in Intelligent Data Analysis VI. IDA 2005. Springer Berlin Heidelberg,
21 Yang, Z., Jin, L. and Wang, M.(2011), "Forecasting Baltic Panamax Index with Support Vector Machine," Journal of Transportation Systems Engineering and Information Technology, Vol. 11, No. 3, pp. 50-57.   DOI
22 Yun, H., Lim, S. and Lee, K.(2016), "Trading Strategies in Bulk Shipping: the Application of Artificial Neural Networks," Journal of Korean Navigation and Port Research, Vol. 40, No. 5, pp. 337-343.   DOI
23 Zeng, Q. et al.(2016), "A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks," Maritime Economics & Logistics, Vol. 18, No. 2, pp. 192-210.   DOI
24 Zeng, Q. and Qu, C.(2014), "An approach for Baltic Dry Index analysis based on empirical mode decomposition," Maritime Policy & Management. Routledge, Vol. 41, No. 3, pp. 224-240.   DOI
25 Zhang, G., Patuwo, B. E. and Hu, M. Y.(1998), "Forecasting with artificial neural networks: The state of the art," International Journal of Forecasting, Vol. 14, No. 1, pp. 35-62.   DOI