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Forecasting Tanker Market Using Artificial Neural Networks

Author

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  • D V Lyridis

    (School of Naval Architecture and Marine Engineering, National Technical University of Athens, 9, Iroon Polytechniou St., 15773 Athens, Greece.)

  • P Zacharioudakis

    (School of Naval Architecture and Marine Engineering, National Technical University of Athens, 9, Iroon Polytechniou St., 15773 Athens, Greece.)

  • P Mitrou

    (School of Naval Architecture and Marine Engineering, National Technical University of Athens, 9, Iroon Polytechniou St., 15773 Athens, Greece.)

  • A Mylonas

    (School of Naval Architecture and Marine Engineering, National Technical University of Athens, 9, Iroon Polytechniou St., 15773 Athens, Greece.)

Abstract

Investing in the tanker market, especially in the VLCC sector constitutes a risky undertaking due to the volatility of tanker freight rates. This paper attempts to uncover the benefits of using Artificial Neural Networks (ANNs) in forecasting VLCC spot freight rates. This is achieved by analysing the period from October 1979 to December 2002, in order to detect possible causes of fluctuations, thus determine the independent variables of the analysis, and then use them to construct reliable ANNs. The aim is to reduce error and, most important, allow the model to maintain a stable error variance during high volatility periods. Among the findings are: ANNs can, with the appropriate architecture and training, constitute valuable decision-making tools especially when the tanker market is volatile; the use of variables in differential form enhances the ANN performance in high volatility periods while variables in normal form demonstrated better performance in median periods; ANN demonstrated mean errors comparable to the naïve model for 1-month forecasts but significantly outperformed it in the 3-, 6-, 9- and 12-month cases; finally, the use of informative variables such as the arbitrage between types of crude oil as well as Capesize rates can improve ANN performance. Maritime Economics & Logistics (2004) 6, 93–108. doi:10.1057/palgrave.mel.9100097

Suggested Citation

  • D V Lyridis & P Zacharioudakis & P Mitrou & A Mylonas, 2004. "Forecasting Tanker Market Using Artificial Neural Networks," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 6(2), pages 93-108, June.
    • Handle: RePEc:pal:marecl:v:6:y:2004:i:2:p:93-108
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    Citations

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    Cited by:

    1. Zhi Heng & Tsz Leung Yip, 2018. "Impacts of Kra Canal and its toll structures on tanker traffic," Maritime Policy & Management, Taylor & Francis Journals, vol. 45(1), pages 125-139, January.
    2. Zaili Yang & Esin Erol Mehmed, 2019. "Artificial neural networks in freight rate forecasting," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 21(3), pages 390-414, September.
    3. Jiao Zhang & Qingcheng Zeng, 2017. "Modelling the volatility of the tanker freight market based on improved empirical mode decomposition," Applied Economics, Taylor & Francis Journals, vol. 49(17), pages 1655-1667, April.
    4. Payman Eslami & Kihyo Jung & Daewon Lee & Amir Tjolleng, 2017. "Predicting tanker freight rates using parsimonious variables and a hybrid artificial neural network with an adaptive genetic algorithm," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(3), pages 538-550, August.
    5. Alizadeh, Amir H. & Talley, Wayne K., 2011. "Vessel and voyage determinants of tanker freight rates and contract times," Transport Policy, Elsevier, vol. 18(5), pages 665-675, September.
    6. Amir Alizadeh & Wayne Talley, 2011. "Microeconomic determinants of dry bulk shipping freight rates and contract times," Transportation, Springer, vol. 38(3), pages 561-579, May.
    7. N.D. Geomelos & E. Xideas, 2014. "Forecasting spot prices in bulk shipping using multivariate and univariate models," Cogent Economics & Finance, Taylor & Francis Journals, vol. 2(1), pages 1-37, December.

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