Journal of Navigation and Port Research (한국항해항만학회지)

Korean Institute of Navigation and Port Research (한국항해항만학회)
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Supramax Bulk Carrier Market Forecasting with Technical Indicators and Neural Networks

  • Lim, Sang-Seop (Division of Shipping Management, Korea Maritime and Ocean University) ;
  • Yun, Hee-Sung (Centre for Shipping Big Data Analytics, Korea Maritime Institute)
  • Received : 2018.09.11
  • Accepted : 2018.09.10
  • Published : 2018.10.31
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Abstract

Supramax bulk carriers cover a wide range of ocean transportation requirements, from major to minor bulk cargoes. Market forecasting for this segment has posed a challenge to researchers, due to complexity involved, on the demand side of the forecasting model. This paper addresses this issue by using technical indicators as input features, instead of complicated supply-demand variables. Artificial neural networks (ANN), one of the most popular machine-learning tools, were used to replace classical time-series models. Results revealed that ANN outperformed the benchmark binomial logistic regression model, and predicted direction of the spot market with more than 70% accuracy. Results obtained in this paper, can enable chartering desks to make better short-term chartering decisions.

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References

  1. Alizadeh, A. H. and Nomikos, N. K. (2006), "Trading strategies in the market for tankers," Maritime Policy & Management, Vol. 33, No. 2, pp. 119-140. https://doi.org/10.1080/03088830600612799
  2. Alizadeh, A. H. and Nomikos, N. K. (2009), "Shipping Derivatives and Risk Management". London: Palgrave Macmillan UK.
  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. https://doi.org/10.1016/j.ijforecast.2006.07.004
  4. 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.
  5. Cybenko, G. (1989), "Approximation by superpositions of a sigmoidal function," Mathematics of Control, Signals, and Systems, Vol. 2, No. 4, pp. 303-314. https://doi.org/10.1007/BF02551274
  6. Fan, S. et al. (2013), "Forecasting Baltic Dirty Tanker Index by Applying Wavelet Neural Networks," Journal of Transportation Technologies, Vol. 3, January, pp. 68-87. https://doi.org/10.4236/jtts.2013.31008
  7. Forte, R. M. (2015), "Matering Predictive Analytics with R," Birmingham-Mumbai: Packt Publishing.
  8. Investopedia(2018), Definition of Technical Analysis, https://www.investopedia.com/term/. [2018.10.08. Accessed]
  9. Kara, Y., Acar, B. M. and Baykan, O. K. (2011), "Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange," Expert Systems with Applications, Vol. 38, No. 5, pp. 5311-5319. https://doi.org/10.1016/j.eswa.2010.10.027
  10. Kavussanos, M. G. and Alizadeh, A. H. (2002), "The Expectations Hypothesis of the Term Structure and Risk Premia in Dry Bulk Shipping Freight Markets," Journal of Transport Economics and Policy, Vol. 36, No. 2, pp. 267-304.
  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. https://doi.org/10.1080/03088839700000053
  12. Patel, J. et al. (2015), "Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques," Expert Systems with Applications. Vol. 42, No. 1, pp. 259-268. https://doi.org/10.1016/j.eswa.2014.07.040
  13. 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. https://doi.org/10.5394/KINPR.2011.35.1.83
  14. 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. https://doi.org/10.5394/KINPR.2008.32.1.081
  15. Shinde, A. S. and Takale, K. C. (2012), "Study of Black-Scholes model and its applications," in International Conference on Modelling, Optimisation and Computing, pp. 270-279.
  16. Shynkevich, Y. et al. (2017), "Forecasting price movements using technical indicators: Investigating the impact of varying input window length," Neurocomputing. Vol. 264, pp. 71-88. https://doi.org/10.1016/j.neucom.2016.11.095
  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. https://doi.org/10.1016/S0965-8564(97)00002-5
  18. 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. https://doi.org/10.5394/KINPR.2016.40.5.337
  19. 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. https://doi.org/10.1016/S0169-2070(97)00044-7