DOI QR코드

DOI QR Code

파나막스 중고선가치 추정모델 연구

Panamax Second-hand Vessel Valuation Model

  • 임상섭 (한국해양대학교 해운경영학부) ;
  • 이기환 (한국해양대학교 해운경영학부) ;
  • 양혁준 (부산연구원 부산공공투자관리센터) ;
  • 윤희성 (한국해양수산개발원 해운빅데이터연구센터)
  • Lim, Sang-Seop (Division of Shipping Management, Korea Maritime and Ocean University) ;
  • Lee, Ki-Hwan (Division of Shipping Management, Korea Maritime and Ocean University) ;
  • Yang, Huck-Jun (Busan Development Institute) ;
  • Yun, Hee-Sung (Centre for Shipping Big Data Analytics, Korea Maritime Institute)
  • 투고 : 2019.01.25
  • 심사 : 2019.02.28
  • 발행 : 2019.02.28

초록

중고선은 신조선과 달리 시장참여자에게 즉각적인 시장 진출입 기회를 제공하기 때문에 해운산업에서 중요한 시장이라 할 수 있다. 중고선 거래 시 정확한 선가 추정은 향후 장기적인 자본비용의 부담과 직접적인 관련이 있기 때문에 투자의사결정에서 상당히 중요한 요소가 된다. 기존의 중고선시장과 관련된 연구들은 시장의 효율성검증에 치우쳐 있어 정확한 중고선가 추정을 위한 연구는 부족한 실정이다. 본 연구에서는 중고선박 가치추정에 전통적인 계량모델보다 기존연구에서 시도되지 않았던 인공신경망모델을 새롭게 제안하였다. 문헌연구를 통해 중고선 가격에 영향을 미치는 6개 요인(운임, 신조선가격, 총 선복대비 발주량, 해체선 가격, 선령, 사이즈)을 선정하였고, 데이터는 2016년 1월부터 2018년 12월까지 Clarkson에 보고된 파나막스 중고선의 실거래 기록 366건을 이용하였다. 변수선정을 위하여 상관분석과 단계적 회귀분석 실시한 결과 최종적으로 운임, 선령, 사이즈 3개의 변수가 채택되었다. 모델의 설계는 10분할 교차검증으로 인공신경망모델의 파라미터들을 추정하여 진행되었다. 인공신경망 모델의 중고선 가치추정치를 단순 단계적 회귀모형과 비교한 결과 인공신경망모델의 성능이 우수함을 확인하였다. 이 연구는 중고선 선가추정에 미치는 요인들에 대한 통계적인 검증, 성능개선을 위한 기계학습기반의 인공신경망 모델활용이라는 측면에서 차별적 의미가 있다. 또한 정확한 선가 추정이 요구되는 실무에서 통계적인 합리성과 결과의 정확성이 동시에 만족되는 과학적 모델을 제시하여 실무적으로도 도움이 될 것으로 기대한다.

The second-hand ship market provides immediate access to the freight market for shipping investors. When introducing second-hand vessels, the precise estimate of the price is crucial to the decision-making process because it directly affects the burden of capital cost to investors in the future. Previous studies on the second-hand market have mainly focused on the market efficiency. The number of papers on the estimation of second-hand vessel values is very limited. This study proposes an artificial neural network model that has not been attempted in previous studies. Six factors, freight, new-building price, orderbook, scrap price, age and vessel size, that affect the second-hand ship price were identified through literature review. The employed data is 366 real trading records of Panamax second-hand vessels reported to Clarkson between January 2016 and December 2018. Statistical filtering was carried out through correlation analysis and stepwise regression analysis, and three parameters, which are freight, age and size, were selected. Ten-fold cross validation was used to estimate the hyper-parameters of the artificial neural network model. The result of this study confirmed that the performance of the artificial neural network model is better than that of simple stepwise regression analysis. The application of the statistical verification process and artificial neural network model differentiates this paper from others. In addition, it is expected that a scientific model that satisfies both statistical rationality and accuracy of the results will make a contribution to real-life practices.

키워드

GHMHD9_2019_v43n1_72_f0001.png 이미지

Fig. 1 Ratio of the number and deadweight of Panamax in bulk shipping Source : Clarkson Research(2019.01.25.)

GHMHD9_2019_v43n1_72_f0002.png 이미지

Fig. 2 Match between ship size and cargo Source : Yun(2018)’s presentation at KMI 36th Seminar

GHMHD9_2019_v43n1_72_f0003.png 이미지

Fig. 3 Structure of artificial neural networks

GHMHD9_2019_v43n1_72_f0004.png 이미지

Fig. 4 Correlation matrix between variables

GHMHD9_2019_v43n1_72_f0005.png 이미지

Fig. 5 Optimal number of variables

GHMHD9_2019_v43n1_72_f0006.png 이미지

Fig. 6 Ranks of importance in variables

Table 1 Description of factors

GHMHD9_2019_v43n1_72_t0001.png 이미지

Table 2 Performance measurements

GHMHD9_2019_v43n1_72_t0002.png 이미지

Table 3 Model performances

GHMHD9_2019_v43n1_72_t0003.png 이미지

참고문헌

  1. Adland, R. and Koekebakker, S. (2004), "Market Efficiency in the Second-hand Market for Bulk Ships". Maritime Economics and Logistics, Vol. 6, No. 1, pp. 1-15. https://doi.org/10.1057/palgrave.mel.9100092
  2. Adland, R. and Koekebakker, S. (2007), "Ship Valuation Using Cross-Sectional Sales Data : A Multivariate Non-Parametric Approach", Maritime Economics and Logistics, Vol. 9, pp. 105-118. https://doi.org/10.1057/palgrave.mel.9100174
  3. Beenstock, M. (1985), "A theory of ship prices", Maritime Policy and Management, Vol. 12, No. 3, pp. 215-225. https://doi.org/10.1080/03088838500000028
  4. Cai, J. et al. (2018), 'Feature selection in machine learning: A new perspective", Neurocomputing, Vol. 300, pp. 70-79. https://doi.org/10.1016/j.neucom.2017.11.077
  5. Campbell, J. Y. and Shiller, R. J. (1987), "Cointegration and Tests of Present Value Models", Journal of Political Economy, Vol. 95, No. 5, pp. 1062-1088. https://doi.org/10.1086/261502
  6. 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
  7. Fama, E. F. (1970), "Efficient Capital Markets-A Review of Theory and Empirical Work", Journal of Finance, Vol. 25, No. 2, pp. 383-417. https://doi.org/10.1111/j.1540-6261.1970.tb00518.x
  8. Glen, D. R. (1997), "The market for second-hand ships: Further results on efficiency using cointegration analysis", Maritime Policy and Management, Vol. 24, No. 3, pp. 245-260. https://doi.org/10.1080/03088839700000029
  9. Hale, C. and Vanags, A. (1992), "The market for second-hand ships: Some results on efficiency using cointegration", Maritime Policy and Management, Vol. 19, No. 1, pp. 31-39. https://doi.org/10.1080/03088839200000003
  10. Kavussanos, M. G. and Alizadeh, A. H. (2002), "Efficient pricing of ships in the dry bulk sector of the shipping industry", Maritime Economics and Logistics, Vol. 29, No. 3, pp. 303-330.
  11. Kohn, S. (2008), "Generalized Additive Models in the Context of Shipping Economics". Thesis, Department of Economics, University of Leicester.
  12. 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
  13. Lim, S. and Yun, H. (2018), "Supramax Bulk Carrier Market Forecasting with Technical Indicators and Neural Networks", Journal of Navigation and Port Research, Vol. 42, No. 5, pp. 341-346. https://doi.org/10.5394/KINPR.2018.42.5.341
  14. Lyridis, D. et al. (2004), "Forecasting Tanker Market Using Artificial Neural Networks", Maritime Economics and Logistics, Vol. 6, pp. 93-108. https://doi.org/10.1057/palgrave.mel.9100097
  15. 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. https://doi.org/10.1016/j.eswa.2007.10.005
  16. Pruyn, J. F. J., et al. (2011), "Second hand vessel value estimation in maritime economics : A review of the past 20 years and the proposal of an elementary method", Maritime Economics and Logistics, Vol. 13, No. 2, pp. 213-236. https://doi.org/10.1057/mel.2011.6
  17. Shepperd, M. and MacDonell, S. (2012), "Evaluating prediction systems in software project estimation", Information and Software Technology, Vol. 54, No. 8, pp. 820-827. https://doi.org/10.1016/j.infsof.2011.12.008
  18. Silhavy, R. et al. (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. https://doi.org/10.1016/j.jss.2016.11.029
  19. Sodal, S. et al. (2009), "Value based trading of real assets in shipping under stochastic freight rates", Applied Economics, Vol. 41, No. 22, pp. 2793-2807. https://doi.org/10.1080/00036840701720853
  20. Stopford, M. (2009), "Maritime Economics". Routledge.
  21. Thalassinos, E. I. and Politis, E. D. (2014), "Valuation Model for a Second-hand Vessel : Econometric Analysis of the Dry Bulk Sector", Journal of Global Business and Technoloty, Vol. 10, No. 1, pp. 1-17.
  22. Tsolakis, S. D. et al. (2003), "Econometric Modelling of Second-hand Ship Prices", Maritime Economics and Logistics, Vol. 5, pp. 347-377. https://doi.org/10.1057/palgrave.mel.9100086
  23. Yun, H. (2016), "Trading Strategies in Bulk Shipping : the Application of Artificial Neural Networks", Journal of Navigation and Port Research, Vol. 40, No. 5, pp. 337-343. https://doi.org/10.5394/KINPR.2016.40.5.337
  24. Zhang, G. et al. (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