Supramax Bulk Carrier Market Forecasting with Technical Indicators and Neural Networks
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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.
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.
참고문헌 (19)
- Alizadeh, A. H, "Trading strategies in the market for tankers", Maritime Policy & Management, 33(2), pp.119~140 (2006)
- Alizadeh, A. H, "Shipping Derivatives and Risk Management" (2009), Palgrave Macmillan UK
- Batchelor, R., "Forecasting spot and forward prices in the international freight market", International Journal of Forecasting, 23(1), pp.101~114 (2007)
- Celik, M, "An integrated fuzzy QFD model proposal on routing of shipping investment decisions in crude oil tanker market", Expert Systems with Applications, 36(2), pp.6227~6235 (2009)
- Cybenko, G., "Approximation by superpositions of a sigmoidal function", Mathematics of Control, Signals, and Systems, 2(4), pp.303~314 (1989)
- Fan, S, "Forecasting Baltic Dirty Tanker Index by Applying Wavelet Neural Networks", Journal of Transportation Technologies, 3, pp.68~87 (2013)
- Forte, R. M., "Matering Predictive Analytics with R" (2015), Packt Publishing
- "Definition of Technical Analysis" (2018)[URL]
- Kara, Y., "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, 38(5), pp.5311~5319 (2011)
- Kavussanos, M. G, "The Expectations Hypothesis of the Term Structure and Risk Premia in Dry Bulk Shipping Freight Markets", Journal of Transport Economics and Policy, 36(2), pp.267~304 (2002)
- Li, J, "Forecasting tanker freight rate using neural networks", Maritime Policy and Management, 24(1), pp.9~30 (1997)
- Patel, J, "Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques", Expert Systems with Applications, 42(1), pp.259~268 (2015)
- 신창훈, "항만물동량 예측력 제고를 위한 ARIMA 및 인공신경망모형들의 비교 연구", 한국항해항만학회지, 35(1), pp.83~91 (2011), 한국항해항만학회
- 신창훈, "하이브리드 ARIMA-신경망 모델을 통한 컨테이너물동량 예측에 관한 연구", 한국항해항만학회지, 32(2), pp.81~88 (2008), 한국항해항만학회
- Shinde, A. S, "Study of Black-Scholes model and its applications", International Conference on Modelling, Optimisation and Computing, pp.270~279 (2012)
- Shynkevich, Y, "Forecasting price movements using technical indicators: Investigating the impact of varying input window length", Neurocomputing, 264, pp.71~88 (2017)
- Veenstra, A. W, "A co-integration approach to forecasting freight rates in the dry bulk shipping sector", Transportation Research Part A: Policy and Practice, 31(6), pp.447~458 (1997)
- 윤희성, "Trading Strategies in Bulk Shipping: the Application of Artificial Neural Networks", 한국항해항만학회지, 40(5), pp.337~343 (2016), 한국항해항만학회[DOI]
- Zhang, G., "Forecasting with artificial neural networks:: The state of the art", International Journal of Forecasting, 14(1), pp.35~62 (1998)