Trading Strategies in Bulk Shipping: the Application of Artificial Neural Networks
0
조회
0
다운로드
4
인용
0
참고문헌
키워드
초록
The core decisions of bulk shipping businesses can be summarized as the timing and the choice of period for which carrying capacity is traded. In particular, frequent decisions to trade freight either with repeated spot transactions or with a one-off long-term deal critically impact business performance. Even though a variety of freight trading strategies can be employed to facilitate the decisions, chartering practitioners have not been active in utilizing these strategies, and academic research has rarely proposed applicable solutions. The specific properties of freight as a tradable commodity are not properly reflected in existing studies, and limitations have been reported in their application to the real world. This research focused on the establishment of applicable freight trading strategies by taking into account two properties of freight: time perishability and term-dependant pricing. In addition to traditional trading strategies, artificial neural networks were applied for the first time to the test of freight trading strategies. The performances of the trading strategies were measured and compared to produce a remarkable outperformance of the ANN. This research is expected to make a significant contribution to chartering practices by enhancing the quality of chartering decisions and eventually enabling the effective management of freight rate risk. In addition to methodological expansion, the result will propose a way to approach the controversial issue of freight market efficiency.
Abstract
The core decisions of bulk shipping businesses can be summarized as the timing and the choice of period for which carrying capacity is traded. In particular, frequent decisions to trade freight either with repeated spot transactions or with a one-off long-term deal critically impact business performance. Even though a variety of freight trading strategies can be employed to facilitate the decisions, chartering practitioners have not been active in utilizing these strategies, and academic research has rarely proposed applicable solutions. The specific properties of freight as a tradable commodity are not properly reflected in existing studies, and limitations have been reported in their application to the real world. This research focused on the establishment of applicable freight trading strategies by taking into account two properties of freight: time perishability and term-dependant pricing. In addition to traditional trading strategies, artificial neural networks were applied for the first time to the test of freight trading strategies. The performances of the trading strategies were measured and compared to produce a remarkable outperformance of the ANN. This research is expected to make a significant contribution to chartering practices by enhancing the quality of chartering decisions and eventually enabling the effective management of freight rate risk. In addition to methodological expansion, the result will propose a way to approach the controversial issue of freight market efficiency.
참고문헌 (15)
- Adland, A., "Market Efficiency in the Second-hand Market for Bulk ships", Maritime Economics and Logistics, 6, pp.1~15 (2004)
- Adland, A. O., "Market Efficiency in the Bulk Freight Market revisited", Maritime Policy and Management, 33(2), pp.107~117 (2006)
- Alizadeh, A. H., "Trading strategies in the market for tankers", Maritime Policy and Management, 33(2) (2006)
- Alizadeh, A. H., "Investment timing and trading strategies in the sale and purchase market for ships", Transportation Research Part B, 34, pp.126~143 (2007)
- Alizadeh, A. H., "Shipping Derivatives and Risk Management", pp.181~216 (2009), Palgrave Macmillan
- Cybenko, G., "Approximation by superpositions of a sigmoidal function", Mathematics of Control, Signals, and Systems, 2(4), pp.303~314 (1989)
- Fama, E. F., "Ranom Walks in Stock Market Prices", Financial Analysts Journal, pp.55~59 (1965)
- Fama, E. F., "Efficient Capital Markets : A Review of Theory and Empirical Work", Journal of Finance, 25(1), pp.383~417 (1970)
- Fama, E. F., "Efficient Capital Market:Ⅱ", Journal of Finance, 46(5), pp.1575~1617 (1991)
- Kaastra, I., "Designing a neural network for forecasting financial and economic time series", Neurocomputing, 10(3), pp.215~236 (1996)
- Mostafa, M. M., "Forecasting the Suez Canal Traffic: A Neural Network Analysis", Maritime Policy and Management, 31, pp.139~156 (2004)
- 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
- Smith, K. A., "Neural networks in business : techniques and applications for the operations researcher", Computers & Operations Research, 27(11), pp.1023~1044 (2000)
- Wong, B. K., "Neural network applications in business : A review and analysis of the literature(1988–1995)", Decision Support Systems, 19(4), pp.301~320 (1997)