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Supramax Bulk Carrier Market Forecasting with Technical Indicators and Neural Networks
임상섭(Sang-Seop Lim),윤희성(Hee-Sung Yun) 한국항해항만학회 2018 한국항해항만학회지 Vol.42 No.5
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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임상섭(Sang Seop Lim),윤희성(Hee Sung Yun) 한국항해항만학회 2018 한국항해항만학회지 Vol.42 No.6
운임시장의 심한 변동성과 시계열 데이터의 불안정성으로 해운시황 예측에 대한 연구가 큰 성과를 내지 못하고 있지만 최근 대표적인 비선형 모델인 기계학습모델을 적용한 연구들이 활발히 진행되고 있다. 대부분의 기존 연구가 계량모델의 설계단계에서 입력변수에 해당하는 요인들을 기존 문헌연구와 연구자의 직관에 의존하여 선정했기 때문에 요인선정에 대한 체계적인 연구가 필요하다. 본 연구에서는 케이프선 운임을 대상으로 단계적 회귀모형과 랜덤포레스트모델을 이용하여 중요 영향요인을 분석하였다. 해운시장에서 비교적 단순한 수급구조를 가져 요인파악이 용이한 케이프선 운임을 대상으로 하였으며 총 16개의 수급요인들을 사전 추출하였다. 요인간의 상호관련성을 파악하여 단계적 회귀는 8개 요인, 랜덤포레스트는 10개 요인을 분석대상으로 선정하였으며 선정된 변수를 입력변수로 하여 예측한 결과를 비교하였다. 랜덤포레스트의 예측성능이 아주 우수하였는데 수요요인이 주로 선정된 단계적 회귀분석과는 달리 공급요인이 비중 있게 선정되었기 때문인 것으로 판단된다. 본 연구는 운임예측 연구에 있어 운임결정요인에 대한 과학적인 근거를 마련하였으며 이를 위해 기계학습 기반의 모델을 활용하였다는데 연구적 의의가 있다. 또한 시장정보의 분석에 있어 실무자들이 어떤 변수에 중점을 두어야 하는지에 대해 합리적 근거를 제시한 측면에서 해운기업의 의사결정에 실질적 도움이 될 것으로 기대된다. In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.
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임상섭(Sang Seop Lim),윤희성(Hee Sung Yun) 한국해양비즈니스학회 2018 해양비즈니스 Vol.- No.40
One of the characteristics of the shipping market is extreme volatility. The play in the volatile market requires players to make cautious decisions based on scientific analysis. Market risk management is of utmost importance in the shipping market and market forecasting is an important element in the management process. This paper deals with forecasting issues in the dry bulk shipping market. Despite the fact that bulk shipping is dominated by spot trading, there are few papers that apply scientific models to short-term forecasting. This paper employs recurrent neural networks that currently draws attention in time-series forecasting. More specifically, Elman neural networks and Jordan neural networks were applied to improve the forecasting performance over traditional econometric models and simple multi-layer perceptron models. Monthly observations of the BDI, BCI and BPI were used for spot forecasting. The result showed that the proposed two models outperformed the ARIMA model and the MLP model. The Elman model performed better for the time series with high volatility and the Jordan model demonstrated better performance for the time series with a modest volatility. The BDI is composed of sub-indices with varying levels of volatility. Hence in the case of the BDI forecast, the Jordan networks performed better than the Elman networks. The results will provide scientific grounds for chartering managers to make better decisions concerning the most active spot transactions.
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