• Journal of Navigation and Port Research
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• v.43 no.2
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• pp.134-141
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• 2019

The objective of this study was to establish whether global macroeconomic indicators affect the profitability of Korean shipping companies by using panel regression analysis. OROA (operating return on assets) and ROA (ratio of net profit to assets) were selected as proxy variables for profitability. OROA and ROA were used as dependent variables. The world GDP growth rate, interest rate, exchange rate, stock index, bunker price, freight, demand and supply of the world shipping market were set as independent variables. The size of the firm was added to the control variable. For small-sized firms, OROA was not affect by macroeconomic indicators. However, ROA was affected by variables such as interest rates, bunker prices, and size of firms. For medium-sized firms, OROA was affected by demand, supply, GDP, freight, and asset variables. However, macroeconomic indicators did not affect ROA. For large-sized firms, freight, GDP, and stock index (SCI; Shanghai Composite Index) have an effect on OROA. ROA was analyzed to be influenced by bunker price and SCI.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.385-391
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• 2022

In this study, a multivariate time series analysis was conducted to identify various variables that impact ocean freight rates in addition to supply and demand factors. First, we used the ClarkSea Index, Clarksons Average Bulker Earnings, and Clarksons Average Tanker Earnings provided by the Shipping Intelligence as substitute variables for the dependent variable, ocean freight. The following ndependent variables were selected: World Seaborne Trade, World Fleet, Brent Crude Oil Price, World GDP Growth Rate, Industrial Production (IP OECD) Growth Rate, Interest Rate (US$ LIBOR 6 Months), and Inflation (CP I OECD) through previous studies. The time series data comprise annual data (1992-2020), and a regression analysis was conducted. Results of the regression analysis show that the World Seaborne Trade and Brent Crude Oil P rice impacted the ClarkSea Index. Only the World Seaborne Dry Bulk Trade impacted the Clarksons Average Bulker Earnings, World Seaborne Oil Trade, Brent Crude Oil Price, IP, and CP I on the Clarksons Average Tanker Earnings.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.5
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• pp.574-583
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• 2021

Predicting shipping markets is an important issue. Such predictions form the basis for decisions on investment methods, fleet formation methods, freight rates, etc., which greatly affect the profits and survival of a company. To this end, in this study, we propose a shipping freight rate prediction model for container ships using gated recurrent units (GRUs) and long short-term memory structure. The target of our freight rate prediction is the China Container Freight Index (CCFI), and CCFI data from March 2003 to May 2020 were used for training. The CCFI after June 2020 was first predicted according to each model and then compared and analyzed with the actual CCFI. For the experimental model, a total of six models were designed according to the hyperparameter settings. Additionally, the ARIMA model was included in the experiment for performance comparison with the traditional analysis method. The optimal model was selected based on two evaluation methods. The first evaluation method selects the model with the smallest average value of the root mean square error (RMSE) obtained by repeating each model 10 times. The second method selects the model with the lowest RMSE in all experiments. The experimental results revealed not only the improved accuracy of the deep learning model compared to the traditional time series prediction model, ARIMA, but also the contribution in enhancing the risk management ability of freight fluctuations through deep learning models. On the contrary, in the event of sudden changes in freight owing to the effects of external factors such as the Covid-19 pandemic, the accuracy of the forecasting model reduced. The GRU1 model recorded the lowest RMSE (69.55, 49.35) in both evaluation methods, and it was selected as the optimal model.

• Journal of Korean Society of Transportation
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• v.24 no.2 s.88
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• pp.53-63
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• 2006

The objective of this study is to estimate Korean transportation service index (KTSI), and to explore possible uses of the KTSI. The KTSI was monthly index to represent the level of passenger and freight services by road, railroad, air and maritime modes, which was developed from eight series. Four of these series measure the level of passenger services (passenger-kilometers) by road, railroad. air and maritime modes : monthly data from January 1995 to December 2004. Similarly. the remaining four series measure the level or freight activity (tonnage) by four modes during the same period. Given the weights of modal revenues, component series were aggregated into two indexes (passenger index and freight index) and a composite index using Chained Fisher Ideal index. which was a geometric mean of the Laspeyres index and the Passche index. The Fisher Ideal index is one of the 'superlative' indexes, which diminish 'substitution bias' as current-weighted indexes. As a result, the freight index and the composite index explain economic conditions better than the passenger index. Based on the composite index. the newly estimated KTSI shows an average lag time of one and a half years at peaks and three months at troughs in comparison with domestic business cycles. Nonetheless. the following efforts are needed for more credible and useful estimates; establishment of data collection scheme in time. credibility uplift of used data, development of various indexation methods.

• Journal of Korea Port Economic Association
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• v.38 no.4
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• pp.107-118
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• 2022

In this study, daily data from January 2002 to June 2022 were used to investigate the relationship between risk-return relationship and market fear, uncertainty, stock market, and maritime freight index for the crude oil market. For this study, the time varying EGARCH-M model was applied to the risk-return relationship, and the wavelet consistency model was used to analyze the relationship between market fear, uncertainty, stock market, and maritime freight index. The analysis results of this study are as follows. First, according to the results of the time-varying risk-return relationship, the crude oil market was found to be related to high returns and high risks. Second, the results of correlation and Granger causality test, it was found that there was a weak correlation between the risk-return relationship and VIX, EPU, S&P500, and BDI. In addition, it was found that there was no two-way causal relationship in the risk-return relationship with EPU and S&P500, but VIX and BDI were found to affect the risk-return relationship. Third, looking at the results of wavelet coherence, it was found that the degree of the risk-return relationship and the relationship between VIX, EPU, S&P500, and BDI was time-varying. In particular, it was found that the relationship between each other was high before and after the crisis period (financial crisis, COVID-19). And it was found to be highly associated with organs. In addition, the risk-return relationship was found to have a positive relationship with VIX and EPU, and a negative relationship with S&P500 and BDI. Therefore, market participants should be well aware of economic environmental changes when making decisions.

• Korea Trade Review
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• v.48 no.1
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• pp.127-163
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• 2023

The objective of this study is to investigate the volatility spillover effects among BDI, CCFI and SCFI. This paper will divide the empirical analysis section into two periods to analyze and compare the differences in volatility spillover effect between shipping freight indices before and after the outbreak of COVID-19 separately. First, in order to compare the mean spillover impact and index lead-lag correlations in BDI and CCFI indices, along with BDI and SCFI indices before and after COVID-19, the co-integration analysis and the test of Granger causality built on the VAR model were utilized. Second, the impulse response and variance decomposition are employed in this work to investigate how the shipping freight index responds to shocks experienced by itself and other freight indices in a short period. Before the COVID-19 epidemic, the results demonstrated that the BDI freight index is the Granger cause of the variable CCFI freight index. But the BDI and CCFI freight indices have no apparent lead-lag relationships after COVID-19, and this empirical result echoes the cointegration test result. After the COVID-19 epidemic, the SCFI index leads the BDI index. This study employs the VAR-BEKK-GARCH joint model to explore the volatility spillover results between dry bulk and container transport markets before and after COVID-19. The empirical results demonstrate that after COVID-19, fluctuations in the BDI index still affect the CCFI index in the maritime market. However, there is no proof of a volatility spillover relationship between the BDI and SCFI after the COVID-19 epidemic. This study will provide an insight into the volatility relationship among BDI, CCFI and SCFI before and after the the COVID-19 epidemic occurred.

• Korea Trade Review
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• v.44 no.2
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• pp.127-141
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• 2019

In this study, the relationship between Baltic Dry Index(BDI) and maritime trade volume in the dry cargo market was verified using the vector autoregressive (VAR) model. Data was analyzed from 1992 to 2018 for iron ore, steam coal, coking coal, grain, and minor bulks of maritime trade volume and BDI. Granger causality analysis showed that the BDI affects the trade volume of coking coal and minor bulks but the trade volume of iron ore, steam coal and grain do not correlate with the BDI freight index. Impulse response analysis showed that the shock of BDI had the greatest impact on coking coal at the two years lag and the impact was negligible at the ten years lag. In addition, the shock of BDI on minor cargoes was strongest at the three years lag, and were negligible at the ten years lag. This study examined the relationship between maritime trade volume and BDI in the dry bulk shipping market in which uncertainty is high. As a result of this study, there is an economic aspect of sustainability that has helped the risk management of shipping companies. In addition, it is significant from an academic point of view that the long-term relationship between the two time series was analyzed through the causality test between variables. However, it is necessary to develop a forecasting model that will help decision makers in maritime markets using more sophisticated methods such as the Bayesian VAR model.

• Journal of Navigation and Port Research
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• v.41 no.1
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• pp.25-32
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• 2017

The purpose of this investigation is to analyze the synchronization between the representative global freight index, the Baltic Dry bulk Index (BDI) and the China Container Freight Index (CCFI) with monthly data from 2000 to 2016. Using the non-stationarity of the business cycle that is able to include common trends, we employ the Engle-Granger 2 stage co-integration test and found no synchronization. On the contrary, we additionally estimated the causality between the markets and revealed the causality, which implies that the Chinese economy has a significant effect on the global market. The results of this empirical analysis demonstrate that the CCFI of China is appropriate for analyzing the shipping industry. In practice, this means that it is more appropriate to include CCFI in the global market outlook than use it as a substitute for the global freight rate index, the BDI. This is a case study of the synchronization of the economic fluctuations of the shipping industry. It suggests that the economic fluctuations of China need to be considered in the unstable global market forecast. In particular, this case applies to the fluctuations in the shipping industry synchronism and provides important results in scientific terms.

• Journal of Navigation and Port Research
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• v.48 no.1
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• pp.62-77
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• 2024

In the maritime shipping industry, imbalance between supply and demand has persistently increased, leading to the utilization of blank sailings by major shipping companies worldwide as a key means of flexibly adjusting vessel capacity in response to shipping market conditions. Traditionally, blank sailings have been frequently implemented around the Chinese New Year period. However, due to unique circumstances such as the global pandemic starting in 2020 and trade tensions between the United States and China, shipping companies have recently conducted larger-scale blank sailings compared to the past. As blank sailings directly impact freight transport delays, they can have negative repercussions from perspectives of both businesses and consumers. Therefore, this study employed Poisson regression models and negative binomial regression models to analyze the influence of maritime freight rate determinants on shipping companies' decisions regarding blank sailings, aiming to proactively address potential consequences. Results of the analysis indicated that, in Poisson regression analysis for 2M, significant variables included global container shipping volume, container vessel capacity, container ship scrapping volume, container ship newbuilding index, and OECD inflation. In negative binomial regression analysis, ocean alliance showed significance with global container shipping volume and container ship order volume, the alliance with container ship capacity and interest rates, non-alliance with international oil prices, global supply chain pressure index, container ship capacity, OECD inflation, and total alliance with container ship capacity and interest rates.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.329-330
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• 2021

벌크화물운송은 해상운송시장에서 가장 큰 규모이고 철강 및 에너지 산업을 뒷받침 하는 중요한 시장이다. 또한 운임의 변동성이 가장 큰 시장으로 상당한 수익을 기대할 수 있는 반면에 파산에 이르는 큰 손실이 발생할 수 있기때문에 시장 참여자들은 합리적이고 과학적인 예측을 기반하여 의사결정을 해야 한다. 그러나 해운시장에서는 과학적 의사결정보다는 경험기반의 의사결정에 의존하기 때문에 시황변동성에 취약하다. 본 논문은 벌크운임예측에 신호 분해 방법인 EMD와 인공신경망을 결합한 하이브리드 모델을 적용하여 과학적 예측방법을 제시하고자 한다. 본 논문은 학문적으로 해운시장 운임예측연구에서 거의 시도되지 않았던 시계열분해법과 기계학습기법을 결합한 하이브리드 모델을 제시하였다는데 의미가 있으며 실무적으로는 해운시장에서 빈번이 일어나는 의사결정의 질이 제고되는데 기여할 것으로 기대된다.