본 연구는 해운기업의 경영안정성을 분석하기 위해 운임지수와 선사 영업이익을 바탕으로 안정성 지수를 설계하고 10개 선사에 대해 적용하였다. 또한 해상운임지수의 변화에 따른 매출액 변화를 추적하기 위하여 VAR 모형의 결과를 바탕으로 충격반응함수를 적용하였다. 분석 결과, 운임지수의 변화에 따라 안정성 지수가 등락하였으며 2012~2013년 기간, 시황의 완만한 회복세로 지수가 급등하는 선사도 있는 것으로 나타났다. 충격반응함수 분석 결과, BDI의 충격이 선사에 미치는 영향은 작았으며 CCFI는 상대적으로 높은 충격을 주었으며 HRCI의 충격은 높지 않은 것으로 나타났다. 본 연구는 해운기업의 경영안정성 지수를 설계하고 상위 10개 기업을 대상으로 적용하였다는 점에서 의의가 있다고 할 수 있으며 대표적인 해상운임지수의 충격이 선사 매출액에 미치는 영향을 추적하였다는 점에서 학문적 의의가 있다고 하겠다.
The shipping industry suffered an important shock due to the merger of two prominent shipping lines, COSCO and China Shipping, in December 2015. It is important to prepare the exit strategy for the domestic shipping lines, Hajin Shipping (HIS) and Hyundai Merchant Marine (HMM), as the Korean financial authorities proposed the merger of HIS and HMM. As the shipping industry is a cycle industry, it is well known that the end of the depression should result in a boom for the industry. During a situation of balance between vessel supply and demand, the cargo volume positively affects the shipping rate; otherwise, the used vessels’ price would be higher. The number of used vessels and newly built ones affects vessel oversupply, causing the shipping rate to be lower.
In 2006, the shipping industry had undergone a large economic boom influenced by the Chinese special procurement demand and the global economic situation. As such, the Baltic Dry Index had increased from 773P on January 3, 2009, to 4,482P in November2009, and, subsequently, decreased to 1,594P in 2011. Due to the shipping recession triggered by the global financial crisis, most international and domestic shipping lines underwent severe liquidity risks, affecting the lines having stable profits, such as Korealines, STX-Pan Ocean, etc. During the recession, the future of the domestic lines was uncertain, because the lines’ profits depend more on sea transport. In the profit structure, heavily dependent on transportation, as the ships sail more, the company incurs losses.
There are several studies regarding the risk management of shipping lines. For example, Yoo (2008) researches risk management due to changing shipping rates. Na et al. (2015) weigh the priority factors, such as the market, operational, and financial perspectives, using an analytical hierarchy process (AHP) model. Using shock-resilience (S-R), Hwang et al. (2012) compare the performance of Korean and Japanese lines before and at the end of the recession. However, there is limited quantitative research on this matter. Therefore, this study generates a stability index for the lines and adopts an impulse response function to trace changes due to typical shipping indices, BDI, China Container Freight Index (CCFI), and the Howe Robinson Container Index (HRCI) during 11 years.
As a result, the stability index would be higher or lower due to BDI, CCFI, and HRCI. Subsequently, the impulse of BDI is relatively smaller than the one of CCFI or HRCI.
This study is similar to Kim (2014) and Kim et al. (2010), as it analyzes stability, profitability, productivity, growth, and change of the shipping lines’ financial status. However, this study creates the stability index and analyzes ten Korean domestic lines, thus differing from previous studies. Additionally, it is academically meaningful in regard to tracing the impulses of changing representative shipping indices. Nonetheless, this study has limitations, such as analyzing only domestic lines.