When will the next shipping recovery arrive?
The dry bulk shipping market has been very weak the past few years due to a lack of a demand-surge induced growth or a supply-shortage induced growth. A recovery in the dry bulk market is when supply and demand gap narrows and market sentiments improves. Every day, there are many events occurring in...
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Format: | Final Year Project |
Language: | English |
Published: |
2017
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Online Access: | http://hdl.handle.net/10356/71226 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | The dry bulk shipping market has been very weak the past few years due to a lack of a demand-surge induced growth or a supply-shortage induced growth. A recovery in the dry bulk market is when supply and demand gap narrows and market sentiments improves. Every day, there are many events occurring in the shipping world, but only certain few triggers would lead to an upturn. Studies had found out that past shipping cycles had similar repetitive events that impact supply and demand. Hence, this report aims to derive an estimated time as to when the market supply and demand narrows by examining past events and determine the triggers that leads to it.
Using qualitative and quantitative analysis, triggers were identified from supply and demand indicators. On the supply side, interviews, surveys and research were collected to observe the live status of tonnage, financing, ship yards, and age profile of the fleet. On the demand side, the same methods were employed to observe China’s economic growth and development plans, and global demand preferences. Triggers that are identified are oil and gas price, vessel deliveries and demolition rates, banks’ sentiments, China’s government policies, and IMO conventions. These can be used to predict future recoveries.
The results would tell if the next recovery is a demand-surge induced growth or a supply-shortage induced recovery based on the triggers identified. Projection of the next recovery is based on correlations with past events. The limitation was that certain peace-time assumptions and parameters had to be set to predict the result. |
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