Back

Ship Investment Valuation Using Real Option Analysis (ROA).

  • Published In: Journal of Coastal Research, 2023, v. 116. P. 423 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Kim, Hanna; Park, Sunghwa; Choi, Sooho; Kim, Taeil 3 of 3

Abstract

Kim, H.; Park, S.; Choi, S., and Kim, T., 2023. Ship investment valuation using Real Option Analysis (ROA). In: Lee, J.L.; Lee, H.; Min, B.I.; Chang, J.-I.; Cho, G.T.; Yoon, J.-S., and Lee, J. (eds.), Multidisciplinary Approaches to Coastal and Marine Management. Journal of Coastal Research, Special Issue No. 116, pp. 423-427. Charlotte (North Carolina), ISSN 0749-0208. As ship investment entails an extensive amount of investment, it is critical to conduct the valuation of investment plans prior to undertaking the investment. In this study, comparison analysis was conducted to empirically analyze a traditional method of ship investment valuation, Net Present Value (NPV) and Real Option Analysis (ROA). This study intends to suggest improvement measures through a real option valuation methodology for ship investment that can factor into uncertainty. This analysis was conducted through the option to defer deferable for two years and the option to expand placing additional orders for 8 vessels depending on market conditions within 2 years after the initial order of 8 vessels. As a result of the comparative analysis, it can be confirmed that the real option would show higher value of ship investment than the traditional valuation and affect investment decision-making, since it considers uncertainty in the event of ship investment. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Coastal Research. 2023/11, Vol. 116, p423
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2023
  • ISSN:0749-0208
  • DOI:10.2112/JCR-SI116-086.1
  • Accession Number:174603510
  • Copyright Statement:Copyright of Journal of Coastal Research is the property of KnowledgeWorks Global, Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.