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Yıl 2023, Cilt: 12 Sayı: 2, 191 - 200, 30.06.2023

Hüseyin Gencer Tolga Tuzcuoğlu

https://doi.org/10.33714/masteb.1277580

Öz

Kaynakça

  • Agresti, A., & Franklin, C. (2013). Statistics, the art and science of learning from data (3rd ed.). Pearson.
  • Chander, B. (2020). Clustering and Bayesian networks (pp. 50–73). In Marquez, F. P. G. (Ed.), Handbook of Research on Big Data Clustering and Machine Learning. IGI Global. https://doi.org/10.4018/978-1-7998-0106-1.ch004
  • Charbonnaux, A., Chaumette, P., & Fedi, F. (2020). Santé au travail, relève des équipages, dialogue social et emploi maritime: retour d’expériences sur la gestion en France de la crise de la COVID-19. Droit Maritime Français, 827, 686–694.
  • Clarksons Research. (2021). Shipping Intelligence Network. https://sin.clarksons.net/
  • Gençer, H. (2022). COVID-19 pandemisi sürecinde konteyner taşımacılığında öne çıkan gelişmeler (Prominent Developments in Container Shipping During the COVID-19 Pandemic Period) (pp. 59-68). In Karabulut, Ş. (Ed.), Bilimsel gelişmeler işığında yönetim ve strateji araştırmaları Management and Strategy Studies in the Light of Scientific Developments). Ekin Yayınevi.
  • Giordani, P., Ferraro, M. B., & Martella, F. (2020). An introduction to clustering with R, (1). Springer Singapore. https://doi.org/10.1007/978-981-13-0553-5
  • Guerrero, D., Letrouit, L., & Pais-Montes, C. (2022). The container transport system during COVID-19: An analysis through the prism of complex networks, Transport Policy, 115, 113-225. https://doi.org/10.1016/j.tranpol.2021.10.021
  • HajKacem, M. A. B., N’Cir, C. -E. B., & Essoussi, N. (2019). Overview of scalable partitional methods for big data clustering (pp. 1–23). In Nasraoui, O., & N’Cir, C. -E. B. (Eds.), Clustering methods for big data analytics: Techniques, toolboxes and applications. Springer Cham. https://doi.org/10.1007/978-3-319-97864-2_1
  • Jayashree, K., & Chithambaramani, R. (2020). Big data and clustering techniques (pp. 1–9). In Marquez, F. P. G. (Ed.), Handbook of research on big data clustering and machine learning. IGI Global. https://doi.org/10.4018/978-1-7998-0106-1.ch001
  • Jin, L., Chen, J., Chen, Z., Sun, X., & Yu, B. (2022). Impact of COVID-19 on China’s international liner shipping network based on AIS data. Transport Policy, 121, 90-99. https://doi.org/10.1016/j.tranpol.2022.04.006
  • Ksciuk, J., Kuhlemann, S., Tierney, K., & Koberstein, A. (2023). Uncertainty in maritime ship routing and scheduling: A Literature Review, European Journal of Operational Research, 308, 499-524. https://doi.org/10.1016/j.ejor.2022.08.006
  • Mo, J., Gao, R., Liu, J., Du, L., & Yuen, K. F. (2022). Annual dilated convolutional LSTM network for time charter rate forecasting, Applied Soft Computing, 126, 109259. https://doi.org/10.1016/j.asoc.2022.109259
  • Monge, M. (2022). Bunker fuel, commodity prices and shipping market indices following the COVID-19 pandemic. A time-frequency analysis, International Economics, 172, 29-39. https://doi.org/10.1016/j.inteco.2022.08.003
  • Munim, Z. H. (2022). State-space TBATS model for container freight rate forecasting with improved accuracy, Maritime Transport Research, 3, 100057. https://doi.org/10.1016/j.martra.2022.100057
  • Notteboom, T., Pallis, T., & Rodrigue, J. -P. (2021). Disruptions and resilience in global container shipping and ports: The COVID-19 pandemic versus the 2008–2009 financial crisis. Maritime Economics & Logistics, 23, 179–210. https://doi.org/10.1057/s41278-020-00180-5
  • Notwinska, A., & Schramm, H. J. (2021). Uncertainty, status-based homophily, versatility, repeat exchange and social exchange in the container shipping industry, Journal of Business Research, 128, 524-536. https://doi.org/10.1016/j.jbusres.2021.02.021
  • Rather, S. A., & Bala, P. S. (2020). Analysis of gravitation-based optimization algorithms for clustering and classification (pp. 74–99). In Marquez, F. P. G. (Ed.), Handbook of Research on Big Data Clustering and Machine Learning. IGI Global. https://doi.org/10.4018/978-1-7998-0106-1.ch005
  • Saeed, N., Nguyen, S., Cullinane, K., Gekara, V., & Chhetri, P. (2023). Forecasting container freight rates using the Prophet forecasting method. Transport Policy, 133, 86-107. https://doi.org/10.1016/j.tranpol.2023.01.012
  • UNCTAD. (2021). Review of Maritime Transport 2021. Geneva.
  • Weerth, C. (2020). WCO and IMO on COVID-19: Joint statement on the integrity of the global supply chain during the COVID-19 pandemic. Technical report.

Financial Implications of the COVID-19 Pandemic on the Container Ship Time Charter Business

Yıl 2023, Cilt: 12 Sayı: 2, 191 - 200, 30.06.2023

Hüseyin Gencer Tolga Tuzcuoğlu

https://doi.org/10.33714/masteb.1277580

Öz

This study examines the financial implications of the COVID-19 pandemic on the container ship time charter business. In this context, the container charter transactions were derived from the Clarksons Research Database, which included the ship types, daily charter fees, ship ages, and total charter days. The empirical analysis employed the K-Means Algorithm to cluster the observations in which the elbow curves revealed three cluster centers in the pre-COVID period and four in the post-COVID era, respectively. Based on the industry-wide used threshold definitions, the clusters were then named according to the mean value of given features. In addition, the relative weight of each cluster was disclosed based on the number of transactions falling into the respective cluster. Accordingly, the pre-COVID period clusters were described as intermediate-rated middle-termed young-aged intermediate-TEU container ships; low-rated middle-termed middle-aged feeders; and intermediate-rated long-termed middle-aged upper intermediate-TEU container ships. As for the post-COVID era, the cluster definitions were determined as intermediate-rated middle-termed young-aged feeders; intermediate-rated middle-termed old-aged feeders; high-rated long-termed middle-aged intermediate-TEU container ships; and high-rated short-termed middle-aged intermediate-TEU container ships. The findings suggested that the pandemic boosted the demand for relatively lower TEU container ships such as the feeders in which the criterium of ship age lost its importance due to availability reasons in the market. In addition, the pandemic led to higher charter rates which was a prioritized factor over the charter period.

Anahtar Kelimeler

Container Ship Covid-19 Pandemic K-Means Clustering Time Charter

Kaynakça

  • Agresti, A., & Franklin, C. (2013). Statistics, the art and science of learning from data (3rd ed.). Pearson.
  • Chander, B. (2020). Clustering and Bayesian networks (pp. 50–73). In Marquez, F. P. G. (Ed.), Handbook of Research on Big Data Clustering and Machine Learning. IGI Global. https://doi.org/10.4018/978-1-7998-0106-1.ch004
  • Charbonnaux, A., Chaumette, P., & Fedi, F. (2020). Santé au travail, relève des équipages, dialogue social et emploi maritime: retour d’expériences sur la gestion en France de la crise de la COVID-19. Droit Maritime Français, 827, 686–694.
  • Clarksons Research. (2021). Shipping Intelligence Network. https://sin.clarksons.net/
  • Gençer, H. (2022). COVID-19 pandemisi sürecinde konteyner taşımacılığında öne çıkan gelişmeler (Prominent Developments in Container Shipping During the COVID-19 Pandemic Period) (pp. 59-68). In Karabulut, Ş. (Ed.), Bilimsel gelişmeler işığında yönetim ve strateji araştırmaları Management and Strategy Studies in the Light of Scientific Developments). Ekin Yayınevi.
  • Giordani, P., Ferraro, M. B., & Martella, F. (2020). An introduction to clustering with R, (1). Springer Singapore. https://doi.org/10.1007/978-981-13-0553-5
  • Guerrero, D., Letrouit, L., & Pais-Montes, C. (2022). The container transport system during COVID-19: An analysis through the prism of complex networks, Transport Policy, 115, 113-225. https://doi.org/10.1016/j.tranpol.2021.10.021
  • HajKacem, M. A. B., N’Cir, C. -E. B., & Essoussi, N. (2019). Overview of scalable partitional methods for big data clustering (pp. 1–23). In Nasraoui, O., & N’Cir, C. -E. B. (Eds.), Clustering methods for big data analytics: Techniques, toolboxes and applications. Springer Cham. https://doi.org/10.1007/978-3-319-97864-2_1
  • Jayashree, K., & Chithambaramani, R. (2020). Big data and clustering techniques (pp. 1–9). In Marquez, F. P. G. (Ed.), Handbook of research on big data clustering and machine learning. IGI Global. https://doi.org/10.4018/978-1-7998-0106-1.ch001
  • Jin, L., Chen, J., Chen, Z., Sun, X., & Yu, B. (2022). Impact of COVID-19 on China’s international liner shipping network based on AIS data. Transport Policy, 121, 90-99. https://doi.org/10.1016/j.tranpol.2022.04.006
  • Ksciuk, J., Kuhlemann, S., Tierney, K., & Koberstein, A. (2023). Uncertainty in maritime ship routing and scheduling: A Literature Review, European Journal of Operational Research, 308, 499-524. https://doi.org/10.1016/j.ejor.2022.08.006
  • Mo, J., Gao, R., Liu, J., Du, L., & Yuen, K. F. (2022). Annual dilated convolutional LSTM network for time charter rate forecasting, Applied Soft Computing, 126, 109259. https://doi.org/10.1016/j.asoc.2022.109259
  • Monge, M. (2022). Bunker fuel, commodity prices and shipping market indices following the COVID-19 pandemic. A time-frequency analysis, International Economics, 172, 29-39. https://doi.org/10.1016/j.inteco.2022.08.003
  • Munim, Z. H. (2022). State-space TBATS model for container freight rate forecasting with improved accuracy, Maritime Transport Research, 3, 100057. https://doi.org/10.1016/j.martra.2022.100057
  • Notteboom, T., Pallis, T., & Rodrigue, J. -P. (2021). Disruptions and resilience in global container shipping and ports: The COVID-19 pandemic versus the 2008–2009 financial crisis. Maritime Economics & Logistics, 23, 179–210. https://doi.org/10.1057/s41278-020-00180-5
  • Notwinska, A., & Schramm, H. J. (2021). Uncertainty, status-based homophily, versatility, repeat exchange and social exchange in the container shipping industry, Journal of Business Research, 128, 524-536. https://doi.org/10.1016/j.jbusres.2021.02.021
  • Rather, S. A., & Bala, P. S. (2020). Analysis of gravitation-based optimization algorithms for clustering and classification (pp. 74–99). In Marquez, F. P. G. (Ed.), Handbook of Research on Big Data Clustering and Machine Learning. IGI Global. https://doi.org/10.4018/978-1-7998-0106-1.ch005
  • Saeed, N., Nguyen, S., Cullinane, K., Gekara, V., & Chhetri, P. (2023). Forecasting container freight rates using the Prophet forecasting method. Transport Policy, 133, 86-107. https://doi.org/10.1016/j.tranpol.2023.01.012
  • UNCTAD. (2021). Review of Maritime Transport 2021. Geneva.
  • Weerth, C. (2020). WCO and IMO on COVID-19: Joint statement on the integrity of the global supply chain during the COVID-19 pandemic. Technical report.
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme
Bölüm Makaleler
Yazarlar

Hüseyin Gencer 0000-0002-4945-4420

Tolga Tuzcuoğlu 0000-0002-5269-9701

Erken Görünüm Tarihi 20 Haziran 2023
Yayımlanma Tarihi 30 Haziran 2023
Gönderilme Tarihi 5 Nisan 2023
Kabul Tarihi 7 Haziran 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 12 Sayı: 2

Kaynak Göster

APA Gencer, H., & Tuzcuoğlu, T. (2023). Financial Implications of the COVID-19 Pandemic on the Container Ship Time Charter Business. Marine Science and Technology Bulletin, 12(2), 191-200. https://doi.org/10.33714/masteb.1277580
AMA Gencer H, Tuzcuoğlu T. Financial Implications of the COVID-19 Pandemic on the Container Ship Time Charter Business. Mar. Sci. Tech. Bull. Haziran 2023;12(2):191-200. doi:10.33714/masteb.1277580
Chicago Gencer, Hüseyin, ve Tolga Tuzcuoğlu. “Financial Implications of the COVID-19 Pandemic on the Container Ship Time Charter Business”. Marine Science and Technology Bulletin 12, sy. 2 (Haziran 2023): 191-200. https://doi.org/10.33714/masteb.1277580.
EndNote Gencer H, Tuzcuoğlu T (01 Haziran 2023) Financial Implications of the COVID-19 Pandemic on the Container Ship Time Charter Business. Marine Science and Technology Bulletin 12 2 191–200.
IEEE H. Gencer ve T. Tuzcuoğlu, “Financial Implications of the COVID-19 Pandemic on the Container Ship Time Charter Business”, Mar. Sci. Tech. Bull., c. 12, sy. 2, ss. 191–200, 2023, doi: 10.33714/masteb.1277580.
ISNAD Gencer, Hüseyin - Tuzcuoğlu, Tolga. “Financial Implications of the COVID-19 Pandemic on the Container Ship Time Charter Business”. Marine Science and Technology Bulletin 12/2 (Haziran 2023), 191-200. https://doi.org/10.33714/masteb.1277580.
JAMA Gencer H, Tuzcuoğlu T. Financial Implications of the COVID-19 Pandemic on the Container Ship Time Charter Business. Mar. Sci. Tech. Bull. 2023;12:191–200.
MLA Gencer, Hüseyin ve Tolga Tuzcuoğlu. “Financial Implications of the COVID-19 Pandemic on the Container Ship Time Charter Business”. Marine Science and Technology Bulletin, c. 12, sy. 2, 2023, ss. 191-00, doi:10.33714/masteb.1277580.
Vancouver Gencer H, Tuzcuoğlu T. Financial Implications of the COVID-19 Pandemic on the Container Ship Time Charter Business. Mar. Sci. Tech. Bull. 2023;12(2):191-200.
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