Forecasting tanker market using artificial neural networks

Lyridis, DV; Zacharioudakis, P; Mitrou, P; Mylonas, A

dc.contributor.author	Lyridis, DV	en
dc.contributor.author	Zacharioudakis, P	en
dc.contributor.author	Mitrou, P	en
dc.contributor.author	Mylonas, A	en
dc.date.accessioned	2014-03-01T01:20:32Z
dc.date.available	2014-03-01T01:20:32Z
dc.date.issued	2004	en
dc.identifier.issn	13881973	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15952
dc.subject	Artificial neural networks	en
dc.subject	Forecasting	en
dc.subject	Tanker market	en
dc.title	Forecasting tanker market using artificial neural networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1057/palgrave.mel.9100097	en
heal.identifier.secondary	http://dx.doi.org/10.1057/palgrave.mel.9100097	en
heal.publicationDate	2004	en
heal.abstract	Investing in the tanker market, especially in the VLCC sector constitutes a risky undertaking due to the volatility of tanker freight rates. This paper attempts to uncover the benefits of using Artificial Neural Networks (ANNs) in forecasting VLCC spot freight rates. This is achieved by analysing the period from October 1979 to December 2002, in order to detect possible causes of fluctuations, thus determine the independent variables of the analysis, and then use them to construct reliable ANNs. The aim is to reduce error and, most important, allow the model to maintain a stable error variance during high volatility periods. Among the findings are: ANNs can, with the appropriate architecture and training, constitute valuable decision-making tools especially when the tanker market is volatile; the use of variables in differential form enhances the ANN performance in high volatility periods while variables in normal form demonstrated better performance in median periods; ANN demonstrated mean errors comparable to the naïve model for 1-month forecasts but significantly outperformed it in the 3-, 6-, 9- and 12-month cases; finally, the use of informative variables such as the arbitrage between types of crude oil as well as Capesize rates can improve ANN performance.	en
heal.journalName	Maritime Economics and Logistics	en
dc.identifier.doi	10.1057/palgrave.mel.9100097	en
dc.identifier.volume	6	en
dc.identifier.issue	2	en
dc.identifier.spage	93	en
dc.identifier.epage	108	en