Analysis and forecast of the capesize bulk carriers shipping market using Artificial Neural Networks
Name
77464366-MIT.pdf
Description
Full printable version
Size
17.02 MB
Format
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Checksum (MD5)
711321b6a8846aa6b2ae0fe56f6b51d1
Author(s)
Voudris, Athanasios V
Advisor(s)
Henry S. Marcus.
Date Issued
2006
Publisher
Massachusetts Institute of Technology
Abstract
Investing in the bulk carrier market constitutes a rather risky investment due to the volatility of the bulk carrier freight rates. In this study it is attempted to uncover the benefits of using Artificial Neural Networks (ANNs) in forecasting the Capesize Ore Voyage Rates from Tubarao to Rotterdam with a 145,000 dwt Bulk carrier. Initially, market analysis allows the assessment of the relation of some parameters of the dry bulk market with the evolution of freight rates. Subsequently, ANNs with an appropriate architecture are constructed and sufficient data, in terms of quantity and quality, are collected and organized so as to establish both the training and the testing data sets. The use of ANNs along with genetic algorithms allows the prediction of bulk freight rates with considerable accuracy for as long as eighteen months ahead and this is quantified by calculating the relative and absolute errors. It is concluded that ANNs offer a promising approach to forecasting the bulk market when coupled with efficient market modeling.
Description
Thesis (S.M. in Ocean Systems Management)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.
Includes bibliographical references (leaves 188-189).
Subjects
Mechanical Engineering.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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