Analysis and forecast of the capesize bulk carriers shipping market using Artificial Neural Networks

Thesis (S.M. in Ocean Systems Management)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.

Bibliographic Details
Main Author: Voudris, Athanasios V
Other Authors: Henry S. Marcus.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2007
Subjects:
Online Access:http://hdl.handle.net/1721.1/36269
_version_ 1826201021520543744
author Voudris, Athanasios V
author2 Henry S. Marcus.
author_facet Henry S. Marcus.
Voudris, Athanasios V
author_sort Voudris, Athanasios V
collection MIT
description Thesis (S.M. in Ocean Systems Management)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.
first_indexed 2024-09-23T11:45:25Z
format Thesis
id mit-1721.1/36269
institution Massachusetts Institute of Technology
language eng
last_indexed 2024-09-23T11:45:25Z
publishDate 2007
publisher Massachusetts Institute of Technology
record_format dspace
spelling mit-1721.1/362692019-04-11T03:06:26Z Analysis and forecast of the capesize bulk carriers shipping market using Artificial Neural Networks Voudris, Athanasios V Henry S. Marcus. Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Mechanical Engineering. Thesis (S.M. in Ocean Systems Management)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006. Includes bibliographical references (leaves 188-189). 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. by Athanasios V. Voudris. S.M.in Ocean Systems Management 2007-02-21T13:18:55Z 2007-02-21T13:18:55Z 2006 2006 Thesis http://hdl.handle.net/1721.1/36269 77464366 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 197 leaves application/pdf Massachusetts Institute of Technology
spellingShingle Mechanical Engineering.
Voudris, Athanasios V
Analysis and forecast of the capesize bulk carriers shipping market using Artificial Neural Networks
title Analysis and forecast of the capesize bulk carriers shipping market using Artificial Neural Networks
title_full Analysis and forecast of the capesize bulk carriers shipping market using Artificial Neural Networks
title_fullStr Analysis and forecast of the capesize bulk carriers shipping market using Artificial Neural Networks
title_full_unstemmed Analysis and forecast of the capesize bulk carriers shipping market using Artificial Neural Networks
title_short Analysis and forecast of the capesize bulk carriers shipping market using Artificial Neural Networks
title_sort analysis and forecast of the capesize bulk carriers shipping market using artificial neural networks
topic Mechanical Engineering.
url http://hdl.handle.net/1721.1/36269
work_keys_str_mv AT voudrisathanasiosv analysisandforecastofthecapesizebulkcarriersshippingmarketusingartificialneuralnetworks