Tools
Tools
Yang, Z and Mehmed, EE (2019) Artificial neural networks in freight rate forecasting. Maritime Economics and Logistics. ISSN 1479-2931
|
Text
Artificial neural networks in freight rate forecasting.pdf - Accepted Version Download (910kB) | Preview |
Abstract
Reliable freight rate forecasts are essential to stimulate ocean transportation and ensure stakeholder benefits in a highly volatile shipping market. However, compared to traditional time-series approaches, there are few studies using artificial intelligence techniques (e.g. artificial neural networks, ANNs) to forecast shipping freight rates, and fewer still incorporating forward freight agreement (FFA) information for freight rate forecasts. The aim of this paper is to examine the ability of FFAs to improve forecasting accuracy. We use two different dynamic ANN models, NARNET and NARXNET, and we compare their performance for 1, 2, 3 and 6 months ahead. The accuracy of the forecasting models is evaluated with the use of mean squared error (MSE), based on actual secondary data including historical Baltic Panamax Index (BPI) data (available online), and primary data on Baltic forward assessment (BFA) collected from the Baltic Exchange. The experimental results show that, in general, NARXNET outperforms NARNET in all forecast horizons, revealing the importance of the information contained in FFAs in improving forecasting accuracy. Our findings provide better forecasts and insights into the future movements of freight markets and help rationalise chartering decisions. © 2019, Springer Nature Limited.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 1402 Applied Economics, 1503 Business and Management, 1604 Human Geography |
| Subjects: | H Social Sciences > HE Transportation and Communications T Technology > TC Hydraulic engineering. Ocean engineering |
| Divisions: | Maritime & Mechanical Engineering (merged with Engineering 10 Aug 20) |
| Publisher: | Palgrave Macmillan |
| Date Deposited: | 13 May 2019 09:53 |
| Last Modified: | 04 Sep 2021 09:26 |
| DOI or ID number: | 10.1057/s41278-019-00121-x |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/10666 |
 |
View Item |