Freight-Forward Agreement Time series Modelling Based on Artificial Neural Network Models

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LYRIDIS, Dimitrios ;ZACHARIOUDAKIS, Panayotis ;IORDANIS, Stylianos ;DALEZIOU, Sophia .
Freight-Forward Agreement Time series Modelling Based on Artificial Neural Network Models. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 59, n.9, p. 511-516, june 2018. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/article/freight-forward-agreement-time-series-modelling-based-on-artificial-neural-network-models/>. Date accessed: 30 mar. 2020. 
doi:http://dx.doi.org/10.5545/sv-jme.2013.947.
Lyridis, D., Zacharioudakis, P., Iordanis, S., & Daleziou, S.
(2013).
Freight-Forward Agreement Time series Modelling Based on Artificial Neural Network Models.
Strojniški vestnik - Journal of Mechanical Engineering, 59(9), 511-516.
doi:http://dx.doi.org/10.5545/sv-jme.2013.947
@article{sv-jmesv-jme.2013.947,
	author = {Dimitrios  Lyridis and Panayotis  Zacharioudakis and Stylianos  Iordanis and Sophia  Daleziou},
	title = {Freight-Forward Agreement Time series Modelling Based on Artificial Neural Network Models},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {59},
	number = {9},
	year = {2013},
	keywords = {freight rates; trading strategy; artificial neural networks; shipping market modeling; freight rate forecasting},
	abstract = {Over the last thirty years, there has been an extraordinary growth in the financial derivatives market, in the field of shipping. This can be attributed to the fact that financial derivatives are contracts that allow all players participating in the shipping market to reduce their exposure to fluctuations in freight rates, bunker prices, interest rates, foreign exchange rates and vessel values. This paper employs an artificial neural network (ANN) in order to forecast the future price of freight derivatives. More specifically, drawing on historical data for the period between January 2005 and March 2009, an ANN is built and trained, and its estimates lead to two individual results. The resulting model indicates to the investor which position to take in the derivatives market (short for sale of agreements and long for the purchase of agreements).},
	issn = {0039-2480},	pages = {511-516},	doi = {10.5545/sv-jme.2013.947},
	url = {https://www.sv-jme.eu/article/freight-forward-agreement-time-series-modelling-based-on-artificial-neural-network-models/}
}
Lyridis, D.,Zacharioudakis, P.,Iordanis, S.,Daleziou, S.
2013 June 59. Freight-Forward Agreement Time series Modelling Based on Artificial Neural Network Models. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 59:9
%A Lyridis, Dimitrios 
%A Zacharioudakis, Panayotis 
%A Iordanis, Stylianos 
%A Daleziou, Sophia 
%D 2013
%T Freight-Forward Agreement Time series Modelling Based on Artificial Neural Network Models
%B 2013
%9 freight rates; trading strategy; artificial neural networks; shipping market modeling; freight rate forecasting
%! Freight-Forward Agreement Time series Modelling Based on Artificial Neural Network Models
%K freight rates; trading strategy; artificial neural networks; shipping market modeling; freight rate forecasting
%X Over the last thirty years, there has been an extraordinary growth in the financial derivatives market, in the field of shipping. This can be attributed to the fact that financial derivatives are contracts that allow all players participating in the shipping market to reduce their exposure to fluctuations in freight rates, bunker prices, interest rates, foreign exchange rates and vessel values. This paper employs an artificial neural network (ANN) in order to forecast the future price of freight derivatives. More specifically, drawing on historical data for the period between January 2005 and March 2009, an ANN is built and trained, and its estimates lead to two individual results. The resulting model indicates to the investor which position to take in the derivatives market (short for sale of agreements and long for the purchase of agreements).
%U https://www.sv-jme.eu/article/freight-forward-agreement-time-series-modelling-based-on-artificial-neural-network-models/
%0 Journal Article
%R 10.5545/sv-jme.2013.947
%& 511
%P 6
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 59
%N 9
%@ 0039-2480
%8 2018-06-28
%7 2018-06-28
Lyridis, Dimitrios, Panayotis  Zacharioudakis, Stylianos  Iordanis, & Sophia  Daleziou.
"Freight-Forward Agreement Time series Modelling Based on Artificial Neural Network Models." Strojniški vestnik - Journal of Mechanical Engineering [Online], 59.9 (2013): 511-516. Web.  30 Mar. 2020
TY  - JOUR
AU  - Lyridis, Dimitrios 
AU  - Zacharioudakis, Panayotis 
AU  - Iordanis, Stylianos 
AU  - Daleziou, Sophia 
PY  - 2013
TI  - Freight-Forward Agreement Time series Modelling Based on Artificial Neural Network Models
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2013.947
KW  - freight rates; trading strategy; artificial neural networks; shipping market modeling; freight rate forecasting
N2  - Over the last thirty years, there has been an extraordinary growth in the financial derivatives market, in the field of shipping. This can be attributed to the fact that financial derivatives are contracts that allow all players participating in the shipping market to reduce their exposure to fluctuations in freight rates, bunker prices, interest rates, foreign exchange rates and vessel values. This paper employs an artificial neural network (ANN) in order to forecast the future price of freight derivatives. More specifically, drawing on historical data for the period between January 2005 and March 2009, an ANN is built and trained, and its estimates lead to two individual results. The resulting model indicates to the investor which position to take in the derivatives market (short for sale of agreements and long for the purchase of agreements).
UR  - https://www.sv-jme.eu/article/freight-forward-agreement-time-series-modelling-based-on-artificial-neural-network-models/
@article{{sv-jme}{sv-jme.2013.947},
	author = {Lyridis, D., Zacharioudakis, P., Iordanis, S., Daleziou, S.},
	title = {Freight-Forward Agreement Time series Modelling Based on Artificial Neural Network Models},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {59},
	number = {9},
	year = {2013},
	doi = {10.5545/sv-jme.2013.947},
	url = {https://www.sv-jme.eu/article/freight-forward-agreement-time-series-modelling-based-on-artificial-neural-network-models/}
}
TY  - JOUR
AU  - Lyridis, Dimitrios 
AU  - Zacharioudakis, Panayotis 
AU  - Iordanis, Stylianos 
AU  - Daleziou, Sophia 
PY  - 2018/06/28
TI  - Freight-Forward Agreement Time series Modelling Based on Artificial Neural Network Models
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 59, No 9 (2013): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2013.947
KW  - freight rates, trading strategy, artificial neural networks, shipping market modeling, freight rate forecasting
N2  - Over the last thirty years, there has been an extraordinary growth in the financial derivatives market, in the field of shipping. This can be attributed to the fact that financial derivatives are contracts that allow all players participating in the shipping market to reduce their exposure to fluctuations in freight rates, bunker prices, interest rates, foreign exchange rates and vessel values. This paper employs an artificial neural network (ANN) in order to forecast the future price of freight derivatives. More specifically, drawing on historical data for the period between January 2005 and March 2009, an ANN is built and trained, and its estimates lead to two individual results. The resulting model indicates to the investor which position to take in the derivatives market (short for sale of agreements and long for the purchase of agreements).
UR  - https://www.sv-jme.eu/article/freight-forward-agreement-time-series-modelling-based-on-artificial-neural-network-models/
Lyridis, Dimitrios, Zacharioudakis, Panayotis, Iordanis, Stylianos, AND Daleziou, Sophia.
"Freight-Forward Agreement Time series Modelling Based on Artificial Neural Network Models" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 59 Number 9 (28 June 2018)

Authors

Affiliations

  • National Technical University Athens, Laboratory for Maritime Transport, Greece 1
  • National Technical University of Athens, School of Applied Mathematical and Physical Sciences, Greece 2

Paper's information

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)9, 511-516

https://doi.org/10.5545/sv-jme.2013.947

Over the last thirty years, there has been an extraordinary growth in the financial derivatives market, in the field of shipping. This can be attributed to the fact that financial derivatives are contracts that allow all players participating in the shipping market to reduce their exposure to fluctuations in freight rates, bunker prices, interest rates, foreign exchange rates and vessel values. This paper employs an artificial neural network (ANN) in order to forecast the future price of freight derivatives. More specifically, drawing on historical data for the period between January 2005 and March 2009, an ANN is built and trained, and its estimates lead to two individual results. The resulting model indicates to the investor which position to take in the derivatives market (short for sale of agreements and long for the purchase of agreements).

freight rates; trading strategy; artificial neural networks; shipping market modeling; freight rate forecasting