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).
Highlights
Artificial neural networks are a technology that has been adopted in many disciplines, including neuroscience, mathematics, statistics, physics, computer science and engineering
Freight derivatives or freight forward agreements (FFAs) were developed. aiming at managing risks resulting from fluctuations in freight rates, the cost of storage, ship prices, scrap prices, interest rates, and foreign exchange rates [2]
The increased capital liquidity prevailing in the shipping market, the ease of creating standardized agreements offered by sea transportation, the fact that the shipping market is subject to a common valuation structure / methodology, and generally increased transparency are all factors that have contributed to the success of derivatives in shipping
Summary
Artificial neural networks are a technology that has been adopted in many disciplines, including neuroscience, mathematics, statistics, physics, computer science and engineering Their ability to learn from data has endowed them with powerful properties and has made them invaluable tools in financial forecasting. Li and Parsons [5] were the first to attempt applying neural network modelling within the context of crude oil freight rate prediction for the Mediterranean line (Med-Med) using data from 1980 to 1995 and three variables: the actual rate time series, demand index for tankers and Drewry’s total active tanker capacity. The following study focuses on freight futures, in terms of the type of the derivative product It is based on three-month agreements for a specific dry freight transportation route issued by IMAREX, according to the Baltic Exchange indexes.
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