Support Vector Machine Algorithms: An Application to Ship Price Forecasting

Theodore Syriopoulos (), Michael Tsatsaronis and Ioannis Karamanos
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Michael Tsatsaronis: University of the Aegean
Ioannis Karamanos: University of the Aegean

Computational Economics, 2021, vol. 57, issue 1, No 4, 55-87

Abstract: Abstract A novel and innovative forecasting framework is proposed to generate newbuilding ship price predictions for different vessel types and shipping markets, incorporating recent developments in the dynamic field of artificial intelligence and machine learning algorithms. Based on the advantages of the support vector machine framework, an appropriate support vector regression (SVR) model is specified, tested, and validated for ship price forecasts. The SVR predictive performance is subsequently comparatively evaluated against standard time-series forecast models, such as the ARIMA models, based on convenient statistical criteria. The predictive power of the SVR model is found to be superior to that of the ARIMA model, delivering satisfactory, robust, and promising results. This is the first empirical application of an SVR model to ship price forecasts and can contribute valuable feedback to investment, financing, and risk management decisions in the global shipping business.

Keywords: Support vector machine learning; Predictive SVR models; ARIMA models; Ship price forecasting; Shipping investment; financing and risk management decisions (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10614-020-10032-2

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