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A Comparison of Forecasting Approaches for Capital Markets

McDonald, Scott, Coleman, SA, McGinnity, TM, Li, Yuhua and Belatreche, Ammar (2014) A Comparison of Forecasting Approaches for Capital Markets. In: IEEE Computational Intelligence for Financial Engineering and Economics. IEEE. 8 pp. [Conference contribution]

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Abstract

In recent years, machine learning algorithms havebecome increasingly popular in financial forecasting. Theirflexible, data-driven nature makes them ideal candidates fordealing with complex financial data. This paper investigates theeffectiveness of a number of machine learning algorithms, andcombinations of these algorithms, at generating one-step aheadforecasts of a number of financial time series. We find thathybrid models consisting of a linear statistical model and a nonlinearmachine learning algorithm are effective at forecastingfuture values of the series, particularly in terms of the futuredirection of the series.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:29837
Deposited By: Dr Sonya Coleman
Deposited On:24 Sep 2014 15:15
Last Modified:24 Sep 2014 15:15

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