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A comparison of forecasting approaches for capital markets

McDonald, S, Coleman, SA, McGinnity, TM, Li, Y and Belatreche, A 2014, A comparison of forecasting approaches for capital markets , in: The Institute of Electrical and Electronics Engineers (IEEE) : Computational Intelligence for Financial Engineering and Economics Conference, 27-28 March 2014, London.

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Abstract

In recent years, machine learning algorithms have become increasingly popular in financial forecasting. Their flexible, data-driven nature makes them ideal candidates for dealing with complex financial data. This paper investigates the effectiveness of a number of machine learning algorithms, and combinations of these algorithms, at generating one-step ahead forecasts of a number of financial time series. We find that hybrid models consisting of a linear statistical model and a nonlinear machine learning algorithm are effective at forecasting future values of the series, particularly in terms of the future direction of the series.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Computational Intelligence for Financial Engineering & Economics (CIFEr), 2104 IEEE Conference
Publisher: The Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Related URLs:
Funders: Non funded research
Depositing User: Yuhua Li
Date Deposited: 19 Jun 2015 18:26
Last Modified: 05 Apr 2016 18:18
URI: http://usir.salford.ac.uk/id/eprint/33100

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