A comparison of forecasting approaches for capital markets

McDonald, S; Coleman, SA; McGinnity, TM; Li, Y; Belatreche, A

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.

Citation

McDonald, S., Coleman, S., McGinnity, T., Li, Y., & Belatreche, A. (2014, March). A comparison of forecasting approaches for capital markets. Presented at The Institute of Electrical and Electronics Engineers (IEEE) : Computational Intelligence for Financial Engineering and Economics Conference, London

• Presentation Conference Type: Other
• Conference Name: The Institute of Electrical and Electronics Engineers (IEEE) : Computational Intelligence for Financial Engineering and Economics Conference
• Conference Location: London
• Start Date: Mar 27, 2014
• End Date: Mar 28, 2014
• Publication Date: Mar 27, 2014
• Deposit Date: Jun 19, 2015
• Publisher: Institute of Electrical and Electronics Engineers
• Publisher URL: http://dx.doi.org/10.1109/CIFEr.2014.6924051
• Related Public URLs: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6901616
• Additional Information: Event Type : Conference