A Comparison of Forecasting Approaches for Capital Markets

Scott McDonald, SA Coleman, TM McGinnity, Yuhua Li, Ammar Belatreche

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Pages32-39
Number of pages8
Publication statusPublished - 27 Mar 2014
EventIEEE Computational Intelligence for Financial Engineering and Economics -
Duration: 27 Mar 2014 → …

Conference

ConferenceIEEE Computational Intelligence for Financial Engineering and Economics
Period27/03/14 → …

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  • Cite this

    McDonald, S., Coleman, SA., McGinnity, TM., Li, Y., & Belatreche, A. (2014). A Comparison of Forecasting Approaches for Capital Markets. In Unknown Host Publication (pp. 32-39). IEEE.