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.
Full text not available from this repository.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: | 06 Sep 2021 07:38 |
| URI: | https://usir.salford.ac.uk/id/eprint/33100 |
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