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Forecasting market returns: bagging or combining?

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journal contribution
posted on 12.08.2016 by Steven J. Jordan, Andrew Vivian, Mark Wohar
This paper provides a rigorous and detailed analysis of the methods of bagging, which addresses both model and parameter uncertainty. We provide a multi-country study of bagging, of which there are very few to date, that examines out-of-sample forecasts for the G7 and a broad set of Asian countries. We find that, when portfolio weight restrictions are applied, bagging generally improves forecast accuracy and generates economic gains relative to the benchmark. Bagging also performs well compared to forecast combinations in this setting. We incorporate data mining critical values for appropriate inference on bagging and combination forecast methods. We provide new evidence that the results for bagging cannot be fully explained by data mining concerns. Finally, forecasting gains are highest for countries with high trade openness and high FDI. The potentially substantial economic gains could well be operational given the existence of index funds for most of these countries.

History

School

  • Business and Economics

Department

  • Business

Published in

International Journal of Forecasting

Issue

forthcoming

Citation

JORDAN, S.J., VIVIAN, A. and WOHAR, M.E., 2016. Forecasting market returns: bagging or combining? International Journal of Forecasting, 33 (1), pp. 102-120.

Publisher

© International Institute of Forecasters. Published by Elsevier

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

29/07/2016

Publication date

2016-10-19

Notes

This paper was accepted for publication in the journal International Journal of Forecasting and the definitive published version is available at http://dx.doi.org/10.1016/j.ijforecast.2016.07.003

ISSN

0169-2070

eISSN

1099-131X

Language

en

Exports