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Forecasting market returns: bagging or combining?

Steven J. Jordan, Andrew Vivian and Mark Wohar ()

International Journal of Forecasting, 2017, vol. 33, issue 1, 102-120

Abstract: This paper provides a rigorous and detailed analysis of bagging methods, which address both model and parameter uncertainty. We provide a multi-country study of bagging, of which there have been very few to date, that examines out-of-sample forecasts for the G7 and a broad set of Asian countries. We find that bagging generally improves the forecast accuracy and generates economic gains relative to the benchmark when portfolio weight restrictions are applied. 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 explained fully by data mining concerns. Finally, the 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.

Keywords: Return forecasting; Fundamentals; Macro variables; Technical indicators; Emerging markets; Asia; G7; Data mining; Bootstrapping (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1016/j.ijforecast.2016.07.003

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Handle: RePEc:eee:intfor:v:33:y:2017:i:1:p:102-120