Forecasting market returns: bagging or combining?

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• 作者：Steven J. Jordan^aAuthor Vitae ; Andrew Vivian^b ; ^{a.j.vivian@lboro.ac.uk}Author Vitae ; Mark E. Wohar^c ; ^bAuthor Vitae
• 关键词：Return forecasting ; Fundamentals ; Macro variables ; Technical indicators ; Emerging markets ; Asia ; G7 ; Data mining ; Bootstrapping
• 刊名：International Journal of Forecasting
• 出版年：2017
• 出版时间：January-March 2017
• 年：2017
• 卷：33
• 期：1
• 页码：102-120
• 全文大小：741 K
• 卷排序：33

文摘

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.