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Forecasting market returns: bagging or combining?

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  • Jordan, Steven J.
  • Vivian, Andrew
  • Wohar, Mark E.

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.

Suggested Citation

  • Jordan, Steven J. & Vivian, Andrew & Wohar, Mark E., 2017. "Forecasting market returns: bagging or combining?," International Journal of Forecasting, Elsevier, vol. 33(1), pages 102-120.
  • Handle: RePEc:eee:intfor:v:33:y:2017:i:1:p:102-120
    DOI: 10.1016/j.ijforecast.2016.07.003
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    Cited by:

    1. Rangan Gupta & Christian Pierdzioch & Andrew J. Vivian & Mark E. Wohar, 2018. "The Predictive Value of Inequality Measures for Stock Returns: An Analysis of Long-Span UK Data Using Quantile Random Forests," Working Papers 201809, University of Pretoria, Department of Economics.
    2. Prokopczuk, Marcel & Tharann, Björn & Wese Simen, Chardin, 2017. "Predicting the Equity Market with Option Implied Variables," Hannover Economic Papers (HEP) dp-619, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.

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