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Title: Ambiguity with Machine Learning: An Application to Portfolio Choice

Author(s): Eric Ghysels, Yan Qian and Steve Raymond

Publication Date: November 2021

Keyword(s):

Programme Area(s): Financial Economics

Abstract: To characterize ambiguity we use machine learning to impose guidance and discipline on the formulation of expectations in a data-rich environment. In addition, we use the bootstrap to generate plausible synthetic samples of data not seen in historical real data to create statistics of interest pertaining to uncertainty. While our approach is generic we focus on robust portfolio allocation problems as an application and study the impact of risk versus uncertainty in a dynamic mean-variance setting. We show that a mean-variance optimizing investor achieves economically meaningful wealth gains (33%) across our sample from 1996-2019 by internalizing our uncertainty measure during portfolio formation.

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Bibliographic Reference

Ghysels, E, Qian, Y and Raymond, S. 2021. 'Ambiguity with Machine Learning: An Application to Portfolio Choice'. London, Centre for Economic Policy Research. https://cepr.org/active/publications/discussion_papers/dp.php?dpno=16748