Portfolio Pretesting with Machine Learning

36 Pages Posted: 24 Jun 2020

See all articles by Ekaterina Kazak

Ekaterina Kazak

University of Manchester

Winfried Pohlmeier

University of Konstanz - Department of Economics & Center of Finance & Econometrics (CoFE)

Date Written: December 2, 2019

Abstract

This paper exploits the idea of pretesting to choose between competing portfolio strategies.

We propose a strategy that optimally trades off between the risk of going for a false positive strategy choice versus the risk of making a false negative choice.

Various different data-driven approaches are proposed based on an optimal choice of the pretested certainty equivalent and Sharpe Ratio.

Our approach belongs to the class of shrinkage portfolio estimators. However, contrary to previous approaches the shrinkage intensity is continuously updated to incorporate the most recent information in the rolling window forecasting set-up.

We show that the bagged pretest estimator performs exceptionally well, especially when combined with adaptive smoothing. The resulting strategy allows for a flexible and smooth switch between the underlying strategies and is shown to outperform the corresponding stand-alone strategies.

Keywords: Pretest Estimation, Bagging, Portfolio Allocation, Adaptive Learning

Suggested Citation

Kazak, Ekaterina and Pohlmeier, Winfried, Portfolio Pretesting with Machine Learning (December 2, 2019). Available at SSRN: https://ssrn.com/abstract=3615666 or http://dx.doi.org/10.2139/ssrn.3615666

Ekaterina Kazak (Contact Author)

University of Manchester ( email )

Arthur Lewis Building
Oxford Road
Manchester, M13 9PL
United Kingdom

Winfried Pohlmeier

University of Konstanz - Department of Economics & Center of Finance & Econometrics (CoFE) ( email )

Konstanz, D-78457
Germany

HOME PAGE: http://econometrics.wiwi.uni-konstanz.de

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