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Portfolio Models For Optimizing Drawdown Duration

Author

Listed:
  • ANDREI VEDERNIKOV

    (Management Science Group, Department of Information and Service Management, Aalto University School of Business, P.O. Box 21220, FI-00076 Aalto, Finland)

  • JUUSO LIESIÖ

    (Management Science Group, Department of Information and Service Management, Aalto University School of Business, P.O. Box 21220, FI-00076 Aalto, Finland)

  • TOMI SEPPÄLÄ

    (Management Science Group, Department of Information and Service Management, Aalto University School of Business, P.O. Box 21220, FI-00076 Aalto, Finland)

Abstract

The drawdown duration, which measures the time elapsed since the portfolio obtained its maximum value, is an important criterion in active portfolio management for institutional investors. Although several optimization models exist for controlling portfolio drawdown magnitude (i.e. the percentage drop in portfolio value from its latest peak value), developing similar models for the drawdown duration has received minimal attention in the literature. Therefore, this paper develops a family of models for optimizing average, maximum and tail drawdown duration formulated as mixed-integer linear programming (MILP) problems, allowing the utilization of powerful solvers to identify optimal asset portfolios. We apply the developed models to real data on historical returns to compare their performance against traditional and drawdown-based portfolio selection models. The results indicate that the developed models lead to decrease in drawdown duration levels both in in-sample and out-of-sample tests. The constructed efficient frontiers also show a clear trade-off between minimizing drawdown duration and maximizing expected returns.

Suggested Citation

  • Andrei Vedernikov & Juuso Liesiã– & Tomi Seppã„Lã„, 2024. "Portfolio Models For Optimizing Drawdown Duration," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 27(02), pages 1-44, March.
    • Handle: RePEc:wsi:ijtafx:v:27:y:2024:i:02:n:s0219024924500146
    DOI: 10.1142/S0219024924500146
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