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Quantitative Portfolio Management: Review and Outlook

Author

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  • Michael Senescall

    (Bond Business School, Bond University, Robina, QLD 4229, Australia)

  • Rand Kwong Yew Low

    (Bond Business School, Bond University, Robina, QLD 4229, Australia
    UQ Business School, The University of Queensland, St Lucia, QLD 4072, Australia)

Abstract

This survey aims to provide insightful and objective perspectives on the research history of quantitative portfolio management strategies with suggestions for the future of research. The relevant literature can be clustered into four broad themes: portfolio optimization, risk-parity, style integration, and machine learning. Portfolio optimization attempts to find the optimal trade-off of future returns per unit of risk. Risk-parity attempts to match the exposure of various asset classes such that no single asset class dominates portfolio risk. Style integration combines risk factors on a security level such that rebalancing differences cancel out. Finally, machine learning utilizes large arrays of tunable parameters to predict future asset behavior and solve non-convex optimization problems. We conclude that machine learning will likely be the focus of future research.

Suggested Citation

  • Michael Senescall & Rand Kwong Yew Low, 2024. "Quantitative Portfolio Management: Review and Outlook," Mathematics, MDPI, vol. 12(18), pages 1-25, September.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:18:p:2897-:d:1479653
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