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Active Preference Learning for Personalized Portfolio Construction

Author

Listed:
  • Kevin Tee
  • Michael McCourt
  • Ruben Martinez-Cantin
  • Ian Dewancker
  • Frank Liu

Abstract

In financial asset management, choosing a portfolio requires balancing returns, risk, exposure, liquidity, volatility and other factors. These concerns are difficult to compare explicitly, with many asset managers using an intuitive or implicit sense of their interaction. We propose a mechanism for learning someone's sense of distinctness between portfolios with the goal of being able to identify portfolios which are predicted to perform well but are distinct from the perspective of the user. This identification occurs, e.g., in the context of Bayesian optimization of a backtested performance metric. Numerical experiments are presented which show the impact of personal beliefs in informing the development of a diverse and high-performing portfolio.

Suggested Citation

  • Kevin Tee & Michael McCourt & Ruben Martinez-Cantin & Ian Dewancker & Frank Liu, 2017. "Active Preference Learning for Personalized Portfolio Construction," Papers 1708.07567, arXiv.org.
    • Handle: RePEc:arx:papers:1708.07567
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    File URL: http://arxiv.org/pdf/1708.07567
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