Hyperparameter Optimization for Portfolio Selection

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift


Portfolio optimization involves a tradeoff between maximizing expected return
and minimizing risk. In practice, useful formulations also include various costs and constraints that regularize the problem and reduce the risk due to estimation errors, resulting in solutions that depend on a number of hyperparameters. As the number of hyperparameters grows, selecting their value becomes increasingly important and difficult. In this article we propose a systematic approach to hyperparameter optimization by leveraging recent advances in automated machine learning and multi-objective optimization. We optimize hyperparameters on a train set to yield the best result subject to market-determined realized costs. For example, we show that when the signal-to-noise ratio of return forecasts deteriorates, the optimal level of transaction costs imposed in portfolio optimization increases to prevent excessive
noise trading. In applications to single- and multi-period portfolio optimization,
we show that sequential hyperparameter optimization finds solutions with better
return/risk tradeoffs than manual, grid, and random search over hyperparameters using fewer function evaluations. At the same time, the solutions found are more stable from in-sample training to out-of-sample testing, suggesting they are less likely to be extremities that randomly happened to yield good performance in training.


Enheter & grupper
Externa organisationer
  • Technical University of Denmark

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Beräkningsmatematik
  • Reglerteknik
TidskriftThe Journal of Financial Data Science
Utgåva nummer2
StatusPublished - 2020 jun 18
Peer review utfördJa