Consistent and Efficient Dynamic Portfolio Replication with Many Factors

Lars Stentoft, Sha Wang

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Abstract

Factor investing involves choosing securities to construct portfolios with particular risk-return profiles. With the proliferation of benchmark-tracking exchange-traded funds (ETFs) virtually any risk-return profile can be reconstructed; the challenge is to find the right ETFs because the number of relevant ETFs is very large. This article proposes an innovative modification to the resampling procedure used in many popular machine learning methods for reducing the dimensionality of this problem. The proposed method allows selection of the specific ETFs used to replicate returns, taking the total costs of using the optimal portfolio to dynamically track returns into consideration. Existing variable selection algorithms are not designed to incorporate rebalancing costs, which are accumulated over time. The methodology is illustrated by replicating hedge fund returns with ETFs. The results show that, by selecting the right replication instruments in a way that is consistent with an investor's economic utility instead of using purely statistical losses, the investor can save around 60 bps per year.

Original languageEnglish
JournalThe Journal of Portfolio Management
Volume46
Issue2
Pages (from-to)79-91
Number of pages13
ISSN0095-4918
DOIs
Publication statusPublished - 2020
Externally publishedYes

Keywords

  • Big data/machine learning*
  • Exchange-traded funds and applications
  • Simulations
  • Statistical methods

Fingerprint

Dive into the research topics of 'Consistent and Efficient Dynamic Portfolio Replication with Many Factors'. Together they form a unique fingerprint.

Cite this