Alternative Risk Premium: Workhorse or Trojan Horse? Diversified alternative risk premium (ARP) portfolios seek to generate absolute returns using a broad range of systematic ...
Partner, Head of Multi-Asset Portfolio Management at Wellington Management Company, USA
Alternative Risk Premium: Workhorse or Trojan Horse? Diversified alternative risk premium (ARP) portfolios seek to generate absolute returns using a broad range of systematic trading strategies incorporating multiple investment styles covering all the major asset classes. Following a period of rapid adoption, disappointing performance over the 2018-2020 period has produced considerable soul searching regarding the role of ARP in institutional portfolios. To examine this very topical issue, this paper utilizes a unique array of benchmarks designed using a proprietary database of 2,000 tradable bank indices. The paper evaluates whether recent returns are consistent with long-term expectations, in the process considering the extent to which data mining, unique environmental headwinds, capacity pressure, or a lack of true breadth across ARP strategies contributed to this outcome.
The Data Dilemma in Alternative Risk Premium: Why Is a Benchmark So Elusive? Alternative risk premium (ARP) is an investment category consisting of a wide range of rules-based trading strategies targeting returns representing either compensation for bearing risk or behavioral biases among market participants. These strategies span all the major asset classes, trading equity indices, government bonds, currencies, commodities, credit spreads, volatility, and individual stocks. ARP constituents generally share the following three characteristics: (1) clear economic rationale supported by empirical research, (2) persistent risk-adjusted return distinct from that of traditional beta, and (3) liquid (scalable), rules-based and transparent, with a predominantly long-short trading profile. Assets under management in ARP have increased significantly over the past decade, but benchmarks remain elusive, making performance evaluation challenging. Focus on this topic has intensified with recent disappointing performance. This paper introduces comprehensive categorical and statistical families of ARP benchmarks, using a proprietary database of tradable bank indices. The exercise includes a detailed and overdue discussion of the many nuances of ARP data, including classification, curation and interpretation. These benchmarks mark an important foundational milestone for analysis in this evolving space.
|Thesis Committee :||
Supervisor: Frank J. Fabozzi, EDHEC Business School
External reviewer: Hossein B. Kazemi, University of Massachusets Amherst
Other committee members: Emmanuel Jurczenko, Nikolaos Tessaromatis, and Enrique Schroth, EDHEC Business School