Capturing hedge fund risk factor exposures: Hedge fund return replication with ETFs

• Published date: 01 August 2020
• DOI: http://doi.org/10.1111/fire.12221
• Author: Jun Duanmu,Alexey Malakhov,Yongjia Li
• Date: 01 August 2020

DOI: 10.1111/fire.12221

ORIGINAL ARTICLE

Capturing hedge fund risk factor exposures: Hedge

fund return replication with ETFs

Jun Duanmu1Yongjia Li2Alexey Malakhov3

1College of Business, LouisianaTech University,

Ruston, Louisiana

2College of Business and Economics, Boise State

University, Boise, Idaho

3Sam M. WaltonCollege of Business, University

of Arkansas, Fayetteville,Arkansas

Correspondence

AlexeyMalakhov,Sam M. Walton College of

Business,University of Arkansas, WCOB 343,

Fayetteville,AR 72701.

Email:amalakhov@walton.uark.edu

Abstract

We develop a new factor selection methodology of spanning the

space of hedge fund risk factors with all available exchange traded

funds (ETFs). We demonstrate the efficacy of the methodology with

out-of-sample individual hedge fund return replication by ETF clone

portfolios. This is consistent with our interpretation of ETF returns

as proxies to risk factors driving hedge fund returns. We further

consider portfolios of “cloneable” and “noncloneable” hedge funds,

defined as top and bottom in-sample R2matches, and demonstrate

that our ETF clone portfolios slightly outperform cloneable hedge

funds out of sample.

KEYWORDS

factor selection, hedge funds, return replication, risk factor expo-

sures

JEL CLASSIFICATIONS

G11, G23

1INTRODUCTION

Hedge funds have experienced tremendous growth in recent years, with more than $3.2 trillion currently investedin

hedge funds globally,1and are now considered an essential part of alternative investment strategiesby institutional

investors and financial institutions. Hedge funds have been able to produce returns with relatively low correlations

with major asset classes, such as stocks and bonds, due to the multitude of investment opportunities available to fund

managers. Unlike managers of more traditional mutual funds, hedge fund managers have the flexibility to invest in

nontraditionalasset classes (including derivative securities), employ leverage, and engage in short sales. However,such

strategies also expose investors to alternative risk factors that may not be easy to quantify, given the opacity of the

hedge fund industry. It is then natural to question whether the returns earned by hedge fund managers are due to

1According to Hedge Fund Research, Inc., April 19, 2018, press release. Retrieved from https://www.hedgefundresearch.com/news/hfr-global-hedge-fund-

industry-report-q1-2018-published

Financial Review.2020;55:405–431. wileyonlinelibrary.com/journal/fire c

2019 The Eastern Finance Association 405

406 DUANMU ETAL.

managerial skill or merely compensation for exposure to alternative risk factors.2If a significant portion of hedge fund

returns comes from alternative risk factor exposures, then it is reasonable to presume that it is possible for investors

to replicate that part of hedge fund returns at a lower cost by taking on these risk exposures themselves. However,

such an exercise hinges on the investor’s ability to identify and quantify these alternative risk factors via proxiesof

portfolios of tradable and liquid securities.3That is why the issue of choosing appropriate risk factors is central to

any study of hedge fund performance, and currently there is no set of factors that is universally accepted across the

literature.4

The main research objective of the paper is to focus on the factor-driven component of hedge fund returns5and

capturing it with easily tradable investment instruments of exchangetraded funds (ETFs). Our approach builds on the

methodology of return attribution, and it relies on proper identification and selection of risk factors relevant for each

individual hedge fund. We argue that passive ETFs can be interpreted as proxies for risk factors, as these return pat-

terns are executedformulaically without human discretion. We argue that the full universe of ETFs currently provides

comprehensive coverage of the space of risk factors that investors find attractivefrom the risk-and-return perspec-

tive. We span the space of potential risk factors with passively managed ETFs from 1997 to 2015. This period saw an

explosion in ETFs available, with the number of U.S.-listedpassively managed ETFs going from 19 in 1997 to 1,711 in

2015. Meanwhile, the ETF coverage of alternative risk factors went from almost nonexistent in 1997 to being com-

prehensive, with ETFs currently providing access to a great variety of alternativestrategies that were previously avail-

able only to hedge funds or institutional investors.6This provides us with a unique opportunity to investigate how

the expanding space of alternative risk factors affects the quality of hedge fund replication with ETFs available at the

time.

Although the large number of ETFs available in the later years of our study allows for more complete spanning of

the space of risk factors, it also increases potential for spurious results due to excessive data mining. We develop a

new methodology for linear hedge fund return replication that overcomesmulticollinearity among ETFs and minimizes

data mining bias while utilizing all ETFs available. Comparing the performance of hedge funds with their ETF clones

in and out of sample, we find high accuracy of hedge fund replication with ETFs when there is sufficient number of

ETFs available. We demonstrate that in the subperiod starting in 2005, the overallout-of-sample performance of the

portfolio of all hedge funds is not statistically different from the portfolio of clones. We attribute thisto the sufficiently

large number of available ETFs in the later years, which allow us to successfully span the space of hedge fund risk

factors.

In a departure from previous hedge fund replication studies, we go beyond considering replicating hedge fund

indexes or average hedge fund performance. Weconsider portfolios of “cloneable” and “noncloneable” hedge funds,

defined as top and bottom in-sample adjusted R2matches. Intuitively, we should not expect success in hedge

fund return replication for a truly skilled hedge fund manager who pursues investment opportunities uncorrelated

with risk factors, delivering true alpha to investors. On the other hand, we fully expect success in return replica-

tion for a manager who follows a rigid formulaic strategy, such as writing out of the money put options on the

2Forexample, John H. Cochrane observes, “AsI look across the hedge fund universe, 90% of what I see is not ‘picking assets to exploit information not reflected

in prices,’ it is ‘taking exposure to factors that managers understand and can tradebetter than clients’” (John H. Cochrane’s “Hedge Funds” lecture notes at

http://faculty.chicagobooth.edu/john.cochrane/teaching/35150_advanced_investments/hedge_notes_and_questions.pdf).

3Notethat if there is no tradable option available to investors for a particular alternative risk factor, then it could be argued that hedge funds are valuable by

merelyproviding access to that risk exposure. Such exposure through hedge funds comes at a high premium in the form of management and incentive fees.

4For example,return attribution studies (Agarwal & Naik, 2004; Fung & Hsieh, 2001, 2004) introduce new trend following and option-based risk factors in

addition to Fama and French(1993) and Carhart (1997) factors. On the other hand, hedge fund replication studies (Amenc, Martellini, Meyfredi, & Ziemann,

2010;Giamouridis & Paterlini, 2010; Hasanhodzic & Lo, 2007) employ liquid index portfolios available to investors.

5Such return patterns are commonly associated with either “passive” or “smart beta” investmentstyles (the difference between “passive” and “smart beta”

investmentstyles is typically in the degree of sophistication in utilizing exotic risk factors).

6As an exampleof available ETF strategies, consider ALPS U.S. Equity High Volatility Put Write Index Fund (ticker HVPW) that tracksNYSE Arca U.S.Equity

High VolatilityPut Write Index with an annual expense ratio of .95%. The ETF benchmark tracks the performance of options sold on a basketof 20 stocks

chosen from the largest capitalized equities that have the highest volatility,as determined by NYSE Arca Inc. Other examples include currency carry ETFs,

volatilityETFs, value ETFs, momentum ETFs, and so on.