Exchange traded funds and asset return correlations

• DOI: http://doi.org/10.1111/eufm.12137
• Author: Zhi Da,Sophie Shive
• Date: 01 January 2018
• Published date: 01 January 2018

DOI: 10.1111/eufm.12137

ORIGINAL ARTICLE

Exchange traded funds and asset return

correlations

Zhi Da

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Sophie Shive

Mendoza College of Business, University

of Notre Dame, Notre Dame,

IN 46556

Emails: sshive1@nd.edu; zda@nd.edu

Abstract

We provide novel evidence supporting the notion that

arbitrageurs can contribute to return comovement via

exchange trade funds (ETF) arbitrage. Using a large sample

of US equity ETF holdings, we document the link between

measures of ETF activity and return comovement at both the

fund and the stock levels, after controlling for a host of

variables and fixed effects and by exploiting the ‘disconti-

nuity’between stock indices. The effect is also stronger

among small and illiquid stocks. An examination of ETF

return autocorrelations and stock lagged beta provides

evidencefor price reversal, suggesting that someETF-driven

return comovement may be excessive.

KEYWORDS

exchange-traded-fund, correlation, arbitrage

JEL CLASSIFICATION

G23, G12

Thanks to an anonymous referee, participants in the 2012 State of Indiana Finance Conference, 2013 China International

Conference in Finance, 2nd Luxembourg Asset Management Summit, 2014 American Finance Association Annual Meeting,

and seminars at Nanyang Technological University, National University of Singapore, Singapore Management University,

University of Cincinnati, University of Illinois at Urbana-Champaign, University of Notre Dame, Vanderbilt University,

Malcom Baker, Robert Battalio, Hendrik Bessembinder, Martijn Cremers, Ben Golez, Robin Greenwood, Bing Han, Paul

Schultz, David Solomon, Mao Ye and Xiaoyan Zhang for helpful comments. This article has been previously circulated

under the title ‘When the bellwether dances to noise: Evidence from exchange-traded funds’. Errors are ours.

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© 2017 John Wiley & Sons, Ltd. wileyonlinelibrary.com/journal/eufm Eur Financ Manag. 2018;24:136–168.

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INTRODUCTION

Perhaps due to a half-century of encouragement from finance academics, investment assets are

increasinglyindexed, but the implications forasset prices of large amounts of indexedinvestment are not

well understood.Citing evidence of mispricing and increasedcorrelations among asset returns, Wurgler

(2010) warns that over-indexing may result in contagion and mispricing risk. Exchange-traded funds

(ETFs), baskets of equities traded on an exchange like stocks, are a growing asset class that has made

indexing cheaper and more convenient for many investors. US-based exchange-traded funds had US

$ 1.7 trillion in assets under management by the end of 2013.

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Since these funds will by all measures

play a large role in the future of saving and investing, it is important to understand if and how they

will affect prices, both in absolute and compared to traditional mutual funds and institutions.

Along with information, ETFs have a potential to transmit non-fundamental shocks. Demand for

ETFs results in price pressure, which is then transmitted to the underlying basket of shares as

arbitrageurs simultaneously take opposite positions in the ETF and the underlying shares.

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As a result,

stocks held by ETFs might comove more with each other than warranted by common exposure to

fundamentals. Arbitrageurs, who are generally enforcers of price efficiency, can thus at times

contribute to excess comovement, consistent with the results in Shleifer & Vishny (1997), Hong,

Kubik, & Fishman (2012) and Lou & Polk (2013). While correlated trading of stocks in the same sector

or style category may also create non-fundamental shocks, to the extent that investors have some

discretion in deciding when and what to trade, ETF arbitrage is more likely than other types of

correlated order flow in driving return comovement among its component stocks.

A large literature on stock comovement has found that adding a stock to an index affects its price

(Harris & Gurel, 1986; Kaul, Mehrotra, & Morck, 2002; Lynch & Mendenhall, 1997; Shleifer, 1986;

Wurgler and Zhuravskaya, 2002) and correlation between the newly added stocks and other stocks in

the index increases (Barberis et al., 2005; Goetzmann & Massa, 2003 for the S&P 500; and Greenwood

& Sosner, 2007 for the Nikkei 225). This literature is subject to the caveat that missing fundamental

factors are driving both the index addition and deletion decision and comovement.

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Examining

arbitrage-driven ETF turnover helps to alleviate this concern since the relative mispricing between ETF

and its underlying stocks is not directly related to index addition and deletion decision. Throughout our

empirical analysis, we do control for other forms of index trading in order to isolate the incremental

impact of ETF arbitrage on return comovement.

Using a large panel of 549 US equity ETFs and 4,887 stocks from July 2006 to December 2013, we

show that ETFs contribute to equity return comovement. An ETF-level analysis reveals that the higher

turnover an ETF has, the more its component stocks move together at monthly frequency, controlling

for time trends, fund- and time-fixed effects, in addition to a host of fund-level control variables.

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See: http://www.icifactbook.org/fb_ch3.html

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The transparency of an ETF's holdings make such arbitrage possible. According to Investor Company Institute Website,

‘ETFs contract with third parties (typically market data vendors) to calculate an estimate of an ETF's Intraday Indicative

Value (IIV), using the portfolio information an ETF publishes daily. IIVs are disseminated at regular intervals during the

trading day (typically every 15 to 60 seconds). Some market participants for whom a 15- to 60-second latency is too long

will use their own computer programs to estimate the underlying value of the ETF on a more real-time basis.’

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Greenwood (2008) that takes advantage of the index weighting scheme is a notable exception.

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Fund fixed effects alleviate the selection bias that arises when similar stocks are selected by the same ETF. Time fixed

effects are also crucial since both ETF activities and stock comovement can be driven by the same macroeconomic

variables. For example, Forbes and Rigobon (2002) show that equity correlation tends to increase during volatile periods

when the trading volumes are also high.

DA AND SHIVE

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To alleviate concerns that a com mon trend in both ETF activity and r eturn comovement drives

their link, we also include an interaction term between th e fund fixed effect and a time trend.

Finally, our analysis also cor rects for cross-correlation in error terms arisin g from common holdings

across ETFs.

At the fund level, a one-standard-deviation increase in the turnover of a typical ETF in our sample is

associated with a 1% increase in the average correlation among its component stocks. This relationship

is not driven by ETFs on large indices with futures and options traded.

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This effect is stronger among

larger ETFs and ETFs that are often traded simultaneously with their underlying stock portfolios,

supporting our conjecture that the comovement is driven by arbitrage between ETFs and the underlying

stock portfolios.

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ETF arbitrage can occur in a different form via ETF creation and redemption activity. Consider the

case when an ETF is trading at a discount, the authorized participants (APs) could buy the ETF shares

and sell short the underlying securities. At the end of the day, APs return the ETF shares to the fund in

exchange for the ETF's redemption basket of securities, which they use to cover their short positions.

We find that our measure of creation and redemption activity is less strongly related to comovement

than are ownership or turnover. This is not surprising as APs can borrow the underlying shares from or

return these shares to large institutional investors such as pension funds without actually trading the

underlying shares and causing excessive correlations.

A key challenge is that the stocks in the same ETF may comove due to their common exposures to

fundamental shocks. To better control for fundamentals-driven return comovement, we focus on a

‘discontinuity’between two stock indices, namely, the large-cap S&P100 index and the mid-cap

S&P400 index which together combine to form the S&P500 index. At the end of each month, we define

three portfolios: Portfolio A contains the smallest stocks in the S&P100; Portfolio B contains

the largest stocks in the S&P400 and Portfolio C contains the remaining S&P400 stocks. We model the

next-month daily returns on these three portfolios using the framework of Greenwood and Thesmar

(2011). Since Portfolios A and B contain similar stocks by construction, the covariance between their

return spread and the return on Portfolio C should more cleanly isolate correlated trading induced by

arbitrage activities on the S&P400 index ETFs. Indeed, we find this covariance to significantly load on

measures of activities on the S&P400 index ETFs. In addition, the average stock correlation in

Portfolio B is strongly linked to the turnover on the S&P400 index ETF, even after controlling for the

average stock correlation in Portfolio A. The evidence suggests that return comovement is driven by

common ETF membership, rather than general demand for the market portfolio or other fundamental

factors that may result in correlated trading in similar stocks.

We also conduct our analysis at the stock level. While arbitr age trading on one ETF only makes

a stock in that ETF comove more with the stock basket underly ing the same ETF, the average stock

in our sample is held simultaneously by 26 ETFs . As such, when the average arbitrage acti vity on

these 26 ETFs increases, we expe ct a stock to comove more with its ‘super-portfolio’that

holds all 26 underlying stock b askets. Empirically, we fin d the stock's beta with respect to its

‘super-portfolio’to highly correlate with the stock's CAPM beta with a cor relation coefficient of

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Only three indices have futures, options or futures options traded on them during our sample period. They are S&P500,

NASDAQ 100 and Dow Jones Industrial Average. Out of the 549 ETFs in our sample, only 7 are based on these three

indices.

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We do not use the daily difference between ETF price and ETF NAV as a proxy for arbitrage trading for two reasons.

First, there is a potential non-synchronicity issue between the ETF price and its NAV, making their difference a noisy

measure of mispricing. Second and more importantly, a price difference can reflect either an actual opportunity for arbitrage

trade or the presence of limits-to-arbitrage.

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DA AND SHIVE