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