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Is there a dark side to exchange traded funds? An information perspective

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Abstract

We examine whether an increase in ETF ownership is accompanied by a decline in pricing efficiency for the underlying component securities. Our tests show an increase in ETF ownership is associated with (1) higher trading costs (bid-ask spreads and market liquidity), (2) an increase in “stock return synchronicity,” (3) a decline in “future earnings response coefficients,” and (4) a decline in the number of analysts covering the firm. Collectively, our findings support the view that increased ETF ownership can lead to higher trading costs and lower benefits from information acquisition. This combination results in less informative security prices for the underlying firms.

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Notes

  1. See, for example, Grossman and Stiglitz (1980), Hellwig (1980), Diamond and Verrecchia (1981), Verrecchia (1982), Admati (1985), and Kyle (1985, 1989).

  2. We use the terms “pricing efficiency” and “informational efficiency” interchangeably. Both refer to the speed and efficiency with which price incorporates new information. Empirically, we use several proxies to measure informational efficiency, including “price synchronicity” (SYNCH), “future earnings response coefficients” (FERC), and the number of analysts covering a firm (ANALYST).

  3. Several models predict noise investors will migrate to index-like instruments because their losses to informed traders are lower in these markets than in the market for individual securities (Rubinstein 1989; Subrahmanyam 1991; Gorton and Pennacchi 1993). Empirically, we have observed such a migration from actively managed assets to passively managed ETFs in recent years. As of June 2015, total ETF trading is close to 28% of the total daily value traded on US equity exchanges (Pisani 2015).

  4. Note that the siphoning of liquidity from component securities can occur with other basket securities as well, such as open-end index funds. However, a key difference between ETFs and index-linked open-end funds is that ETF shares can be traded throughout the day, while transactions with open-end funds occur only at the end of the day and only at net asset value (NAV). Thus ETFs are a more attractive instrument for uninformed traders who trade for speculative reasons, while index funds are better suited to longer term buy-and-hold investors. In section 2, we explain in detail the implications of this difference for our tests.

  5. We use annual holding periods to test our hypotheses because we expect the information-related effects of ETF ownership changes to be experienced gradually over time. Our inferences are the same if we use quarterly panels.

  6. To improve our ability to identify the consequences of increased ETF ownership in a cleaner setting, we focus mainly on analyzing the associations of lagged changes in ETF ownership with firms’ trading costs and measures of pricing efficiency.

  7. For reasons detailed in section 3, we decompose the Amihud (2002) measure of price impact of trades and investigate the effect of increased ETF ownership on the numerator of the Amihud (2002) measure, ILLIQ_N, controlling for the denominator of the Amihud (2002) measure, ILLIQ_D.

  8. Compared to Hamm (2014), we use alternative measures of stock liquidity, include different control variables, examine annual versus quarterly observations, and use a more complete firm-level longitudinal data set. Our main findings with respect to the effect of ETF ownership on stock liquidity are consistent with those of Hamm (2014). It should be noted that Hamm (2014) does not examine the implications of ETF ownership on the informational efficiency of security prices.

  9. These measures have been featured in prior literature on pricing efficiency (Roll 1984; Durnev et al. 2003; Piotroski and Roulstone 2004; Ettredge et al. 2005; Choi et al. 2011).

  10. Sullivan and Xiong (2012) note that, while passively managed funds represent only about one-third of all fund assets, their average annual growth rate since the early 1990s is 26%, double that of actively managed assets. Much of this increase has been in the form of ETFs. According to Madhavan and Sobczyk (2014), as of June 2014, there were 5217 global ETFs representing $2.63 trillion in total net assets.

  11. Specifically, unlike ETFs, open-end funds do not provide a ready intraday market for deposits and redemptions with a continuous series of available transaction prices. Hence investors may not know with sufficient certainty the cash-out value of redemption before they must commit it.

  12. Note that ETFs are most likely to be successful when the underlying securities are relatively less liquid or difficult to borrow (thus creating an equilibrium demand for the ETF shares, with its lower trading costs). For example, the highly popular small-cap ETF, IWM, is based on the Russell 2000 index. While the underlying securities are typically less liquid (they represent the 2000 stocks in the Russell Index that are below the largest 1000), IWM itself is over $26 billion in assets and trades at extremely low costs.

  13. What happens if the cost of private information remains constant? In that case, pricing efficiency may not be affected by an exodus of uninformed traders. This result derives because the following two opposing forces are at work.

    a. As uninformed traders exit the market the profits from trading with them as an informed trader becomes smaller.

    b. As fewer informed traders purchase private signals, the value of being one of the remaining informed traders becomes larger.

    The net effect is that fewer informed traders will individually make more money, with no net change in the economy-wide value of becoming informed (which remains equal to the information cost). Although the source of noise differs in the models of Verrecchia (1982) and the Grossman and Stiglitz (1980), the same result obtains in both. In both, pricing efficiency will be unaffected by an exodus of uninformed traders if information costs remain constant. We are grateful to the editor for pointing this out.

  14. We test our hypotheses using annual panels because we expect the effect of increased ETF ownership to manifest itself gradually over time after an increase in ETF ownership. Figure 2 presents a sample construction timeline for the key empirical variables used in our tests. Most of our analyses are done using annual changes in ETF ownership, returns, and earnings (Panel A). However, in our replication and reconciliation of the Glosten et al. results, we used quarterly data (Panel B) to match their analyses.

  15. Prior research on the relation between bid-ask spreads and institutional ownership is mixed. Glosten and Harris (1999) suggest that higher levels of concentrated institutional ownership will increase bid-ask spreads, while higher levels of dispersed institutional ownership might encourage competition that reduces bid-ask spreads.

  16. Our inferences are the same when we use the residual from the regression model ∆ETF it  = β 0 + β 1∆INST it  + ε it as a measure of change in ETF ownership that is orthogonal to the change in the level of institutional ownership.

  17. In untabulated analyses, we explore the sensitivity of our inferences to the inclusion of year fixed effects. We do so to address concerns that the inclusion of year fixed effects limits our analyses to the variation in changes in ETF ownership relative to other firms in the same year, while ignoring the variation in total average year-over-year changes in ETF ownership (which may also have a significant explanatory power for variation in the dependent variables). Our inferences remain the same under the alternative specification that excludes year fixed effects. We tabulate results controlling for year fixed effects, because we believe that controlling for unobserved time-specific effects helps us better isolate the effects of changes in ETFs on variables of interest. We thank the referee for raising this issue.

  18. We adopt this model of returns to measure firm-specific adjusted R2 (and consequently synchronicity) because it is the most frequently used in the literature (Piotroski and Roulstone 2004; Hutton et al. 2009; Chan and Chan 2014). To ensure that our inferences are not affected by the method chosen to estimate firm-specific adjusted R2, we also estimate synchronicity using the methodology outlined by Crawford et al. (2012) and Li et al. (2014). Our inferences are the same when we use these alternate measurement techniques.

  19. In computing SYNCH it , we exclusively use adjusted\( {R}_{it}^2 \) values. Following Crawford et al. (2012), we truncate the sample of adjusted \( {R}_{it}^2 \) values at 0.0001.

  20. Note that our main identification strategy is to link changes in ETF ownership to subsequent changes in the variables of interest. An alternative approach is to identify a discontinuity in ETF ownership arising from an exogenous event (an event unrelated to firms’ trading costs or information environment). For example, Chang et al. (2015) use a regression discontinuity (RD) design to study the effect of Russell 2000 index membership on stock returns. In an attempt to adopt the same strategy, we obtained their dataset of instrumented Russell membership changes and closely follow their approach. Unfortunately, we found that ETF ownership does not change significantly immediately surrounding Russell 2000 index inclusions/exclusions. While this result is consistent with their finding of no relation between this event and changes in overall institutional ownership, it unfortunately means that the Russell 2000 membership reconstitution is not an effective instrument for changes in ETF ownership.

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Acknowledgements

We gratefully acknowledge research assistance from Woo Young Park and Padmasini Venkatachari. We thank Inessa Liskovich and Harrison Hong for kindly providing us with their data on Russell 2000 reconstitutions. We are grateful for helpful suggestions and comments from Russell Lundholm (Editor), Ira Yeung (Discussant), an anonymous referee, Will Cong, Larry Glosten, Ananth Madhavan, Ed Watts, Frank Zhang as well as seminar participants at Emory University, Interdisciplinary Center (IDC) Herzliya, Tel Aviv University, UCLA, the University of Iowa, Duke University, and Harvard University (IMO Conference 2016).

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Authors and Affiliations

  1. Arison School of Business, The Interdisciplinary Center (IDC) Herzliya, Herzliya, Israel

    Doron Israeli

  2. Graduate School of Business, Stanford University, Stanford, CA, USA

    Charles M. C. Lee

  3. Goizueta Business School, Emory University, Atlanta, GA, USA

    Suhas A. Sridharan

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  1. Doron Israeli
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  2. Charles M. C. Lee
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  3. Suhas A. Sridharan
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Correspondence to Suhas A. Sridharan.

Appendix

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Fig. 2
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Sample construction timeline

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Table 8 Variable definitions
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Table 9 Sample ETFs ranked by assets under management
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Israeli, D., Lee, C.M.C. & Sridharan, S.A. Is there a dark side to exchange traded funds? An information perspective. Rev Account Stud 22, 1048–1083 (2017). https://doi.org/10.1007/s11142-017-9400-8

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