Trading Down: The Effects of Active Trading on One-Month ETF Returns
Ian Gray (Loyola Marymount University)
December 15, 2021
Ark Investment Management (ARK), led by CIO Cathie Wood, has risen to prominence over the past few years because of its remarkable performance. Because of requirements for active ETFs to publish daily holdings, market participants have gained unprecedented access to following the path to market-beating performance. ARK is celebrated for both its stock picking and active trading abilities. In this paper, I study how active trading affects alpha from ARK’s funds, and I create static synthetic portfolios to strip out the effects of active trading. I find that active trading on average reduces ARK’s one-month returns by 1.36 percentage points.

Index Providers: Whales Behind the Scenes of ETFs
Yu An (Johns Hopkins Carey Business School)
July 30, 2021
Most ETFs passively replicate the performance of an index that is constructed and maintained by an index provider. We show that index providers wield strong market power and charge large markups to ETFs, which are passed on to investors through management fees. We document three stylized facts about index providers: (i) the ETF indexing market is highly concentrated; (ii) when choosing ETFs, investors care about the identities of index providers, although index providers explain little variation in ETF returns; and (iii) about one-third of all ETF management fees are paid as index licensing fees to index providers. Using a structural model that incorporates two-tiered competition between index providers for ETFs and between ETFs for investors, we estimate that 60% of licensing fees are markups charged by index providers. Eliminating index providers’ market power can reduce ETF management fees by 30%.

Tax-loss Selling and the January Effect Revisited: Evidence from Municipal Bond Closed-end Funds and Exchange-traded Funds
Allen Carrion (U. of Memphis) and Jiang Zhang (U. of Virginia)
August 18, 2021
We revisit the tax-loss selling hypothesis as a potential explanation of the well-known January effect in securities markets. We expand on prior empirical evidence from municipal bond closed-end funds (CEFs) by extending the sample period by 19 years and adding exchange-traded funds (ETFs). Our sample covers the recent introduction and rapid growth of municipal bond ETFs, significant changes to municipal bond market structure, and the modernization of tax-loss selling practices. These developments all potentially impact the January effect. We find that the January effect in municipal bond CEFs has become stronger in recent years and show evidence that largely supports the tax-loss hypothesis. We also find some evidence indicating a smaller discrepancy between the abnormal returns of the funds and underlying bonds. We find a smaller January effect in municipal bond ETFs that cannot be explained by the tax-loss selling hypothesis.

Blessing or Curse? Institutional Investment in Leveraged ETFs
Luke DeVault (Clemson University), et al.
May 1, 2021
We document the increasing role leveraged exchange traded funds (ETFs) play in institutional portfolios over time. A subset of independent investment advisors, quasi-indexers, and transient portfolio managers all make substantive use of these tools. Leveraged ETFs can be used for diversification or to implement strategic bets. Empirical tests suggest that institutional holders of leveraged ETFs predict weak portfolio performance in aggregate, consistent with manager hubris, especially among the set of institutional managers most likely to lack management skill. Interestingly, managers appear to reduce positions in leveraged ETFs following good past performance, potentially to lock in good returns, consistent with compensation-based incentives.

ETFs, Illiquid Assets, and Fire Sales
John J. Shim (U. of Notre Dame) and Karamfil Todorov (LSEPS)
July 14, 2021
We document several novel facts about exchange-traded funds (ETFs) holding corporate bonds. First, the portfolio of bonds that are exchanged for new or existing ETF shares (called creation or redemption baskets) often represents a small fraction of ETF holdings – a fact that we call “fractional baskets.” Second, creation and redemption baskets exhibit high turnover. Third, creation (redemption) baskets tend to have longer (shorter) durations and smaller (larger) bid-ask spreads relative to holdings. Lastly, ETFs with fractional baskets exhibit persistent premiums and discounts, which is related to the slow adjustment of NAV returns to ETF returns. We develop a simple model to show that an ETF’s authorized participants (APs) can act as a buffer between the ETF market and the underlying illiquid assets, and help mitigate fire sales. Our findings suggest that ETFs may be more effective in managing illiquid assets than mutual funds.

Closet Active Management of Passive Funds
Pat Akey (University of Toronto), et al.
March 1, 2021
Ostensibly passive index funds and ETFs are surprisingly active. A third of these funds exhibit more activeness than the median actively managed fund, as measured by conventional proxies. Using hand-collected prospectus data, we find that “passive” funds offer an increasingly wide assortment of styles and provide more extreme factor exposures than active funds. We also identify a new dimension of activeness: the use of an index that is explicitly proprietary to the index fund or ETF. In contrast with actively managed funds, more active index funds and ETFs—“closet activists”—underperform. A one-standard deviation increase in activeness is associated with a 55 basis-point decrease in annual alpha. Our results point to the increasingly blurred line between “active” and “passive” funds.

Information Inertia and Limited Information Processing Capacity in Selecting Index ETFs
Yizhen Xie (Carnegie Mellon U.) and Darcy Pu (London Business School)
April 6, 2021
We investigate the role of information inertia and limited information processing capacity in creating an anomaly in the US index ETF industry. We fi nd that tracking errors and long-term returns are significant and consistent predictors of future returns, while there is little evidence that investors’ flows are sensitive to them. In line with this finding, we provide a trading strategy that chases small tracking errors, which outperforms the equal-weighted portfolio of all S&P 500 index ETFs by 1.65% annually over 2003-2020. Mutual fund managers who just joined the fund are more likely to switch to better index ETFs than existing managers, suggesting investors face information inertia. Unsophisticated investors have limited information processing capacity as index ETFs held by more institutional investors deliver higher returns. Overall, our results suggest that the anomaly in low-return and low-tracking error sensitivity in the index ETF industry can be explained by information inertia and limited information processing capacity.

Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return
By James Picerno