ETF Dividend Cycles
Pekka Honkanen (University of Georgia), et al.
February 2023
Exchange-traded funds (ETFs) collect approximately 7% of all U.S. corporate dividends, which they are required to redistribute to investors. How do the funds manage these dividend flows, and does such management have spillover effects on other financial markets? In this paper, we document a new stylized fact of the “ETF dividend cycle:” ETFs gradually invest in money market funds (MMFs) when they accumulate dividend receipts and periodically withdraw from MMFs when they distribute dividends. This cycle creates periodic liquidity shocks to MMFs and, consequently, to the Treasury markets as the affected MMFs liquidate some of their short-term Treasury holdings to satisfy ETFs’ dividend-driven withdrawals. As a result, ETF dividend cycles can explain flows to MMFs and fluctuations in Treasury yields.

Hedge Fund Investment in ETFs
Douglas J. Cumming (Florida Atlantic U.) and Pedro Monteiro (U. of Scranton)
January 2023
This paper examines the causes and consequences of hedge fund investments in exchange traded funds (ETFs) using U.S. data from 1998 to 2018. The data indicate that transient hedge funds and quasi-indexer hedge funds are substantially more likely to invest in ETFs. Unexpected hedge fund inflows cause a rise in ETF investments, and the economic significance of unexpected flow is more than twice as large for transient than quasi-indexer hedge funds. ETF investment is in general associated with lower hedge fund performance. But when ETF investment is accompanied by an increase in total flow and unexpected flow, the negative impact of ETF holdings on performance is mitigated. The data are consistent with the view that hedge fund ETF investment unrelated to unexpected flow is an agency cost of delegated portfolio management.

Do Stock Retail Investors Show Better Portfolio Performance When They Hold Passive ETFs?
Younes Elhichou Elmaya (UCLouvain), et al.
June 2022
We investigate the portfolio performance of retail investors who combine stocks and passive exchange traded funds (P-ETFs) by relying on both proprietary trading records and survey data. We use propensity score matching to control for all the key investor characteristics and better identify the contribution of holding P-ETFs in retail portfolios. We find that heavy P-ETFs investors trade more passively, while light P-ETF investors trade as actively as investors who hold individual stocks only. The level of total and systematic risks are lower in portfolios held by P-ETF investors. The risk-adjusted performance remains negative for all retail investors, irrespective of their exposure to P-ETFs. Nevertheless, retail investors who hold P-ETFs generate higher risk-adjusted returns than those who trade individual stocks only.

A Tax-Loss Harvesting Horserace: Direct Indexing vs. ETFs
Roni Israelov (NDVR) and Jason Lu (IMF)
February 2023
This paper proposes and analyzes an enhanced, but easily implemented, heuristic for tax-loss harvesting within a portfolio of stocks. Because stock returns are correlated within and across sectors, harvesting opportunities may simultaneously arise across many stocks that also concentrate in individual sectors, and the active risk of undertaking all available harvests may become undesirable. Our algorithm balances harvesting yield, active risk, portfolio rebalancing, and turnover. We evaluate the performance of our heuristic using Monte Carlo simulations across time and within different market environments. We show that harvesting yield for a portfolio of stocks is superior to market ETFs.

On the Anomaly Tilts of Factor Funds
Markus S. Broman (Ohio University) and Fabio Moneta (University of Ottawa)
February 2023
By analyzing portfolio holdings, we find that a significant subset of Hedged Mutual Funds (HMFs) and smart-beta Exchange-Traded Funds (ETFs) tilt their portfolios towards well-known anomaly characteristics and that such tilts are highly persistent. Short positions of HMFs amplify their factor tilts. Most single-factor ETFs target multiple factors, while many also exhibit offsetting tilts to other factors. HMFs with large factor tilts outperform corresponding ETFs, which is driven by short positions and higher factor-related returns. Overall, we show the superior factor replication ability of HMFs over ETFs, and that HMFs achieve similar (or better) performance as the academic factors.

‘OK Google’: ETF’s Online Visibility and Fund Flows
Olga Obizhaeva (Stockholm School of Economics)
August 2022
Using the unique dataset on web analytics and exploiting institutional features of search engine technology, I study the relationship between an ETF’s visibility on the Internet and its fund flows. I consider three channels of online visibility: Organic or “free” search, paid search, and website referrals. I find that ETFs with webpages ranked higher on Google, funds that engage in search engine marketing, and those with webpages referred to by more websites attract higher fund flows. Online visibility of an ETF is as important for an investor’s purchase decision as the fund’s past performance. ETF issuers use online marketing tools to reduce investors’ search costs and gain an advantage in attracting investors’ flows.

Impact of Financial Innovation on Fund Performance: Hedging with Industry ETFs
Yigit Atilgan (Sabanci University), et al.
February 2023
Extant research provides evidence for financial innovation’s contribution to market efficiency by documenting that hedge funds which bet on positive earnings surprises manage their sector risk by shorting industry exchange-traded funds (ETFs). We add to this literature by considering a hypothetical hedge fund that can anticipate positive earnings news. We construct return series for a naked strategy that only takes long stock positions and a hedged strategy that also holds short positions in industry ETFs around earnings announcements with positive content. Our main result is that hedging with industry ETFs improves fund performance based on various reward-to-risk ratios. This finding holds in various equity subsamples and both strategies tend to perform better among riskier stocks.

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