Do Sector ETFs Outperform Treasury Bills?
Gow-Cheng Huang (Tuskegee U.) and Kartono Liano (Mississippi State U.)
Unlike individual stocks, more than 67% of sector ETFs have lifetime buy-and-hold returns that are higher than the T-bill rates. Thus, the majority of sector ETFs outperform T-bills. However, less than 26% of sector ETFs have lifetime buy-and-hold returns that are higher than SPY, an index ETF that is a proxy for the overall stock market. Consequently, most sector ETFs underperform the market index.
ETF Rebalancing, Hedge Fund Trades, and Capital Market
George Jiaguo Wang (Lancaster University Management School), et. al
We study the interaction between ETF rebalancing and hedge fund “front-running” trades and its implications for the capital market. First, we document that ETF rebalancing has a strong negative relation with future stock returns. Second, we observe that hedge funds gradually increase (decrease) their net arbitrage positions before ETF rebalancing. Strikingly, the “front-running” stocks bought by hedge funds significantly outperform stocks not subject to hedge funds front-running by 0.86% (with a t-statistic of 3.86) before the month of ETF rebalancing. Our findings raise the question of the potential cost of ETFs rebalancing due to their embedded transparency and predictability, which creates anticipatory arbitrage trading by hedge funds.
Hidden Gem or Fool’s Gold: Can Passive ESG ETFs Outperform the Benchmarks?
Ariadna Dumitrescu (ESADE Business School), et al.
Using a unique and extensive dataset of 121 socially responsible investing (SRI) equity exchange-traded funds (ETFs) from January 2010 to December 2020, this study examines how passive SRI ETFs perform compared with their non-SRI benchmarks composed of S&P500 ETFs. Over the full sample period, our results show that an equally weighted SRI ETF portfolio underperforms its benchmark portfolio. Notably, we do not find significant differences in the two portfolios’ performance in the second half of our sample period. However, in the last two years, the SRI ETF portfolio significantly outperforms the benchmark. For the SRI investment strategies, we show that positive screening (or inclusion) rather than negative screening (or exclusion) can beat the benchmark portfolio. In particular, environmental inclusion screen provides significantly higher abnormal returns. Finally, we find that SRI ETFs’ performance can be explained by increasing industry competition and declining market concentration.
Price Discovery or Overreaction? A Study on the Reaction of Asia Pacific Country ETFs to the U.S. Stock Market
Rongzhao Ou (McMaster University)
In theory, large premiums or discounts are not sustainable in ETFs due to the arbitrage mechanism. However, the time gap of trading hours between U.S. market and Asia Pacific markets makes short-term premiums and discounts possible. The price of Asia Pacific country ETFs traded in the U.S. is not fully driven by net asset value; rather, it is affected by the information released during U.S. trading hours. I introduce an innovative model to predict the return of net asset value and find that the trading time of local markets has great influence on the predictive power of country ETFs and S&P500 Index . The returns of these ETFs do not necessarily overreact to the U.S. market; instead, they reflect the short-term expectations toward the performance of underlying indices. In the investigation of overnight and daytime returns, I identify the price correction during daytime trading hours.
Do Investors and Managers of Active ETFs React to Social Media Activities?
Sha Liu (Southwestern University of Finance and Economics)
Social media activities are a proxy for non-fundamental forces that contribute to ETF premium. Greater social media coverage of an active ETF’s holdings predicts a greater ETF premium on the next trading day. Active-ETF managers are more likely to sell underlying stocks which experience more bearish social media sentiment the previous day. The findings suggest that social media activities help investors who are subject to limited attention decide which ETFs to buy, and that active-ETF managers may cater to investors’ preferences by considering social media sentiment in their investment decision making.
Do Stock Retail Investors Show Better Portfolio Performance When They Hold Passive ETFs?
Younes Elhichou Elmaya (UCLouvain), et al.
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.
Navigating Climate Risks: Clean Tech vs Fossil Fuel ETFs
Minh Nhat Nguyen and Ruipeng Liu (Deakin University)
Using non-parametric estimates with imposing inequality restrictions, we compare unconditional to conditional estimators for “green” and “brown” ETFs, namely fossil fuel, and clean energy. Specifically, while unconditional tests could not indicate that the “green” outperforms the “brown”, the outperformance of the “green” is statistically significant in conditional testing incorporating climate-related information. Similarly, the conditional tests show that “brown” ETFs are riskier than “green” ETFs, especially downside risk beta. Interestingly, we document that non-fundamental demand proxied by fund flows for the “green” is higher than that for the “brown” only when incorporating the relevant information in the test. In general, we provide formal tests on controversial questions about the “green” and “brown” assets.
Expected Returns on Oil Futures ETFs
Gonzalo Cortazar (Pontificia Universidad Catolica de Chile)
This paper develops a new methodology to estimate oil ETF returns using WTI futures prices and analysts’ forecasts. Futures prices are obtained from the New York Mercantile Exchange, and analysts’ forecasts from Bloomberg and the U.S. Energy Information Administration.We use the United States Oil Fund ETF (USO) to illustrate our methodology. We first show that the USO Net Asset Value (NAV) and ETF historical returns can be replicated when using USO´s investment strategy and account fees.We estimate USO’s expected returns using a three-factor stochastic model estimated with a Kalman Filter. Results show that expected returns and volatility are higher for shorter horizons, which is consistent with previous findings for commodity returns.We compare our results with those from using the traditional CAPM model, finding similar average expected returns but higher volatility which is consistent with the behavior of oil futures prices.Finally, we study the macroeconomic determinants of USO expected returns and find that using S&P500 returns, VIX returns, Oil Inventories variations, 5-year Treasury Bill, Default Premium and Term Premium rates, can explain 60% of the variations of USO expected returns for maturities up to 5 years and 40% of variations for the 10-year maturity.
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