Mutual/Hedge Funds

Are hedge fund-oriented strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider six ETFs, two live and four dead (in order of inception dates):

• IQ Hedge Multi-Strategy Tracker (QAI) – seeks to track, before fees and expenses, risk-adjusted returns of a collection of long/short equity, global macro, market neutral, event-driven, fixed income arbitrage and emerging markets hedge funds.
• ProShares Hedge Replication (HDG) – seeks to track, before fees and expenses, an equally weighted composite of over 2000 hedge funds.
• AlphaClone Alternative Alpha (ALFA) – seeks to track price and yield, before fees and expenses, of U.S.-traded equity securities to which hedge funds and institutional investors have disclosed significant exposures. (This fund is dead as of August 2022.)
• IQ Hedge Market Neutral Tracker (QMN) – seeks to track, before fees and expenses, risk-adjusted returns of market neutral hedge funds. (This fund is dead as of January 2023.)
• ProShares Morningstar Alternatives Solution (ALTS) – seeks to track, before fees and expenses, performance of a diversified set of alternative ETFs. (This fund is dead as of April 2022.)
• JPMorgan Diversified Alternatives (JPHF) – aims to provide direct, diversified exposure to hedge fund strategies via a bottom-up approach across equity long/short, event-driven and global macro strategies. (This fund is dead as of May 2020.)

We focus on monthly return statistics, including compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). We use two benchmarks, SPDR S&P 500 (SPY) and the Eurekahedge Hedge Fund Index (HFI). Using monthly returns for the six hedge fund ETFs and SPY and for HFI as available through August 2023, we find that: Keep Reading

Should investors in low-cost mutual funds consider active ones? In his April 2023 paper entitled “Vanguard and Fidelity Domestic Active Stock Funds: Both Beat their Style Mimicking Vanguard Index Funds, & Vanguard Beats by More”, Edward Tower compares returns of active Vanguard and Fidelity stock mutual funds to those of style-mimicking portfolios of Vanguard index funds. He segments active funds into three groups: U.S. diversified, sector/specialty and global/international. For U.S. diversified funds, for which samples are relatively large, he regresses monthly net returns of each active fund versus monthly net returns of Vanguard index funds to construct an index fund portfolio that duplicates the active fund return pattern (style). For sector/specialty and global/international segments, for which samples are small, he instead compares active fund net returns to those for respective benchmarks. He uses Vanguard Admiral class funds when available, and Investor class funds otherwise. He applies monthly rebalancing for all fund portfolios. Using fund descriptions and monthly net returns during January 2013 through March 2023, he finds that:

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Do hedge funds rapidly move to exploit, and thereby weaken/extinguish, newly discovered stock return anomalies? In the December 2022 version of their paper entitled “Anomaly Discovery and Arbitrage Trading”, Xi Dong, Qi Liu, Lei Lu, Bo Sun and Hongjun Yan measure the post-publication role of hedge funds on 99 published stock return anomalies (or latest working paper dates if unpublished). For each anomaly, they:

1. Calculate a five-year rolling correlation of monthly returns between the extreme tenths (deciles 1 and 10) of anomaly stock sorts, minus the correlation between deciles 5 and 6 to control for unrelated trends.
2. Analyze via quarterly SEC Form 13F holdings aggregate U.S. hedge fund differential trading of extreme decile stocks.

Using monthly returns for the 99 anomalies as available starting in 1926 and hedge fund SEC Form 13F filings as available starting 1981, both through 2020, they find that: Keep Reading

Do factor investing (smart beta) mutual funds capture for investors the premiums found in academic factor research? In their November 2022 paper entitled “Factor Investing Funds: Replicability of Academic Factors and After-Cost Performance”, Martijn Cremers, Yuekun Liu and Timothy Riley analyze the performance of funds seeking to capture of published (long-side) factor premiums. They group factor investing funds into four styles: dividend, volatility, momentum and q-factor (profitability and investment). They separately measure how closely fund holdings adhere to the long sides of academic factor specifications. They measure fund outperformance (alpha) relative to the market factor via the Capital Asset Pricing Model (CAPM) and via a multi-factor model (CPZ6) that accounts for the market factor and for granular size/value interactions. Using monthly returns for 233 hand-selected factor investing mutual funds and for the academic research factors during January 2006 (16 funds available) through September 2020 (207 funds available), they find that:

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Is past rolling maximum drawdown, a simple measure of recent downside risk, a useful indicator of future mutual fund performance? In their June 2022 paper entitled “Maximum Drawdown as Predictor of Mutual Fund Performance and Flows”, Timothy Riley and Qing Yan investigate whether style-adjusted maximum drawdown based on daily returns over the last 12 months usefully predicts mutual fund performance. To adjust for fund style differences, they subtract from each individual unadjusted drawdown the average unadjusted drawdown across all funds in the same style during the measurement interval. Their principal performance metric is alpha based on a 4-factor (market, size, book-to-market, momentum) model of stock returns. Using daily net returns for 2,188 actively managed long-only U.S. equity mutual funds that are at least two years old and have at least $20 million in assets during January 1999 through December 2019, they find that: Keep Reading

A reader requested evaluation of the Fosback Index and its Ned Davis variant. The creators of these indicators argue that a high (low) ratio of cash equivalents to assets among equity mutual funds indicates strong (weak) potential demand for stocks. The Investment Company Institute (ICI) surveys mutual fund managers monthly (with a lag of about a month) to measure the aggregate equity mutual fund liquidity ratio (LR). Only past year-end values of LR are readily available. Norman Fosback adjusts raw LR based on current interest rates, reasoning that mutual fund managers have more (less) incentive to hold cash when interest rates are high (low). We adjust the effect of interest rates via linear regression of annual LR against year-end yield of the 3-month U.S. Treasury bill (T-bill). We then define the difference between raw and adjusted values as Excess LR and relate this variable to annual returns of the Fidelity Fund (FFIDX) as a proxy for U.S. stock market total performance. Using year-end values of aggregate equity mutual fund LR from the 2021 Investment Company Fact Book, Table 15, year-end T-bill yield and annual returns for FFIDX during December 1984 through December 2021 ( 36 years), we find that: Keep Reading

How have active equity investment managers performed over the past three decades? In his November 2021 paper entitled “Active Equity Management, 1991-2020”, Gene Hochachka examines whether: (1) active equity managers as a group beat their benchmarks over the last 30 years; and, (2) active equity manager relative performance is persistence. By active equity managers, he means:

• Live and dead U.S. mutual funds tracked by Morningstar Direct and not classified as an index fund or fund-of-funds, segmented into US LargeCap, US MidCap, US SmallCap and Foreign (International) LargeCap.
• Institutional strategies tracked as self-reported by Mercer Global Investment Manager Database and not classified as passive in mid-2021, segmented into US LargeCap, US MidCap, US SmallCap, US Small/MidCap, US AllCap and International LargeCap.

Fund/strategy and benchmark returns are for calendar years, including dividends/distributions, and are gross of all fees and expenses. Some analyses compare net-of-expense fund/strategy and net-of-expense benchmark returns. Using the specified annual returns during 1991 through 2020, he finds that: Keep Reading

How has the marketplace for exchange-traded funds (ETFs) evolved? What performances do its “species” deliver? In their January 2021 paper entitled “Competition for Attention in the ETF Space”, Itzhak Ben-David, Francesco Franzoni, Byungwook Kim and Rabih Moussawi summarize evolution of broad-based ETFs (tracking broad market and style indexes) and specialized ETFs (tracking sectors and narrow investment themes). They apply a 4-factor model of stock returns (accounting for market, size, value and momentum factors) to assess risk-adjusted performance (alphas) of these categories. Using monthly data for 1,080 U.S. equity ETFs (ignoring non-equity, foreign equity, inverse and leveraged ETFs) and monthly 4-factor model returns during 1993 through 2019, they find that: Keep Reading

How do mutual funds and hedge funds change their stock holdings in response to a sharp market crash? In their July 2020 paper entitled “Where Do Institutional Investors Seek Shelter when Disaster Strikes? Evidence from COVID-19”, Simon Glossner, Pedro Matos, Stefano Ramelli and Alexander Wagner analyze changes in institutional and retail stock holdings during the first quarter of 2020. Using a February-March 2020 snapshot of returns and firm accounting data for non-financial stocks in the Russell 3000 Index, institutional holdings of these stocks as percentages of shares outstanding during the fourth quarter of 2018 through the first quarter of 2020, and number of Robinhood clients (representing retail investors) holding these stocks on December 31, 2019 and March 31, 2020, they find that:

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Is the nominal incentive fee charge by hedge funds (typically 20% of profits exceeding a previous high-water mark) representative of the actual aggregate incentive fee paid by fund investors? In the July 2020 revision of their paper entitled “The Performance of Hedge Fund Performance Fees”, Itzhak Ben-David, Justin Birru and Andrea Rossi (1) quantify the actual aggregate incentive fee paid by investors across a large sample of hedge funds over a 22-year sample period and (2) explore reasons for the difference between actual aggregate and nominal fees. Using return and management/performance fee data for 5,917 live and dead hedge funds during 1995 through 2016, they find that: Keep Reading