Mutual/Hedge Funds

Do some mutual fund managers game Morningstar ratings/benchmarks by shifting the styles of their funds? In their September 2024 paper entitled “Box Jumping: Portfolio Recompositions to Achieve Higher Morningstar Ratings”, Lauren Cohen, David Kim and Eric So investigate how mutual fund managers exploit investor reliance on Morningstar ratings by adjusting holdings to jump their funds into size/value styles with low benchmarks. They focus on active U.S. and global equity mutual funds during the period from five years before to five years after June 2002, when Morningstar began rating funds by style. They include dead funds to avoid survivorship bias. Using Morningstar style assignments, Morningstar ratings and performance data for active equity mutual funds during 1997 through 2007 (with some data through 2022), they find that:

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Are hedge fund-oriented strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider 10 ETFs, six live and four dead (in order of inception dates):

• NYLI Hedge Multi-Strategy Tracker ETF (QAI) – seeks investment results that track, before fees and expenses, the price and yield performance of the NYLI Hedge Multi-Strategy Index, which attempts to replicate the risk-return characteristics of hedge funds generally.
• ProShares Hedge Replication (HDG) – seeks to track, before fees and expenses, an equally weighted composite of over 2000 hedge funds.
• NYLI Merger Arbitrage ETF (MNA) – seeks to track, before fees and expenses, the price and yield performance of the NYLI Merger Arbitrage Index, which: (1) invests in global companies for which there has been a public announcement of a takeover by an acquirer; and, (2) includes short exposure to global equities as a partial equity market hedge.
• 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. (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. (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. (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. (Dead as of May 2020.)
• iMGP DBi Hedge Strategy ETF (DBEH) – seeks long-term capital appreciation via long and short positions in derivatives, primarily futures contracts and forward contracts, across the broad asset classes of equities, fixed income and currencies.
• AltShares Merger Arbitrage ETF (ARB) – seeks to track, before fees and expenses, the performance of the Water Island Merger Arbitrage USD Hedged Index, a global merger arbitrage strategy investing in publicly announced mergers and acquisitions.

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 10 hedge fund ETFs and SPY and for HFI as available through August 2024, we find that: Keep Reading

Can researchers train machine learning models to mimic top mutual fund managers? In his August 2024 paper entitled “Machine Learning from the Best: Predicting the Holdings of Top Mutual Funds”, Jean-Paul van Brakel seeks to anticipate and exploit the stock picking of top-performing U.S. equity mutual fund managers by:

• Using a large set of holdings-based measures and component firm/stock factor exposures and accounting ratios to rank stocks on the likelihood that top managers will pick them. He considers five models: three machine learning models (decision tree, random forest and gradient boosting), a linear (logit regression) model and simple selection based on recent holdings. For machine learning models, he each quarter uses quarterly data from four years ago to one year ago for training and quarterly data from last year for generating holdings likelihoods for next quarter.
• Calculating average absolute Shapley value for each firm/stock characteristic to assess its importance in the decision process.
• Assessing economic value of predictions for each model by each quarter creating six groups of stocks, first sorting stocks into large and small and then sorting each size sort into thirds based on likelihood that top fund managers will pick the stocks. He then each quarter reforms a probable-minus-improbable (PMI) factor portfolio that is long large and small stocks with the highest likelihoods and short those with the lowest.

He applies a 6-factor (market, size, book-to-market, profitability, investment, momentum) model based on daily calculations to compare alphas across funds, features and the PMI factor portfolio. Using quarterly/daily data for U.S. broad equity mutual fund holdings/returns and associated individual firm/stock data starting December 1998 and ending December 2022 and December 2023, respectively, he finds that: Keep Reading

Do narrow exchange-traded funds (ETF), such as specific technology-focused funds, exhibit a predictable lifecycle of fund inflows that inflate prices of holdings followed by fund outflows that depress prices of holdings? In their May 2024 paper entitled “Ponzi Funds”, Philippe van der Beck, Jean-Philippe Bouchaud and Dario Villamaina decompose ETF returns into price pressure (self-inflated) and fundamental components, with the former a function of the concentration of ETF holdings and flows of investor money into and out of the fund. They then compare performances of funds with relatively high and relatively low self-inflated returns. Using daily holdings of U.S. equity ETFs during 2019 through 2023, they find that: Keep Reading

Do short-selling hedge funds consistently extract alpha from exuberant retail traders? In their March 2024 paper entitled “Short-Selling Hedge Funds”, Jialin Qian, Zhen Shi and Baozhong Yang examine the performance of hedge funds engaged in short-selling, as follows:

1. Which hedge funds are likely short-sellers, and how do they compare with other hedge funds?
2. What factors contribute to the performance of short-selling hedge funds?
3. How has the 2021 Meme stock phenomenon affected short-selling hedge funds?

They each month identify short-selling hedge funds as those with positive return betas over the past 24 months versus a monthly rebalanced portfolio of short stock positions with weights proportional to their respective short interests. They relate behaviors of short-selling funds to those of other hedge funds and to those of retail traders. Using monthly data for 11,054 U.S. hedge funds, returns and short interests for a broad sample of U.S. stocks and data to measure retail stock trading/sentiment during 2010 through 2022, they find that: Keep Reading

Should qualified investors count on global macro (GM) and managed futures (MF, or alternatively CTA for commodity trading advisors) hedge funds to beat the market? In their November 2023 paper entitled “Global Macro and Managed Futures Hedge Fund Strategies: Portfolio Differentiators?”, Rodney Sullivan and Matthew Wey assess the performances of GM and MF hedge fund categories, defined as:

• GM – try to anticipate how political trends and global economic activity will affect valuations of global equities, bonds, currencies and commodities.
• MF – rely systematic trading programs based on historical prices/market trends across stocks, bonds, currencies and commodities.

For comparison, they also look at the long-short equity (LSE) hedge fund category. They decompose category returns into components driven by exposures to U.S. stock and bond market return factors, other factor premiums and unexplained alpha. They focus on how fund categories have changed since the 2008 financial crisis, emphasizing performances during market downtowns. Using index returns from Hedge Fund Research (equal-weighted) and Credit Suisse (asset-weighted) during January 1994 through December 2022, they find that:

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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