Do investors in mutual funds and hedge funds get their fair share of returns, or are they perpetually disadvantaged by fees and underperforming fund managers? Are there ways to exploit fund behaviors? These blog entries relate to mutual funds and hedge funds.
The 2024 S&P Indices Versus Active (SPIVA) Scorecard finds that a supermajority of active U.S. funds underperform respective benchmarks. Is that finding representative of investor experience? In the May 2026 draft of their paper entitled “How the SPIVA U.S. Scorecard Understates the Performance of Actively Managed Mutual Funds”, Martijn Cremers, Jon Fulkerson and Timothy Riley restate the question addressed by the SPIVA Scorecard.
The Scorecard asks: what percentage of active funds either do not survive the full horizon or underperform the respective category benchmark indexes?
The authors ask: what percentage of active fund assets underperform equivalent passive funds?
They therefore adjust the SPIVA methodology, as follows:
Instead of treating a fund that exits the sample as an underperformer, they use the actual returns of such a fund until its exit.
Instead of weighting all funds equally, even though most assets are in a few large funds, they consistently weight by fund assets.
Instead of using hypothetical benchmarks (total return indexes), they use existing equivalent passive funds.
They then compare SIVA results, replicated SPIVA results and adjusted results. Using categories and total returns from the same fund database used to generate SPIVA reports, along with returns for matched S&P benchmark indexes and passive tracking funds, during 2005 through 2024, they find that:Keep Reading
What do the latest S&P Indices Versus Active (SPIVA) Scorecards say about active management investing expertise? In the “SPIVA U.S. Year-End 2025” Scorecard, Anu Ganti, Davide Di Gioia, Nick Didio and Liam Flaherty review the 2025 performance of active mutual funds and exchange-traded funds (ETF) per the following SPIVA Scorecard principles:
Account for discontinued funds, thereby eliminating survivorship bias.
Compare fund performance to that of a benchmark index matched to the fund investment category.
When aggregating funds, consider both equal-weighted and asset-weighted average returns.
Monitor investment style consistency over time to account for fund style drift.
For a given fund, use only the share class with the most assets to avoid double-counting classes.
Exclude all index funds, leveraged/inverse funds and other index-linked products.
Using categories and total returns for active U.S. mutual funds and ETFs and their associated S&P indexes during 2001 through 2025, they find that:Keep Reading
How should investors think about active exchange-traded funds (ETF)? In their February 2026 paper entitled “The Fast-Growing Market of Active ETFs”, Rachel Li and Nadia Winn review the state of active ETFs. They consider only ETFs offered by investment companies registered under the Investment Company Act of 1940. They identify active versus passive ETFs based on fund manager responses on SEC forms. Using assets under management (AUM), holdings and expense ratio data for the specified ETFs from SEC filings and fund tracking data from Morningstar during 2020 through 2024, they find that:
Are hedge fund-oriented strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider 10 ETFs, five live and five 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. (Dead as of September 2024)
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.
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:
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:
Which hedge funds are likely short-sellers, and how do they compare with other hedge funds?
What factors contribute to the performance of short-selling hedge funds?
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:
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:
