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Mutual/Hedge Funds

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.

When Institutional Investors Seek Safety

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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Actual vs. Nominal Hedge Fund Performance Fees

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

Performance of ETFs Employing Rule-based Hedging

Do exchange-traded funds (ETF) that operate like rule-based (passive) hedge funds offer attractive performance? In their December 2019 paper entitled “The Performance of Passively-Managed Hedged ETFs”, Jason Cheng, Joseph Fung and Eric Lam examine performance of passively-managed hedged ETFs (HETF) as of the end of 2017. These funds attempt to replicate a hedge fund index (either global macro or long-short equity), generally allocating about 80% to replication 20% to buffer market movements. The study looks at raw returns and alphas relative to a 3-factor (equity market, volatility, interest rate) and more complex 7-factor and 8-factor models of hedge fund returns. They test each HETF individually and equal-weighted portfolios of HETFs. Using month-end prices, net asset values (NAV), assets under management (AUM), and bid and ask quotes for 23 HETFs available at the end of 2017 and monthly hedge fund factor model inputs during January 2008 through December 2017, they find that: Keep Reading

More Stock Funds Than Stocks?

What does it mean when the number of stock funds exceeds the number of stocks they hold? In their October 2019 paper entitled “What Happens with More Funds than Stocks?”, Ananth Madhavan, Aleksander Sobczyk and Andrew Ang tackle this question by examining holdings over time for all U.S. active equity mutual funds and equity exchange-traded funds (ETF). The look at commonalities and differences across all funds and between mutual funds and ETFs, including dominant equity factor exposures. Using quarterly holdings of all U.S.-listed U.S. equity mutual funds and ETFs during January 2007 through December 2018, they find that:

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Exploiting Consensus Hedge Fund Conviction Stock Picks

Can investors exploit information about hedge fund stock holdings in SEC Form 13F filings? In their October 2019 paper entitled “Systematic 13F Hedge Fund Alpha”, Mobeen Iqbal, Farouk Jivraj and Luca Angelini investigate whether carefully culled “best ideas” of equity hedge funds produce significantly beat the S&P 500 Total Return (TR) Index. Using quarterly Form 13Fs for U.S. equity long-short, equity market neutral, equity long-only and equity event-driven hedge funds, they measure: individual hedge fund manager conviction regarding a stock based on size of position; and, hedge fund manager consensus regarding a stock based on the number of funds holding it. Using proprietary data, they identify hedge funds exhibiting long-term investment approaches. They then 47 days after the end of each quarter (to ensure availability of Form 13Fs), reform a portfolio from among long-term hedge funds holding at least five stocks, as follows:

  1. Exploit conviction by identifying all stocks comprising at least 7.5% of a fund portfolio.
  2. Exploit consensus by buying the equal-weighted top 50 of these stocks in terms of number of hedge managers holding them. 

Using processed quarterly data from hedge fund Form 13Fs, the specified proprietary data on hedge fund investment approaches and returns for associated stocks during the first quarter of 2004 through the second quarter of 2019, they find that:

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Misleading Mutual Fund Classifications?

Are Morningstar mutual fund profiles accurate? In their October 2019 paper entitled “Don’t Take Their Word For It: The Misclassification of Bond Mutual Funds”, Huaizhi Chen, Lauren Cohen and Umit Gurun examine whether aggregate credit risks of actual of U.S. fixed income (corporate bond) mutual fund portfolios match those presented by Morningstar in respective fund profiles. They focus on recent data (first quarter of 2017 through second quarter of 2019), during which Morningstar includes percentages of fund holdings by risk category. Using Morningstar profiles, actual holdings as reported to the SEC, detailed credit ratings of holdings and returns for 1,294 U.S. corporate bond funds during January 2003 through June 2019, they find that:

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Mutual Fund Managers Harmfully Biased?

Are there relationships between (1) the stock market outlook expressed by a U.S. equity mutual fund manager in semi-annual reports and (2) positioning and performance of that fund? In his October 2019 preliminary paper entitled “Are Professional Investors Prone to Behavioral Biases? Evidence from Mutual Fund Managers”, Mehran Azimi examines these relationships. Specifically, for each such U.S. equity mutual fund semi-annual report, he:

  1. Uses a word list to identify parts of fund reports that may contain stock market outlooks.
  2. Applies machine learning to isolate sentences most likely to present outlooks.
  3. Manually reads and rates these sentences as bearish, neutral or bullish.
  4. Computes fund manager “Belief” as number of bullish sentences minus number of bearish sentences divided by the total number of sentences isolated. Positive (negative) Belief indicates a net bullish (bearish) outlook.

He then employs regressions to relate fund manager Belief to fund last-year return, asset allocation, portfolio risk and next-year 4-factor (adjusting for market, size, book-to-market and momentum) alpha. Using 40,731 semi-annual reports for U.S. equity mutual funds and associated fund characteristics, holdings and returns during February 2006 through December 2018, he finds that:

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ETFs No Better Than Mutual Funds?

Is the conventional wisdom that exchange-traded funds (ETF) are efficient, low-cost alternatives to mutual funds correct? In their September 2019 paper entitled “The Performance of Exchange-Traded Funds”, David Blitz and Milan Vidojevic evaluate the performance of a comprehensive, survivorship bias-free sample of U.S. equity ETFs. They first divide the sample into three groups: (1) broad market index trackers; (2) inverse and leveraged funds; and, (3) others. They then subdivide group 3 into equity factor subgroups (small, value, dividend, momentum, quality or low-risk) based on either their names or their empirical exposures to widely accepted factor premiums. Finally, they compare performances of value-weighted ETF groups to those of the broad U.S. stock market and specified factors, focusing on data starting January 2004 when there are at least 100 ETFs of some variety. Using trading data and descriptions for 918 U.S. equity ETFs (642 live and 276 dead by the end of the sample period) and equity factor returns during January 1993 through December  2017, they find that: Keep Reading

Long/short Equity Mutual Fund Performance Update

How well have long/short equity mutual funds done in recent years? In their April 2019 paper entitled “Hedge Funds Versus Hedged Mutual Funds: An Examination of Long/Short Funds; A Performance Update”, David McCarthy and Brian Wong present an out-of-sample update of a prior performance assessment of long/short equity mutual funds (see “Multialternative Mutual Fund Performance”). They track the same universe as the prior paper and therefore do not include funds launched after January 2013. They construct an equally weighted index of long/short equity mutual funds, rebalanced monthly. They compare performance of this index to those of the S&P 500 Total Return Index, HFRI Equity Hedge Fund Index (HFRI Index) and the Dow Jones Credit Suisse Long/Short Equity Hedge Fund Index (DJ-CS Index). Using monthly returns of 26 live, 14 dead and 4 changed (up to date of change) long/short equity mutual funds established as of January 2013 along with contemporaneous returns for benchmark indexes during July 2013 through December 2018, they find that:

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Vanguard vs. Fidelity Funds

Which fund family is better, Vanguard or Fidelity? In their April 2019 paper entitled “Vanguard versus Fidelity: Multidimensional Comparison of the Index Funds and ETFs of the Two Largest Mutual Fund Families”, Chong Li, Edward Tower and Rhona Zhang compare 21 matched Vanguard and Fidelity fund pairs in five ways: (1) before-tax and after-tax performance, (2) tax efficiency, (3) cost (expense ratio, turnover and short-term redemption fees), (4) diversification and (5) benchmark tracking precision. They consider 10 domestic equity and international equity index mutual funds and 11 sector exchange-trade funds (ETF). Their objective is to aid investors in selecting a fund provider. Using fund performance, cost, holdings and benchmark as of the end of 2018, they find that: Keep Reading

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