Mutual/Hedge Funds

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

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

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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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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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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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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