Mutual/Hedge Funds

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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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

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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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

Do retail investors rationally account for risks as modeled in academic research when choosing actively managed equity mutual funds? In their March 2019 paper entitled “What Do Mutual Fund Investors Really Care About?”, Itzhak Ben-David, Jiacui Li, Andrea Rossi and Yang Song investigate whether simple, well-known signals explain active mutual fund investor behavior better than academic asset pricing models. Specifically, they compare abilities of Morningstar’s star ratings and recent returns versus formal pricing models to predict net fund flows. They consider the Capital Asset Pricing Model (CAPM) and alphas calculated with 1-factor (or market-adjusted), 3-factor (plus size and book-to-market) and 4-factor (plus momentum) models of stock returns. They consider degree of agreement between signals for a fund (such as number of Morningstar stars and sign of a factor model alpha) and the sign of net capital flow for that fund. They also analyze spreads between net flows to top and bottom funds ranked according to Morningstar stars and fund alphas, taking the number of 5-star and 1-star funds to determine the number of top-ranked and bottom-ranked funds, respectively. Using monthly returns and Morningstar ratings for 3,432 actively managed U.S. equity mutual funds and contemporaneous market, size, book-to-market and momentum factor returns during January 1991 through December 2011 (to match prior research), they find that:

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A subscriber inquired about a “hot hand” strategy that each year picks the top performer from a family of diversified equity mutual funds (not including sector funds) and holds that winner the next year. To evaluate this strategy, we consider Vanguard diversified equity mutual funds with inceptions no later than September 2011. The test period is the lifetime of SPDR S&P 500 (SPY), which serves as a benchmark. We assume no costs or holding period constraints/delays for switching from one fund to another. We also simplify calculations by assuming that end-of-year “hot hand” fund identification and fund switches occur simultaneously (in other words, we can accurately rank mutual funds one day before the end of the year). Using monthly total returns for SPY and for Vanguard diversified equity mutual funds as available during December 1992  through December 2018, we find that:

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Does combining the wisdom of multiple stock-picking models via ensemble methods, as done in forecasting landfall of hurricanes, improve investment portfolio performance? In their September 2018 paper entitled “Ensemble Active Management”, Alexey Panchekha, Robert Tull and Matthew Bell test the application of ensemble methods to active portfolio management, looking for consensus or near-consensus among multiple, independent stock picking sources. Ensemble diversification tends to neutralize biases among individual sources when: (1) sources are independent; (2) sources employ different approaches; and, (3) most sources achieve at least 50% individual accuracies. As sources, they use the holdings and weights of 37 actively managed U.S. equity large-capitalization mutual funds, focusing on high-conviction stock selections (those with large mismatches with respect to market capitalization). Specifically, every two weeks they:

• Reform 30,000 randomly generated clusters of 10 mutual funds.
• For each cluster, reform a long-only Ensemble Active Management (EAM) portfolio consisting of the 50 stocks with the highest consensus overweights within the cluster.
• Calculate total returns for EAM portfolios, their respective clusters and the S&P 500 Index.

They debit performance of each EAM portfolio by the average contemporaneous expense ratio of the 37 mutual funds (average 0.94% across all years). To aggregate results, they calculate rolling 1-year and 3-year performances of EAM portfolios, mutual fund clusters and the index. Using daily estimated stock holdings and weights for the 37 mutual funds and associated stock prices as available during July 2007 through December 2017, they find that:

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