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.

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A Few Notes on Invest with the House

Mebane Faber states in the first chapter of his 2016 book Invest with the House: Hacking the Top Hedge Funds: “We make two assumptions…: 1. There are active managers that can beat the market… 2. Superior active managers can be identified. …There is a general feeling that the market can’t be beat, and it is tough to get past that belief. A big challenge is separating luck from skill. But would anyone deny that some people are better than others at stock picking? Just like any other profession, the investment field has top experts who are paid handsomely for what they do. …You have access to the stock picks made by fund managers who often spend millions of dollars and every waking moment thinking and obsessing about the financial markets. …The best ones know everything there is to know about a company before they invest. …You can then build a stable of these managers and use them…for stock ideas to research and possibly implement in your own portfolio.” Based on prior research/experience and performances of the top ten (long) holdings from quarterly Form 13F filings for selected fund managers during January 2000 through December 2014, he concludes that: Keep Reading

Hedge Funds vs. Mutual Funds: Give and Take

Who are the givers and who are the takers among mutual funds and hedge funds? In their January 2016 paper entitled “Style and Skill: Hedge Funds, Mutual Funds, and Momentum”, Mark Grinblatt, Gergana Jostova, Lubomir Petrasek and Alexander Philipov analyze quarter-to-quarter changes in Form 13F stock holdings to assess investment styles and sources of performance for hedge funds and mutual funds. They focus on the interaction between portfolio weight changes and future stock returns to measure investing skill. They calculate fund alpha via adjustments for stock size, book-to-market ratio and (when appropriate) momentum. Using quarterly 13F filings of 589 mutual funds and 1,342 hedge funds during 1998 to 2012, they find that: Keep Reading

Mutual Fund Hot Hand Performance

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 the Vanguard family of diversified equity mutual funds over the period during which SPDR S&P 500 (SPY) is available as a total return benchmark. We assume that there are no costs or holding period constraints/delay for switching from one fund to another. We also simplify calculations by assuming that end-of-month “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 month). Using monthly total returns for SPY since inception and for Vanguard diversified equity mutual funds as available from Yahoo!Finance since December 1992, all through December 2015, we find that: Keep Reading

Overall Findings from a Decade of Hedge Fund Research

What are the principal themes of research on hedge funds published in top journals over the past decade? In their August 2015 paper entitled “Hedge Funds: A Survey of the Academic Literature”, Vikas Agarwal, Kevin Mullally and Narayan Naik summarize 121 papers on hedge funds and commodity trading advisors from four leading finance journals. They focus on the 105 papers published since 2005. They organize this research into five categories:

  1. Fund performance over time and by type, including return drivers, risks and assessment of manager skill.
  2. Relationships between fund characteristics (such as contractual terms, size, age and manager background) and fund performance.
  3. Investor risks, including manager incentives and capital flows.
  4. Role of hedge funds in the financial system.
  5. Biases in and limitations of data.

Based on this review, they conclude that: Keep Reading

Recent Hedge Fund Performance and Research

What is the state of the hedge fund industry? In the July 2015 draft of their paper entitled “Hedge Funds: A Dynamic Industry In Transition”, Mila Getmansky, Peter Lee and Andrew Lo review recent academic research on hedge funds and update industry performance statistics. They surmise that  hedge fund data from 10 years ago may be unrepresentative of today’s environment, especially in the aftermath of the 2007-2009 financial crisis. Their review considers four perspectives: investor, portfolio manager, regulator and academician. Based on this review and self-reported hedge fund performance data during January 1996 through December 2014, they conclude that: Keep Reading

Testing the Equity Mutual Fund Liquidity Ratio

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 2015 Investment Company Fact Book, Table 15, year-end T-bill yield and annual returns for FFIDX during December 1984 through December 2014 ( 30 years), we find that: Keep Reading

Effects of Mutual Fund Aging on Performance

Should investors adopt a mutual fund for the long term, or should they occasionally switch to funds with fresh ideas and energy? In the July 2015 draft of their paper entitled “Milk or Wine: Mutual Funds’ (Dis)economies of Life”, Laura Dahm and Christoph Sorhage investigate whether mutual fund performance tends to decline or improve with age. They measure fund performance via four alphas: Jensen’sthree-factor (market, size, book-to-market); four-factor (adding momentum) alpha; and, five-factor (adding liquidity). All alpha calculations employ 36-month rolling window regressions of net fund returns in excess of the risk-free rate. The regression methodology allows measurement of the performance difference between a mature fund and its younger self. Using returns, characteristics and holdings data for 3,489 actively managed U.S. domestic equity funds during 1991 through 2014, they find that: Keep Reading

Debating Active Share as Fund Performance Predictor

“Measuring the Level and Persistence of Active Fund Management” (pro) and “Fund Activeness Predicts Performance?” (con) summarize debate on the ability of Active Share, how much portfolio holdings differ from a benchmark index, to predict mutual fund performance. The authors of the con paper summarized in the latter (principals of AQR Capital Management) assert that “neither theory nor data justify the expectation that Active Share might help investors improve their returns.” In his June 2015 paper entitled “AQR in Wonderland: Down the Rabbit Hole of ‘Deactivating Active Share’ (and Back Out Again?)”, Martijn Cremers rejoins the debate by examining the methodology and motives of the con paper. Using data on active U.S. equity mutual funds from the original research, and holdings/performance data for seven AQR Capital Management funds offered to retail investors that concentrate in U.S. stocks as available through December 2014, he finds that: Keep Reading

Competitive Market Perspective on Fund Manager Skill

Do any mutual funds reliably generate significant alpha and, if so, do fund investors receive this alpha? In their June 2015 paper entitled “Active Managers Are Skilled”, Jonathan Berk and Jules Van Binsbergen examine interactions among equity mutual fund gross alpha, assets under management, fees and net alpha. To measure a practical gross alpha, they benchmark active mutual fund gross performance against an historical best-fit linear combination of net returns from contemporaneously available Vanguard funds. To account for the effects of mutual fund size, they measure monthly dollar value added by the fund manager as gross alpha times assets under management. This approach accounts for competition among funds, whereby investors chase an outperforming fund until its alpha drops to zero. They then estimate fund manager skill as average monthly valued added divided by the standard error of the monthly value added series. Using gross monthly returns and fees for a broad survivorship bias-free sample of of active equity mutual funds and net monthly returns for Vanguard mutual funds during January 1977 through March 2011, they find that: Keep Reading

Real-world Equity Fund Performance Benchmarks

Do equity style mutual funds look more attractive when benchmarked to matched style stock indexes than to more theoretical factor models of stock returns? In their April 2015 paper entitled “On Luck versus Skill When Performance Benchmarks are Style-Consistent”, Andrew Mason, Sam Agyei-Ampomah, Andrew Clare and Steve Thomas compare alphas for U.S. equity style mutual funds as calculated with conventional factor models and as calculated with matched Russell style indexes. The factor models they consider are the 1-factor capital asset pricing model (CAPM), the Fama-French 3-factor model (market, size, book-to-market) and the Carhart 4-factor model (adding momentum). They consider both value (net asset value)-weighted and equal-weighted portfolios of mutual funds. They also perform simulations to control for differences in the precision of alpha estimates due to differences in fund sample sizes. Using monthly gross and net returns and equity styles for 2,384 surviving and dead U.S. diversified equity funds, and returns for Russell equity style indexes and market/size/value/momentum factors, during January 1990 through December 2011, they find that: Keep Reading

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