Mutual/Hedge Funds

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

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’s; three-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

“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

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

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

Are mutual fund managers whose holdings deviate most from their benchmarks the best performers? In their April 2015 paper entitled “Deactivating Active Share”, Andrea Frazzini, Jacques Friedman and Lukasz Pomorski investigate whether Active Share is a reliable indicator of future mutual fund performance. Active Share measures the distance between a portfolio and its benchmark, ranging from zero for a portfolio that is identical to its benchmark to one for a portfolio with no holdings in common with its benchmark. They consider both theoretical arguments and empirical analysis, with the latter focused on disentangling Active Share and benchmark effects. Using holdings and performance data for actively managed U.S. equity mutual funds during 1980 through 2009, they find that: Keep Reading

Do multi-class mutual funds exhibit good asset class allocation timing? In their April 2015 paper entitled “Multi-Asset Class Mutual Funds: Can They Time the Market? Evidence from the US, UK and Canada”, Andrew Clare, Niall O’Sullivan, Meadhbh Sherman and Steve Thomas investigate whether mutual fund managers time allocations across asset classes skillfully. They focus on three asset classes: equities, government bonds and corporate bonds. They apply two alternative methodologies: (1) returns-based, relating each asset class beta for a fund to next-month return for that class; and, (2) holdings-based, relating changes in asset class weights within a fund to next-month class returns. Using monthly returns and holdings for 617 U.S., UK and Canadian multi-asset class mutual funds during 2000 through 2012, they find that:

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When and why do investors make changes in asset class allocations? In the March 2015 version of their paper entitled “Global Asset Allocation Shifts”, Tim Kroencke, Maik Schmeling and Andreas Schrimpf examine the asset reallocation decisions of U.S. mutual fund investors. They focus on shifts between U.S. equities and U.S. bonds (rotation) and between U.S. assets and non-U.S. assets (diversification). Specifically, they address: (1) principal factors explaining reallocations; (2) the link between monetary policy announcements and allocation shifts; and, (3) the search for bond yield and asset returns as drivers of allocation shifts. Using detailed U.S. mutual fund data on investor allocations to U.S. equities, non-U.S. equities and fixed income (comprising a total of about $6.6 trillion in assets) during January 2006 through December 2014, they find that: Keep Reading

Can investors exploit the combination of unusual changes in hedge fund long positions and unusual changes in short interest for individual stocks? In the February 2015 version of their paper entitled “Arbitrage Trading: The Long and the Short of It”, Yong Chen, Zhi Da and Dayong Huang examine the power of three variables to predict stock returns:

1. Abnormal hedge fund holdings (AHF), the current quarter aggregate hedge fund long positions in a stock divided by the total shares outstanding minus the average of this ratio over the four prior quarters.
2. Abnormal short interest (ASR), the current quarter short interest in a stock divided by the total number of shares outstanding minus the average of this ratio over the four prior quarters.
3. The difference between AHF and ASR as a measure of imbalance in hedge fund trading.

They also examine how AHFSR interacts with ten widely used stock return predictors: book-to-market ratio; gross profitability; operating profit; momentum; market capitalization; asset growth; investment growth; net stock issuance; accruals; and, net operating assets. To measure the effectiveness of each predictor, they each quarter rank stocks into fifths (quintiles) based on the predictor and then calculate the difference in average gross excess (relative to the risk-free rate) returns of extreme quintiles. Using quarterly hedge fund SEC Form 13F filings and short interest data for a broad sample of U.S. stocks (excluding small and low-priced stocks), along with data required to compute stock return predictors and risk factors for these stocks, during 1990 through 2012, they find that: Keep Reading

Do changes in hedge fund holdings and short interest in a stock together predict its returns? In their January 2015 paper entitled “Short Selling Meets Hedge Fund 13F: An Anatomy of Informed Demand”, Yawen Jiao, Massimo Massa and Hong Zhang test whether joint changes in aggregate hedge fund holdings and short interest of a stock relate to its future returns. They define a contemporaneous increase (decrease) in aggregate hedge fund holdings and decrease (increase) in short interest as indicative of informed long (short) demand for a stock. They relate informed demand to abnormal return, the return of the stock relative to that of its style benchmark based on size, book-to-market and prior-period return. Using size/value characteristics, monthly returns, quarterly short interest and holdings from quarterly SEC Form 13F filings of 1,397 hedge funds for 5,357 U.S. stocks during 2000 through 2012, they find that: Keep Reading