Mutual/Hedge Funds

Can equity funds exploit widely accepted stock return anomalies? In their July 2013 paper entitled “Academic Knowledge Dissemination in the Mutual Fund Industry: Can Mutual Funds Successfully Adopt Factor Investing Strategies?”, Eduard Van Gelderen and Joop Huij investigate whether mutual funds that materially adopt investment strategies based on published asset pricing anomalies consistently outperform the stock market. They first use monthly regressions to measure degrees of use of six factor investing strategies (low-beta, small cap, value, momentum, short-term reversal and long-term reversion) across U.S. equity mutual funds. They then calculate market-adjusted returns to determine whether funds employing the strategies outperform those that do not and the market. Using monthly returns for 6,814 U.S. equity mutual funds, and contemporaneous monthly returns for the specified factors, during 1990 through 2010, they find that: Keep Reading

How much difference does it make to calculate mutual fund alphas with exchange-traded funds (ETF) rather than ideal (frictionless) indexes/factors? In their November 2012 paper entitled “Mutual Fund’s Net Economic Alpha: Definition and Evidence” Sharon Garyn-Tal and Beni Lauterbach investigate how benchmarking mutual funds with ETFs differs from traditional benchmarking with ideal performance models based on one to five factors (market, fund style, size, book-to-market ratio and momentum). They calculate traditional alphas via regressions against a specified number of factors. They calculate net economic alphas by adding to the traditional alphas costs of implementing associated factors with one or several actual ETFs. Net economic alpha therefore represents the actual value of a fund to investors relative to mimicking ETF alternatives. While accounting for ETF expense ratios, they ignore trading frictions associated with periodic (monthly) rebalancing sets of ETFs to maintain alignment with multi-factor models. They also ignore mutual fund redemption fees and loads, hoping that ETF and mutual fund cost omissions cancel. They focus on post-2000 data because factor-implementing ETFs are not available earlier. Using returns and style designations for over 1,000 open-end, non-specialized U.S. equity funds and values for traditional performance model factors during 2001 through 2009 (segmented into three equal subperiods), they find that: Keep Reading

What are the trading behaviors of the best-performing hedge funds? In his June 2013 paper entitled “How do Hedge Fund ‘Stars’ Create Value? Evidence from Their Daily Trades”, Russell Jame uses transaction-level data to investigate the magnitude and source of hedge fund equity trading profits. His sample includes name, equity trade dates (but not non-equity trades, if any), execution prices and transaction costs for 74 hedge funds and 579 other institutions over a 12-year period. He estimates performance by constructing buy and sell portfolios from trades and computing portfolio-level returns over intervals of the next 21, 63, 126 and 252 trading days (emphasizing 252 days as closest to the average holding period of a typical hedge fund). He excludes portfolios with fewer than ten stocks as too noisy. He considers gross return, gross DGTW-adjusted return (return on a stock less the value-weighted return on a benchmark portfolio with the same size, book-to-market and momentum characteristics as the stock) and net DGTW-adjusted return. Using detailed trading data as described during January 1999 through December 2010 and associated stock prices through December 2011, he finds that: Keep Reading

What proportion of long-short equity hedge fund managers effectively time the stock market? In their January 2013 paper entitled “Hedge Fund Managers’ Market Timing Skills”, Xin Li and Hany Shawky investigate whether long-short equity hedge funds (the oldest and largest hedge fund category) exhibit market timing skill by adjusting positions with market trends. Specifically, they examine hedge fund return correlations with the Fama-French model factors (market, size and book-to-market ratio) during three major crises: the Long-Term Capital Management (LTCM) collapse in the fall of 1998; the quant crisis in August 2007; and, the financial crisis in 2008. They also examine market timing behaviors of individual hedge funds over their respective lifetimes by relating fund beta to market return via a nonlinear function accounting for risk aversion and/or market liquidity. Using monthly returns for 1,571 long-short equity hedge funds having at least 48 months of returns, and contemporaneous Fama-French factor returns, during January 1994 through January 2011, they find that: Keep Reading

Do mutual fund managers who employ technical analysis outperform those who do not? In their January 2013 paper entitled “Head and Shoulders above the Rest? The Performance of Institutional Portfolio Managers who Use Technical Analysis”, David Smith, Christophe Faugere and Ying Wang compare the aggregate investment performance of mutual funds that (self-reportedly) using technical analysis to that of funds not using technical analysis. Self-reported importance of technical analysis is on a five-level scale: “very important,” “important,” “utilized,” “not important” or “not utilized.” Using technical analysis importance levels and monthly returns for 10,452 actively managed U.S. equity, global equity, U.S. balanced and global balanced mutual funds during January 1993 through March 2012 (231 months), they find that: Keep Reading

Are managed futures programs good portfolio diversifiers? In his September 2012 paper entitled “Revisiting Kat’s Managed Futures and Hedge Funds: A Match Made in Heaven”, Thomas Rollinger updates prior research exploring the diversification effects of adding managed futures to traditional portfolios of stocks and bonds and to portfolios including stocks, bonds and hedge funds. His proxies for the four asset classes are: (1) for stocks, the S&P 500 Total Return Index; (2) for bonds, the Barclays U.S. Aggregate Bond Index; (3) for hedge funds, the HFRI Fund Weighted Composite Index; and, (4) for managed futures programs, the Barclay Systematic Traders Index (focused on systematic trend-following strategies). He assumes monthly (frictionless) portfolio rebalancing. Using monthly returns for the four asset class indexes during June 2001 through December 2011, he finds that: Keep Reading

Does a low-beta strategy work for mutual funds? In his September 2012 paper entitled “Capitalizing on the Greatest Anomaly in Finance with Mutual Funds”, David Nanigian examines portfolios of funds sorted on lagged beta to determine whether mutual fund investors can capitalize on outperformance of low-beta assets. He calculates rolling betas for each mutual fund based on monthly returns over the prior 60 months (or less, as few as 24 months, when 60 months of returns are unavailable) or 12 months. He then ranks funds based on lagged betas into asset-weighted fifths each month and holds for one month, or each year and holds for one year. Using monthly net returns and total assets for a broad sample of U.S. equity open-end mutual funds, along with contemporaneous U.S. stock market returns, the risk-free rate and commonly applied risk factors, during December 1990 through April 2012, he finds that: Keep Reading

How effective is technical cloning of hedge funds (attempting to capture a hedge fund’s future returns via a portfolio of liquid assets that empirically replicates the fund’s historical returns)? In the July 2012 version of their paper entitled “Send in the Clones? Hedge Fund Replication Using Futures Contracts”, Nicolas Bollen and Gregg Fisher test whether a replication process can capture some of the benefits of hedge funds (diversification and high Sharpe ratio) while avoiding associated high fees, illiquidity and opacity. They choose one broad and nine strategy-focused hedge fund indexes as targets for replication. They seek to replicate hedge fund index returns with combinations of five fully collateralized futures contracts: U.S. Dollar Index; 10-year T-Note; Gold; Crude Oil; and, S&P 500 Index. Fully collateralized means that they cover potential exposure (positive or negative) with cash earning the risk-free rate (one-month LIBOR). Specifically, they set weights for the futures contracts each month based on linear regression of monthly returns for a hedge fund index versus returns for the five futures contracts over a rolling historical window (see the figure below). They calculate futures contract returns based on holding the nearest-to-expiration contract and rolling to the next maturity five days before expiration. While this process could exploit hedge fund index timing of market factors, it cannot capture any idiosyncratic (non-factor) alpha. Using monthly returns for the ten hedge fund indexes and the five futures contract series during January 1994 through December 2011, they find that: Keep Reading

Do top-performing mutual funds reliably continue to be top performers. In their June 2012 semiannual report entitled “Does Past Performance Matter? S&P Persistence Scorecard”, Standard and Poor’s summarizes performance persistence statistics for U.S. mutual funds overall and for funds grouped by capitalization focus of holdings. They measure persistence of the top 25% (quartile) and top half of funds across multiple subsequent years and frequency of migration of all performance quartiles from one multi-year interval to the next. Using annual performance data for a broad sample of U.S. mutual funds during March 2002 through March 2012, they find that: Keep Reading

Does momentum investing work when implemented via mutual fund alpha? In his February 2012 paper entitled “Short Term Alpha as a Predictor of Future Mutual Fund Performance” (the National Association of Active Investment Managers’ 2012 Wagner Award runner-up), Michael Hartmann examines a momentum-based approach for selecting outperforming equity mutual funds by investment style. He considers nine equity investment styles: Large Capitalization Growth, Large Capitalization Blend, Large Capitalization Value, Mid Capitalization Growth, Mid Capitalization Blend, Mid Capitalization Value, Small Capitalization Growth, Small Capitalization Blend and Small Capitalization Value. He measures momentum based on fund alpha calculated by linear regression of returns versus those of the S&P 500 Index over the past 20, 40, 60, 80 and 100 calendar days. He then forms non-overlapping portfolios of the three highest-alpha funds (weighted equally) for each style every 45, 70, 95, 120, 135 and 170 calendar days over the entire sample period and compares compound annual return rates for these portfolio series to those for corresponding Russell total return style indexes. Using daily total returns for open-ended mutual funds currently available via the no-transaction mutual fund platform at Charles Schwab & Co. and daily returns for the S&P 500 Index from the end of June 1999 through December 2011, along with sample period compound rates of return for Russell benchmark indexes, he finds that:

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