Mutual/Hedge Funds

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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Have researchers unfairly treated actively managed mutual funds by using non-investable benchmarks? In their February 2012 paper entitled “Another Look at the Performance of Actively Managed Equity Mutual Funds”, David Blitz and Joop Huij evaluate the performance of actively managed equity mutual funds against a set of passively managed market, small capitalization, growth and value index funds rather than modeled risk factors. They select four Vanguard funds as passive benchmarks: 500 Index Investor (VFINX); Small Cap Index Investor (NAESX); Value Index Investor (VIVAX); and, Growth Index Investor (VIGRX). At the beginning of every month, they rank active funds into deciles based on 12-month lagged returns and calculate average fund returns by decile over the next month. They then relate the decile series returns to: (1) the commonly used market, size, book-to-market (value) and momentum factors; and, (2) factor-mimicking combinations of the Vanguard passive index funds. Using monthly returns for those of 6,814 living and dead U.S. equity mutual funds which are not obviously index funds from April 1993 (when all four Vanguard index funds become available) through March 2010, they find that: Keep Reading

Do hedge funds trade on market risk, idiosyncratic risk or tail risk? In their November 2011 paper entitled “Systematic Risk and the Cross-Section of Hedge Fund Returns”, Turan Bali, Stephen Brown and Mustafa Caglayan explore the predictability of hedge fund returns based on distinct market-related (systematic), idiosyncratic (residual) and tail risk measures. They alternatively consider four-factor (equity market, size, book-to-market and momentum), six-factor (adding two bond factors) and nine-factor (adding currency, bond and commodity momentum) models of market risk. They employ both three-year rolling regressions and equally weighted quintile portfolios formed from monthly sorts to relate hedge fund risks and returns. They ignore funds with less than 24 months history and avoid a measured 1.87% annual backfill bias (only funds with good first years volunteer performance) by ignoring the first 12 months of returns for each fund. Using monthly net returns and characteristics for a sample of 14,228 hedge funds (8,201 dead and 6,027 live) during January 1994 through June 2010, they find that: Keep Reading

A reader suggested reviewing the detailed track record of SweetSpot Investments LLC, consisting of 29 closed trades over the past 12 years. The basic SweetSpot strategy posits market-beating three-year reversion of the three least popular “sectors” out of 100 formed from 500 non-diversified mutual funds and exchange-traded funds (ETF). Popularity is a function of fund assets and prior-year fund flows and returns. From a practical perspective, this strategy results in a steady-state portfolio of nine “sector” funds, each year selling the three oldest holdings and adding three new ones. Since 2009, the strategy includes as a hedge a short position in a market index fund or a position in an inverse market index fund “whenever the market’s intermediate-term trend falls below its long term trend.” The detailed track record includes no trades since that change in strategy. Using results from 29 SweetSpot trades from the end of 1998 through the beginning of 2011, we find that: Keep Reading

Do hedge funds rely on off-the-beaten-track (illiquid) positions to fuel performance? In his April 2011 paper entitled “Hedge-Fund Performance and Liquidity Risk”, Ronnie Sadka investigates aggregate market liquidity as a predictor of hedge fund performance. His calculates liquidity based on trade-by-trade price impact estimated monthly for individual stocks and aggregated by averaging. Using net monthly returns for a broad sample of live and dead hedge funds during 1994 thorugh 2009 and contemporaneous trade-by-trade stock prices for liquidity calculations, he finds that: Keep Reading

Do active exchange-traded funds (ETF), which realistically incorporate management costs and trading frictions, offer value to investors? In his June 2011 paper entitled “Active ETFs and Their Performance vis-à-vis Passive ETFs, Mutual Funds and Hedge Funds”, Panagiotis Schizas examines the returns and risks of the first active ETFs, including comparisons with alternative passive ETFs, mutual funds and hedge funds. The active ETFs [and passive counterparts] he considers are:

PowerShares Active Low Duration (PLK) [iShares Barclays 1-3 Year Treasury Bond (SHY)]
PowerShares Active Mega Cap (PMA) [SPDR S&P 500 (SPY)]
PowerShares Active AlphaQ (PQY) [PowerShares QQQ (QQQ)]
PowerShares Active Alpha Multi-Cap (PQZ) [SPDR S&P 500 (SPY)]
PowerShares Active U.S. Real Estate (PSR) [iShares FTSE NAREIT Real Estate 50 (FTY)]

Using matched ETF, mutual fund and hedge fund performance data (daily for ETFs and mutual funds and monthly for hedge funds) as available from active ETF inception (4/14/08 for the first four and 11/21/08 for the fifth) through 3/4/10, he finds that: Keep Reading

Are many mutual fund managers worldwide so fixated on benchmarks that they substantially emulate index funds, while charging shareholders “active” fees? In the April 2011 version of their paper entitled “The Mutual Fund Industry Worldwide: Explicit and Closet Indexing, Fees, and Performance”, Martijn Cremers, Miguel Ferreira, Pedro Matos and Laura Starks address the prevalence and consequences of index versus active investing in the mutual fund industry around the world. They focus on “closet index” funds that are nominally active but do not deviate much from benchmark compositions, applying an “Active Share” measurement to quantify the degree of deviation. Using data for large samples of alive and dead open-end equity mutual and exchange-traded funds across 30 countries during 2002 through 2007, they find that: Keep Reading

Hedge fund databases are prone to: (1) self-selection bias (only good performers report); (2) backfill bias (only funds with good recent past performance retroactively report it); (3) survivorship bias (exclusion of dead fund performance); and; (4) liquidation bias (poor performers stop reporting but continue to operate for some period). Do hedge fund indexes therefore inaccurately portray industry performance? In the April 2011 revision of their paper entitled “Hedge Fund Biases After the Financial Crisis”, Dieter Kaiser and Florian Haberfelner estimate three of the four hedge fund database biases and explore how these biases evolved during the 2007-2009 financial crisis. They focus on liquidation bias, which leading commercial hedge fund databases do not attempt to control. Their principal analytic technique is to form hypothetical funds of hedge funds from actual single funds and compare the resulting hypothetical returns, after correcting for observable backfill and survivorship biases, with returns from real funds of hedge funds. Using data for 8,935 hedge funds (6,088 single funds and 2,847 funds of funds) for the period January 2002 through September 2010, they find that: Keep Reading