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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.

Active Mutual Funds Beat Fair Benchmarks?

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

Hedge Fund Risk and Return

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

SweetSpot: Market-beating Reversion of Unloved Niches?

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

Liquidity Risk Premium Dominant in Hedge Fund Returns?

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

Active ETF Performance

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

Focus on the Most Intensely Active Mutual Funds?

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 Benchmark Bias?

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

Taxonomy of Mutual Fund Fees, Expenses and Costs

The variety and sometime abstruseness of mutual fund fees and expenses suggest that fund manager incentives do not always align with the interest of fund investors in net return. In an April 2011 update entitled “Mutual Funds: Revised New Total Expense Ratio and Costs with Soft-Dollar Commissions and Rebates”, John Haslem notes that the SEC “regulatory scheme of expense ratio disclosure is short on transparency and long on opaque” and offers an alternative taxonomy of mutual fund costs intended to be transparent for investors. As described in the article, the categories/elements and typical magnitudes of annual fund expenses are: Keep Reading

Holdings Return Skewness as a Luck-Skill Discriminator

Can investors discriminate between lucky and skillful equity fund managers by examining the distribution of returns across fund holdings? In the September 2010 preliminary draft of their paper entitled “Home-Run Sluggers vs. Contact Hitters: Stock Performance Distribution inside Mutual Funds and Fund Managers’ Stock Picking Ability”, Peter Chung and Thomas Kim relate the skewness of the return distribution of equity mutual fund holdings to performance persistence. Specifically, they calculate the skewness of the distribution of four-factor (adjusted for market, size, book-to-market, momentum) alphas of individual fund holdings weighted according to position size. A fund manager who consistently picks outperforming stocks (gets lucky with one big winner) would have a negatively (positively) skewed distribution of alphas. Using reported holdings for 1,604 U.S. equity mutual funds and data to calculate the lagged six-month alphas for each of these holdings from the end of July 2002 through February 2006, they find that: Keep Reading

Institutional Ownership, Idiosyncratic Volatility and Stock Returns

Is the number of institutional owners of a stock, arguably a proxy for general investor awareness and demand, an important factor in current and future pricing of the stock? In their February 2011 paper entitled “What Makes Stock Prices Move? Fundamentals vs. Investor Recognition”, Scott Richardson, Richard Sloan and Haifeng You investigate the role of institutional ownership breadth in size-adjusted stock price dynamics. They focus on institutional investors with greater than $100 million in equity holdings, as reported quarterly to the SEC via Form 13F. They measure institutional ownership breadth as the number of institutions holding a particular stock relative to the number of institutions holding any given stock. They measure firm size based on total assets. They impose a three-month lag on data to ensure calculations use only publicly available information. Using stock returns, institutional ownership data and accounting data for a broad sample of U.S. firms over the period 1986 through 2008 (35,526 firm years), they find that: Keep Reading

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