Mutual/Hedge Funds

Are the sources of active mutual fund risk mostly common (systematic) or unique (idiosyncratic)? In his July 2014 paper entitled “Components of Portfolio Variance: R2, SelectionShare and TimingShare”, Anders Ekholm decomposes mutual fund return variance (risk) into three sources: (1) passive systematic factor exposure (R-squared); (2) active security selection or stock picking (SelectionShare); and, (3) active systematic factor timing (TimingShare). He demonstrates estimation of these three components based on mutual fund returns (reflecting daily manager actions) rather than holdings (known only via quarterly snapshots). He employs the widely used four-factor (market, size, book-to-market, momentum) model of stock returns to define systematic risk. Using daily returns for a broad sample of actively managed U.S. equity mutual funds and for the four factors during 2000 through 2013, he finds that: Keep Reading

Are alternative mutual funds attractive for retail investors as hedge fund surrogates? In their June 2014 paper entitled “Performance of Alternative Mutual Funds: The Average Investors Hedge Fund”, Srinidhi Kanuri and Robert McLeod analyze the performance of alternative mutual funds that employ strategies similar to those of hedge funds and seek returns uncorrelated with the equity market. These funds can sell short and use leverage, derivatives, options and swaps to shape and enhance returns. However, they must offer daily liquidity, cover short positions, limit borrowing to a third of assets and limit illiquid investments to 15% of assets. The authors consider nine categories of alternative mutual funds, ranging in population from just three inverse commodity funds to 109 long-short equity funds. They apply both a four-factor (equity market, size, book-to-market, momentum) mutual fund model and a seven-factor (equity market, size, bond market, credit spread, bond trend, currency trend, commodity trend) hedge fund model to measure alternative mutual fund alpha. They aggregate across all funds and within categories based on equal weight. Using monthly data for 256 surviving and 62 dead alternative mutual funds during January 1998 through December 2011, they find that: Keep Reading

Do individuals who actively reallocate funds within their pension accounts outperform passive counterparts? In the March 2014 update of their paper entitled “Individual Investor Activity and Performance”, Magnus Dahlquist, Jose Vicente Martinez and Paul Soderlind examine the activity and performance of individual participants in Sweden’s Premium Pension System. This system allows individual participants to reallocate among available mutual funds on a daily basis with no switching fees/impediments. Information about the 1,230 funds offered during the sample period includes type (fixed income, balanced, life-cycle and equity), return and risk measured at several horizons, fee and major holdings. Most are equity funds, about half of which invest primarily in international equities. The government assigns individuals who make no choice to a default fund. Using daily net returns, fund trades and demographics for 70,755 individuals (from a random draw of individuals in the system over the entire period) and contemporaneous returns for several benchmarks during September 2000 through May 2010, they find that: Keep Reading

Is past performance a useful indicator of future performance for some kinds of mutual funds? In their April 2014 paper entitled “Differences in Short-Term Performance Persistence by Mutual Fund Equity Class”, Larry Detzel and Andrew Detzel evaluate performance persistence among diversified U.S. equity mutual funds categorized per the Morningstar Equity Style Box: Large Value (LV), Large Blend (LB), Large Growth (LG), Mid-Cap Value (MV), Mid-Cap Blend (MV), Mid-Cap Growth (MG), Small Value (SV), Small Blend (SB) or Small Growth (SG). Each quarter, they sort funds into styles and then rank them into fifths (quintiles) based on four-factor alpha (adjusting for market, size, book-to-market and momentum risks) calculated with daily returns. They then calculate average four-factor alphas for these quintiles during the next four quarters. Using quarterly Morningstar style assignments and daily returns for a large sample of live and dead diversified U.S. equity mutual funds, along with data for associated stocks and contemporaneous returns for risk factors, during January 1999 through December 2011, they find that: Keep Reading

Do Morningstar’s analyst ratings predict which mutual funds will do best? In their January 2014 paper entitled “Going for Gold: An Analysis of Morningstar Analyst Ratings”, Will Armstrong, Egemen Genc and Marno Verbeek examine the performance of mutual funds after Morningstar assigns analyst ratings to them. Morningstar initiated these substantially qualitative ratings (Gold, Silver, Bronze, Neutral and Negative) in September 2011, as a supplement to star ratings, to convey expected risk-adjusted performance of funds with respect to peers over a full market cycle of at least five years. Ratings take into account past performance, fees and trading costs, quality of investment team, parent organization and investment process.  The study considers both raw returns and four-factor (market, size, book-to-market, momentum) alphas during intervals of one, three and six months after each rating initiation. It also takes into account differences in time frame, fund investment style and fund star rating. Using analyst ratings initiated during September 2011 through December 2012, associated fund characteristics and associated fund returns through June 2013, they find that:

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Do active investment managers beat the market? In their January 2014 paper entitled “Active Manager Performance: Alpha and Persistence”, Frank Benham and Edmund Walsh assess the performance of active investment managers relative to appropriate benchmarks across asset classes over long periods. They consider six basic investment classes: core bonds; high-yield bonds; domestic large capitalization stocks; domestic small capitalization stocks; foreign large capitalization stocks; and, emerging markets stocks. They focus on whether investment managers beat benchmarks in the past and whether past outperformers become future outperformers. They take steps to avoid survivorship bias, selection bias and fund classification errors. Using a sample of 5,379 live and dead funds assembled from Morningstar Direct by filtering to avoid classification errors and to eliminate redundant funds run by the same manager from benchmark inceptions (ranging from January 1979 for domestic stocks to January 1988 for emerging markets stocks) through 2012, they find that: Keep Reading

Does the expanding set of exchange-traded funds (ETF) support reliable replication (cloning) of future hedge fund returns? In their March 2014 paper entitled “In Search of Missing Risk Factors: Hedge Fund Return Replication with ETFs”, Jun Duanmu, Yongjia Li and Alexey Malakhov investigate the use of ETFs as factors in constructing hedge fund clones. They note that the number of U.S.-listed passive ETFs increases from 19 in 1997 to 1,313 in 2012, now comprising a large set of proxies for many factor/characteristic strategies. They use this set of factor proxies to clone a hedge fund via a three-step in-sample replication process based on two years of historical data. Specifically, each year they:

1. Iterate cluster analysis 100 times to identify ETFs most representative (highest correlation of monthly returns with the mean return of the cluster) of up to 100 clusters to serve as factor proxies.
2. Use an optimization tool on each of the 100 cluster analyses to combine representative ETFs into 100 clone models of pre-fee (risk factor perspective) monthly returns for each target hedge fund.
3. Apply the Bayesian information criterion (which addresses data snooping bias via a penalty for model complexity) to select the best clone model for each target hedge fund.

They then test the ability of winning clone models to match post-fee (investor perspective) monthly returns of target hedge funds for one year out-of-sample. They mitigate backfill bias in hedge fund returns (only funds with good starts begin publicizing their returns) by excluding the first 24 months of reported returns. They suppress survivorship bias by including funds that later stop reporting. Using monthly returns, fees and characteristics for 3,190 hedge funds and monthly returns and fees for 1,313 passive ETFs as available during 1997 through 2012, they find that: Keep Reading

Research on hedge fund performance derives from voluntary reports by hedge funds to commercial databases. This environment encourages: (1) backfill bias (non-reporting funds doing well are most likely to begin reporting, including historical data that arguably involves some good luck); and, (2) delisting bias (reporting funds doing poorly, arguably due in part to poor strategies, are most likely to stop reporting). Also, young databases tend to have survivorship bias because they have not accumulated much data on “dead” funds. How material is the delisting bias? In their August 2013 paper entitled “The Delisting Bias in Hedge Fund Databases”, Philippe Jorion and Christopher Schwarz compare information across three commercial hedge fund databases to estimate delisting bias. Their estimating process exploits the fact that hedge funds often do not terminate reporting to all three databases at the same time. Using matched monthly hedge fund return data across Tremont Advisory Shareholders Services, the Center for International Securities and Derivatives Markets, and Hedge Fund Research databases during 1994 through 2008 (9,970 funds), they find that: Keep Reading

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