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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.

Technical Analysis as a Mutual Fund Discriminator

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

Managed Futures as Portfolio Diversifier

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

Betting Against Mutual Fund Beta

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

Technical Cloning of Hedge Funds with Futures

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

Mutual Fund Performance Persistence

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

Mutual Fund Alpha Momentum

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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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

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