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.

Page 1 of 1212345678910...Last »

Assessing Active Investment Managers

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

Cloning Hedge Funds with ETFs

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

Testing the Equity Mutual Fund Liquidity Ratio

A reader requested an evaluation of the Fosback Index and its Ned Davis variant. The creators of these indicators argue that a high (low) ratio of cash equivalents to assets among equity mutual funds indicates strong (weak) potential demand for stocks. The Investment Company Institute (ICI) surveys mutual fund managers monthly to measure the aggregate mutual fund liquidity ratio. However, only the most recent survey results and past year-end values of the liquidity ratio are publicly available. Monthly values are available with a lag of about one month. Norman Fosback adjusts the raw liquidity ratio based on current interest rates, reasoning that mutual fund managers have more (less) incentive to hold cash when interest rates are high (low). We adjust the raw liquidity ratio from ICI for interest rates by debiting the contemporaneous 13-week U.S. Treasury bill (T-bill) yieldUsing January and February closes of the S&P 500 index and year-end values of the equity mutual fund liquidity ratio and T-bill yield during December 1984 through February 2014 ( about 30 years), we find that: Keep Reading

FundX Upgrader Funds of Funds Performance

A subscriber requested review of FUNDX momentum-oriented funds of funds. We focus on the following three funds: FundX Upgrader (FUNDX)FundX Aggressive Upgrader (HOTFX); and, FundX Conservative Upgrader (RELAX). The offeror describes the upgrading process as follows: “…we sort funds and ETFs by risk, separating more speculative sector and single-country funds from more diversified funds, and we rank these funds each month based on relative performance. We buy highly ranked funds and ETFs and sell these funds when they fall in our ranks. By continually following this active process of buying leaders and selling laggards, the Upgrading strategy seeks to align the FundX Upgrader Funds portfolios with current market leadership and change the Fund portfolios as market leadership changes.” Strategy details are proprietary. As benchmarks, we consider SPDR S&P 500 (SPY) as a large-capitalization index proxy, iShares Russell 2000 (IWM) as a small-capitalization index proxy and the very simple mutual fund “hot hand” strategy. Using monthly total returns for FUNDX, HOTFX, RELAX, SPY and IWM during July 2002 (limited by availability for HOTFX and RELAX) through January 2014, and annual total returns for all these funds plus the “hot hand” strategy during 2003 through 2013, we find that: Keep Reading

Mutual Fund Hot Hand Performance

A subscriber inquired about a “hot hand” strategy that each year picks the top performer from a family of diversified equity mutual funds (not including sector funds) and holds that winner the next year. To evaluate this strategy, we consider the Vanguard family of diversified equity mutual funds over the period during which SPDR S&P 500 (SPY) is available (inception January 1993) as a realistic total return benchmark. We arbitrarily pick end of June to end of the next June as a return ranking and holding interval. To simplify analysis, we assume the “hot hand” mutual fund on the next-to-last trading day of June is the same as that for the end of June. We assume that there are no costs or holding period constraints/delay for switching from one fund to another. Using monthly total returns for SPY and Vanguard diversified equity mutual funds as available from Yahoo!Finance during June 1992 through June 2013, we find that: Keep Reading

Hedge Fund Benchmark Biases

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

Mutual Funds Successfully Exploiting Academic Research?

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

Fair Benchmarks for Mutual Funds

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

Trading Habits of Highly Successful Hedge Fund Managers

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

Hedge Fund Market Timing Proficiency

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

Page 1 of 1212345678910...Last »
Login
Current Momentum Winners

ETF Momentum Signal
for April 2014 (Final)

Momentum ETF Winner

Second Place ETF

Third Place ETF

Gross Momentum Portfolio Gains
(Since August 2006)
Top 1 ETF Top 2 ETFs
217% 197%
Top 3 ETFs SPY
197% 68%
Strategy Overview
Recent Research
Popular Posts
Popular Subscriber-Only Posts