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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.

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Beta Males Make Hedge Fund Alpha

Does appearance-based masculinity predict hedge fund manager performance? In their January 2018 paper entitled “Do Alpha Males Deliver Alpha? Testosterone and Hedge Funds”, Yan Lu and Melvyn Teo use facial width-to-height ratio (fWHR) as a positively related proxy for testosterone level to investigate the relationship between male hedge fund manager testosterone level and hedge fund performance. They each year in January sort hedge funds into tenths (deciles) based on fund manager fWHR and then measure the performance of these decile portfolios over the following year. Their main performance metric is 7-factor hedge fund alpha, which corrects for seven risks proxied by: (1) S&P 500 Index excess return; (2) difference between Russell 2000 Index and S&P 500 Index returns; (3) 10-year U.S. Treasury note (T-note) yield, adjusted for duration, minus 3-month U.S. Treasury bill yield; (4) change in spread between Moody’s BAA bond and T-note, adjusted for duration; and, (5-7) excess returns on straddle options portfolios for currencies, commodities and bonds constructed to replicate trend-following strategies in these asset classes. They collect 3,228 hedge fund manager photographs via Google image searches, choosing the best for each manager based on resolution, degree of forward facing and neutrality of expression. They use these photographs to measure fWHR as the distance between the two zygions (width) relative to the distance between the upper lip and the midpoint of the inner ends of the eyebrows (height). Using these fWHRs, monthly net-of-fee returns and assets under management of 3,868 associated live and dead hedge funds, and monthly risk factor values during January 1994 through December 2015, they find that:

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Are Hedge Fund ETFs Working?

Are hedge fund-oriented strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider six ETFs, all currently available (in order of decreasing assets):

  • IQ Hedge Multi-Strategy Tracker (QAI) – seeks to track, before fees and expenses, risk-adjusted returns of a collection of long/short equity, global macro, market neutral, event-driven, fixed income arbitrage and emerging markets hedge funds.
  • ProShares Hedge Replication (HDG) – seeks to track, before fees and expenses, an equally weighted composite of over 2000 hedge funds.
  • AlphaClone Alternative Alpha (ALFA) – seeks to track price and yield, before fees and expenses, of U.S.-traded equity securities to which hedge funds and institutional investors have disclosed significant exposures.
  • IQ Hedge Market Neutral Tracker (QMN) – seeks to track, before fees and expenses, risk-adjusted returns of market neutral hedge funds.
  • ProShares Morningstar Alternatives Solution (ALTS) – seeks to track, before fees and expenses, performance of a diversified set of alternative ETFs.
  • JPMorgan Diversified Alternatives (JPHF) – aims to provide direct, diversified exposure to hedge fund strategies via a bottom-up approach across equity long/short, event-driven and global macro strategies.

We consider both daily and monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). We use two benchmarks, SPDR S&P 500 (SPY) and the Eurekahedge Hedge Fund Index (HFI). Using daily and monthly returns for the six hedge fund ETFs and SPY as available through September 2017 and monthly returns for HFI through August 2017, we find that: Keep Reading

Hedge Fund Breakdown?

Can investors confidently pick hedge funds that will do well? In their September 2017 paper entitled “Hedge Fund Performance Prediction”, Nicolas Bollen, Juha Joenväärä and Mikko Kauppila examine the forecasting power of 26 hedge fund performance predictors identified in past research. These predictors span five categories: seven broad manager skills; four market timing skills; six systematic risks; four tail risks; and, five incentive metrics. They test the predictors individually and in combinations based on an average of rankings by category and overall. Specifically, for their main tests, they each year:

  1. Sort funds into fifths (quintiles) based on one predictor or a combination of predictors as measured over the prior 24 months.
  2. Randomly select several funds (baseline 15) from the top quintile to represent a feasible long-only hedge fund portfolio.
  3. Hold the selected funds with initial equal weights but no interim rebalancing for one year.
  4. Calculate the performance of a succession of such one-year portfolios over the sample period.

They run 1,000 trials for each predictor/combination to obtain a performance distribution. Their benchmark is an 80% allocation to the S&P 500 Total Return Index and a 20% allocation to the Vanguard Total Bond Market Index mutual fund (VBTIX), rebalanced annually. They collect data starting in January 1994 but delete the first 12 months to control for backfill bias (reporting of a successful year after the fact). As a robustness test, they repeat the analysis on two subperiods with break point at the end of February 2009. Using monthly returns after fees and characteristics for a broad sample of hedge funds during January 1995 through December 2016, they find that: Keep Reading

FundX Upgrader Funds of Funds Performance

A subscriber requested review of FUNDX momentum-oriented funds of funds. We focus on 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 and competition, we consider SPDR S&P 500 (SPY) for large-capitalization stocks, iShares Russell 2000 (IWM) for small-capitalization stocks and Simple Asset Class ETF Momentum Stategy (SACEMS) Top 1 and equal-weighted (EW) Top 3 variations. Using monthly total returns for FUNDX, HOTFX, RELAX, SPY and IWM since July 2002 (limited by HOTFX and RELAX), SACEMS Top 1 since January 2003 and SACEMS EW Top 3 since August 2006, all through July 2017, we find that: Keep Reading

Do Hedge Funds Effectively Exploit Real-time Economic Data?

Do hedge funds demonstrate the exploitability of real-time economic data? In their June 2017 paper entitled “Can Hedge Funds Time the Market?”, Michael Brandt, Federico Nucera and Giorgio Valente evaluate whether all or some equity hedge funds vary equity market exposure in response to real-time economic data, and (if so) whether doing so improves their performance. Their proxy for real-time economic data available to a sophisticated investor is the 20-day moving average of an economic growth index derived from principal component analysis of purely as-released industrial output, employment and economic sentiment. They relate this data to hedge fund performance by:

  1. Applying a linear regression to measure the sensitivity (economic data beta) of each hedge fund to monthly changes in economic data.
  2. Sorting funds into tenths (deciles) based on economic data beta and calculating average next-month equally weighted risk-adjusted performance (7-factor alpha) by decile. The seven monthly factors used for risk adjustment are: equity market excess return; equity size factor; change in 10-year U.S. Treasury note (T-note) yield; change in yield spread between BAA bonds and T-notes; and trend following factor for bonds, currencies and commodities.

Using sample of 2,224 dead and alive equity hedge funds having at least 36 months of net-of-fee returns and average assets under management of at least $10 million, and contemporaneous daily values of the economic growth index, during January 1994 through December 2014, they find that:

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Faked Out by Mutual Funds?

Do investors view (mechanical) smart beta returns from mutual funds as (skillful) alpha? In the April 2017 update of their paper entitled “Fake Alpha”, Marcel Müller, Tobias Rosenberger and Marliese Uhrig-Homburg investigate the conflation of smart beta (“fake alpha”) and true alpha (incremental to smart beta and generated by skill) by mutual fund managers and investors. In estimating smart beta returns, they consider size, value and momentum factors. Using monthly returns for 3,292 actively managed mutual funds focused on U.S. stocks and contemporaneous market, size, book-to-market and momentum factor returns during March 1993 to December 2014, they find that: Keep Reading

Effects of Smart Beta ETFs on Mutual Funds

Has availability of liquid exchange-traded funds (ETF) designed to exploit predictive stock market factors (smart beta ETFs) affected the mutual fund industry? In their May 2017 paper entitled “How Do Smart Beta ETFs Affect the Asset Management Industry? Evidence from Mutual Fund Flows”, Jie Cao, Jason Hsu, Zhanbing Xiao and Xintong Zhan examine the impact of ETFs that do not track the market (smart beta ETFs) on mutual funds. They focus on U.S. equity and assess effects of smart beta ETFs by measuring mutual fund investment flow sensitivities to equity factor alphas over time. They quantify alphas using a 5-year rolling window of historical data. They split their sample into to subperiods, an early one with low smart beta ETF trading volumes and a late one with high volumes. Using monthly trading volumes, returns and assets (sizes) for 4,587 U.S. equity mutual funds and for 747 U.S. equity ETFs, and contemporaneous U.S. equity factor model returns, during January 2000 through December 2015, they find that: Keep Reading

Good and Bad High-fee Mutual Funds

Should investors shun mutual funds with high fees? In their February 2017 paper entitled “Cheaper is Not Better: On the Superior Performance of High-Fee Mutual Funds”, Jinfei Sheng, Mikhail Simutin and Terry Zhang re-examine the conventionally accepted negative relationship between expense ratio and future net performance of actively managed equity mutual funds. They measure fund performance as alpha from each of four factor models of stock returns:

  • 1-factor: the Capital Asset Pricing Model, which controls for the market factor.
  • 3-factor: the widely used Fama-French model, which controls for market, size and value (book-to-market ratio) factors.
  • 4-factor: the widely used Carhart model, which adds the momentum factor to the 3-factor model.
  • 5-factor: the recent Fama-French model that adds profitability and investment factors to the 3-factor model.

They calculate alpha for each fund each month as the difference between next-month excess return minus expected return based on fund factor loadings from a regression over the last 60 months. They then use additional regressions and a ranking of funds into tenths (deciles) by fee to analyze relationships between alphas and fees. Using survivorship bias-free performance, sales channel and holding data for active U.S. domestic equity funds with at least five years of history and substantial holdings/assets during 1980 through 2014, they find that: Keep Reading

Hedge Fund Manager Personal Risk Taking vs. Investment Performance

Do hedge fund managers who seek excitement as indicated by choice of cars invest differently from those who do not? In their December 2016 paper entitled “Sensation Seeking, Sports Cars, and Hedge Funds”, Yan Lu, Sugata Ray and Melvyn Teo investigate the relationship between hedge fund manager personal car selection (body style, maximum horsepower, maximum torque, passenger volume and safety ratings) and fund performance. After identifying a large set of hedge fund managers, they match managers to cars and car characteristics via VIN Place, Autocheck, cars.com, cars-data and the Insurance Institute for Highway Safety, categorizing cars as sports cars, minivans or other based on body style. They then relate hedge fund manager car data as available to subsequent performance and characteristics of associated hedge funds. Using car data and monthly net-of-fee returns, assets under management and other fund characteristics for 1,774 vehicles (including 163 sports cars and 101 minivans) purchased by 1,144 hedge fund managers during January 1994 through December 2015, they find that: Keep Reading

The Value of Fund Manager Discretion?

Are there material average performance differences between hedge funds that emphasize systematic rules/algorithms for portfolio construction versus those that do not? In their December 2016 paper entitled “Man vs. Machine: Comparing Discretionary and Systematic Hedge Fund Performance”, Campbell Harvey, Sandy Rattray, Andrew Sinclair and Otto Van Hemert compare average performances of systematic and discretionary hedge funds for the two largest fund styles covered by Hedge Fund Research: Equity Hedge (6,955 funds) and Macro (2,182 funds). They designate a fund as systematic if its description contains “algorithm”, “approx”, “computer”, “model”, “statistical” and/or “system”. They designate a fund as discretionary if its description contains none of these terms. They focus on net fund alphas, meaning after-fee returns in excess of the risk-free rate, adjusted for exposures to three kinds of risk factors well known at the start of the sample period: (1) traditional equity market, bond market and credit factors; (2) dynamic stock size, stock value,  stock momentum and currency carry factors; and, (3) a volatility factor specified as monthly returns from buying one-month, at‐the‐money S&P 500 Index calls and puts and holding to expiration. Using monthly after-fee returns for the specified hedge funds (excluding backfilled returns but including dead fund returns) during June 1996 through December 2014, they find that: Keep Reading

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