Mutual/Hedge Funds

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 Vanguard diversified equity mutual funds with inceptions no later than September 2011. The test period is the lifetime of SPDR S&P 500 (SPY), which serves as a benchmark. We assume no costs or holding period constraints/delays for switching from one fund to another. We also simplify calculations by assuming that end-of-year “hot hand” fund identification and fund switches occur simultaneously (in other words, we can accurately rank mutual funds one day before the end of the year). Using monthly total returns for SPY and for Vanguard diversified equity mutual funds as available during December 1992  through December 2018, we find that:

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Does combining the wisdom of multiple stock-picking models via ensemble methods, as done in forecasting landfall of hurricanes, improve investment portfolio performance? In their September 2018 paper entitled “Ensemble Active Management”, Alexey Panchekha, Robert Tull and Matthew Bell test the application of ensemble methods to active portfolio management, looking for consensus or near-consensus among multiple, independent stock picking sources. Ensemble diversification tends to neutralize biases among individual sources when: (1) sources are independent; (2) sources employ different approaches; and, (3) most sources achieve at least 50% individual accuracies. As sources, they use the holdings and weights of 37 actively managed U.S. equity large-capitalization mutual funds, focusing on high-conviction stock selections (those with large mismatches with respect to market capitalization). Specifically, every two weeks they:

• Reform 30,000 randomly generated clusters of 10 mutual funds.
• For each cluster, reform a long-only Ensemble Active Management (EAM) portfolio consisting of the 50 stocks with the highest consensus overweights within the cluster.
• Calculate total returns for EAM portfolios, their respective clusters and the S&P 500 Index.

They debit performance of each EAM portfolio by the average contemporaneous expense ratio of the 37 mutual funds (average 0.94% across all years). To aggregate results, they calculate rolling 1-year and 3-year performances of EAM portfolios, mutual fund clusters and the index. Using daily estimated stock holdings and weights for the 37 mutual funds and associated stock prices as available during July 2007 through December 2017, they find that:

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Does recent research on active mutual fund performance challenge conventional wisdom that: (1) the average fund underperforms passive benchmarks on a net basis; and, (2) individual fund outperformance does not persist. In their September 2018 paper entitled “Challenging the Conventional Wisdom on Active Management: A Review of the Past 20 Years of Academic Literature on Actively Managed Mutual Funds”, Martijn Cremers, Jon Fulkerson and Timothy Riley review academic research on active mutual funds from the last 20 years to assess the degree to which it supports this conventional wisdom. They focus on U.S. equity mutual funds but also consider bond funds, hybrid stock-bond funds, socially responsible funds, target date funds, real estate investment trust (REIT) funds, sector funds and international funds. Based on this research, they conclude that: Keep Reading

Do active U.S. equity mutual funds beat their passive counterparts in recent years? In the September 2018 version of his paper entitled “The Historical Record on Active vs. Passive Mutual Fund Performance”, David Nanigian compares risk-adjusted annual performance of active versus passive U.S. equity mutual funds as categorized and monitored in the Morningstar Direct survivorship bias-free database. He measures rise-adjusted performance based on the Carhart 4-factor model (accounting for market, size, book-to-market and momentum factors) alpha. He considers both value-weighted (VW), based on fund assets under management at the end of the prior month, and equal-weighted (EW) combinations of funds. In addition to the full sample, he considers separately funds in the bottom fifth (quintile) of expense ratios. He also compares active and passive funds paired based on similar expense ratios. Using monthly fund data as specified during 2003 through 2017, he finds that: Keep Reading

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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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

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 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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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

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