Mutual/Hedge Funds

Do changes in hedge fund holdings and short interest in a stock together predict its returns? In their January 2015 paper entitled “Short Selling Meets Hedge Fund 13F: An Anatomy of Informed Demand”, Yawen Jiao, Massimo Massa and Hong Zhang test whether joint changes in aggregate hedge fund holdings and short interest of a stock relate to its future returns. They define a contemporaneous increase (decrease) in aggregate hedge fund holdings and decrease (increase) in short interest as indicative of informed long (short) demand for a stock. They relate informed demand to abnormal return, the return of the stock relative to that of its style benchmark based on size, book-to-market and prior-period return. Using size/value characteristics, monthly returns, quarterly short interest and holdings from quarterly SEC Form 13F filings of 1,397 hedge funds for 5,357 U.S. stocks during 2000 through 2012, they find that: Keep Reading

Does the average mutual fund investor accrue the average fund performance, or do investor timing practices alter the equation? In their July 2014 paper entitled “Timing Poorly: A Guide to Generating Poor Returns While Investing in Successful Strategies“, Jason Hsu, Brett Myers and Ryan Whitby compare the average dollar-weighted and buy-and-hold returns of different U.S. equity mutual fund styles, with focus on the value style. Dollar weighting adjusts the return stream based on the timing and magnitude of fund flows and is a more accurate measure than buy-and-hold of the returns realized by fund investors who may trade in and out of funds. Using monthly returns, monthly total assets and quarterly fund style information for a broad sample of U.S. equity mutual funds during 1991 through 2013, they find that: Keep Reading

Should investors expect mutual fund managers to generate value via timely trades? In their November 2014 paper entitled “Do Funds Make More When They Trade More?”, Lubos Pastor, Robert Stambaugh and Lucian Taylor investigate the relationship between mutual fund turnover and performance. They measure mutual fund performance at a monthly frequency as gross fund return minus the return on its Morningstar benchmark index. They measure turnover over the last 12 months via a methodology that largely excludes trading due solely to fund inflows and outflows. Using returns and turnovers for 3,126 active U.S. equity mutual funds during 1979 through 2011, they find that: Keep Reading

Does the expanding set of exchange-traded funds (ETF) support reliable replication (cloning) of future returns for some hedge funds? In their December 2014 paper entitled  “Smart Beta ETF Portfolios: Cloning Beta Active Hedge Funds”, Jun Duanmu, Yongjia Li and Alexey Malakhov test replication of top risk factor-driven (beta-active) hedge funds using portfolios of ETFs. The selected hedge funds perform well historically and are especially suited to cloning because of their dependence on known risk factors. The hedge fund selection and cloning process involves repeating four steps annually based on two years of monthly historical data. Specifically, each year the authors:

1. Identify the fourth of hedge funds with returns most strongly correlated with known risk factors.
2. 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 risk factor proxies.
3. 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.
4. 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, starting in 2005 (when enough historical ETF data become available), 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 net returns for 2,014 hedge funds (963 live and 1,051 dead) during 1994 through 2012 and monthly returns for 1,313 passive ETFs as available during 1997 through 2012, they find that: Keep Reading

Do predictable monthly outflows from and inflows to mutual funds drive the Turn-of-the-Month (TOTM) effect, a concentration of positive stock market returns around the turns of calendar months? In their November 2014 paper entitled “Dash for Cash: Month-End Liquidity Needs and the Predictability of Stock Returns”, Kalle Rinne, Matti Suominen and Lauri Vaittinen explore TOTM with focus on the effects of: (1) month-end flows from mutual funds to retirees and dividend-collecting investors; and, (2) beginning-of-month flows from working investors to mutual funds. To account for trade settlement rules, funds must sell stocks at least three trading days before the end of the month to raise cash for expected month-end outflows. The authors therefore define a TOTM interval from three trading days before through three trading days after the last trading day of the month. They also consider intervals of five trading days before TOTM to measure the effect of fund selling and five trading days after TOTM  to measure reversion from fund buying. Using daily value-weighted, (mostly) total return stock market indexes for the U.S. since 1926 and for 24 other developed markets as available during January 1980 through January 2014, and data for individual U.S. stocks and mutual funds during January 1980 through December 2013, they find that: Keep Reading

Do hedge fund-like mutual funds work like hedge funds? In his September 2014 paper entitled “Hedge Funds versus Mutual Funds (2): An Examination of Multialternative Mutual Funds”, David McCarthy evaluates mutual funds categorized by Morningstar as “multialternative” after further subcategorizing them as: Global Asset Allocation (active asset allocation across a broad set of global markets); Multistrategy (investing across distinct investment styles); and, Replication (quantitatively mimicking the returns of a hedge fund index). He profiles these groups, compares their asset class and factor exposures to those of hedge fund indexes, and compares their performances to those of hedge fund indexes. He considers three benchmarks: the Hedge Fund Research (HFR) Relative Value Multistrategy Index; the HFR Fund of Funds Diversified Index; and, the long-only Morningstar Global Allocation Index. Using monthly returns as available for 30 Global Asset Allocation, 33 Multistrategy and four Replication mutual funds established as of January 2013 along with contemporaneous returns for asset class proxies, factors and benchmarks during January 2008 through December 2013, he finds that: Keep Reading

How successful are active equity mutual fund managers in timing their domestic markets worldwide? In their August 2014 paper entitled “Market Timing Around the World”, Javier Vidal-Garcia, Marta Vidal and Duc Khuong Nguyen employ daily returns to measure the effectiveness of mutual fund market exposure adjustments made more frequently than monthly. They also examine fund timing performance under different economic conditions. Their fund universe consists of 8,680 actively managed, open-end, diversified, domestic live and dead equity mutual funds registered in 35 countries (about 69% are U.S.-registered). Using daily total returns in local currencies and characteristics for these funds, along with contemporaneous country economic data, during January 1990 through December 2013, they find that: Keep Reading

How should investors use Morningstar mutual fund ratings/grades to select mutual funds? In his July 2014 paper entitled “Morningstar Mutual Fund Measures and Selection Model”, John Haslem surveys the five kinds of Morningstar mutual fund ratings and grades: (1) Morningstar star ratings (one to five stars); (2) analyst ratings (gold, silver, bronze, neutral and negative); (3) total pillar ratings (positive, neutral or negative for fund people, process, parent, performance and price); (4) upside/downside capture ratios; and, (5) stewardship ratings (culture, incentives, fees, board quality and regulatory history). Based on the body of research about the predictive power of Morningstar ratings/grades, he chooses three criteria for screening mutual funds:

1. Star rating of 4 or 5 and analyst rating of gold or silver.
2. Upside capture ratios greater than downside capture ratios for all three of 3-year, 5-year and 10-year past performance intervals.
3. Total stewardship grade of A.

He applies these criteria to the set of Vanguard actively managed diversified (not sector) U.S. equity mutual funds. His selections are current winners, with empirical testing requiring future performance data. Applying the chosen criteria to the specified set of Vanguard funds (about 20 funds), he finds that: Keep Reading

Are widely used volatility-adjusted investment performance metrics, such as Sharpe ratio, robust to different measurement intervals? In the July 2014 version of their paper entitled “The Divergence of High- and Low-Frequency Estimation: Implications for Performance Measurement”, William Kinlaw, Mark Kritzman and David Turkington examine the sensitivity of such metrics to the length of the return interval used to measure it. They consider hedge fund performance, conventionally estimated as Sharpe ratio calculated from monthly returns and annualized by multiplying by the square root of 12. They also consider mutual fund performance, usually evaluated as excess return divided by excess volatility relative to an appropriate benchmark (information ratio). Finally, they consider Sharpe ratios of risk parity strategies, which periodically rebalance portfolio asset weights according to the inverse of their return standard deviations. Using monthly and longer-interval return data over available sample periods for each case, they find that: Keep Reading

Is there a best way to exploit unusual closed-end fund discounts to net asset value? In their July 2014 paper entitled “Exploiting Closed-End Fund Discounts: The Market May Be Much More Inefficient Than You Thought”, Dilip Patro, Louis Piccotti and Yangru Wu construct two regression models to predict closed-end fund returns:

1. One model is a simple regression based on the past relationship between monthly fund discount and next-month fund return.
2. The other augments the first by including a term that accounts for effects of changes in the discount over a recent (optimized) interval.

They test whether these models beat a naive strategy that trades only on current closed-end fund discounts. They focus on Sharpe ratio as a key performance metric. Using monthly prices, net asset values and classifications for 377 U.S. closed-end funds as available during August 1984 through December 2011 and contemporaneous monthly four-factor (market, size, book-to-market, momentum) and liquidity factor returns, they find that: Keep Reading