Mutual/Hedge Funds

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

“Mutual Fund Hot Hand Performance” tests a “hot hand” strategy that each year picks the top performer from the Vanguard family of diversified equity mutual funds (not including sector funds) and holds that winner the next year. A subscriber suggested a robustness test using the Fidelity family of diversified equity mutual funds. To support the test, we select all Fidelity diversified U.S. and international equity mutual funds that bear no transaction fee, are open to new investors and have a history of at least three years. We consider the total return on the S&P 500 Index (with dividends estimated from Robert Shiller’s data) and SPDR S&P 500 (SPY) as benchmarks. As in the prior analysis of Vanguard funds, we pick end of June to end of the next June for annual return measurement intervals. 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 annual returns for the S&P 500 Index plus Shiller’s dividend data and annual returns for SPY and Fidelity diversified equity mutual funds as available from Yahoo!Finance during June 1980 through June 2014, we find that: Keep Reading

How do hedge funds electing not to report to a commercial database differ from those that do? In their July 2014 paper entitled “What Happens ‘Before the Birth’ and ‘After the Death’ of a Hedge Fund?”, Vikas Agarwal, Vyacheslav Fos and Wei Jiang compare performances of equity hedge funds before they begin self-reporting, while they are self-reporting; and after they stop self-reporting to commercial databases. They develop a sample of hedge funds that do and do not self-report by matching hedge fund Securities and Exchange Commission (SEC) Form 13F filings to listings of hedge funds that self-report to any of five major hedge fund commercial databases. They then identify subsamples of hedge funds that: (1) initially do not but later do self-report; and, (2) initially do but later do not self-report. They then use the long-only equity holdings in series of Form 13F to analyze performances and characteristics within subsamples. Using 1,199 series of Form 13Fs for firms that are clearly hedge funds during 1980 through 2008 and contemporaneous data for hedge funds self-reporting to commercial databases, they find that: Keep Reading

Are the sources of active mutual fund risk mostly common (systematic) or unique (idiosyncratic)? In his July 2014 paper entitled “Components of Portfolio Variance: R2, SelectionShare and TimingShare”, Anders Ekholm decomposes mutual fund return variance (risk) into three sources: (1) passive systematic factor exposure (R-squared); (2) active security selection or stock picking (SelectionShare); and, (3) active systematic factor timing (TimingShare). He demonstrates estimation of these three components based on mutual fund returns (reflecting daily manager actions) rather than holdings (known only via quarterly snapshots). He employs the widely used four-factor (market, size, book-to-market, momentum) model of stock returns to define systematic risk. Using daily returns for a broad sample of actively managed U.S. equity mutual funds and for the four factors during 2000 through 2013, he finds that: Keep Reading

Are alternative mutual funds attractive for retail investors as hedge fund surrogates? In their June 2014 paper entitled “Performance of Alternative Mutual Funds: The Average Investors Hedge Fund”, Srinidhi Kanuri and Robert McLeod analyze the performance of alternative mutual funds that employ strategies similar to those of hedge funds and seek returns uncorrelated with the equity market. These funds can sell short and use leverage, derivatives, options and swaps to shape and enhance returns. However, they must offer daily liquidity, cover short positions, limit borrowing to a third of assets and limit illiquid investments to 15% of assets. The authors consider nine categories of alternative mutual funds, ranging in population from just three inverse commodity funds to 109 long-short equity funds. They apply both a four-factor (equity market, size, book-to-market, momentum) mutual fund model and a seven-factor (equity market, size, bond market, credit spread, bond trend, currency trend, commodity trend) hedge fund model to measure alternative mutual fund alpha. They aggregate across all funds and within categories based on equal weight. Using monthly data for 256 surviving and 62 dead alternative mutual funds during January 1998 through December 2011, they find that: Keep Reading

Do individuals who actively reallocate funds within their pension accounts outperform passive counterparts? In the March 2014 update of their paper entitled “Individual Investor Activity and Performance”, Magnus Dahlquist, Jose Vicente Martinez and Paul Soderlind examine the activity and performance of individual participants in Sweden’s Premium Pension System. This system allows individual participants to reallocate among available mutual funds on a daily basis with no switching fees/impediments. Information about the 1,230 funds offered during the sample period includes type (fixed income, balanced, life-cycle and equity), return and risk measured at several horizons, fee and major holdings. Most are equity funds, about half of which invest primarily in international equities. The government assigns individuals who make no choice to a default fund. Using daily net returns, fund trades and demographics for 70,755 individuals (from a random draw of individuals in the system over the entire period) and contemporaneous returns for several benchmarks during September 2000 through May 2010, they find that: Keep Reading

Is past performance a useful indicator of future performance for some kinds of mutual funds? In their April 2014 paper entitled “Differences in Short-Term Performance Persistence by Mutual Fund Equity Class”, Larry Detzel and Andrew Detzel evaluate performance persistence among diversified U.S. equity mutual funds categorized per the Morningstar Equity Style Box: Large Value (LV), Large Blend (LB), Large Growth (LG), Mid-Cap Value (MV), Mid-Cap Blend (MV), Mid-Cap Growth (MG), Small Value (SV), Small Blend (SB) or Small Growth (SG). Each quarter, they sort funds into styles and then rank them into fifths (quintiles) based on four-factor alpha (adjusting for market, size, book-to-market and momentum risks) calculated with daily returns. They then calculate average four-factor alphas for these quintiles during the next four quarters. Using quarterly Morningstar style assignments and daily returns for a large sample of live and dead diversified U.S. equity mutual funds, along with data for associated stocks and contemporaneous returns for risk factors, during January 1999 through December 2011, they find that: Keep Reading

Do Morningstar’s analyst ratings predict which mutual funds will do best? In their January 2014 paper entitled “Going for Gold: An Analysis of Morningstar Analyst Ratings”, Will Armstrong, Egemen Genc and Marno Verbeek examine the performance of mutual funds after Morningstar assigns analyst ratings to them. Morningstar initiated these substantially qualitative ratings (Gold, Silver, Bronze, Neutral and Negative) in September 2011, as a supplement to star ratings, to convey expected risk-adjusted performance of funds with respect to peers over a full market cycle of at least five years. Ratings take into account past performance, fees and trading costs, quality of investment team, parent organization and investment process.  The study considers both raw returns and four-factor (market, size, book-to-market, momentum) alphas during intervals of one, three and six months after each rating initiation. It also takes into account differences in time frame, fund investment style and fund star rating. Using analyst ratings initiated during September 2011 through December 2012, associated fund characteristics and associated fund returns through June 2013, they find that:

Keep Reading