Mutual/Hedge Funds

Does trading by expert investors boost performance (profitably exploit information), or depress performance (unprofitably exploit information or wastefully churn on noise)? In their September 2016 paper entitled “Trading Frequency and Fund Performance”, Jeffrey Busse, Lin Tong, Qing Tong and Zhe Zhang investigate the relationship between trading frequency and performance among institutional investors (funds). They specify fund daily trading frequency as number of trades divided by the number of unique stocks traded. They calculate fund quarterly trading frequency as average daily trading frequency during the quarter. For each buy or sell, they calculate the return from execution date (at execution price) to end of the quarter, including stock splits, dividends and sometimes commissions. They estimate quarterly fund trading performance by aggregating performances of buys and sells separately, weighted either equally or by trade size, such that the average holding interval is about half a quarter. They subtract fund benchmark return over the same holding interval to calculate abnormal return. They then examine the relationship between abnormal return and fund size. Using daily common stock transaction details for 843 fund managers and 5,277 unique funds, along with associated stock return and firm data, during January 1999 through December 2009, they find that: Keep Reading

What is the single best performance metric an investor can use to rank performances of competing portfolios (such as mutual funds)? In his September 2016 paper entitled “Measuring Portfolio Performance: Sharpe, Alpha, or the Geometric Mean?”, Moshe Levy compares Sharpe ratio, 5-factor (market, size, book-to-market, profitability, investment) alpha and geometric mean return as portfolio performance metric. The widely used Sharpe ratio is optimal when return distributions are normal and the investor can borrow at the lending (risk-free) rate without limit for leverage. However, asset return distributions may not be normal, investors generally borrow at an interest rate above the risk-free rate and Federal Reserve Regulation T restricts borrowing to 100% of an investor’s initial capital. Moreover, investors typically restrict themselves to much lower borrowing levels. His methodology is to compare the ranking of a set of actual equity mutual funds under realistic assumptions based on each of the three metrics with the ranking produced by utility maximizing allocations for each fund paired with the risk-free asset. The better the ranking produced by the metric aligns with the utility maximization ranking, the better the metric. His baseline assumption is that actual annual borrowing rate is 3.5% above the lending rate. For robustness, he considers several levels of investor risk aversion in determining utility maximization and other gaps between borrowing and lending rates. Using theory, monthly returns for 10,145 U.S. domestic equity mutual funds, the risk-free (lending) rate and returns for the five Fama-French factors during July 2005 through June 2015, he finds that: Keep Reading

Should mutual fund investors go with trendy new funds? In their August 2016 paper entitled “What’s Trending? The Performance and Motivations for Mutual Fund Startups”, Jason Greene and Jeffrey Stark examine the interactions of mutual fund trendiness with growth in assets under management, fees and performance. They quantify fund trendiness by each month:

1. Relating each key word found in fund names to industry fund flows over the past 12 months.
2. Subtracting the average key word-flow relationship for the entire sample period from the monthly relationship to indicate current key word trendiness.
3. Ranking key words by trendiness.
4. Averaging the trendiness ranks for each key word in each fund name to measure fund trendiness.

They then relate fund trendiness to fund flows over the next 12 months, fund fee level at fund inception and fund performance over its first five years of existence. Using fund names and monthly fund returns, fund assets and factor returns for alpha calculations during 1993 through 2014 (7,072 distinct funds), they find that: Keep Reading

Can hedge fund managers reliably time eight factors explaining multi-class asset returns: equity market; size; bond market; credit spread; trend-following for bonds, currencies and commodities; and, emerging markets? In their July 2016 paper entitled “Timing is Money: The Factor Timing Ability of Hedge Fund Managers”, Bart Osinga, Marc Schauten and Remco Zwinkels study the magnitude, determinants and persistence of factor timing ability among hedge fund managers. To minimize biases, they: include live and dead funds; remove the first 18 months of returns for each fund; consider only funds that have at least 36 monthly returns and average assets under management $10 million; and, consider only funds that report net monthly excess returns in U.S. dollars. They also exclude the top and bottom 1% of all returns to suppress outlier effects. Using monthly returns for 2,132 dead and 992 live hedge funds encompassing nine investment styles, and contemporaneous factor returns, during January 1994 through April 2014, they find that: Keep Reading

Should investors believe that the financial industry can offer low-cost, liquid funds that reliably mimic Commodity Trading Advisor (CTA) hedge funds? In their June 2016 paper entitled “Just a One Trick Pony? An Analysis of CTA Risk and Return”, Jason Foran, Mark Hutchinson, David McCarthy and John O’Brien identify and examine performances of CTA-like hedge funds across eight distinct categories defined via iterative correlation clustering. Their goal is to determine whether category performance is amenable to modeling (cloning) via liquid exposures to four futures risk factor premiums:

1. Value – long (short) high-value (low-value) futures, with “value” based on book-to-market ratios for stock index futures and 5-year change in yields/spot prices/purchasing power for government bonds/commodities/currency forwards.
2. Carry – long (short) futures with high (low) roll returns.
3. Time series momentum – long (short) futures with positive (negative) 12-month past returns.
4. Options-based trend following – from Fung and Hsieh, correlated with trends shorter than time series momentum.

They estimate these premiums from monthly returns of rolling nearest contracts for: 12 global equity index futures series; eight global 10-year government bond synthetic futures series; 22 commodity futures series; and, nine global currency forward series versus the U.S. dollar. They employ a hedge fund screening process that suppresses backfill bias (lucky starts). Using monthly net returns and assets under management (AUM) for specific (not fund-of-funds) and distinct CTA funds with at least 12 months of returns denominated in U.S. dollars and monthly data required to estimate futures risk factor premiums as available during January 1987 through July 2015, they find that: Keep Reading

How well do mutual funds and exchange-traded funds (ETF) designed to track hedge fund indexes work? In their October 2015 paper entitled “Synthetic Hedge Funds”, Mario Fischer, Matthias Hanauer and Robert Heigermoser examine the performance of synthetic hedge funds, defined as open-end mutual funds and ETFs that explicitly employ hedge fund indexes as their primary benchmarks. They assess replication success: (1) based on both return distribution shapes and risk-adjusted performance; and, (2) overall, for mutual funds and ETFs separately as groups, and by specific hedge fund strategy (when enough synthetic funds exist for a strategy). They group funds via value-weighted portfolios. Using monthly returns for 72 synthetic hedge funds (52 mutual funds and 20 ETFs) and associated Credit Suisse hedge fund index benchmarks during January 2009 through December 2013, they find that: Keep Reading

Is selecting mutual funds based on strong performance over the last three years helpful (discovering fund manager skill) or harmful (signaling imminent fund strategy mean reversion)? In the February 2016 version of their paper entitled “The Harm in Selecting Funds that Have Recently Outperformed”, Bradford Cornell, Jason Hsu and David Nanigian investigate future mutual fund performance based on recent past performance relative to stated benchmarks. They focus on a past performance interval of three years because: institutional consultants cite this measurement as one of the most important criterion for fund selection; and, Morningstar’s rating algorithm emphasizes three-year past performance. Specifically, every three years they:

1. Rank funds by expense ratio and exclude the highest tenth as likely poor choices.
2. Define Winner, Median and Loser funds as the tenths of the rest with the highest, middle (centered on the 50th percentile) and lowest benchmark-adjusted returns the past three years.
3. Track the performance of the equally weighted and monthly rebalanced Winner, Median and Loser groups over the next three years.

Using benchmark-adjusted returns for actively managed U.S. equity mutual funds during January 1994 through December 2015, they find that: Keep Reading

Mebane Faber states in the first chapter of his 2016 book Invest with the House: Hacking the Top Hedge Funds: “We make two assumptions…: 1. There are active managers that can beat the market… 2. Superior active managers can be identified. …There is a general feeling that the market can’t be beat, and it is tough to get past that belief. A big challenge is separating luck from skill. But would anyone deny that some people are better than others at stock picking? Just like any other profession, the investment field has top experts who are paid handsomely for what they do. …You have access to the stock picks made by fund managers who often spend millions of dollars and every waking moment thinking and obsessing about the financial markets. …The best ones know everything there is to know about a company before they invest. …You can then build a stable of these managers and use them…for stock ideas to research and possibly implement in your own portfolio.” Based on prior research/experience and performances of the top ten (long) holdings from quarterly Form 13F filings for selected fund managers during January 2000 through December 2014, he concludes that: Keep Reading

Who are the givers and who are the takers among mutual funds and hedge funds? In their January 2016 paper entitled “Style and Skill: Hedge Funds, Mutual Funds, and Momentum”, Mark Grinblatt, Gergana Jostova, Lubomir Petrasek and Alexander Philipov analyze quarter-to-quarter changes in Form 13F stock holdings to assess investment styles and sources of performance for hedge funds and mutual funds. They focus on the interaction between portfolio weight changes and future stock returns to measure investing skill. They calculate fund alpha via adjustments for stock size, book-to-market ratio and (when appropriate) momentum. Using quarterly 13F filings of 589 mutual funds and 1,342 hedge funds during 1998 to 2012, they find that: Keep Reading

What are the principal themes of research on hedge funds published in top journals over the past decade? In their August 2015 paper entitled “Hedge Funds: A Survey of the Academic Literature”, Vikas Agarwal, Kevin Mullally and Narayan Naik summarize 121 papers on hedge funds and commodity trading advisors from four leading finance journals. They focus on the 105 papers published since 2005. They organize this research into five categories:

1. Fund performance over time and by type, including return drivers, risks and assessment of manager skill.
2. Relationships between fund characteristics (such as contractual terms, size, age and manager background) and fund performance.
3. Investor risks, including manager incentives and capital flows.
4. Role of hedge funds in the financial system.
5. Biases in and limitations of data.

Based on this review, they conclude that: Keep Reading