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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Fund Activeness Predicts Performance?

Are mutual fund managers whose holdings deviate most from their benchmarks the best performers? In their April 2015 paper entitled “Deactivating Active Share”, Andrea Frazzini, Jacques Friedman and Lukasz Pomorski investigate whether Active Share is a reliable indicator of future mutual fund performance. Active Share measures the distance between a portfolio and its benchmark, ranging from zero for a portfolio that is identical to its benchmark to one for a portfolio with no holdings in common with its benchmark. They consider both theoretical arguments and empirical analysis, with the latter focused on disentangling Active Share and benchmark effects. Using holdings and performance data for actively managed U.S. equity mutual funds during 1980 through 2009, they find that: Keep Reading

Timing of Asset Class Allocations by Multi-class Funds

Do multi-class mutual funds exhibit good asset class allocation timing? In their April 2015 paper entitled “Multi-Asset Class Mutual Funds: Can They Time the Market? Evidence from the US, UK and Canada”, Andrew Clare, Niall O’Sullivan, Meadhbh Sherman and Steve Thomas investigate whether mutual fund managers time allocations across asset classes skillfully. They focus on three asset classes: equities, government bonds and corporate bonds. They apply two alternative methodologies: (1) returns-based, relating each asset class beta for a fund to next-month return for that class; and, (2) holdings-based, relating changes in asset class weights within a fund to next-month class returns. Using monthly returns and holdings for 617 U.S., UK and Canadian multi-asset class mutual funds during 2000 through 2012, they find that:

Keep Reading

When and Why U.S. Mutual Fund Investors Reallocate

When and why do investors make changes in asset class allocations? In the March 2015 version of their paper entitled “Global Asset Allocation Shifts”, Tim Kroencke, Maik Schmeling and Andreas Schrimpf examine the asset reallocation decisions of U.S. mutual fund investors. They focus on shifts between U.S. equities and U.S. bonds (rotation) and between U.S. assets and non-U.S. assets (diversification). Specifically, they address: (1) principal factors explaining reallocations; (2) the link between monetary policy announcements and allocation shifts; and, (3) the search for bond yield and asset returns as drivers of allocation shifts. Using detailed U.S. mutual fund data on investor allocations to U.S. equities, non-U.S. equities and fixed income (comprising a total of about $6.6 trillion in assets) during January 2006 through December 2014, they find that: Keep Reading

Exploiting Unusual Changes in Hedge Fund Holdings and Short Interest

Can investors exploit the combination of unusual changes in hedge fund long positions and unusual changes in short interest for individual stocks? In the February 2015 version of their paper entitled “Arbitrage Trading: The Long and the Short of It”, Yong Chen, Zhi Da and Dayong Huang examine the power of three variables to predict stock returns:

  1. Abnormal hedge fund holdings (AHF), the current quarter aggregate hedge fund long positions in a stock divided by the total shares outstanding minus the average of this ratio over the four prior quarters.
  2. Abnormal short interest (ASR), the current quarter short interest in a stock divided by the total number of shares outstanding minus the average of this ratio over the four prior quarters.
  3. The difference between AHF and ASR as a measure of imbalance in hedge fund trading.

They also examine how AHFSR interacts with ten widely used stock return predictors: book-to-market ratio; gross profitability; operating profit; momentum; market capitalization; asset growth; investment growth; net stock issuance; accruals; and, net operating assets. To measure the effectiveness of each predictor, they each quarter rank stocks into fifths (quintiles) based on the predictor and then calculate the difference in average gross excess (relative to the risk-free rate) returns of extreme quintiles. Using quarterly hedge fund SEC Form 13F filings and short interest data for a broad sample of U.S. stocks (excluding small and low-priced stocks), along with data required to compute stock return predictors and risk factors for these stocks, during 1990 through 2012, they find that: Keep Reading

Exploiting Interaction of Hedge Fund Holdings and Short Interest

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

Investor Return versus Mutual Fund Performance

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

Mutual Fund Trading Drives Performance?

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

Cloning Risk Factor-driven Hedge Funds with ETFs

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

Monthly Mutual Fund Flow Pattern as Driver of TOTM Effect

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

Multialternative Mutual Fund Performance

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

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