Mutual/Hedge Funds

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

Should investors shun mutual funds with high fees? In their February 2017 paper entitled “Cheaper is Not Better: On the Superior Performance of High-Fee Mutual Funds”, Jinfei Sheng, Mikhail Simutin and Terry Zhang re-examine the conventionally accepted negative relationship between expense ratio and future net performance of actively managed equity mutual funds. They measure fund performance as alpha from each of four factor models of stock returns:

• 1-factor: the Capital Asset Pricing Model, which controls for the market factor.
• 3-factor: the widely used Fama-French model, which controls for market, size and value (book-to-market ratio) factors.
• 4-factor: the widely used Carhart model, which adds the momentum factor to the 3-factor model.
• 5-factor: the recent Fama-French model that adds profitability and investment factors to the 3-factor model.

They calculate alpha for each fund each month as the difference between next-month excess return minus expected return based on fund factor loadings from a regression over the last 60 months. They then use additional regressions and a ranking of funds into tenths (deciles) by fee to analyze relationships between alphas and fees. Using survivorship bias-free performance, sales channel and holding data for active U.S. domestic equity funds with at least five years of history and substantial holdings/assets during 1980 through 2014, they find that: Keep Reading

Do hedge fund managers who seek excitement as indicated by choice of cars invest differently from those who do not? In their December 2016 paper entitled “Sensation Seeking, Sports Cars, and Hedge Funds”, Yan Lu, Sugata Ray and Melvyn Teo investigate the relationship between hedge fund manager personal car selection (body style, maximum horsepower, maximum torque, passenger volume and safety ratings) and fund performance. After identifying a large set of hedge fund managers, they match managers to cars and car characteristics via VIN Place, Autocheck, cars.com, cars-data and the Insurance Institute for Highway Safety, categorizing cars as sports cars, minivans or other based on body style. They then relate hedge fund manager car data as available to subsequent performance and characteristics of associated hedge funds. Using car data and monthly net-of-fee returns, assets under management and other fund characteristics for 1,774 vehicles (including 163 sports cars and 101 minivans) purchased by 1,144 hedge fund managers during January 1994 through December 2015, they find that: Keep Reading

Are there material average performance differences between hedge funds that emphasize systematic rules/algorithms for portfolio construction versus those that do not? In their December 2016 paper entitled “Man vs. Machine: Comparing Discretionary and Systematic Hedge Fund Performance”, Campbell Harvey, Sandy Rattray, Andrew Sinclair and Otto Van Hemert compare average performances of systematic and discretionary hedge funds for the two largest fund styles covered by Hedge Fund Research: Equity Hedge (6,955 funds) and Macro (2,182 funds). They designate a fund as systematic if its description contains “algorithm”, “approx”, “computer”, “model”, “statistical” and/or “system”. They designate a fund as discretionary if its description contains none of these terms. They focus on net fund alphas, meaning after-fee returns in excess of the risk-free rate, adjusted for exposures to three kinds of risk factors well known at the start of the sample period: (1) traditional equity market, bond market and credit factors; (2) dynamic stock size, stock value,  stock momentum and currency carry factors; and, (3) a volatility factor specified as monthly returns from buying one-month, at‐the‐money S&P 500 Index calls and puts and holding to expiration. Using monthly after-fee returns for the specified hedge funds (excluding backfilled returns but including dead fund returns) during June 1996 through December 2014, they find that: Keep Reading

Does a flexible robo advisor (offering automated, passive investment strategies tailored to investor situation/preferences) perform well in comparison to mutual fund/stock portfolios they might replace? If so, what inhibits investors from switching to them? In their November 2016 paper entitled “Robo Advisers and Mutual Fund Stickiness”, Michael Reher and Celine Sun compare actual mutual fund/stock portfolios held by individuals to Wealthfront robo advisor portfolios constructed by assigning weights to 10 exchange-traded funds based on investor responses to questions about financial situation and risk tolerance. The robo advisor portfolio construction process includes a critique of original portfolio diversification, fees and cash holdings. They focus on stock, mutual fund and ETF holdings in retirement (non-taxable) portfolios. They project net portfolio performance at the asset level based principally on the Capital Asset Pricing Model (CAPM, alpha plus market beta) of asset returns. They group findings by: individuals who manage their own portfolios versus those who rely on mutual funds; and, individuals who choose to set up robo advisor accounts versus those who do not. Using original investor portfolio and corresponding robo advisor portfolio holdings collected during mid-January 2016 through early November 2016, fund loads and fees as of September 2016, and monthly returns for all assets and factors as available since January 1975, they find that: Keep Reading

How well does the Morningstar fund rating system (one star to five stars) work? In their November 2016 paper entitled “The Morningstar Rating for Funds: Analyzing the Performance of the Star Rating Globally”, suggested for review by a subscriber, Jeffrey Ptak, Lee Davidson, Christopher Douglas and Alex Zhao analyze the global performance of star ratings in terms of ability to predict fund performance. They use two test methodologies:

1. Monthly two-stage regressions that test the ability of fund star ratings to add value to a linear factor model for each asset class at a one-month horizon. The first stage estimates fund dependencies (betas) on commonly used predictive factors over the past 36 months. The second stage measures the ability of fund star ratings to add predictive power to those betas in the following month. For stock funds, they consider fee, equity market, size, value and momentum factors. For bond funds, they consider fee, credit and term factors. For stock-bond funds, they consider all these factors. For alternative asset class funds, they consider fee and equity market factors.
2. An event study that tracks performances of equally weighted portfolios of funds formed by prior-month star rating over the next 1, 3, 6, 12, 36 and 60 months.

Using fund categories, monthly fund star ratings and returns, and asset class factor returns during January 2003 through December 2015, they find that: Keep Reading

Does a model based on factors extracted from investable exchange-traded funds (ETF) work as well in evaluating fund alphas as models based on factors from more conceptional portfolios? In their October 2016 paper entitled “Bringing Order to Chaos: Capturing Relevant Information with Hedge Fund Factor Models”, Yongjia Li and Alexey Malakhov examine a hedge fund performance evaluation model that identifies risk factors dynamically based on the universe of index-tracking ETFs, focusing on data since 2005 when more than 100 ETFs become available. They first suppress redundancy among these ETFs via cluster analysis, iteratively grouping ETFs by return series similarity (up to 100 clusters) to find the best set of clusters and selecting the ETF most representative of each cluster. They apply regression techniques to identify each year the optimal set of factors (weighted ETFs) for explaining hedge fund returns over the prior 24 months. They compare the power of the ETF-based factor model to explain (in-sample) hedge fund returns with the predictive powers of seven published hedge fund return models that have fixed sets of 1 to 15 factors. They also test hedge fund performance persistence based on out-of-sample performance of funds ranked by in-sample alphas for ETF-based and conventional factor models. Using net monthly returns and descriptions for 10,506 unique hedge funds (2,404 live and 8,102 dead), excluding the initial 24 months of reported returns for each fund, and monthly returns for all index-tracking ETFs with at least 24 months of history as available during 2003 through 2012, they find that: Keep Reading

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