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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.

The Value of Fund Manager Discretion?

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

Robo Advisor Expected Performance and Acceptance

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

Self-grading of the Morningstar Fund Rating System

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

ETF-based Model of Hedge Fund Returns

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

Institutional Stock Trading Expertise

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

Sharpe Ratio, Alpha or Geometric Mean?

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

Trendy Mutual Fund Performance

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

Factor Timing among Hedge Fund Managers

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

Feasibility of Cloning CTA-like Funds

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

Mutual and Exchange-traded “Hedge Funds”

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

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