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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.

Mutual Fund Investors Underperform Their Underperforming Funds?

Mutual fund investors have two ways to beat the market: (1) pick the right funds, and (2) time their purchases and sales. How effectively does the average fund investor execute the latter goal? In their December 2007 paper entitled “Investor Timing and Fund Distribution Channels”, Mercer Bullard, Geoff Friesen and Travis Sapp examine the investment timing performance of equity mutual fund investors and the relationship of this performance to the fund distribution channel. Using data on returns and funds flows for 6,164 U.S. equity mutual funds during 1991-2004, they conclude that: Keep Reading

Stock Picking or Industry Picking?

Which path, stock picking (company analysis) or industry picking (economic trend analysis) is the more direct to investing outperformance? In their December 2007 paper entitled “Mutual Fund Industry Selection and Persistence”, Jeffrey Busse and Qing Tong examine the relative importance of industry selection and stock selection in the performance of actively managed mutual funds. Using quarterly stockholdings during 1980-2006 for a large sample of actively managed U.S. equity mutual funds, along with associated stock return data, they find that: Keep Reading

Do Funds Focused on Just a Few Stocks Outperform?

Do the most skilled stock pickers among fund managers gravitate toward funds that focus on a few good ideas, thereby outperforming diversified peers? In their recent paper entitled “Security Concentration and Active Fund Management: Do Focused Funds Offer Superior Performance?”,  Travis Sapp and Xuemin Yan examine whether funds concentrated in relatively few securities outperform. Using price and holdings data for a broad sample of U.S. equity mutual funds operating at any time during 1984-2002 (2,278 funds encompassing 16,399 fund-years), they conclude that: Keep Reading

How Fund Managers React to Success and Failure

How do fund managers behave when they have recently outperformed or underperformed? Do winners hunker down and protect their gains, while losers ratchet up risk to recover. In two recent papers, Manuel Ammann and Michael Verhofen use a variety of risk measures to analyze the impact of prior performance on the risk-taking behavior of mutual fund managers. Their October 2006 paper entitled “Prior Performance and Risk-Taking of Mutual Fund Managers: A Dynamic Bayesian Network Approach” examines year-to-year changes in fund risk levels based on a large sample of U.S. mutual funds and contemporaneous risk premium data (market, size, value, momentum) over the period 1985-2003. Their subsequent November 2007 paper entitled “The Impact of Prior Performance on the Risk-Taking of Mutual Fund Managers” examines changes in fund risk levels from the first half of the year to the second half based on daily return data for a large sample of U.S. mutual funds and contemporaneous risk premium data over the period 2001-2005. In both papers, they conclude that: Keep Reading

Measuring the Level and Persistence of Active Fund Management

What is the best approach for measuring the stock picking, industry concentration and factor risk aspects of active fund management? In the October 2007 update of their paper entitled “How Active Is Your Fund Manager? A New Measure That Predicts Performance”, Martijn Cremers and Antti Petajisto introduce “Active Share” to quantify active portfolio management in terms of the share of portfolio holdings that differ from the makeup of an appropriate benchmark index. They then apply Active Share (measuring stock/industry selection) in combination with index tracking error (measuring factor bets) to evaluate equity mutual funds. Using data on holdings and returns for 2,647 U.S. equity mutual funds and 19 associated benchmark indexes spanning 1980-2003, they conclude that: Keep Reading

Evaluating Hedge Fund Managers on Walk, Not Talk

Picking the right benchmark is critical when assessing the performance of a fund manager. Benchmark selection is especially difficult for hedge fund managers because of: (1) the number of style options available to them, and (2) the difficulty of assigning specific funds to styles. Should evaluators simply accept the style claims of fund managers for benchmarking purposes? In their recent paper entitled “Hedge Funds: Ability Persistence and Style Bias”, Matteo Belleri and Marco Navone do not. Instead, they calculate a benchmark for each hedge fund by fitting its actual performance over the past three years to a weighted portfolio of ten hedge fund indexes (Convertible Arbitrage, Dedicated Short Bias, Emerging Markets, Market Neutral, Event Driven, Fixed Income Arbitrage, Global Macro, Long/Short Equity, Managed Futures and Multi-Strategy). This approach essentially makes each manager accountable for modifications of fund strategy to benefit from current market conditions. Using the benchmark index data and return data for 3,627 hedge funds over the period 1994-2004, they conclude that: Keep Reading

No Fire Exit at the Overcrowded Hedge Fund Party?

Have hedge funds proliferated, grown and leveraged to the point that groups of them with similar quantitative strategies can crash as they try to exit common positions in response to some external trigger? In their September 2007 paper entitled “What Happened To The Quants In August 2007?”, Amir Khandaniy and Andrew Lo investigate the hypothesis that similar market-neutral and long/short equity hedge funds suffered a cascading fire sale liquidation (one-month losses of 5%-30%) during early August 2007. Using daily return data for a broad set of stocks to model hedge fund performance over the period 1/95-8/07, they tentatively conclude that: Keep Reading

Characteristics of Persistently Outperforming Hedge Funds

In his recent PhD thesis entitled “An Analysis of Hedge Fund Strategies”, Daniel Capocci offers an epic study of hedge fund properties, results and potential benefits. Specifically, he: (1) applies a multi-factor performance analysis model to determine the degree to which hedge funds persistently produce alpha; (2) measures the extent to which market-neutral hedge funds are really neutral; and, (3) examines the mean, volatility, skewness and kurtosis of hedge fund returns to evaluate their potential benefits to investors. Using hedge fund performance data from several sources spanning 1993-2003, he finds that: Keep Reading

The Truly Active Part of Active Fund Management

In his May 2007 paper entitled “Where Do Alphas Come From?: A New Measure of the Value of Active Investment Management”, Andrew Lo proposes a decomposition of the economic value of a fund’s management into two components, one measuring security selection (a weighted average of portfolio asset returns) and the other measuring timing (the correlation between portfolio asset weights and asset returns). When correlation between portfolio weights and returns is positive, management is moving assets toward optimization of overall portfolio returns. In other words, a manager can add value by: (1) picking the right assets; and, (2) continually growing positions with the highest future returns and shrinking positions with the lowest future returns. Using multiple examples, he argues that: Keep Reading

(Low) Volatility as an Indicator of Persistent Hedge Fund Outperformance

Market conditions vary considerably across the business cycle, presumably affecting the opportunity set for a given investing style/strategy. What are the return characteristics that predict which hedge funds can best navigate changing economic conditions? In his 2007 paper entitled “The Sustainability of Hedge Fund Performance: New Insights”, Daniel Capocci decomposes hedge fund returns to determine how investors can reliably identify funds that outperform equity and bond indexes in both bull and bear markets. Using monthly return data for the 1994-2002 business cycle from two sources (3,060 individual funds and 907 funds of funds) to investigate 14 potentially useful persistence discriminators, he concludes that: Keep Reading

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