Ebook Hedge funds, managerial skill, and macroeconomic variables
2008 was a difficult year for hedge funds. Many hitherto successful hedge fund managers who had consistently delivered stellar returns were hit with significant losses. Investors long conditioned to expect high alpha from such financial cognoscenti were sorely disappointed and withdrew funds en masse. For example, despite illustrious multi-year track records, both Kenneth Griffin of Citadel Investment Group and Daniel Ziff of Och-Ziff Capital Management posted significant losses in 2008. As a result of Citadel’s poor performance, Mr. Griffin was forced to waive management fees and erect gates to stanch the massive wave of redemptions. Have hedge fund managers lost their edge or are they simply victims of the prevailing market conditions? How should fund managers be evaluated given that their performance may be affected by macroeconomic factors? The fact that some investment styles like global macro and managed futures thrive under the volatile conditions while others do not, suggests that conditioning on the economy may be important when evaluating hedge fund performance.
In this paper, we confront these issues by analyzing the performance of portfolio strategies that invest in hedge funds. These strategies exploit predictability, based on macroeconomic variables, in (i) fund manager asset selection and benchmark timing skills, (ii) hedge fund risk loadings, and (iii) benchmark returns. By examining the out-of-sample investment opportunity set, we show that allowing for predictability based on macroeconomic variables is important in ex-ante identifying subgroups of hedge funds that deliver significant outperformance. Our analysis leverages on the Bayesian framework proposed by Avramov and Wermers (2006) who study the performance of optimal portfolios of equity mutual funds that utilize conditional return predictability. In particular, they find that long-only strategies that incorporate predictability in managerial skills outperform their Fama and French (1993) and momentum benchmarks by 2-4 percent per year by timing industries over the business cycle, and by an additional 3-6 percent per year by choosing funds that outperform their industry benchmarks.
We argue that the Avramov-Wermers framework is even more relevant to the study of hedge fund performance because hedge funds engage in a much more diverse set of strategies than do mutual funds. Hedge funds trade in different markets, with different securities, and at different frequencies. They may employ leverage, complex derivatives, and short-selling. The multitude of hedge fund strategies include global macro, managed futures, convertible arbitrage, short selling, statistical arbitrage, equity long/short, distressed debt, etc. Anecdotal evidence suggests that the success of these strategies hinges on the behaviour of various economic indicators like the credit spread and volatility. In contrast, the mutual fund universe is a lot less diverse. Equity mutual funds, for instance, differ mainly according to the style of securities that they invest in (e.g., small cap versus large cap and value versus growth). Therefore, macroeconomic variables are likely to be more important for explaining the cross-sectional variation in managerial skill for hedge funds.
To adjust for risk, we employ the methodology of Fung and Hsieh (2004). Fung and Hsieh (1999, 2000, 2001), Mitchell and Pulvino (2001), and Agarwal and Naik (2004) show that hedge fund returns relate to conventional asset class returns and option-based strategy returns. Building on this, Fung and Hsieh (2004) show that their parsimonious asset-based style factor model can explain up to 80 percent of the variation in global hedge fund portfolio returns. The Fung and Hsieh (2004) factor model includes bond factors derived from changes in term and credit spreads. We adjust these factors appropriately for duration so that they represent returns on traded portfolios. In sensitivity tests, to account for hedge funds’ exposure to emerging market equities, distress risk, stock momentum, and illiquidity, we augment the Fung and Hsieh (2004) model with the MSCI emerging markets index excess return, the Fama and French (1993) high-minus-low book-to-market factor, the Jegadeesh and Titman (1993) momentum factor, and the Pástor and Stambaugh (2003) liquidity factor, respectively. We also redo the analysis using option-based factors from the Agarwal and Naik (2004) model to ensure that our results are not artefacts of the risk model we use.
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