Hedge Fund Benchmarks: A Risk-Based Approach

By William Fung, David A. Hsieh. Financial analysts journal, 2004

1,031 citations recorded by the source corpus.

Following a review of the data and methodological difficulties in applying conventional models used for traditional asset class indexes to hedge funds, this article argues against the conventional approach. Instead, in an extension of previous work on asset-based style (ABS) factors, the article proposes a model of hedge fund returns that is similar to models based on arbitrage pricing theory, with dynamic risk-factor coefficients. For diversified hedge fund portfolios (as proxied by indexes of hedge funds and funds of hedge funds), the seven ABS factors can explain up to 80 percent of monthly return variations. Because ABS factors are directly observable from market prices, this model provides a standardized framework for identifying differences among major hedge fund indexes that is free of the biases inherent in hedge fund databases. Conventional models for constructing asset class indexes rest on the assumptions that the underlying assets have homogeneous performance characteristics and that the dominant investment strategy is to buy and hold assets. In contrast, the performance characteristics of hedge funds are diverse, the investment styles are dynamic, and bets may be highly levered. These hedge fund characteristics, together with the lack of standardized reporting of historical hedge fund performance, greatly limit the information content of hedge fund indexes that are constructed by using conventional methods. At times, using such indexes can even produce misleading results. In the study reported here, we used a method to create hedge fund benchmarks that captures the common risk factors in hedge funds by using asset-based style (ABS) factors. Model construction began by extracting common sources of risk from hedge fund returns. These sources of risk were identified by directly linking them to various market risk factors. These ABS factors were then used to construct a hedge fund risk-factor model similar to the approach in arbitrage pricing theory, in which the factor loadings (betas) are permitted to vary over time. Thus far, researchers have identified seven risk factors. Equity long-short hedge funds are exposed to two equity risk factors—market risk (as proxied by the S&P 500 Index) and the spread between small-capitalization stock returns and large-capitalization stock returns. Fixed-income hedge funds are exposed to two interest-rate-related risk factors—the change in 10-year U.S. Treasury yields and the change in the yield spread between 10-year T-bonds and Moody's Investors Service Baa bonds. Trend-following funds are exposed to the same risk factors as three portfolios of “lookback” options—on bond futures, on currency futures, and on commodity futures. Empirical evidence shows that these seven risk factors can jointly explain a major portion of return movements in diversified hedge fund portfolios, as proxied by a fund-of-funds index. Applying the risk-factor model to hedge fund indexes, we show that the model can identify risk differences inherent in these indexes, which in turn, helps explain anomalous return differences among them. An out-of-sample check on the usefulness of the risk-factor model with 2003 data indicates that the model explains a significant amount of the return differences among major hedge fund indexes. The ABS risk-factor model can be applied to circumvent the lack of transparency in hedge fund investments. It helps investors relate hedge fund strategies to a set of common risk factors, which can be key inputs for portfolio construction, risk management, and performance evaluation. Because ABS factors are measured in market prices, investors can frequently approximate the performance of their hedge fund investments to match the changing conditions of global markets without having to rely on normal net-asset-value reporting as the only input. Hedge fund managers can also use ABS factors to communicate the systematic risk inherent in a strategy to investors in a format that is consistent with the qualitative description of the strategy's style. Thus, risk disclosure and transparency can be brought to a satisfactory aggregated level without having to analyze the huge volume of individual hedge fund transactions. The same framework can be used by regulators to monitor aggregate exposures to systematic risks. This use would provide important input to the management of stressful events, such as the bond market decline of 1994.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/c0ea31451e2c4501074b875c0ef1bed54ac67336

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Citations in the corpus

Do Hedge Fund Managers Misreport Returns? Evidence from the Pooled Distribution (2009, neutral): “In contrast, the hedge fund 8 Fung and Hsieh (1997) argue that hedge fund returns feature option-like payoffs relative to the return of underlying assets, consistent with dynamic trading strategies.”
"Momentum Strategies in Futures Markets and Trend-following Funds" (2013, positive): “Interestingly, the inclusion of the time-series strategies among the benchmark factors of the Fung and Hsieh (2004) 7-factor model for hedge fund returns dramatically increases its explanatory power, while the statistical significance of some of the straddle factors is driven out.”
Uncovering Hedge Fund Skill from the Portfolio Holdings They Hide (2013, neutral): “We do not resort to Fung and Hsieh (2004) hedge fund factors because they are meant for alternative asset classes while we are analyzing equity positions exclusively.”
Can Hedge Funds Time Market Liquidity (2012, positive): “In this paper, we estimate liquidity-timing ability for hedge funds using the seven-factor model proposed by Fung and Hsieh (2004) as the main benchmark model.”
Side-by-Side Management of Hedge Funds and Mutual Funds (2010, neutral): “Instead, our tests show that side-by-side hedge fund managers are at best on par with their primary strategy peers, based on 7-factor alphas (see Fung and Hsieh, 2004) or 6-factor alphas (see Agarwal and Naik, 2004).”
Hedge Funds, Managerial Skill, and Macroeconomic Variables (2011, methodology): “For example, the ND portfolio that excludes all forms of predictability yields a relatively modest Fung and Hsieh (2004) alpha of 3.89 percent per year.”
Hedge funds: an industry in its adolescence (2006, methodology): “…(2000b), and they reflect the actual investment experience in hedge funds, netting out the cost of due diligence, portfolio construction, etc. Fung et al. (2006) estimated the alpha of FoFs from the merged TASS, HFR, and CISDM databases, using the seven risk factors from Fung and Hsieh (2004b).”
Hedge Fund Contagion and Liquidity Shocks (2010, neutral): “Since the credit spread measure is already included in the filtering analysis, as it is one of the Fung and Hsieh (2004) factors, and since the REPO volume is only available as of August 1994, we add the other five channel variables (TEDSPRD, STKLIQ, BANK, PBI, and FLOW).”
Do Hedge Funds’ Exposures to Risk Factors Predict Their Future Returns? (2011, neutral): “Gupta and Liang (2005) use the extreme value approach of Bali (2003) to examine value at risk and capital adequacy of individual hedge funds.”