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1 June 2016 CFA Institute Journal Review

The Cross-Section of Liquid Absolute Return Funds (Digest Summary)

  1. Lawrence Gillum, CFA

Examining the performance characteristics of the growing universe of liquid alternative mutual funds, the author finds that, despite the absolute return orientation, monthly excess returns for all categories have been negative over the time period observed. Furthermore, he finds that all investment categories are statistically correlated with the stock market, thus calling into question the implied diversification benefits of these absolute return strategies.

What’s Inside?

Since the global financial crisis, demand for liquid absolute return strategies has increased. Moreover, because of regulatory changes in Europe and North America that relaxed leverage and short position restrictions, the supply of such funds has increased as well. The author investigates the performance and characteristics of these mutual funds that follow hedge fund–like strategies and finds that, despite the funds’ claims, the monthly alpha for all categories of liquid alternatives is negative. Furthermore, he finds that all of the investment categories tested have a statistically and economically significant exposure to the overall stock market.

How Is This Research Useful to Practitioners?

Because of the poor performance of traditional stock and bond investment options during the global financial crisis, investor demand for strategies that offer downside protection and/or positive returns irrespective of the market environment has increased. At the same time, the regulatory environment has changed, thus allowing traditional mutual fund vehicles more flexibility to implement hedge fund–like strategies that involve leverage and short selling. As such, assets allocated to these absolute return strategies have increased tremendously over the past few years. Despite this recent popularity, however, the performance and characteristics of these strategies have not been extensively tested.

To test the strategies’ absolute orientation claims, the author performs a time-series regression analysis to strip out the impact of systematic factor exposures—that is, exposures that could be replicated elsewhere—to determine the monthly alphas associated with each category. Because these strategies can typically invest globally, the author uses international factors rather than US factors. The first test is a four-factor analysis that includes equity market beta, size, value, and momentum. The results demonstrate that a substantial portion of the variation of returns can be explained by these four factors. The adjusted R2 for the categories ranges from 0.27 to 0.65.

Next, the author runs a second test that includes the first four factors as well as interest rates, change in credit spreads, volatility of stock markets, movement of commodity prices, and currencies. Similar to the first analysis, the author finds that the majority of variation in returns for liquid absolute return funds can be explained using these nine factors. Except for the managed futures category (0.39), the adjusted R2 ranges from 0.53 to 0.73.

Therefore, after the author corrects for the various systematic factor exposures, he finds that the resultant average monthly alphas for the different categories are negative, ranging from –0.13% (aggressive allocation) to –1.03% (event driven). Moreover, all absolute return categories show a statistically and economically significant exposure to the stock market factor (beta) that ranges between 0.4 and 0.8, which indicates that they are still sufficiently correlated with the stock market.

How Did the Author Conduct This Research?

Using Morningstar and Lipper databases, the author assigns each fund to 1 of 15 categories according to its investment approach and stated goals. The classification schemes largely follow Morningstar’s classification scheme as well as traditional hedge fund categories. Ultimately, the author classifies 1,140 unique funds with assets under management totaling $463.7 billion as of November 2014. There is insufficient information to classify 259 funds, which are thus excluded from the analysis. Excluded funds are predominantly young and small with a median age of less than three months and a median size of $6.7 million. The author uses the longest track record—between September 1994 (or from the fund’s inception if later) and November 2014. It should be pointed out that the majority of funds (68%) were formed after the most recent major stock market downturn.

Abstractor’s Viewpoint

Although the author’s analysis is certainly comprehensive, given the idiosyncratic nature of these strategies, it may be difficult to translate higher-level classifications and analysis into individual strategies within the classification. That is, there may be enough heterogeneity within the broad classifications to question the applicability of the analysis to individual strategies. For example, although the author points out that the respective category alphas are negative, he also shows that there is a sizable minority of funds with positive alphas over the time periods observed. Thus, manager and strategy selection is important when allocating to these strategies. Nonetheless, the majority of funds observed have a limited track record and have not been tested specifically during negative market events, so investor caution is advised.

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