Skip to main content
June 2024
AAM Magazine
June 2024
Back to April 2020

Are hedge funds just traditional beta?

By Joseph Cherian, Christine Kon and Li Ziyun*   
  • Asia
  • Global
  • USA
As conservative investors turn to alternatives, we closely analyse hedge fund strategies

During our comprehensive academic study as to whether Asian and US hedge fund strategies can be explained by judiciously chosen systematic linear and non-linear risk factors, we stumbled on something interesting. Certain hedge fund strategies are truly market-neutral, while others aren’t. The following commentary constitutes a preview of various hedge fund strategies’ exposure to such factors.

Why is this study important? Institutional investors who traditionally are conservative, such as pension funds, are now exploring allocating a larger portion of their assets under management to alternative investments, including hedge funds, private equity, venture capital, real estate and infrastructure. Top1000funds reports that Australia’s US$113 billion sovereign wealth fund, the Future Fund, recently announced that it will be allocating even more money to its $15.3 billion hedge fund programme.

According to Eurekahedge, as of end-2019, global assets under management in the hedge fund industry had grown to US$2.3 trillion, with $183 billion in Asian funds.

With all this money pouring into hedge funds, and given that they are not cheap – management fees still run into the 2% range and performance fees are at 20% – it behoves translational researchers to determine whether hedge funds are truly worth their salt. This article expounds on that topic.

Alternative beta blockers

Our factor exposure analysis compares period and 24-month rolling betas of the various hedge fund index level strategies against systematic risk factors such as the market portfolio (S&P 500), commodity returns, credit spreads, downside risk (VIX), short-term hedges (PutWrite strategy), and the ubiquitous Fama-French factors. More specifically, we define the risk factors as:

  • S&P 500: S&P 500 market index return in excess of the risk-free rate;
  • Emerging market: MSCI Emerging Markets Index return in excess of the risk-free rate;
  • Bond: Month-end to month-end change in the US Federal Reserve 10-year Treasury constant maturity yield minus the US Federal Reserve 3-month Treasury constant maturity yield;
  • USDX: US Dollar Index return relative to the value of a basket of currencies comprising the US’s most significant trading partners;
  • Credit: Month-end to month-end change in Moody’s BAA yield - US Federal Reserve 10-year Treasury constant maturity yield;
  • DVIX: First difference of VIX, a risk aversion indicator;
  • Commodity: S&P Goldman Sachs Commodity Index (GSCI) return;
  • Size: Russell 2000 return - Russell 1000 return;
  • Value: Russell 1000 Value Index return - Russell 1000 Growth Index return;
  • Momentum: Kenneth French’s momentum factor from his data library;
  • Short put: CBOE S&P 500 PutWrite Index Return, a strategy that earns downside risk premium.

Using comprehensive monthly data obtained from Eurekahedge between January 2000 and December 2019, we plot the 24-month rolling betas of hedge fund strategies against the these risk factors to observe changes in exposure over time. We provide results for Asian hedge fund strategies in addition to the usual North American strategies.

A summary of these results can be found in table 1 and table 2.

The tables include each hedge fund index strategy’s regression beta from January 2000 to December 2019 against a systematic risk factor, which we refer to as the period beta1. We compare this against the standard deviation of the 24-month rolling betas over the same period.

In most cases, a period beta value that is close to zero implies low average exposure to that particular risk factor. Since the bond and credit factors are based on yields, an absolute value that is less than one implies low average exposure. Additionally, due to the inverse relationship between bond prices and yields, a more negative beta for bond and credit implies increased market exposure. All other risk factors are based on total returns.

Overall, the period betas are low for both North American and Asian hedge fund index strategies, except – and understandably so – for some obvious ones. For example, long-short equity’s average positive exposure to the S&P 500 due to its long equity bias; various Asian hedge fund index’s negative exposure to the USDX; and fixed income and debt strategies’ high exposure to the credit factor.

Despite the low period betas, a more careful examination of the time series of the 24-month rolling beta, both by risk factor and hedge fund index strategy, reveals something slightly different.

Time-series analysis

The entries highlighted in red in the tables are those with low overall period betas but which show significant deviation of the 24-month rolling betas when these values are plotted over time. A few strategies with large variation in factor exposures are highlighted below, first by systematic risk factors, and then by hedge fund index strategies.

The first glaring observation is that exposures to several systematic factors on average have increased in recent years.

Increase in exposure to the S&P 500 factor

In 2017, Asia managed futures’ rolling beta increased to 0.7 (chart 1). In 2018, North American (NA) arbitrage increased to 0.5. Finally, in 2019, the rolling beta exposures of NA multi-strategy, NA distressed debt and Asia event-driven increased to 0.6, 0.5 and 0.35, respectively.

In the decade since the global financial crisis from 2009 to 2018, the beta exposure of NA macro on average steadily increased from minus 0.3 to 0.5 (chart 2).

Increase in exposure to short put factor

Similarly, NA multi-strategy, NA macro, NA arbitrage and NA event-driven indexes also showed a trend of increasing exposures to short put since 2008.

Increase in exposure to commodity factor

More recently, from 2018/2019 onwards, the NA and Asia hedge fund index (chart 3), NA and Asia long-short equity, NA multi-strategy, NA distressed debt, NA macro, NA fixed income, NA and Asia relative value, NA and Asia event-driven strategy indexes have shown an increase in beta to the commodity factor, from near zero to 0.3 – 0.5.

Comparing pre- and post- financial crisis exposures, NA distressed debt, NA and Asia event-driven, NA and Asia relative value switched from negative to positive exposure.

However, one caveat is in order here. The correlations of commodity versus S&P 500 and other risk factors have increased post the financial crisis, which implies that the increased beta exposure may be a correlation effect.

Increase in exposure to credit spread factor

Exposure to credit has become more negative for most hedge fund index strategies between 2016 – 2020, indicating that a 1% increase/decrease in the credit spread leads to a bigger decrease/increase in returns on average.

Increase in exposure to size factor

From 2018 to date, there is an overall increase in exposure to size for many hedge fund index strategies, as indicated by an increase in the rolling betas of North American and Asia hedge fund indexes (chart 4), NA and Asia long-short equity, NA multi-strategy, NA distressed debt, NA macro, NA and Asia arbitrage, NA and Asia relative value, NA and Asia event-driven against size.


Other than the aforementioned factors, Asia distressed debt versus USDX increased from negative exposure to 0.25, while Asia arbitrage has increased to 0.4 in recent years against the value factor. Since the financial crisis, Asia and NA event-driven versus value have increased from an overall negative exposure to positive exposure.

Finally, NA and Asia arbitrage, NA multi-strategy and NA long-short equity have been showing increasing beta exposures against the emerging markets factor.

The 24-month rolling beta by hedge fund index strategy reveals similar glaring variations in beta across time that calls for attention.

North American macro

The period beta of NA macro index ranges from minus 0.03 to 0.03 against the systematic total return risk factors, while it is between minus 0.29 and 0.81 for the yield factors (bond and credit). While almost all factor betas are close to zero, fluctuations in the 24-month rolling beta over time indicate this period average to be misleading.

For example, S&P 500 has 0.01 period beta, but rolling beta is close to 0.5 since 2014. In fact, rolling beta was never totally zero, but positive before 2007, and approximately minus 0.25 from 2007 to 2013 (chart 2). We observe the same pattern for the remaining factors, summarised in the following table:

Asia macro

Asia macro index shows a similar trend – period betas tend to be close to zero, except for value, size and bond, which have betas greater than 0.1. However, fluctuations in the rolling betas indicate that beta is far from zero. Its fluctuation level is similar to NA macro, but there are periods where Asia macro beta shows sudden spikes that is much larger than NA macro.

A positive sign is that the variations in the betas for the macro strategy in general have become less extreme after the financial crisis.

Managed futures

NA managed futures
With the exception of the USDX factor, the period beta of managed futures index is consistently close to zero for all factor betas, ranging between minus 0.01 to 0.06 for the total return factors, and minus 0.06 and 0.32 (bond and credit) for yield factors. However, a closer look at the rolling beta graphs shows significant time variation.

For example, S&P 500 shows a period beta of minus 0.01, but in fact, beta was 0.5 from 2004 to 2007, and fluctuated around 0.1 to 0.3 in recent years. We observe the same pattern for the remaining factors, summarised as follows:

Asia managed futures index shows a similar trend:

Similar to the macro strategy, variations in beta of managed futures have become less extreme post the financial crisis for S&P 500, USDX, value, commodity, momentum, DVIX and emerging markets.


In addition to macro and managed futures, several other hedge fund index strategies display low period beta but high volatility of rolling beta (tables 1 and 2 highlighted in red) to momentum, bond and value factors.


We have attempted to systematically categorise hedge fund index strategies’ average and time series exposures to systematic risk factors, which are well accepted and established in academia and the industry. We comprehensively compare 24-month rolling betas and average period betas of the various hedge fund index strategies, for both Asia and North America, against these risk factors.

While hedge fund strategies are on average worth their salt, the time series analysis shows that there is indeed still time-variation to their “skill” in delivering alpha. The importance of due diligence in the hedge fund search process hence cannot be overemphasised.

1 The average of the 24-month rolling betas is similar to the period beta over the sample period January 2000 to December 2019.

*Joseph Cherian is practice professor of finance at Singapore’s NUS Business School and academic adviser to Asia Asset Management. Christine Kon and Li Ziyun are research analysts at Xen Capital and NUS Business School, respectively. All charts and tables for this commentary, plus additional tables, are available at