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Summary­

• In this commentary we attempt to identify the sources of performance in long/short equity strategies.
• Using Kalman Filtering, we attempt to replicate the Credit Suisse Long/Short Liquid Index with a set of common factors designed to capture equity beta, regional, and style tilts.
• We find that as a category, long/short equity managers make significant changes to their equity beta and regional tilts over time.
• Year-to-date, we find that tilts towards foreign developed equities, emerging market equities, and the value premium have been the most significant detractors from index performance.
• We believe that the consistent relative out-performance of U.S. equities against international peers has removed an important alpha source for long/short equity managers when they are benchmarked against U.S. equities.

Please note that analysis performed in this commentary is only through 8/31/2018 despite a publishing date of 10/22/2018 due to data availability.

Introduction

Since 4/30/1994, the Credit Suisse Long/Short Equity Hedge Fund (“CS L/S EQHF”) Index has returned 9.0% annualized with an 8.8% annualized volatility and a maximum drawdown of just 22%.  While the S&P 500 has bested it on an absolute return basis – returning 10.0% annualized – it has done so with considerably more risk, exhibiting 14.4% annualized volatility and a maximum drawdown of 51%.  Capturing 90% of the long-term annualized return of the S&P 500 with only 60% of the volatility and less than half the maximum drawdown is an astounding feat.  Particularly because this is not the performance of a single star manager, but the blended returns of dozens of managers.

Yet absolute performance in this category has languished as of late.  While the S&P 500 has returned an astounding 13.5% annualized over the last five years, the CS L/S EQHF Index has only returned 5.6% annualized.  Of course, returns are only part of the story, but this performance is in stark contrast to the relative performance experienced during the 2003-2007 bull market. From 12/31/2003 to 12/31/2007, the average rolling 1-year performance difference between the S&P 500 and the CS L/S EQHF Index was less than 1 basis point whereas the average rolling 1-year performance differential from 12/31/2010 to 12/31/2017 was 877 basis points. Year-to-date performance in 2018 has been no exception to this trend.  The CS L/S EQHF Index is up just 2.1% compared to a positive 9.7% for the S&P 500, with several popular strategies faring far worse.

Now, before we dive any deeper, we want to address the obvious: comparing long/short equity returns against the S&P 500 is foolish.  The long-term beta of the category is less than 0.5, so it should not come as a surprise that absolute returns have languished during a period where vanilla U.S. equity beta has been one of the best performing asset classes.  Nevertheless, while the CS L/S EQHF typically exhibited higher risk-adjusted returns than equity beta from 1994 through 2011, the reverse has been true since 2012.

Identifying precisely why both absolute and relative risk-adjusted performance has declined over the last several years can be difficult, as the category as a whole is incredibly varied in nature.  Consider this index definition from Credit Suisse:

 The Credit Suisse Long/Short Equity Hedge Fund Index is a subset of the Credit Suisse Hedge Fund Index that measures the aggregate performance of long/short equity funds. Long/short equity funds typically invest in both long and short sides of equity markets, generally focusing on diversifying or hedging across particular sectors, regions or market capitalizations. Managers typically have the flexibility to shift from value to growth; small to medium to large capitalization stocks; and net long to net short. Managers can also trade equity futures and options as well as equity-related securities and debt or build portfolios that are more concentrated than traditional long-only equity funds.

The wide degree of flexibility means that we would expect significant dispersion in individual strategy performance.  Examining a broad index may still be useful, however, as we may be able to decipher the large muscle movements that have driven common performance.  In order to do so, we have to get under the hood and try to replicate the index using common factor exposures.

Figure 1: Credit Suisse Long/Short Equity Indices

Data from 12/1993-8/2018

 Source: Kenneth French Data Library and Credit Suisse. Calculations by Newfound Research. It is not possible to invest in an index.  Past performance does not guarantee future results.

Replicating Long/Short Equity Returns

To gain a better understanding of what is driving long/short equity returns, we attempt to construct a strategy that replicates the returns of the Credit Suisse Long/Short Liquid Index (“CS L/S LAB”).  We have selected this index because return data is available on a daily basis, unlike many other long/short equity indexes which only provide monthly returns.

It is worth noting that this index is itself a replicating index, attempting to track the CS L/S EQHF Index using liquid instruments.  In other words, we’re attempting a rather meta experiment: replicating a replicator. This may introduce unintended noise into our effort, but we feel that the benefit of daily index level data more than offsets this risk.

Based upon the category description above, we pre-construct several long/short indices that aim to isolate equity beta, regional tilts, and style tilt effects.  To capture beta, we construct the following long/short index:

• Long S&P 500 / Short Cash: The excess returns offered by U.S. large-cap equities

To capture regional, size, and industry effects, we construct the following long/short indexes:

• Long Russell 2000 / Short S&P 500: Relative performance of small-cap equities versus large-cap equities
• Long MSCI EAFE / Short S&P 500: Relative performance of international developed equities versus U.S. equities
• Long MSCI EM / Short S&P 500: Relative performance of emerging market equities versus U.S. equities
• Long Nasdaq 100 / Short S&P 500: Relative performance of “concentrated” large-cap equities versus broad large-cap equities¹

To capture certain style premia, we construct the following long/short indexes:

• Long Russell 1000 Value / Short Russell 1000 Growth: Relative performance of large-cap value versus large-cap growth.²
• Long High Momentum / Short Low Momentum: Relative performance of recent winners versus recent losers.

All long/short indexes are assumed to be dollar-neutral in construction and are rebalanced on a monthly basis.

A simple way of implementing index tracking is through a rolling-window regression. In such an approach, the returns of the CS L/S LAB Index are regressed against the returns of the long/short portfolios.  The factor loadings would then reflect the weights of the replicating portfolio.

In practice, the problem with such an approach is that achieving statistical significance requires a number of observations far in excess to the number of factors.  Were we to use monthly returns, for example, we might need to employ upwards of three years of data.  Yet, as we know from the introductory description of the long/short equity category, these strategies are likely to change their exposures rapidly, even on an aggregate scale.  One potential solution is to employ weekly or daily returns.  Yet even when this data is available, we must still determine the appropriate rolling window length as well as consider how to handle statistically insignificant explanatory variables and perform model selection.

With this in mind, we elected to utilize an approach called Kalman Filtering.  This algorithm is designed to produce estimates for a series of unknown variables based upon a series of inputs that may contain statistical noise or other inaccuracies.  The benefit of this model is that we need not specify a lookback window: the model dynamically updates for each new observation based upon how well the model fits the data and how noisy the algorithm believes the data to be.

As it pertains to the problem at hand, we set up our unknown variables to be the weights of the replicating factors in our portfolio.  We feed the algorithm the daily returns of these factors and set it to solve for the weights that will minimize the tracking distance to the daily returns of the CS L/S LAB Index.  In Figure 2 we plot the cumulative returns of the CS L/S LAB Index and our Kalman Tracker portfolio.  We can see that while the Kalman Tracker does not perfectly capture the magnitude of the moves exhibited by the CS L/S LAB Index, it does generally capture the shape and significant transitions within the index.  While not a perfect replica, this may be a “good enough” approximation for us to glean some information from the underlying exposures.

Figure 2: Credit Suisse Long/Short Liquid Index and Hypothetical Kalman Tracker

Source: Kenneth French Data Library, Credit Suisse, and CSI Analytics.  Calculations by Newfound Research. It is not possible to invest in an index.  Past performance does not guarantee future results. Index returns are total returns and are gross of all fees except for underlying ETF expense ratios of ETFs utilized by the Kalman Tracker.  The Kalman Tracker does not reflect any strategy offered or managed by Newfound Research and was constructed exclusively for the purpose of this commentary. 

The Time-Varying Exposures of Long/Short Equity

In Figure 3 we plot the underlying factor weights of our replicating strategy over time, specifically magnifying year-to-date exposures.

Figure 3: Underlying Exposure Weights for Kalman Tracker

We can see several effects:

1. Factor exposures do indeed exhibit significant time-varying behavior. For example, prior to 2008 there was a large tilt towards foreign-developed equities, whereas post-2008 exposure remained largely U.S. focused.
2. Beta exposure is time-varying. While there is latent beta exposure in the long/short factors, we can approximate overall beta exposure by simply isolating S&P 500 exposure.  In March 2008, exposure peaked at 72% and then was cut quickly throughout the year. By January 2009, the index was net short. Post-crisis, exposure was rebuilt back to nearly 70% by September 2011, but has been declining since.  Exposure currently sits at 28%. Has all this equity timing been valuable? In Figure 4 we plot the cumulative return of the index’s long-term average beta exposure and the cumulative return from beta timing.  We can see that beta timing has, over the long run, been neither a significant contributor nor detractor from performance.  Yet crisis-period returns suggest that long/short equity strategies may employ convex trading strategies (e.g. trend-following or constant proportion portfolio insurance).
3. Size, value, and momentum tilts are not particularly significant in magnitude, with the exception of value during the 2008 crisis.  Interestingly, exposure to value was negative during that time period, implying that the index was long growth and short value. Concentrated large-cap exposure has been a rather consistent bet in the post-2008 era, reflecting a tilt towards growth.
4. Regional bets have been largely absent post-2008, at least with respect to their pre-2008 magnitude. We think it is important to pause and acknowledge the impact that benchmarking can have on perceived value add.  Consider Figure 5 where we plot the cumulative returns of regional tilts towards international developed and emerging markets.  We can see that prior to 2008, a tilt away from U.S. equities was successful in both cases, and after 2011 both were a losing bet. In the post-2011 environment, if a manager successfully makes the call to tilt towards U.S. equities, an entirely U.S. equity benchmark will effectively nullify the impact since the bet is already fully encapsulated in the benchmark!  In other words, by choice of benchmark we have eliminated a source of value-add for the manager.  Had we elected a global equity benchmark instead, the manager’s flexibility could potentially create value in both environments.

Figure 4: Cumulative Returns of Kalman Tracker’s Long-Term Average S&P 500 Exposure and Time-Varying Exposure

Source: Kenneth French Data Library, Credit Suisse, and CSI Analytics.  Calculations by Newfound Research. It is not possible to invest in an index.  Past performance does not guarantee future results.

Figure 5: Cumulative Returns of Regional Tilts

Source: CSI Analytics.  Calculations by Newfound Research. It is not possible to invest in an index.  Past performance does not guarantee future results.

What has driven performance in 2018?  We see three primary components.

1. Entering the year, the index carried a nearly 40% allocation to equity beta. While exposure declined to about 33% by the end of the month, it was rapidly cut down to just 20% after the first week February.  By mid-March this position was rebuilt to approximately 30%.We estimate that average beta exposure has been a 3.4% contributor to year-to-date returns, while market timing has been a -0.3% detractor.
2. After Q1, there was an increase in exposure to MSCI EAFE, MSCI EM, and value tilts. We estimate that these tilts have been -1.9%, -2.3%, and -1.1% detractors from performance, respectively.It is possible that these tilts all reflect the same underlying bet towards global value.  Or it may be the case that the global tilts reflect a bet on a weakening dollar.  We should not hesitate to remember that these figures are all statistically derived, so an equally valid possibility is that they are entirely wrong in the first place. It is worth noting that the value tilt – which is expressed as long Russell 1000 Value and short Russell 1000 Growth – does neutralize some of the sectors tilts expressed in the concentrated large-cap position discussed in the next bullet.  The true net effect may not actually be a tilt towards value within the index, but rather just a reduction in the tilt towards growth.
3. The largest positive contributor to returns year-to-date has been the concentrated large-cap tilt. Implemented as long Nasdaq 100 / short S&P 500, this tilt largely expresses a bet on information technology, telecommunication services, and consumer discretionary sectors. Specifically, year-to-date is represents a significant overweight towards individual names like Apple, Amazon, Microsoft, Google, and Facebook.

Conclusion

Has long/short equity lost its mojo?

By replicating index performance using liquid factors, we can extract the common drivers of performance.  What we found was that pre-2008 performance was largely driven by equity beta and a significant tilt towards foreign developed equities.

After 2011, regional tilts were losing bets.  Fortunately, we can see that such tilts were significantly reduced – if not outright removed – from the index composition.  Nevertheless, if we benchmark to a U.S. equity index (even if properly risk-adjusted), the accuracy of this trade will be entirely discounted because it is fully embedded in the index itself.  In other words, by benchmarking against U.S. equities, the best a manager can do during a period when U.S. equities outperform is keep up with the index.  Consider that year-to-date the MSCI ACWI has returned just 3.5%: much closer to the 2.1% of the CS L/S EQHF Index quoted in the introduction.

We can also see a significant tilt towards concentrated U.S. equities in the post-crisis era.  This trade captured the relative performance of sectors like technology, telecommunication services, and consumer discretionary and from 12/31/2009 to 8/31/2018 returned 4.5% annualized.

Taken together, it is hard to argue that aggregate timing skill is not being displayed in the long/short equity category.  We simply have to use the right measuring stick and not expect the timing to work over every shorter-term period.

Of course, this analysis should all be taken with a grain of salt.  Our replicating index is by no means a perfect fit (though it is a very good fit from 2012 onward) and it is entirely possible that we selected the wrong set of explanatory features.  Furthermore, we have only analyzed one index.  The performance of the Credit Suisse Liquid Long/Short Index is not identical to that of the HFRI Equity Hedge Index, the Wilshire BRI Long/Short Equity Index, or the Morningstar Global Long/Short Index. Analysis using those indices may very well lead to different conclusions.  Finally, the mathematics of this exercise does not make the factor tea-leaves any easier to decipher: we are ultimately attempting to create a narrative where one need not apply.

It is worth acknowledging that our analysis is categorical about an asset class where investors have little ability to make an indexed investment.  Rather, allocation to long/short equity is still dominated by individual manager selection.  This means that that investor mileage will vary considerably and that our analysis herein may not apply to any specific manager.  After all, we are attempting to analyze aggregate results and it is impossible to unscramble eggs.

Yet it does raise the question: if the aggregate category has such attractive features and can be tracked well with liquid factors, why have trackers not taken off as a popular – and much lower cost – solution for investors looking to index their long/short equity exposure?  Another potential solution may be for investors to unbundle and rebuild.  For example, we find that the beta exposure of $1 invested in the long/short category can be captured efficiently by $0.5 of trend equity exposure, freeing up $0.5 for other high-conviction alpha strategies.

Diversifying core equity exposure is a goal of many investors. Long/short equity provides one way to do this. In addition to potentially highlighting some of the performance drivers for long/short equity, this replication exercise shows that there may be other, more transparent, ways to achieve this goal.

1. This factor was explicitly included as it is a listed factor within the CS L/S LAB Index construction methodology.
2. As we have written about numerous times in the past, we are not fans of the value versus growth distinction.  However, this factor is explicitly listed within the CS L/S LAB marketing materials, so we use it in effort to better track the returns of the index.

Corey Hoffstein

Corey is co-founder and Chief Investment Officer of Newfound Research. Corey holds a Master of Science in Computational Finance from Carnegie Mellon University and a Bachelor of Science in Computer Science, cum laude, from Cornell University. You can connect with Corey on LinkedIn or Twitter.