Over the next few years, I anticipate that a greater number of hedge fund investors will use liquidity tools to enhance portfolio efficiency and risk management.  Broadly speaking, these liquidity tools encompass investment strategies that seek to deliver hedge fund-like returns but with material structural advantages, like daily liquidity, real-time position-level transparency, no headline risk, scalability, and other features.  I’ve spent a lot of time looking at factor-based models that, in essence, seek to understand the current broad market exposures of the hedge fund industry or a portfolio of hedge funds, and to invest in those market factors directly through liquid futures and ETFs.  I’ve found that there are three key things to understand about these strategies:  the value proposition, why past returns are relevant, and why this approach is fundamentally different from passive indexing in traditional assets.


The goal of a well-designed liquidity program is to deliver similar returns to either a hedge fund index or portfolio.  It generally doesn’t work well for an individual manager – and I wouldn’t recommend trying.  That said, the strategy isn’t designed to outperform, but rather to deliver some or all of the returns of a target.  In practice, there will be periods of outperformance and underperformance, but a well-designed program should deliver the majority of the target returns over time.  Therefore, the principal “value” lies in structural benefits, not outperformance.  Put simply, a 10% return with daily liquidity is more valuable than a 10% return with a long lockup.[1]  Flexibility is valuable:  an investor who could exit the markets in late 2008 would have preserved capital and was far better positioned to re-invest in early 2009.

The question for each investor is how to think about this value proposition and how to deploy it in practice.  Each investor will ascribe a different value to benefits like liquidity, transparency, scalability, lack of headline risk, and other features.  An investor who demands a 300 bps (expected) return premium for investing in an illiquid asset will realize 300 bps of “value” if a liquid investment can deliver the same return.  Other investors may ascribe more value to intangible benefits like the absence of K1s or reduced internal monitoring/administrative costs.  A large institution may place value on the reduction in headline risk, real-time position-level transparency, and low all-in fees.  I personally place a high value on the information about how exposures shift over time — a window into where hedge fund managers see opportunities.


All predictive quantitative models are grounded in the premise that the past is representative of the future.  In factor-based programs, these models rely on an assumption that the composition of the industry and the key (market) drivers of returns are relatively constant over time, not that future returns will mimic past returns.  This is an important distinction and the evidence strongly supports this position.

What this means in practice is that it’s easier to approximate the returns of strategies with relatively stable exposures over time – equity long-short, credit, event-driven, distressed.  Others – CTAs and macro – are more variable over time and hence less predictable.

The factor models require a year or two of historical returns in order to estimate the current weights of the target portfolio/index.  In this sense, the models are always “backward looking.”  What we’ve seen over the past five years is that hedge funds, as a group, shift exposures slowly enough (they do change, but over months and quarters) that the factor models are able to detect material shifts on a timely basis.  While this may not be true for individual managers, it does hold true for diversified portfolios.

For more stable strategies, this “makes sense.”  Equity long-short, event-driven and credit managers generally build up positions over months and quarters based on incremental research and market information.   Consequently, today’s portfolio reflects a series of decisions over the past year or more.  Whether they make or lose money this month will largely be the result of accumulated decisions over the preceding quarters.  The factor models pick up on this.  Therefore, the positions today are, in fact, “forward-looking” in that they reflect the collective views of the underlying managers of where returns will be.

Consequently, an ancillary benefit of the liquidity programs is that they can provide a window into where hedge funds see better returns going forward. In early 2011 we saw a major shift in a typical portfolio away from emerging markets and into US equities – it’s quite clear today that this accurately reflected a reassessment of the relative attractiveness of US equities versus those in developing markets.


The principal difference between hedge fund liquidity tools and traditional passive index products is that you can easily and efficiently invest in the securities underlying the traditional index.  Not so with hedge funds.  A decade ago, some firms sought to create ultra-diversified portfolios of hedge funds – akin to buying 150 of the stocks in the S&P500 – as a means to generate an index-like product for hedge funds; Unfortunately, these “investable index” products suffered from data biases that caused persistent underperformance.

In general, any hedge fund liquidity program will have substantially larger tracking error than that of a passive index.  While we think they’ve proven to be highly effective over time, investors should be prepared for more month-to-month tracking error given that the models provide a (good) approximation of exposures over time, but that idiosyncratic factors can cause divergences.  Also, depending on the target, it may be prudent to expect that the liquidity program will provide only a portion of the target returns; however, given the structural benefits and cost of holding cash, this may still be compelling.

The second issue is that investors are more skeptical about the returns of hedge fund indices.  Many analysts have noted data biases that can skew the results upward or downward over time.  The result is a lack of unanimity as to which benchmark(s) provide the best representation of industry returns.

I’ve generally preferred to use actual portfolios as a target – this better aligns the liquidity program with the investor’s chosen hedge fund portfolio.  However, actual portfolios are more likely to change materially over time, and idiosyncratic manager risks can elevate tracking error.  That said, I’ve found that liquidity tools can effectively deliver the vast majority of the returns of a well-managed portfolio and provide a powerful means to improve risk management and flexibility.

[1]       Liquidity per se is difficult to value.  Endowments generally expect a 400 bps return premium for investing in illiquid assets.  We’ve looked at the value of being able to cut risk in portfolios below a certain loss threshold (i.e., down 5%); depending on the strategy, this can result in benefits of up to 300 bps per annum.


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