HFND ETF: Interesting Concept, But Wait For More Data

The Unlimited HFND Multi Strategy Return Tracker ETF (NYSEARCA:HFND) aims to give retail investors access to hedge fund investment styles through its proprietary machine learning investment process that aims to replicate the gross returns of the hedge fund industry.

While the concept is intriguing, I have reservations about the execution as individual hedge funds can be very diverse. I fear HFND will end up replicating the ‘average’ of each style, which may turn out to be very mediocre returns due to the law of large numbers.

Fund Overview

The Unlimited HFND Multi Strategy Return Tracker ETF is an actively managed ETF that aims to replicate the hedge fund industry’s gross of fees returns. HFND is a new ETF launched in October 2022, so it has limited returns history. HFND charges a 1.03% expense ratio.

Strategy

The HFND ETF aims to replicate the hedge fund industry’s gross returns through a proprietary machine learning algorithm that creates a portfolio that best matches the most recent month’s returns (return, volatility, correlation with other asset classes) for various hedge fund styles: long/short equity, global macro, event-driven, fixed income arbitrage, emerging markets, managed futures, and multi-strategy. The investment manager, Unlimited Funds, Inc. (“Manager”) achieves its objective by taking long and short positions in broad-based ETFs and futures contracts.

Intriguing Investment Process

First, the manager obtains publicly reported returns and fee data for the hedge fund industry, including those for the individual hedge fund styles mentioned above. Next, for each style, the manager determines a portfolio of 10-20 positions that best matches the style’s reported gross returns characteristics. The manager then aggregates the style positions resulting in a total hedge fund industry model. The style portfolios are weighted based upon the relative asset levels of each style based on publicly reported data. Generally, the resulting portfolio will consist of 30 to 50 positions.

Over time, the manager expects this machine learning portfolio will generate returns that approximate the hedge fund industry’s gross returns such that the HFND ETF will outperform the hedge fund industry on a net of fees basis.

What the HFND ETF will not do is invest in hedge funds directly or replicate the direct underlying holdings of individual funds. The HFND ETF will also not engage in the use of excessive leverage or have a significant percentage of assets invested in illiquid investments.

Bridgewater Pedigreed Manager

The HFND ETF is managed by Bob Elliott, a frequent guest of many financial podcasts and a ‘fintwit’ personality with more than 50k followers. Mr. Elliott is a former member of the Investment Committee at Bridgewater Associates, the firm founded by Ray Dalio which manages more than $140 billion in hedge fund assets. Mr. Elliott is the CEO and CIO of Unlimited, and has more than two 2 decades of experience building investment strategies including for Bridgewater’s Pure Alpha fund.

Portfolio Holdings

Figure 1 shows the HFND ETF’s current holdings. The fund has 28% of assets held in cash, and 14% invested in mid-cap equities and 12% invested in global equities. It is also long mortgage backed securities (9%), corporate bonds (7%), and emerging market stocks (7%). The ETF is short technology stocks (-5%), long-term treasuries (-4%), and TIPS bonds (-3%).

Returns

With only 3 months of operating history, it is simply too soon to judge HFND’s performance. The HFND ETF was down 2.1% in December, but up 2.1% since its October inception to December 31, 2022 (Figure 2).

YTD to January 25, 2023, the HFND ETF is up 3.1% (Figure 3).

Distribution & Yield

The HFND ETF paid a token distribution of $0.0875 in December. Based on available disclosure, it is unclear whether this is a periodic distribution or a special year-end distribution. Since the HFND ETF holds some fixed income bond funds, it is possible for the fund to pay a regular distribution funded by investment income. However, hedge fund strategies tend not to be high yielding investments, so investors should not get their hopes up.

Intriguing Concept But The Proof Will Be In The Pudding

The HFND ETF has an intriguing concept of using machine learning to replicate the exposures of hedge fund styles without investing in the actual underlying investments. However, one word of caution is that HFND’s approach may only replicate the ‘average’ fund performance within each strategy.

Hedge funds are notoriously eclectic and diverse, so the returns distribution within each strategy category can be enormous. For example, according to a recent Financial Times article, macro hedge funds had a stellar 2022, as volatility in commodities and fixed income allowed astute managers to make huge returns. Chris Rokos of Rokos Asset Management reportedly gained 45.5% in 2022, while Caxton Associates’ Macro fund gained 30.2% to mid-December.

However, if we look at the ‘average’ macro fund, as represented by Eurekahedge’s Macro Hedge Fund Index, macro hedge funds lost 0.4% in 2022 (Figure 4). So HFND’s approach may replicate the ‘average’ 2022 return of -0.4% and not some of the top performing returns shown in the FT article.

The returns dispersion is even starker for some other styles like Long/Short Equities. For example, while Eurekahedge’s Long/Short Index showed a 2022 return of -5.1% for the style, actual fund performance ranged from Acadian Global Equity Absolute Return Fund’s 29.1% 1 Yr net return to Q2/2022, to growth-focused Tiger Global, which lost 56% in 2022. Once again, if HFND aims to replicate the ‘Long/Short Equity’ style, does it end up replicate the ‘average’ of -5.1%?

Due to the law of large numbers, what HFND ends up replicating could be very mediocre returns over the long-run. For example, Eurekahedge’s Long/Short Equities index has returned a cumulative 44.9% since inception in 2008 (Figure 5). This works out to a CAGR return of 2.5%.

This compares poorly to an equity index fund like the SPDR S&P 500 ETF Trust (SPY), which has delivered 265.8% total returns in the same timeframe for a CAGR of 9.0% (Figure 6).

Conclusion

The HFND ETF aims to give retail investors access to hedge fund investment styles like long/short equity, global macro, and event-driven by using a proprietary machine learning process to design portfolios using ETFs and futures that replicate each style’s return profile. HFND aims to outperform the hedge fund industry overall by replicating the various styles’ gross returns while avoiding the associated high fees.

While the concept is intriguing, I have reservations about the execution as individual hedge funds can be very diverse. Even within a common style like ‘global macro’, there can be a wide range of returns distribution, depending on the managers’ skill and luck. What HFND replicates is the ‘average’ of each style, which may turn out to be very mediocre returns due to the law of large numbers.