Machine Learning-Based Active ETF to Replicate Hedge Fund Returns

Former Bridgewater Associates executive Bob Elliott and NYU economics professor Bruce McNevin launched Unlimited Funds earlier this month to break down and replicate index returns. By feeding return data into machine learning algorithms, the firm hopes to identify the component drivers of returns to infer and replicate real-time positions.

Its flagship exchange-traded fund, the Unlimited HFND Multi-Strategy Return Tracker ETF (HFND), aims to create a portfolio with hedge fund-like return characteristics. However, by charging comparatively lower expenses, the firm intends to outperform the hedge fund industry net of fees, delivering exceptional value to shareholders.

Most hedge funds charge a 2% management fee and a 20% performance fee, commonly known as a 2 and 20 fee structure. By comparison, HFND charges a modest 1.03% expense ratio while leveraging machine learning algorithms to create a portfolio matching each major hedge fund style’s most recent monthly returns, from long/short to multi-strategy.

“After spending many years in the hedge fund industry, we’ve identified that investors are either ill-served by exorbitant fees in the asset class or are unable to access such exclusive strategies,” says Mr. Elloitt. “With HFND, we are bridging what we see as a crucial gap in the market by bringing together the best parts of the hedge fund industry with the democratizing structure of an ETF.”

HFND holds a portfolio of long and short positions in 30 to 50 underlying ETFs and future contracts. By aggregating portfolios based on the relative asset levels in each hedge fund style, the fund offers comparable returns without the excess leverage of illiquidity of hedge funds.

And unlike competing hedge fund-focused ETFs that rely on delayed or misleading public filings, the firm’s machine learning-based approach analyzes real-time investment returns. The result is a more accurate replication approach and returns that may mirror hedge fund performance more closely than ever.

The fund’s largest holdings include:
 

• Cash – 24.76%
• ProShares Short 20+ Year Treasury ETF (TBF) – 10.56%
• Vanguard Short-Term Corporate Bond Index ETF (VCSH) – 9.45%
• Vanguard Total World Stock Index ETF (VT) – 8.31%
• abrdn Bloomberg All Commodity Strategy K-1 Free ETF (BCI) – 5.32%

HFND is innovative in replicating hedge fund returns. Still, it’s not the only way for ETF investors to access hedge fund strategies. Several ETFs implement hedge fund-like strategies to help retail investors capture the benefits of long/short, multi-asset, or other strategies.

Other popular active ETFs based on similar investment themes include the following.

 

Data as of October 18, 2022.

When choosing an ETF, you should consider the expense ratio and potential risk factors. Many hedge fund strategies can hedge against risk or boost returns but, in some cases, these strategies can increase overall portfolio risk or result in opportunity costs. For example, covered call strategies inherently give up some equity upside.

Hedge funds are an excellent way for investors to weather uncertain times, but they’re expensive and off-limits to many investors. Fortunately, the Unlimited HFND Multi-Strategy Return Tracker ETF (HFND) offers everyday investors a window into the hedge fund world by harnessing the power of machine learning.

Take a look at our recently launched Model Portfolios to see how you can rebalance your portfolio.