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Save Some Money, Clone A Hedge Fund

Fees: Still for suckers.

It’s a tough time to be an active manager – unless, of course, you run a hedge fund.

Hedge funds are the lone area in which investors are willing to pay for active investment advice at the moment. While passive investment funds like many index ETFs have seen huge asset inflows, investors have been fleeing actively managed funds in droves. That may eventually change alongside the economic environment, but there is little reason to expect it to right now. Hedge funds occupy a unique position in the securities world, perhaps because of the mystique around them.

Hedge funds rely on using exposure to a variety of esoteric asset classes to generate returns with limited risk. While public perception is that hedge funds have tremendously high investment returns, the reality is that most do not. Firms like Renaissance Technologies have had returns that are much higher than the broader market – in RenTech’s case returns are 35% annually for 24 years according to data from hedge fund website Octafinance – but the average hedge fund return is actually ~30 basis points lower than that of the broader market according to researchers. In other words, while the broader market returns about 11.2% over the last 30 years, hedge fund returns have been about 10.9%.

There are now a variety of new ETFs and mutual funds being launched that aim to replicate the returns of hedge funds at a fraction of the cost. These new replicated hedge funds take varying levels of exposure to 8 different asset classes that have been shown to be closely related to hedge fund returns. The graphic below shows how the clone funds perform.


The results show that in some cases hedge funds outperform their clones, but in other cases, the clones outperform them. Overall, hedge fund clones could be a powerful alternative for fee-conscious investors especially in some sectors of the hedge fund world. The chart below illustrates hedge fund alpha for the basket of all hedge funds against a basket of low cost clones before transactions costs. The alpha column is in monthly basis points.


As the chart shows, the basket of hedge funds outperforms clones by 4 basis points per month on average from 2003-2011. The sample stops in 2011 to avoid survivorship bias – in other words we want to make sure that the basket of actual hedge funds is not being dragged down by a bunch of subpar funds that aren’t going to make it in the long run anyway.

The results here illustrate that with a basket of roughly 100-130 ETFs, one can replicate all of the hedge fund strategies that exist across 3,000+ funds with available returns data from Bloomberg. It takes between 3 and 6 ETFs to replicate a typical hedge fund, and the clone funds as a whole have mean excess monthly returns of roughly 20 basis points or 2.4% annually versus the broader market.

The point here is that most of a hedge fund’s performance can be cloned effectively and in a low cost vehicle. Investors would generally be much better off giving up 48 basis points of pre-expense annual alpha vs. a clone in exchange for paying ETF level fees rather than 2 and 20.

Of course, I’m not the only one that is aware of this result. Many financial economists are helping asset managers to design new products on this basis for their customers. Make no mistake, the product is real and significant. Hedge funds that want to survive in the long run need to articulate their advantages over clone funds – this could include quantitative algorithms that clone funds cannot replicate, or it could include access to investment areas that are not available to clones such as litigation funding and cat bonds.

Funds also need to spend time revamping their attribution analysis. Such analysis is a critical part of the sales proposition for many large institutional investors in my experience. Without such attribution analysis, making an honest case for alpha is much harder.

Mike McDonald is a PhD in finance and a university professor in the subject. He also runs a consulting company doing work on quantitative investing, big data, and machine learning for a variety of financial firms, asset managers, institutional investors, and government regulators. Prior to getting his PhD, Mike worked for a major Wall Street bank and one of the top hedge funds. Comments, questions, and concerns are always welcome – email Mike at or visit his firm’s website at



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