5 Reasons You Should Avoid Hedge Funds
In 1990, there were only about 530 hedge funds managing about $50 billion. By the end of 2009, there were more than 8,000 hedge funds. And despite the retrenchment in 2008, the hedge fund industry now has in excess of $2 trillion dollars under management. Does performance match the hype?
As we've discussed before, much of the alpha claimed by the industry isn't alpha at all, but forms of risk other than beta. Also, hedge funds have exorbitant fees, around 2 percent of assets and 20 percent of profits. If there's any alpha in the gross returns, that alpha is going to the managers and fund sponsors, not investors. And finally, as all of the returns shown are pretax, and because of their high turnover rates hedge funds tend to be highly tax inefficient, the after-tax results would surely be worse. To sum it all up, the evidence suggests that hedge funds really aren't investment vehicles, they are compensation schemes.
However, the returns reported by hedge funds may not be as good as they claim. The following discusses the large biases in the data that you should know.
- Next: Survivorship Bias
Survivorship Bias
The first two we'll review are survivorship bias and backfill bias (which we'll discuss on the next page). In their 2010 study "The ABCs of Hedge Funds: Alphas, Betas and Costs," Roger Ibbotson, Peng Chen and Kevin Zhu analyzed the performance of 8,400 hedge funds for the period 1995-2009. Their aim was to account for these two biases.
It's important to note that hedge funds aren't required to report their returns to databases. This creates a host of problems such as these biases. Survivorship bias means hedge fund databases show inflated returns because they remove the returns of funds that have failed (or gone out of business). For example, imagine that four funds reported returns: -10 percent, -5 percent, 5 percent and 10 percent. The average return of these funds would be 0 percent. However, if the worst performing fund went out of business and the database excluded it from its reporting, the group of funds would show a return of 3.3 percent.
Ibbotson, Chen and Zhu's study looked at hedge funds reporting to the Tass database. The returns to live funds with backtested data were 14.3 percent. Once dead funds were included, the returns dropped to 11.1 percent. Once you control for backfill bias, it becomes an even wider gap -- 12.8 percent for live funds vs. 7.6 percent for live and dead funds combined -- of 5.2 percent.
- Next: Backfill Bias
Backfill bias
As mentioned on the previous page, backfill bias presents another problem when looking at hedge fund returns. Backfill bias occurs when funds that haven't been reporting their returns suddenly add their data after a good run. For example, if a fund shows strong performance for four years, then decided to start reporting its returns in the fourth year, it can report its returns for the full period, making the average return rise for the past four years.
We can see the effect of backfill bias using the same returns we discussed on the previous page. The average return of live funds that included backfilled data was 14.3 percent, while the excluding backfilled data dropped the returns to 12.8 percent, for a gap of 1.5 percent. Once we control for survivorship bias, we see returns of live and dead funds average 11.1 percent, while excluding backfilled data drops the average return to 7.6 percent, for a gap of 3.5 percent.
- Next: Liquidation Bias
Liquidation bias
Liquidation bias occurs in the data because funds that become defunct often fail to report their last returns. While it's impossible to know for certain the full impact of liquidation bias, "A Reality Check on Hedge Funds," a 2003 paper by Nolke Posthuma and Pieter Jelle van der Sluis, estimated the effect based on conversations with employees of Tass.
The assumption is that the hedge funds that terminated their reporting did so due to serious negative performance. After all, it's well known that many terminated funds are unable to return capital to investors. The most famous example is Long-Term Capital Management. It lost 92 percent of its capital from October 1997 through October 1998, and didn't report that loss to public databases.
The authors of the paper added one final month to returns to estimate the impact of liquidation bias. They then created two scenarios; one with an estimate of a 50 percent loss, and the other with an estimated loss of 100 percent. An assumption of a 50 percent loss creates a liquidation bias of about 3 percent. A 100-percent loss assumption creates a liquidation bias of 6 percent. While the 6 percent assumption would be too extreme, clearly there is another large bias in the data.
- Next: Risk Premiums
Risk premiums
For investors to hold risky assets, they should be compensated with higher expected returns. Otherwise, they have no reason to hold the asset if a less risky one will do. That's why stocks have higher expected returns than, say, Treasury bills.
Many hedge funds invest in highly risky assets, for which investors should be compensated. For example, a study by hedge fund AQR Capital Management, covering the six-year period through 2000, found that many hedge funds were taking on significantly greater risk by investing in both illiquid securities and issues of lower credit quality. In other words, the alphas (above-benchmark returns) reported by hedge funds are misleading -- the hurdles are too low. Stated another way, there was no alpha in many cases -- the incremental return was a risk premium.
- Next: Liquidity Loss
Liquidity loss
Finally, we have liquidity loss. This occurs when investors are unable to access their capital. Investments where liquidity may not be readily accessible are riskier assets, and investors should receive higher expected returns.
Investors should be compensated for the loss of liquidity in hedge funds which typically have long lock up periods (allowing them to invest in those illiquid securities and claim alpha). Yet, as we saw, even before considering this issue, there was no there, there.
Photo courtesy of Dee Lees on Flickr
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