Peter Hecht, Ph.D., Senior Investment Strategist for Evanston Capital Management, says he cringes when people from the investment community criticize hedge funds for lagging the S&P 500 amid a broad and historic bull market. Many of these same people were also unduly impressed by hedge funds’ relatively superior performance during the 2008 bear market, in Dr. Hecht’s view, since the nature of hedge funds dictates that they are more likely to underperform in bull markets and outperform in bear markets – they are hedged funds, after all.
MV-MPT & EMH
“Mean-variance model portfolio theory” (MV-MPT) is a mouthful, but vitally important to understanding optimal asset allocation, according to Hecht’s new paper How to Evaluate Hedge Funds or Any New Investment. Theoretician Harry Markowitz revolutionized how investment managers allocate assets with MV-MPT in the 1950s, and economist Eugene Fama – Dr. Hecht’s dissertation adviser – built on MV-MPT for his “efficient market hypothesis” (EMH), which helped win him a Nobel prize in 2013.
But despite these widely accepted theoretical advancements, most people don’t properly understand the importance of “beta-adjusting returns and risk-adjusting alphas,” according to Hecht. He suggests they begin utilizing appraisal ratio (AR), a risk-adjusted performance statistic that accounts for a new investment’s expected return, risk, and diversification attributes “in a manner consistent with MV-MPT.”
Hecht also says investment allocators put too much focus on an individual investment’s Sharpe ratio, expected returns, and correlation properties – what’s really important, according to MV-MPT, is how adding a new investment impacts the entire portfolio. AR, by contrast, is defined as the expected alpha to alpha-volatility ratio, and it can only be calculated in the context of an existing portfolio of investments.
Unlike Sharpe ratio, which doesn’t correctly control for an individual investment’s beta, AR is a risk-adjusted measure of alpha. AR evaluates potential investments based on what they bring to the portfolio and discounts any redundancy and overlap – investments with the highest ARs will improve the total portfolio’s Sharpe ratio the most.
The graphic below shows the theoretical relationship between expected return and volatility. The green line represents the most efficient “tangent” possible, called the “efficient frontier.” The objective of asset allocation is to move the portfolio “north” along its efficient frontier.
According to Hecht, the most efficient portfolios have expected returns that are exactly proportional to beta – even greater expected returns would be “inefficient,” and such a portfolio could be optimized by allocating more to the alpha-generating assets, thereby absorbing them into the portfolio’s adjusted beta. Using AR to evaluate potential new investments ensures that the new investment’s risk and return properties actually contribute to improving the portfolio’s “efficient frontier,” rather than duplicating existing portfolio features and adding little but transaction costs.
Dr. Hecht concludes Evanston Capital’s excellent new whitepaper with a list of common “performance evaluation mistakes and misperceptions;” among them:
- Don’t forget to deduct the risk-free rate when estimating alphas: Hecht says he commonly comes across analyses, even from vended software packages, that forget to work with “excess returns” – defined as returns above the “risk-free” (U.S. government debt) rate of return. This isn’t particularly significant now since short-term interest rates are basically at zero, but as rates move higher, this error could prove costly.
- Don’t be “seduced” by high alphas: Just as portfolio returns need to be adjusted for beta; alphas need risk-adjusting, too.
- Don’t necessarily avoid investments with high correlations to the existing portfolio: Hecht says a low-correlation portfolio with zero alpha “is worthless,” and that high correlation can result in low “residual volatility,” and thus a higher AR.
For more information, download a pdf copy of the whitepaper.