## The term 'Sharpe Ratio' is included in the Investments edition of the Financial Dictionary. Get your copy on Amazon in Kindle, Paperback or Audio edition. Choose your edition here...

Sharpe Ratio refers to a means of calculating returns which are risk-adjusted. This particular ratio has grown into the standard in the investment industry for these types of compilations. Nobel laureate William F. Sharpe originally created and popularized it. It represents the average return which investments earn above the risk free rate per unit of volatility.

It is also the total risk involved with the investment. When individuals subtract from the mean return the risk free rate, it allows for the performance based on risk taking to be determined. One of the facts that this calculation assumes is that those investors whose portfolios represent only investments with zero risk will reflect a zero Sharpe ratio. This type of portfolio would hold U.S. Treasury Bills as an example.

All else being equal, the higher a Sharpe ratio proves to be, the better the return on a risk-adjusted basis are. Over the years, this ratio has expanded to the point that it is now the most commonly utilized means for compiling risk-adjusted returns. There are limitations to it however. It fails when contemplating those portfolios that possess substantial risks which are not linear. This would include stock warrants or options. In the intervening years, there have been alternate risk-adjusted return means of calculating performance put forward. Among these are Return Over Maximum Drawdown RoMaD, the Sortino Ratio, and the Treynor Ratio.

There are many uses for the Sharpe Ratio. It finds common employment as a means of contrasting the risk to return ratio of a portfolio after a new asset class or individual asset becomes added. It always helps to look at a concrete example to better understand the concept. Godfrey the British portfolio manager wants to add a hedge fund in to his presently fifty percent and fifty percent composed portfolio of stocks and bonds. It currently possesses a .67 Sharpe ratio. Once he would add in the hedge fund component, his allocation would be 40 percent, 40 percent, and 20 percent, with stocks, bonds, and a diversified hedge fund holding (that invested in other hedge funds), respectively. This boosts the ratio to .87 in this particular case.

What it tells Godfrey is that while the hedge fund may be higher risk as a standalone investment, it does boost the Sharpe-displayed risk to return features of the newly reallocated portfolio. This improves the diversification factor for Godfrey. Had the additional prospective investment decreased the ratio, this would have indicated to Godfrey that he should not include the new investment in his portfolio.

Another helpful use of the Sharpe ratio lies in its practical ability to describe the excess returns of a portfolio. It helps to reveal whether such higher returns result from an over reliance on risk or intelligent investment and diversification choices. While a single fund or even portfolio might experience out-sized returns to its competitors, such an investment would only be a good recommendation if the greater returns are not a result of a larger amount of risk incurred. In general, the larger the Sharpe ratio for a portfolio, the higher its performance on a risk-adjusted basis has proven to be in the past. Negative ratios describe a portfolio that would have equal performance with a lower risk competitor.

Critics of the Sharpe ratio claim that its interpretations can result in misleading conclusions. Hedge funds also can misuse the ratio as they look to make their returns on a risk-adjusted basis look better than they actually are. They might do this by increasing the measurement intervals. It would reduce the volatility factor over time. They might also compound their monthly returns while not compounding the standard deviations. They could smooth out their returns by only occasionally updating the values of their illiquid assets. Another means at their disposal is to employ price models which underestimate their monthly losses or gains. They might also cut out their extreme returns by eliminating both the worst and best monthly returns for each year. This would lower the standard deviation.