How do you evaluate money managers? (If you want to cut to the chase, a simple and easy way of evaluating their portfolio returns can be found in the second-to-last sentence of this blog post.) First and foremost, you probably judge them based on their portfolio’s performance, right? If so, then how do you do it? Come on, don’t be shy. You probably use the same technique that many use: On a graph, if the portfolio returns are above the S&P 500 returns, then you probably think the portfolio looks good. Lots of us do this. Let’s dub this technique the “eyeball” test or the “it looks good” test.

While we conduct these “eyeball” tests to help evaluate portfolio returns, I think that we all realize in the back of our minds that there are probably more rigorous, sophisticated, and methodical ways to evaluate returns. And, of course, there are. For example, many advisors use the Sharpe Ratio, which is a ratio of a portfolio’s average returns in excess of a risk-free return over the standard deviation of the portfolio’s returns. This is simply the tradeoff between return and risk. It is typically believed that a higher Sharpe Ratio is better than a lower one.

However, one problem with the Sharpe Ratio is that it does not explicitly take into account the portfolio’s length of track record. Wouldn’t a portfolio’s Sharpe Ratio seem more valid if it were based on many years of returns instead of just a few years of returns? For example, if I told you that two portfolios have a Sharpe Ratio of 0.7, but one portfolio has been around for three years while the other has been around for 10 years, then you’d probably think that the Sharpe Ratio of 0.7 for the second portfolio is more reliable since it’s based on more years of data. One very simple way to factor in the length of track record is to take the annualized Sharpe Ratio and multiply it by the square root of the number of years that the portfolio has been in existence. This simple multiplication provides a numerical value that has two huge benefits. First, it’s a measure that directly incorporates the length of track record. This first benefit is easy to see. However, the second benefit is a bit harder to explain. Because we multiplied by the Sharpe Ratio by the square-root of the number of years the portfolio has been in existence, the resulting numerical solution is what is known as a t-statistic.  What’s a t-statistic? It’s actually a really useful statistical measure. Let me explain.

A t-statistic is a measure that comes from the study of statistics. I know that we all hated studying statistics in school (including me), but what I’m about to explain I think is really useful (and also pretty cool). If we believe that stock returns follow a normal distribution (and they pretty much do), then a t-statistic can tell us the probability that returns are not random. Let me illustrate with the following example.

Let’s say that there is a portfolio called “Fooled You” and it has a Sharpe Ratio of 0.7, and it has been around for 10 years. When you multiply 0.7 by the square-root of 10, you get a t-statistic of 2.2. Statistically speaking, a t-statistic of 2.2 is pretty darn high. In “stat-speak,” a t-statistic of 2.2 means that the returns are over two standard deviations (or two sigma) greater than zero returns. If 120 monthly returns are used to estimate this Sharpe Ratio, then statistically speaking a t-statistic of 2.2 implies there is less than a 5 percent chance that these good returns were just random. This suggests that the money manager may have used skill rather than luck to generate those returns. He was able to get two sigma! (Did you know there is a hedge fund called Two Sigma?)

So, if you want to evaluate portfolio returns using some rigor and some standards, then it’s actually pretty simple to do it!  Just take the portfolio’s Sharpe Ratio and multiply it by the square-root of the number of years that it’s been in existence. If the value is greater than 2, then statistically it might appear that the portfolio manager knows how to beat the market.

However, before I conclude, there is one important caveat that I need to state. While the performance of “Fooled You” may look both impressive and real, don’t forget that the analysis is based on a single portfolio. One portfolio. Uno. If you simply took 200 portfolios, all with stocks that are randomly selected, then there can be a very good chance that a few of those portfolios will perform like “Fooled You” and have a t-statistic of 2.2 on their Sharpe Ratios. But, since all of the stocks in each portfolio were randomly selected, the results can reflect random chance.

So, what’s the point of this discussion? Use a higher standard. Don’t be fooled by randomness. (Did you know that biomedical researchers use a four-sigma standard?  This makes you feel secure, right?) As stated in a recent academic white paper by professors at Duke University, “Researchers in finance, whether practitioners or academics, need to realize that they will find seemingly successful trading strategies by chance.”  These professors conclude: “Two sigma is no longer an appropriate benchmark for evaluating trading strategies.” So come on, let’s all have higher standards. To help evaluate portfolio performance, take the portfolio’s Sharpe Ratio and multiply it by the square-root of the number of years that it has been in existence, and if that number is greater than three, then you are using a pretty high standard to help identify good portfolios that have statistically outperformed based upon this approach to the Sharpe Ratio. (Oh, and by the way, you didn’t really think that a portfolio named “Fooled You” was really going to be a good portfolio, did you?)

Kenneth A. Kim is chief financial strategist for Eqis, which provides asset management, practice management and operations automation on an integrated platform for advisors.