By John Netto, author of The Global Macro Edge

john-netto

A debate has been raging in the world of investing about the best way to invest: active versus passive. This debate is vital, as the disparity in fees between the two disciplines can climb to a substantial amount over the lifetime of an investor. However, while this debate is important, it has been conducted through an obscure one-dimensional prism—measuring merely “nominal returns.” It’s a classical apples-to-oranges comparison.

In Warren Buffett’s annual Berkshire Hathaway shareholder letter, the sage investor said, “When trillions of dollars are managed by Wall Streeters charging high fees, it will usually be the managers who reap outsized profits, not the clients.” I could not agree more. However, Buffett and I have two completely different viewpoints of the process by which this conclusion is reached.

Mr. Buffett (and many others like him) are comparing active and passive funds by only measuring their percentage. This means that they simply look at the return of an index fund as well as the return of a hedge fund, and make their call from there.

greenland-mapMercator map of the world, making Greenland look much larger than it really is. (Source: Flickr.com)

This is analogous to using a Mercator map created in 1596 to determine the biggest land masses in the world. By this method, Greenland is about the size of the United States and Mexico. This one-dimensional process of assessing performance can lead to several inaccurate conclusions.

Improving performance comparisons by adding a second dimension

A better way is to add a second dimension—to compare the returns of active and passive funds, adjusting for volatility. Keep in mind that a passive mutual fund like the Vanguard S&P 500 Index Fund (VFINX) has no risk budget or parameters. They essentially have one mandate: be long the S&P 500 regardless of any outside factors.

An active money manager is probably working with risk constraints and looking to take on risk in a controlled manner. This may result in a lower nominal total return, but possibly more stable returns from day to day or month to month.

All things being equal, I would rather invest in a strategy that makes 10% a year and only loses 5% during that year (a 5% drawdown) than a strategy that makes 15% but has a 20% drawdown. Adding this second dimension provides better context. This example highlights where mistakes can happen if only nominal returns are used without factoring in volatility.

Adding a third dimension to get the Netto Number

There is an even better way to measure investment skill—by adding a third dimension. This is the Netto Number. Both dimensions one and two lack one key ingredient in determining true investment skill. This ingredient is the size of the risk budget.

A risk budget is the amount of capital that the investor budgets before trading the strategy. For example, an investor allocates $1 million to a manager and tells him that if the account falls to $800,000, the investor will close the account and pull out his capital. The key is that the amount is determined in advance, so both the manager and investor know the rules of the game.

An example

This is critical because if you have two managers that are both given a $1 million allocation, while one manager has a risk budget of $200,000 and the other has a risk budge of $300,000, this can impact how we assess their skill.

For instance, over a given period, let’s say Manager 1 (with a $200,000 risk budget) has a 30% return, and Manager 2 (with a $300,000 risk budget) also has a 30%, and both have identical volatility. If we use the performance analytics from either a one- or two-dimensional approach, we would conclude they both performed the same. This would be wrong because Manager 1, who had a risk budget that was $100,000 lower than Manager 2, performed better when return is measured on a per unit-of-risk basis.

The Netto Number displays how to calculate all three of these dimensions in one simple formula (that I invented on the back of a napkin). I expound on this formula in detail in my book, The Global Macro Edge: Maximizing Return per Unit of Risk. The key here is that the Netto Number not only redefines what investment skill is but also, through the Risk Factor Compensation Grid, tells us exactly what to pay for it. The higher the Netto Number, the higher the incentive fee an active manager makes, whereas the lower the Netto Number, the lower the incentive fee a manager makes.

This approach helps us completely recalibrate how we assess and pay for investment skill — a recalibration that no longer relies on a Draconian one- or two-dimensional process. With the Netto Number, we’re finally comparing apples to apples.

About John Netto
John Netto is a cross-asset class trader and author of The Global Macro Edge: Maximizing Return Per Unit-of-Risk. He is also the creator of the Netto Number, the Risk Factor Compensation System, and the Protean Strategy, for which he was named by Collective2.com as Strategy Developer of the Month. Netto is an expert in developing, executing, and managing proprietary algorithmic and discretionary trading strategies across a range of time horizons, asset classes, and market regimes.