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The 401k Plan Sponsor’s Dilemma – What’s Wrong with “Risk”

August 23
00:32 2011

(The following is one of a special five part series meant to be shared by professionals and non-professionals alike. This particular series covers only one of the 7 Deadly Sins Every ERISA Fiduciary Must Avoid.)

There’s a demon lurking in nearly every investment analysis of far too many 401k plans. It’s a problem posing as a solution until it inevitably dissolves into a dilemma. It promises the consistency of empiricism, but delivers only unexpected surprises with 380515_7286_dart_stock_xchng_royalty_free_300alarming regularity. It infects some of the most popular 401k plan investment products, including those “blessed” by the Department of Labor. Yet, in terms of fiduciary liability, 401k plan sponsors cannot rely on the supposed protection of regulators. ERISA plan fiduciaries can never fully relinquish their responsibility for knowing and understanding the true nature of the investments in their plans. Worse, they remain liable for how they articulate the selection criteria of those investments to their employees.

What is this devil whose evil temptations even professionals fail to shun? It’s not merely risk, because, as we’ve said in early parts of this series, risk is real. The problem resides in the measurement of risk. For more than half a century, academics have measured risk using some form of the volatility of past investment returns. Why? First and foremost, it’s quantifiable. Academics, and then industry analysts and finally computer programmers use standard deviation statistics to calculate volatility. Both the Capital Asset Pricing Model and the foundations of Modern Portfolio Theory derive from this use of volatility. Heck, the guy started it all – Harry Markowitz – even won a Nobel Prize.

Can this measure of risk do no wrong? It’s easy to calculate – and the dawn of the personal computer made it even easier to calculate and more universally accessible. In addition, it’s easy to graph and, using the typical bell curve of a normal distribution, easy to explain to people without a degree in math or statistics (this includes not only regular investors, but professionals, too). Aren’t graphs a great communication tool? When you get right down to it, there’s something sensual about risk when defining it by volatility. It’s alluringly simple and contains at least a scant appearance of common sense.

Just what is volatility? Markowitz defined it as the mean variance (as measured by the standard deviation) of returns. For such statistical measures to work, we need to assume returns fall within what’s called “a normal distribution.” The following graph shows a normal distribution.

The Famous Bell Curve of a Normal Distribution

The Famous Bell Curve of a Normal Distribution

The real world of investment returns poses a problem to the theoretical world of academia. In practice, returns are generally not normally distributed. The following graph shows the actual distribution of the market based on the data published in the 2010 Ibbotson Yearbook.

Distribution of Market Returns 1945-2010 (Source: 2010 Ibbotson Yearbook)

              Distribution of Annual Market Returns 1945-2010              (Source: 2010 Ibbotson Yearbook)

As you can see, this distribution is far from normal. First, we have a jagged curve, not the smooth bell curve of the normal distribution. Second, if you look close, you’ll see this is a skewed distribution. In cases like this, statisticians have to work hard to justify the use of normal distribution tools like standard deviation.

Nonetheless, academic researchers and industry professionals merely wave their hands and “assume” return distributions behave like normal distributions. This assumption is critical, because the popular Monte-Carlo simulation requires that returns be normally distributed.

Let’s ignore the fact that investment returns aren’t normally distributed and go along with the popular assumption that they are. Implied in the use of standard deviation, especially in doing long-term analyses, stands the supposition standard deviations themselves remain relatively consistent over time. As we’ll learn with the Third Deadly Sin, the volatility of bond returns has grown since 1946. Again, despite the facts, both academic researchers and industry practitioners continue to rely on standard deviation in their models.

But let’s be charitable, let’s disregard this second fact and still assume we should use standard deviation to represent risk. In order to really grasp the problem with looking at risk through a stochastic lens, we need to introduce a real world element to the debate. Imagine the following scenario…

You’re sipping your favorite ale at a pub while visiting London for business. You don’t know anyone in the bar, but your mug is all the friend you need. Suddenly, you notice everyone’s staring at your tie. It turns out you made the most unfortunate wardrobe decision earlier in the morning when you picked from your array of neckwear the one piece carrying the colors of the local’s most hated rival.

With fear for your mortal life, you plead innocence only to find your supplications spurned and a rather large man with legs the size of tree trunks rises from the rear of the room and huffs and puffs his way towards you. The hulk approaches until he towers over you. Your mind racing, you meekly mumble “Game of darts, sir?”

Momentarily stunned by this inane offer, your tormentor smiles deviously and says, “Sure. You lose, you eat your tie.” He turns away.

“But, what if I win,” you instinctively respond despite your brain’s best efforts to stop your mouth.

The mammoth slowly revolves, his sites aimed squarely at your eyes, “Then I’ll eat your tie. Either way, the tie goes… and, oh, by the way, you’ve got to pick your partner.”

You scan the room for someone – anyone – who looks like a good dart thrower. You settle on two, we’ll call them Player A and Player B. The monster has graciously allowed you to test your potential partner with some actual throws. You give each three darts. They toss them and you see the following pattern:

When You Digestive System Depends on It, Which Player Should You Pick?

When Your Digestive System Depends Solely on Volatility, Which Player Should You Pick?

Which player should you pick? As you can see, Player B has a much tighter pattern than Player A. The spread of Player A implies his throws have greater volatility – there’s a larger standard deviation from the midpoint of his throws. Seeing this, it appears the best choice is Player B, right? But wait! There’s something strange about our picture above. There’s something missing.

Think about it first…














The Missing Piece Revealed!

Does This Additional Information Change Your Choice of Preferred Player?

Where Volatility Fails: Does This Additional Information Change Your Choice of Preferred Player?

Viola! That’s what we’ve been missing – the target!

Once we put the target in place, our definition of real risk changes – and so does our decision pertaining to our pick as partner. Yes, Player B’s tighter pattern indicated a smaller standard deviation and greater consistency, but he’s consistently off target! Player A, on the other hand, while a bit wild has, unlike Player B, at least an inkling of a chance to hitting the target. Your choice now comes down to either a sure miss (Player B) or a slight chance (Player A).

Common sense tells you to pick Player A. A statistical analysis using standard deviation to define risk, similar to the way Modern Portfolio Theory does, would have picked Player B.

As a fiduciary, do you think the beneficiaries counting on you want you to use common sense or would they prefer you bet their retirement on some numbers game? The answer might seem obvious here, but the shades of grey found in real life make it less obvious than you think. More on that in our next part.

Part I: 7 Deadly Sins Every ERISA Fiduciary Must Avoid: The 2nd Deadly Sin – The Joy of “Risk”
Part II: Investment Risk and the 401k Fiduciary: An Overview of Components
Part III: The 401k Plan Sponsor’s Dilemma – What’s Wrong With “Risk”
Part IV: Why Risk Doesn’t Matter to the ERISA Fiduciary
Part V: Risk and the 401k Investor: How Plan Sponsors Can Avoid Misleading Employees

About Author

Christopher Carosa, CTFA

Christopher Carosa, CTFA


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