Inflating performance

Excuse me for once again commenting on the subject of trust, but I just learned that Claremont Mckenna College, a prestigious California institution, has admitted to inflating SAT scores to improve it's ranking. There seems to be almost an epidemic in such shenanigans. It hasn't been that long since we learned of teachers in many public schools in the United States changing the answers on student exams, to improve rankings. Cheating has somehow become acceptable, at least in some sectors.

Such actions don't occur without a degree of conspiring on the parts of two or more individuals. How does this happen? Apparently, it isn't so difficult, at least from the rash of cases that have surfaced. Yes, we have our Bernie Madoff and others like him who was successful at getting individuals to work with them in order to do some pretty dishonest things. And yes, over the years we've seen some asset managers inflate their scores to improve their rankings. I find all of this quite disturbing.

And while we can feel good that these are "isolated incidents," when those we hold in the highest regard fail us, that surely impacts our comfort at trusting others, does it not? As a society, we should be concerned that such behavior seems to have almost no limits.

Dispersion relative to what, exactly?

I participated in a panel discussion last week for the New York Society of Security Analysts (NYSSA). Questions arose regarding the use of standard deviation with GIPS(R) (Global Investment Performance Standards). I used  my standard graphic, which distinguishes between this statistic being used as a risk measure (a longitudinal or across time view, looking at 36 months of composite returns) and as a measure of dispersion (for a single period, where we look at the returns of the accounts within the composite, to see how disparate they are).

One individual mentioned that as a dispersion measure, it measures the account returns relative to the composite's return. While this would be, I believe, the ideal, as one should want to know how returns vary relative to the composite, in reality, most firms measure dispersion relative to the average of the accounts that were present for the full period, and this can be quite a different number.

Consider this: We have a composite that begins with 30 accounts; during the year, 10 disappear, and 10 more are added, meaning 20 are present for the full year. The composite's return is derived on a monthly basis, from the accounts present each month; these returns are then linked to produce the composite's return for the year. If one runs standard deviation across the 20 accounts that were present all year, it won't consider the composite's  return whatsoever; in order to bring that return into the mix, one must manually (i.e., employ a step-by-step approach) calculate standard deviation, using the composite's average as the average against which each account return is measured.

My suspicion is that few firms employ this more accurate approach. Is there much of a difference? Probably not. However, I think it unfortunate that we weren't clearer as to how this measure is to be derived. Perhaps we will in the future. I'll address this in greater detail in our February newsletter.

Is consistency overblown?

We, that is, The Spaulding Group, are (or should it be, is?) wrestling with a situation where a client may not be consistent in their pre-2011 adoption of "stub periods" for their GIPS(R) (Global Investment Performance Standards) composites (recall that until 1 January 2011, showing stub period performance in your composite materials was an option; and some might argue, not even permitted!). And so this begs the question: "must they (be consistent, that is)?"

The standards expect consistency, but is this in everything a firm does? I would hope not. Surely asset managers should be granted some degree of flexibility, and not be castigated for an occasional, though intentional, lapse.

The Standards shouldn't be seen as constantly putting up challenges before firms that wish to comply. Surely, it must be challenging and demanding, but not in a silly, nonsensical, unnecessary way.

And while I am the first to criticize those verifiers who "work with their clients" in such a way that they ignore clearly articulated and defined rules (and as a result, put their clients at risk), where the rules haven't been overly prescriptive, let not the verifier be the one to introduce new and unnecessary hurdles. Your thoughts?

Let's take risk reporting to the next level

The Global Investment Performance Standards (GIPS(R)) now require compliant firms to include the 3-year, annualized standard deviation for the composite and its benchmark. And while this was a somewhat controversial move, it's here, so we live with it. But, why stop there?

For example, while conducting a recent GIPS verification for Reams Asset Management, a division of Scout Investments, I found the following shown for their Unconstrained Fixed Income Composite:

What can we tell from this? Not much.

Okay, the composite had a significant out performance relative to the index (more than 200 bps); but, look at that standard deviation; looks like a lot of risk was taken! If one truly believes in the value of standard deviation, might it be a good idea to move to the next  step?  That is, to require a risk-adjusted measure, such as (what seems to be the logical choice in this case, given that the risk measure is standard deviation) the Sharpe ratio?

But also observe that we are showing a one-year return and a three-year standard deviation, meaning the match up isn't perfect (and is arguably misleading), and so, let's report what isn't required (but perhaps should also be?): that is, the three-year annualized returns!

A lot more insightful,  right?

In this particular case, the benchmark is an absolute index, so the differences are a bit more pronounced than they might otherwise be. But the point is, I believe, still valid: to compare one-year returns with three-year risk statistics is, as we like to say, mixing apples and oranges. And, showing returns and a risk measure doesn't quite do the job.

And so, I encourage the GIPS Executive Committee to:

1. Require, in addition to the 3-year annualized standard  deviations, the corresponding 3-year annualized returns
2. Require the Sharpe Ratio.

What do you think?

"Say again?"

It wasn't long after I joined the Field Artillery that I learned that one did not say "repeat" over the radio, especially when speaking to anyone in an artillery battery, as this expression means to "fire again." Instead, one would simply speak the words "say again?" (You can often tell a former army guy, if they say this; you also know when they can spell phonetically (alpha, bravo, charlie, etc.)).

Well, sometimes one is tempted to ask the person one is speaking with to "say again?" when they hear something that is confusing, ambiguous, or unclear. This happened to me recently, when speaking with a client who was wondering about the proper treatment of fees, meaning custodial and management, for "SMA accounts." SMA stands for "separately managed account," and my initial question was "are you able to break the fees out?" Since this firm is a GIPS(R) (Global Investment Performance Standards) verification client of ours, I was a bit confused, since I didn't recall that they had any wrap accounts.

Well, there's the rub. You see, they don't have wrap accounts. The person asking the question is somewhat new to this side of the investment business, and was using the term "SMA" to represent, well, a separately managed account. Sadly, since the wrap fee industry adopted the term "SMA" to represent wrap accounts, confusion often arises; this isn't much different than when someone says "alpha," which can mean (a) excess return, (b) Jensen's alpha, and (c) other things, too! And so, one is forced to qualify what the speaker or writer means.

As I understand it, E.F. Hutton (you recall them, right? "When E.F. Hutton speaks ...") invented "wrap fee" accounts in the early/mid 1980s (an advisor I worked for in the mid 1980s considered introducing wrap fee accounts, too). These accounts "wrap" all the fees (commissions and other trading expenses, advisory fees, custodial fees, broker fees) together into a single fee (e.g., 2.00%; 2.50%), which the client pays. This way, the client doesn't worry about the advisor churning and burning them with lots of trades, which can turn into high commission expenses. My guess is that some in the industry felt that "wrap" didn't have quite the pizazz they wanted, and so the use of "SMA" began. It's probably too bad that no one said "sorry, that term is already in use; pick something else!"

Consequently, when we hear someone say "SMA" or even "separately managed account, qualification is in order. We should try to avoid reusing words and expressions, as this practice often leads to confusion.

The value (and necessity) of trust

I recently listened to Stephen Covey (the son of Stephen Covey of the "7 Habits" fame) speak on trust. It truly resonated with me, and I'll share just a bit here and more in an upcoming newsletter.

Our industry has suffered from a loss of trust. A highly successful and revered leader, Bernie Madoff, turned out to be a charlatan and a crook. Former New Jersey Governor and Senator, and former Goldman Sachs CEO, Jon Corzine ran a company that appears to have misappropriated client segregated funds. If ever the need for trust was evident, it is today.

In our GIPS(R) (Global Investment Performance Standards) and non-GIPS verification, we must have trust in our clients: if we encounter someone who we don't trust; who we think will try to deceive us, then we won't take them as a client.

As Covey points out, there are two important aspects of trust: character and competence. To gain our full trust, one must have both. To have character without confidence, we know that the person will strive hard to do a good job, but won't fully know enough to be successful, and so will need our support, counsel, and guidance. If the person is highly competent but lacks character, then there is nothing we can for them.

But in a relationship such as this, we, too, must win the trust of our clients, by demonstrating our competence and character. We want them to have confidence in our counsel, and see us as a highly trusted advisor. This is critical to success.

Yes, trust is extremely important. And again, more to follow.

The many faces of standard deviation

Confusion abounds when it comes to standard deviation. Some of the issues include:

• Equal-weighted or asset-weighted?
• Divide by "n" or "n-1"?
• Is it a measure of variability, volatility, or dispersion?
• Is it a measure of risk?
• What's the best way to measure relative to the composite's average return?

I'll be brief, but promise to expound further upon this subject in this month's newsletter.

Equal or asset-weighted?

If you've been reading my stuff for any length of time, chances are you know the answer: EQUAL! Okay, so you're allowed to do asset-weighted, but why would you? What does the number mean or represent? This was an idea that some folks thought made sense almost 20 years ago ("since returns are asset-weighted, shouldn't standard deviation?"), but didn't and doesn't. But if you insist on doing asset-weighted, be my guest.

Divide by "n" or "n-1"?

By "n" we mean the number of accounts. I recall that the AIMR-PPS® flip flopped on this one (the first edition (1993) had one form, the second (1997) a different one [perhaps someone was planning to enter politics, and wanted practice]).

We're supposed to use "n" when we're measuring against the population, and "n-1" when against a sample. Dividing by "n" makes standard deviation a bit smaller. Most firms seem to use "n," so I say "why not join them?" We can debate which is appropriate, but why bother?

Is it a measure of variability, volatility or dispersion?

The short answer: yes!

Bill Sharpe, in his 1966 paper used the term "variability" to describe standard deviation (he referred to what we know as the "Sharpe Ratio" as the "reward to variability" (recall it has standard deviation in the denominator) and Jack Treynor's risk-adjusted measure as the "reward to volatility" (it has beta in the denominator)). However, in an email to me not long ago, he said using either the term "variability" or "volatility" is fine. Both of these are used in the context of standard deviation being a measure of risk; what some call "external dispersion."

As for "dispersion," I usually mean this in the same context as some do for "internal dispersion," meaning how the composite's returns compare / vary.

The GIPS® standards (Global Investment Performance Standards) now require both (a) a measure of dispersion (and standard deviation is just one way to accomplish this) and (b) the 36- month, annualized standard deviation for both the composite and benchmark. The former is for a single time period (standard deviation of annual portfolio returns for 2011, for example) and the other across time; a longitudinal measure, if you will (e.g., the 36-month standard deviation of the composite for the period ending 31 December 2011).

Is it a measure of risk?

It depends who you speak to. Since many consider risk to be either (a) the failure to meet the client's objective or (b) losing money, it wouldn't qualify, because it does neither. However, Spaulding Group research has shown that it's the most common measure of risk. And, the GIPS standards now require it (although they've shied away from calling it a "risk measure"). And so, regardless of its detractors, most folks do consider it a measure of risk.

What's the best way to measure relative to the composite's average return?

I saved the best for last. I am conducting a GIPS verification and was validating the client's measure of dispersion; in this case, equal-weighted standard deviation. Because I couldn't match what they had, I tried comparing it to the composite return; let me explain.

If you use Excel, for example, and run the "STDEVP" function against the returns of all account's present for the full year, you're measuring standard deviation against the average of these returns, which in almost all cases will not be the same as the composite's return, meaning it's telling us how disparate the returns are around this average, not the average reported in the presentation. I believe that ideally it should be run against the composite's return. However, this would require several more steps, and couldn't be invoked by simply running a similar function like STDEVP. Too bad.

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And so, standard deviation isn't really so simple, is it?