Alternative Investments ... just scratching the surface

At this week's European Performance Measurement Forum meeting, we touched on the subject of alternative investments. We hear this term bandied about quite a bit; so much so, that it often causes one to grimace when they hear it, because of the fear it can invoke.

While this category can include credit default swaps, as well as other varieties of swaps and swaptions; futures, forwards, and options; along with commodities and fairly esoteric investments, such as guitars, watches, and artwork; it also is where we house [pardon the pun] real estate and private equity.

In the case of real estate and private equity, while we may not know all the answers, many of the rules are fairly well agreed upon. The GIPS(R) standards (Global Investment Performance Standards) after all includes rules specifically for these asset classes.

But there is a broad, make that very broad, collection of instruments for which the rules are less clear. And when we speak about rules, we must consider:

• Valuing the assets
• Deriving returns on the assets
• Measuring risk of the assets
• Determining how to handle them as part of our attribution
• and no doubt a lot more.

This is a topic that we will not attempt to address quickly, as much time is needed for it. Hopefully, we will be able to provide guidance on much of what is faced in the industry today. And so, stay tuned! In the mean time, if you have ideas or questions, please pass them along!

Topology makes it way into performance and risk

Yesterday, The Spaulding Group held a luncheon in NYC, where Jed Schneider, CIPM, FRM reviewed some of the findings from our recent Performance Attribution survey. As with all of these research projects, some very interesting insights can be drawn. And since this is the fourth time we've surveyed asset managers on this topic, we can compare results from one period to another, to identify trends, changes, etc. Our cosponsors for this survey were:

• BiSam
• DST Global
• FirstRate
• Morningstar
• Statpro
• VPD

As with all of our surveys, we invited our cosponsors to come to the luncheon and make brief presentations, and four did. One, Steve Shefras from BiSam, mentioned how it is often said that risk and return are two sides of the same coin. He went on to say that he believes they're on the same side. I immediately thought of a totally different anaology: a Möbious strip!

You may be familiar with them. They come from the mathematical field topology, that deals with "mapping." A Möbious strip is a one-sided object. You can build your own by taking a long strip of paper, twisting it, and then connecting the ends. If you traverse either "side," you'll cover both "sides,"  meaning there is only one side.

And so, rather than saying that risk and return are on the same side of a coin (which encourages one to ask, "what's on the other side?"), we could say that they're both on a Möbious strip, and therefore on the same (and only) side.

M-squared's view of negative Sharpe ratios

In The Spaulding Group's January newsletter, I expanded upon a recent blog post where I introduced a couple graphics in an attempt to "make sense out of" negative Sharpe ratios. Two pillars of the investment performance community, Carl Bacon and Steve Campisi, chimed in with comments, which will appear in the February newsletter. In the mean time, I will take this topic a bit further, with inspiration from both gentlemen.

Would it not be useful to see how the Modigliani-Modigliani risk-adjusted measure responds to negative Sharpes? I believe so. And in truthfulness and full disclosure, I will admit to being kept awake last night thinking about this (graphing it in my head), until I got up to put the materials together.

On the positive side. Let's begin by recalling how the M-squared looks when we're dealing with positive returns.

Recall that we first plot the benchmark (in the risk/return graph), and draw a line from the risk free rate and through it; this is the "market line." We can next plot the portfolio, and draw a similar line. Here I show a case where the portfolio and benchmark have identical returns, but the portfolio has taken on added risk. Note that its line falls below the benchmarks, meaning it will end up with a lower M-squared value.

The fundamental step in this method is to equalize the risks, and this is done graphically here, where we shift the portfolio's point to the left, so that it aligns with the benchmark's risk; and, as predicted, we have a lower return.

What happens on the negative side?

I again chose a case where the portfolio has the same return as the benchmark, and where it also has taken on greater risk. But notice that it plots above the line. We again adjust the portfolio's risk, so that it aligns with the benchmark's, and we see that it has a higher return.

This is what people find confusing: more risk, same negative return, why not a lower Sharpe ratio (risk-adjusted return)? Do the graphics help? Perhaps in some cases, but surely not all.

That's why I hold to the notion that we would expect that by taking on more risk, the portfolio should have a much lower return; however, it doesn't, and thus it gets rewarded. Perhaps if we inverse the thinking a bit: the benchmark took on less risk but did equally bad (i.e., it managed to do as badly as a portfolio that took on more risk, so it somehow captured even greater negativeness than one would have anticipated.

Please let me know your thoughts. I plan to tackle this subject from a beta perspective, too!

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?

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?

Lessons from a former CEO, U.S. Senator, and Governor, on how to avoid risk hurdles

In yesterday's WSJ we saw yet another article regarding MF Global Holdings and its former CEO, Jon Corzine ("MF Probe Targets Back-Office Unit") . This time we learn that just about everyone in the company has been interviewed by federal prosecutors, save for the Honorable former Governor of the great state of New Jersey, and a few other former seniors from the firm.

In the spirit of "full disclosure" I must confess that as a resident of the Garden State, I did not vote for Corzine when he ran for Senator or Governor, and my reasons aren't because he is a Democrat, as I have been known to vote for several Democrats, including the member of the House of Representatives (Rush Holt) who represents my district. It is one thing to persuade lots of people to make significant donations to your campaign, as Barack Obama and many others have done; it's another to spend millions of your own dollars to buy a Senate seat and Governor's position. Perhaps he was fired from Goldman Sachs for a reason. His performance as a U.S. Senator was one with no notable accomplishments, and as Governor, his performance was so bad that this predominantly Democratic state voted him out (something that rarely occurs).

It has been reported that Corzine, when confronted by MF Global's risk officer (who no doubt was paid a sizable amount to guard the firm against taking unnecessary risks) about his desire to invest so much of the company's funds in sovereign debt, said something to the effect of "if you won't let me do this, I'll quit!" At least somewhere in the WSJ I recall reading something to this effect, as incredulous as this may sound.

I am blessed with two beautiful grandsons. But I have confidence that their father (my older son) and their mother (my lovely daughter-in-law), if talked to in a similar fashion, wouldn't budge. But no; not in this case. A CEO who throws the equivalent of a tantrum is told "okay, go ahead."

Risk managers are hired and risk controls are implemented for very good reasons; and one would think that the CEO, who as spent decades on Wall Street, would know as much, respect them, and even serve perhaps as an example in honoring them (so much for the "honorable").

Risk remains a very difficult subject to get our arms around. Risk managers, risk officers, risk controls, risk management rules, etc. are necessary; they must be honored, respected, and ahered to.

Marrying Performance and Risk

I have been invited to join the CFA Institute's Jonathan Boersma and Neuberger Berman's Leah Modigliani to speak on the subject of risk, at an evening event at the NYSSA (New York Society of Security Analysts). The program takes place at 6 o'clock on January 26.

I am particularly looking forward to this, simply to hear Leah once again discuss the risk-adjusted measure she and her Nobel Prize winning grandfather, the late Franco Modigliani, developed; to me, this alone, is "worth the price of admission." As I understand it, Jonathan will discuss GIPS' (Global Investment Performance Standards) new risk reporting requirement; I will briefly provide an overview of a variety of measures, and then Leah will discuss M-squared (I guess we could say that she's "one of the M's" in this model!).

Hope you can join us; I guarantee you'll benefit!

Making sense of negative Sharpe ratios

I'm teaching an in-house Fundamentals of Performance class this week in Canada, and, as usual, we touch upon the Sharpe ratio, and how negative Sharpe ratios can produce results which appear inconsistent with our expectations.

To help try to communicate what's going on, I constructed the following graphic:

What you're seeing are two different cases: one where we're dealing with a positive Sharpe ratio, and the other where we have a negative. In both cases, the portfolio's risk exceeds that of the benchmark, and in both cases the portfolio's return equals that of the benchmark. On the positive side, given the higher risk, we would expect a higher return for the portfolio; but because it failed to do that, we end up with a lower Sharpe ratio. On the negative side, we would expect to see a lower return, given the higher risk; but failing to see this occur, we are rewarded with a higher Sharpe ratio.

This may not be clear enough to comprehend, and I will take the subject up later this month, in our monthly newsletter. So, consider this a "warm up"!

The "regulator's dilemma"

I often save clippings from newspapers and magazines, to refer back to at a future date. I just discovered one from the August 12, 2011 issue of the WSJ: it's from their "op ed" section, titled "S&P 500 and the 'Regulator's Dilemma.'"

The article discusses the U.S. Senate Banking Committee's examination of Standard &Poor's decision to downgrade the long-term U.S. debt. The writer points out something ironic: "S&P's judgements carry such weight because Washington [i.e., the federal government, or perhaps more correctly, the United States Congress] told the markets to pay attention to them. Federal regulators have embedded credit ratings into countless financial rules." Not surprisingly, as a result of Congress' habit of excluding various parties from their regulations (even themselves, on occasion), Treasury debt was exempt.

The author points out that "a critical ingredient in the 2008 financial crisis was the encouragement that regulators gave banks to hold mortgage-backed securities rated by S&P, Moody's and Fitch – the government-created oligopoly of credit judges."

And while Dodd-Frank instructed bureaucrats to remove credit rating references from their rules, bank regulators resisted, because of their struggle to devise better standards to judge an asset's safety.

A key statement in this piece: "as counterintuitive as it may be to politicians, having no federal standard on risk is the best standard of all." [emphasis added]


Those interested in reviewing the article's points regarding banking regulations are welcome to do so. My reason for mentioning this piece is the reference to trying to "standardize risk." It's illusive, ambiguous, impossible to classify with any degree of agreement, and impossible to measure in a way that all would find acceptable.

In my recent post that highlighted the 10 things I like best about GIPS(R) (Global Investment Performance Standards), I (with some hesitation I might add) applauded the introduction of the requirement for a three-year annualized standard deviation. Not because I think it's an ideal measure, because I'm on record objecting to it. However, I also realize that there is no measure that all would agree with, and that this is a formula that is quite easy to calculate and interpret. Firms can include additional risk measures; the new requirement simply aims to have something that prospective clients can see.

In the Standards' 2010 edition exposure draft, the GIPS Executive Committee suggested mandating risk disclosures in composite presentations; this was objected to by most who took the time to comment, and the EC wisely withdrew it. Risk is SO difficult to get one's arms around. Trying to regulate it, much more than this simple requirement, would probably be unwise. And, create additional dilemmas we don't need.

Controlling risks when we can

This past Saturday, my wife and I were driving to a wedding in Hope, NJ, which is about 75 minutes from our home. As you may have heard, New Jersey, as much of the Northeast, received a rare and rather nasty snowstorm. We didn't know what we were in for.

After sitting in traffic on Route 206 for about 30 minutes, we learned that a tree had fallen up ahead, and was blocking the entire roadway; okay, so mystery solved. Our GPS detoured us to another route, which took us on to Schooleys Mountain Road; a road we were somewhat familiar with because it was how we got to the college our younger son, Douglas, went to as an undergrad (Centenary). We were about 15 minutes from our destination and a short distance from where we were to turn, when we encountered a freshly fallen tree, blocking our way (see first photo). So, we had to turn around and find another way (what did we do before the GPS?).

This time we were driving along Route 46, four tenths of a mile from where we were to turn, and again, less than 15 minutes from our destination. Now we were stopped for about 20 minutes and then learned that someone had driven into a utility pole, knocking it down, resulting in a roadblock which wouldn't clear for hours. And so, off again!

We were heading down a road when we found yet another tree blocking the road; not a terribly large one, and one I thought we could maneuver around. But before we had a chance to try, a truck came the other way (second photo). To make a long story short, the truck helped to make our path clearer, but also nearly hit us, as he fishtailed his way up the hill and inches away from our car. We eventually made it to the wedding, missing the ceremony: a trip that took us nearly four and a half hours.

Recognizing that the snow covered roadways required extra care, I, as well as just about every driver we encountered, drove much slower than normal. But there were still accidents and other mishaps. But, we could control much of the risks we encountered.

Contrast this with the 84 year old man from Pennsylvania, who was napping in his recliner at home. A tree fell down, onto his home, and killed him. A totally unanticipated event. We could think of the man's house as a AAA-rated structure, which wasn't able to withstand the onslaught of a tree, whose limbs were made more heavy by snow falling upon leaves, which normally are long gone before the first snow arrives.

Can we see a parallel between these two situations: driving through a snowstorm, and resting at home during a snowstorm? In the first, we can observe risks and take steps to reduce or try to avoid them. In the second, "out of left field" an event appears, one that we couldn't have expected; perhaps not unlike the mortgage crisis which rippled onto the stock market.

Bottom line: we can only do so much to avoid or control risks. We should try to identify the risks we face, and take the necessary steps to minimize them. However, we must also be aware that there are certain risks we cannot anticipate, and must respond to them, as best we can, if we are able to.

Potter Stewart and risk measurement

When Herb Chain (of Deloitte), Matt Forstenhausler (of E&Y), and I used to regularly teach AIMR-PPS(R) and then GIPS(R) courses (first for AIMR, then for the CFA Institute), one thing we could be sure of: Herb would reference former U.S. Supreme Court Justice's famous line about not having a definition for pornography, but knowing what it was when he saw it. Over the years I, like Herb, have used this line metaphorically, as it fits a variety of situations.

Well, when it comes to risk measurement his line doesn't fit, as their are several definitions available. Leslie Rahl (of Capital Market Risk Advisers) has become somewhat famous for her long list of risks, which she further qualifies as being incomplete. There is no commonly used definition for risk, and perhaps this is how it should be. Risk is usually defined in four ways:

• Volatility
• Possibility of a loss
• Possibility of not meeting the client's objective
• Uncertainty.

Volatility probably has the most number of measures, with standard deviation being the most common, as well as the most frequently used measure overall. One cannot assume that because most firms use standard deviation that they therefore interpret risk to mean volatility; it's likely that they look at risk in a different way, but use standard deviation to try to assess risk in line with the other definition (which can be a challenge). Other measures include beta and tracking error.

The possibility of a loss and possibility of not meeting the objective can be measured using the same formula: Sortino ratio. We just adjust the absolute return in our equation, from zero (for a loss) to our goal, or minimal acceptable return (or, if you prefer, minimum funding ratio or liability related benchmark).

Uncertainty is difficult to measure. Scenario analysis can be used here, where we look at different possible future events to see how our portfolio would behave. Value at Risk and Liquidity Risk might also work, too.

Regardless of your definition, we see risk as something that needs to be "ganged up on." That is, approached from multiple angles to get a sense of what is really there. The old television game show, Concentration, might be a good metaphor for risk. We want to get a good look at what risk is, but must take several views to really understand it. But unlike the game show, it never fully reveals itself.

What are the risk statistics for IRR?

Someone recently asked me what risk statistics should be used with the internal rate of return (IRR), (which, as any reader of this blog knows, is my preferred return measure). Sadly, I didn't have an immediate reply.

The plethora of risk statistics that are available for time-weighted rates of return (TWRR) use the intra-period returns. For example:

• standard deviation (we can continue its appropriateness as a risk statistic)
• beta
• tracking error
• downside deviation

as well as the multitude of risk-adjusted return measures that use these risk measures (such as Sharpe ratio).

Recall that the IRR measures the return for a single period; there is no linking. Comparing the portfolio's IRR with the benchmark's only serves the purpose of seeing how well the portfolio did. But how can we measure risk if we use the IRR?

Reflect on this for a bit;  I will return to this matter soon, with some concrete ideas.

Beta ... what is 1.0?

Beta is a risk statistic which tells us essentially how our portfolio behaves relative to the market. It is a derivative of Modern Portfolio Theory, and more specifically, the Capital Asset Pricing Model (CAPM). The purpose of this post isn't to address whether or not beta is dead; Fama & French can tackle that one. It's really a lot simpler than that.

The market gets 1.0; our portfolio's value is measured using the following formula:

We measure the covariance of the portfolio's return stream relative to the market's; that is, how they vary relative to one another. We then divide this value by the variance of the market.

One question that has held firm for CAPM is "what's the market?" Well, that's something that is taken up in the hallowed halls of academia; for our purposes, we have the same question, but not perhaps from such a purist perspective. We want to know what gets the "1.0."

Standard practice seems to be to assign the 1.0 to the portfolio's benchmark. In my opinion, to do anything but this can be of little value. For example, let's say that you use the S&P 500 to represent the broad market, and everything, including your portfolio's benchmark, relative to it. And let's say that as a U.S. small cap manager, your benchmark is the Russell 2000. And so, you first calculate the Russell 2000's beta, which you find to be (for exhibition purposes only; not in reality) 1.53. You next calculate your portfolio's beta relative to the S&P 500, and it turns out to be 1.61. This tells us that both the portfolio's index and the portfolio itself are more volatile than the S&P 500, meaning that they both exhibit more risk. So what? We're interested in the risk you've taken relative to the index your managing against, aren't we? And wouldn't it be more appropriate to set the Russell 2000 to 1.0, and measure the portfolio's beta in comparison to it? We should let the academics try to define "the market." But when we're measuring a portfolio's risk, its market is its benchmark, and risk should be compared against it. This holds for tracking error, information ratio, Jensen's alpha, Modigliani-Modigliani, and any other risk statistic that uses a benchmark in its formula.

p.s., There may be some value to measure a benchmark's beta relative to the S&P 500 (or some other "market"), to exhibit how each behaves relative to it, so I don't want to "pooh-pooh" the idea completely. But from a fundamental, foundational perspective, use the portfolio's benchmark when measuring risk. You're not managing against "the market," other than as represented by the associated benchmark.

Challenges with predicting the future

You may have heard that at last week's Masters Golf Tournament in Augusta, Georgia, young (not quite 22 years of age) Rory McIlroy was leading by four strokes after the third round, with just Sunday to play. The golf pundits expressed great confidence that Rory would achieve victory given his stellar play the first three days, and that no one was likely to be able to catch him. Sadly, this wasn't to happen as he finished Sunday with a rather poor 80. And from out of nowhere, South African Charl Schwartzel won the tournament. My point? With just one day predictions were far from accurate, even when given by the most knowledgeable of sports people.

When a doctor tells a woman that her "due date" is October 31, how likely is it that he'll be correct? Well below 50% it seems: Wikipedia, in an x-rated post, reports that it's below five percent. Mike Brown in How I Killed Pluto and Why It Had It Coming explained how, when his wife was pregnant with their first child, he wondered what percentage of babies were born before versus after the date; how many were born within a day, a week, etc. of the date. He attempted to develop statistics on this. No doubt such stats would be helpful in providing some degree of confidence as to when a baby will, in fact, arrive.

I spoke last Tuesday at a DST Global event in Singapore, where I delivered a talk titled "Performance Measurement: One Size Doesn't Fit All." Later I sat on a panel with Nick Wade of Northfield and an old friend, Trevor Persuad of Russell. The topic of ex ante risk came up, and I commented how difficult it is to make any accurate predictions, thus the need to couch such statistics with a declaration that qualifies what the number(s) represents and the assumptions underlying it.

It's necessary in many cases to offer predictions; it just important to understand what their basis is and potential for being correct.

p.s., As an aside, Trevor mentioned that you can't manage risk with Value at Risk, and I thought this was an insightful comment. Recall that VaR is based on a portfolio's history: how does one adjust their history? This is perhaps another topic that is worthy of some discussion.

I can't hear you because it's too noisy

Odd title for a blog post, right? Well, it's my silly way to introduce a term which is often bantered about but not often discussed: "noise." No, I'm not talking about the sounds that come through the hotel room while you're trying to sleep or the sounds that interrupt your concentration. I'm speaking of "statistical noise."

In a recent post I introduced an animation that provides an overview of standard deviation. This post could have gone on and on and on, as this is a topic that has many angles on which to comment. And perhaps I'll do that in future posts, but for today we'll discuss noise and standard deviation.

What is noise?  Well, the often-criticized-but-frequently-cited Wikipedia defines it as "the colloquialism  for recognized amounts of unexplained variation in a sample." We anticipate that there will be some variance, but some of it comes from unknown sources, which makes it difficult to draw conclusions about what is occurring in our sample size or population.

Interestingly, we tend to get more noise as we shorten our time periods. For example, if we measure standard deviation over the past 36 months using daily returns, we're dealing with a great deal of noise. If, however, we lengthen our periods, our noise reduces. Thus, using months is quite common, though one might argue that quarters are better still, and why not move to years? Well, there are probably two reasons. One, for standard deviation to have much value we need at least 30 observations, and 30 quarters would equal 7 1/2 years, which may be too long as an organization can experience a lot of change over such a period. And 30 years would be even more difficult.

Yes, there's noise even in 36 months. And when (as the video pointed out) the distribution is probably not even normal, the value of the statistic is even more questionable / challengeable. No wonder it's a popular statistic both to use and discuss.

Jane Austen's guidance on risk management

Perhaps it might seem odd that one can draw anything from Jane Austen's classic, Pride And Prejudice, in regards to our profession, but alas it is possible, and I believe I have.

The scene occurs at the start of Chapter 5 in Volume 3, shortly after the main character, Elizabeth, has learned that her younger sister, Lydia, has eloped with the once respected, more recently loathed, military officer Wickham. Elizabeth's uncle reflects upon what has occurred: "It appears to me so unlikely that any young man should form such a design against a girl who is by no means unprotected or friendless, and who was actually in his Colone's family, that I am strongly inclined to hope the best. Could he expect that her friends would not step forward? Could he expect to be noticed again by the regiment after such an affront to Colonel Forster? His temptation is not adequate to the risk." (emphasis added) In other words, Surely this man, Wickham, must weigh the risks of his actions and realize that they are such that to put Elizabeth's sister in any jeopardy isn't worth it.

Being aware of the risks is a critical part of investing, whether it's an investment of our money, time, or emotions. Too often investors don't consider the downside of their actions. As is often stated, risk isn't bad, what is bad is not taking the risks into consideration or knowing what those risks portend. One could argue that the gifted Jane Austen was offering some advice which is worth considering, did she not?

Riskless stocks

In this weekend's WSJ, Jason Zweig reminds his readers of James K Glassman's 1999 best seller, Dow 36,000: The New Strategy for Profiting From the Coming Rise in the Stock Market (which, by the way can apparently be had at Amazon for a mere one penny!). Jason mentions that in his new book (which I won't bother to reference), he admits that he "was wrong."

Recall that Glassman picked up on the hot bull market of that time and made this bold, and apparently rather unrealistic, prediction for the future of the DJIA. Glassman and his coauthor, Kevin A. Hassett felt that "stocks and bonds are equally risky over long horizons," because "stocks had never  lost money over the long term." Zweig further points out that investors back then had apparently learned that "socks weren't risky."

Zweig cites LA-based Oaktree Capital Management's chairman, Howard Marks, who said that this logic is false. Zweig also alerted us of a new "superb forthcoming book" by Marks that "helps explain risk clearly": The Most Important Thing. The book is due out May 1, and I preordered my copy today.

Rather than repeat here any further commentary from Jason, I will leave it to the reader to link across. Some insightful points are raised which are worth having a look at.

How many risk measures are enough?

I was teaching our Fundamentals of Performance Measurement course yesterday and came upon a metaphor to use to justify the need for multiple views on risk: John Godfrey Saxe's poem about the blind men and the elephant. You are no doubt familiar with the story, how the blind men go to the elephant, each approaching it from a different part. One thinks that it's like a wall while another thinks it's like a snake. Since none are able to see the "big picture," their observations are very limited.

Well, to me, risk is like that elephant, and if we only use one or two measures, we only see a limited amount of what lies before us. The more measures, the better perspectives.

I love Yale Endowment Fund CIO David Swensen's statement, from his book Pioneering Portfolio Management, that “Quantitative measures of risk for individual portfolios leave much to be desired." Yes, I suspect that most people would agree that they are limited and have a much to be desired. Our only choice is to employ multiple, though limited, measures. A basket that includes Sharpe ratio, M-squared, tracking error, information ratio, value at risk, liquidity risk, extreme risk analysis, and, if you really want to, standard deviation, is a good start, I believe.

A risk measure to consider...

Bruce Feibel, author of Investment Performance Measurement, spoke at the Performance Measurement Forum's North America chapter's Fall meeting on the topic of risk. I was quite pleased when he expressed his support for the risk-adjusted measure, M-squared, also known as Modigliani-Modigliani, named for its designers, Franco and Leah. This grandfather/granddaughter duo developed what Bruce and I feel is a superior metric to evaluate risk.

Unlike the other risk-adjusted measures, its results are highly intuitive. To me there's a parallel to the way we show gross and net performance, as well as pre- and after-tax results. For example, from a gross/net perspective we might see:

• Gross return = 17.45%
• Net return = 16.68%.

The net result reflects the impact of the advisor's fee. From a tax perspective:

• Pre-tax return = 19.35%
• After-tax return = 16.45%.

Well, with M-squared we could see:

• Before adjusting for risk = 18.27%
• After adjusting for risk = 16.45%.

Granted, we might not label our  results this way, but I'm simply trying to convey how the results align with what we often see today. Our return has been adjusted for risk, unlike the other measures. And unlike the other measures, its reported in percentage terms, which are much clearer to comprehend.

If you're not including M-squared in your reporting, you should be!

And, if you'd like more information on this measure, send me a note.

Everybody complains about the weather, but ...

You've no doubt heard the expression about the weather and no one doing anything about it. Well, we could apply the same idea to information, especially regarding risk, and how firms measure and manage it. There is great interest in understanding what firms are doing, but how many are willing to participate in the discovery?

Well, to learn you have to give; that is, you have to be willing to explain what you do in order to learn what others do; at least when it comes to the survey that The Spaulding Group has undertaken, along with Capital Market Risk Advisors. But time is running out!

We encourage you to take just a few minutes of your time to complete the online form; this will entitle you to receive a complimentary copy of the results.