Corporate Ethics: Summit Seeking?

Earlier this year, world-renowned mountain climber Sir Edmund Hillary criticized changes in climber ethics that caused some to ignore distressed and injured climbers on the mountain in their relentless, self-serving pursuit of the summit. The death of climber David Sharp, who had run out of oxygen on Mount Everest and was ignored by more than 40 climbers, left experienced climbers like Hillary troubled. Hillary noted on ABC Radio's "The World Today":

"On our expedition 50 years ago, would have never considered leaving a man like that. We were very much aware of our responsibilities to look after any person on the mountain who was in distress. There's no doubt at all that there's been a lowering of standards in recent years, with the commercialisation; as a consequence people are being neglected and are dying."

Hillary's recognition of the "summit seeking" ethic is notable. A similar viewpoint extends throughout many corporations and has a significant impact on enterprise risk which analysts, investors and auditors need to consider more carefully. Appropriately defined in corporate environments, summit seeking is the pursuit of return at nearly any cost.

My own experience with summit seeking organizations spans nearly twenty years. Having worked in several high risk tech and telecom organizations, I experienced the same ethic in interactions with investment bankers, corporate partners, carrier and technology vendors and in a few cases, our own organization. First-hand experience with partners and executives at Inacom, Enron, Worldcom, Lucent and Level3 confirmed this ethic was exceptionally prevalent.

Upon first inspection, summit seeking ala "our corporation seeks exceptional return for its investors" sounds great. What shareholder doesn't want outstanding returns? Yet somehow, this practice highly correlates to later catastrophic collapses of the same investments, usually leaving the stakeholders high and dry. Is there an explanation for this correlation? Is the corporate summit seeking ethic complicit in these catastrophic outcomes?

It is my suggestion that this is indeed the case. Having worked through enterprise risk management assessments and operations audits, I would suggest there are significant, observable processes that cause this high-level ethic to eventually allow the organization to implode. Traditional risk management texts discuss the trade-off between risk and return. To an organization, this choice is made in the selection of its corporate risk/return ethic by its senior management. Specifically, organizations can choose one of the following:

1. Summit Seeking Ethic: Return is sought at any cost and processes that impair this pursuit are dismantled and discarded.

2. Risk-Managed Ethic: Risk appetite is defined and budgeted. Return within that budget is maximized given the constraints of the risk-management framework.

While one would hope that the increased regulatory call for ethical corporate governance would predict the choice of the risk-managed ethic, experience indicates otherwise. Recurring corporate implosions, stock-option backdating scandals and periodic hedge fund disasters tell us the summit seeker is alive and well in many companies. It is possible that many organizations fall into the summit-seeking trap by failing to realize this is the default choice should they fail to establish a top-down risk-managed approach.

Observing and predicting the presence of a summit seeking ethic can be difficult, but to the astute analyst, the evidence is present. At the operational level, summit-seeking directives from the top lead to the dismantling of mid-level controls. Credit quality standards get overlooked, maintenance is curtailed to leverage new projects and provide revenue growth, and product or service guarantees are complicated with unrealistic conditions in order to defer or eliminate ever-increasing customer returns, refunds and cancellations.

Notable metrics that illustrate this condition include:

• increasing delays in service delivery
  
• increasing levels of product returns or service credit requests
  
• difficulties in accounts receivables collections which may be attributed to unrecognized returns and credit requests
  
• inventory manipulation, including provisions that require customers take early or complete delivery of products to attain financing
  
• increasing litigation activity, either by the organization's own legal department or by customers in conditions where contractual performance is disputed
  
• indications of relaxed corporate credit, especially in organizations that provide substantial internal financing programs for customer purchases
  

For the risk-aware investor or analyst, a careful inspection of this risk/return condition is prudent. Summit seekers are poor partners as they're quick to ignore risk and will leave investors, creditors and customers behind at a moment's notice. Using this risk/return perspective, what signal does the management of your investment send? Does management's action through its acquisitions, use of funds and operational control suggest risk is their primary metric, or return?

Google Politik?

According to a UK Guardian report yesterday, Google has formed the political action committee "NetPAC" and has loaded up with prominant Democrats to direct its liberal agenda:

Google has an impressive list of players on its team. As well as counting Al Gore among its senior advisers, Google's Washington office was set up about a year and a half ago by Alan Davidson. A well-known Democrat sympathiser, he served for eight years as associate director of the Centre for Democracy and Technology, a thinktank that opposes government and industry control of the web. Alongside him is Robert Boorstin, a former Clinton foreign policy aide from the Centre for American Progress, as Google's communications chief in the capital.

From corporate spending on self-described "one-upmanship" executive toys to questionable acquisitions that establish significant goodwill on the balance sheet such as the recent YouTube purchase, Google appears to be ripe for a dot-com magnitude correction. While ad revenues may be propping up the beast, executive decision-making and ethics appear to be nonexistent.

So what drives Google's executive philosophy? "Do no evil" is the often referenced founding principle. Unfortunately, such vague objectives leave considerable room for interpretation, relativism and activism. Yet true principled behavior is somehow lacking in recent decisions, including serious misteps on the support of Chinese government surveillance efforts using Google data, Chinese censorship efforts, destruction of the objective value of GoogleNews by excluding conservative viewpoints Google employees disagree with and now the formation of a liberal PAC to further attack half of their customer base. The Google definition of evil appears to be seriously modified to reflect corporate gain at the expense of ethics and objectivity.

Certainly Google has quite an unusual approach to its fiduciary caretaking responsibility.

VTI vs SPY

In the ETF portfolios models I work with, I usually use Vanguard's Total Stock Market (VTI) instead of the more well known S&P 500 SPDRs ETF (SPY). Since I usually have a few questions about the purpose of this substition and the significance of the differences, I thought I'd address them here (note: all statistics quoted here are as of October 20, 2006).

As both are index based equities, it's important to pay attention to the index and its characteristics as that drives much of the behavior of the ETF. SPY is based on the S&P 500 Index, holding all of the S&P 500 stocks. For those that are curious, SPY was constructed as as SPDR (an undivided ownership interest) when ETFs were first emerging and has a somewhat different operational model than the more prevelant index-based passive mutual model that most newer ETFs (including VTI) use.

VTI is based on the MSCI US Broad Market Index, which typically spans about 1,300 of the largest stocks traded on NYSE, AMEX and OTC markets. VTI is a Vanguard ETF product.

Comparing the indexes, VTI is much broader than SPY and this accounts for the primary difference. Looking at SPY's top 10 holdings, compared to VTI's top 10, you'll note that SPY a higher concentration of individual holdings in the top rankings. For instance, SPY contains 2.99% of General Electric (GE), compared to VTI's 2.35% GE. This is accounted for by VTI's much broader coverage. Comparatively, SPY is a narrower, deeper pool compared to VTI's wide, shallow pool. Likewise, VTI's holding turnover is nearly double what SPY's is given its breadth. If you're paranoid about the S&P's weighting model and the impact of individual security problems (like investors deciding Google is a dot-com 2.0), SPY's higher concentration provides greater risk.

So what does that mean for an investor in the two equities? Both have comperable yields (1.74 vs 1.72 SPY to VTI), composite P/E ratios (14 vs 15) and basic risk ratios, with 5-year standard deviations hanging around 12.70 for SPY and 12.87 for VTI. In many respects, they perform in a similar manner according to most risk measurements, but historically, VTI's breadth gives a little boost in returns, as illustrated in this five-year comparison chart.

And don't forget to consider the expense ratios. SPY comes in at a modest 0.1%, while VTI is a low 0.07% (common for Vanguard ETF products). While 0.03% is insignificant to many, that's still extra money in your pocket.

Downsides to VTI include underperformance when the largest of largecaps are outperforming the market comparatively (as has occurred this year), causing VTI to have a bit of penalty for its breadth. Likewise, VTI lacks the shear trading volume, market cap and liquidity that SPY has. SPY's average daily volume is around 50-60 million shares a day, compared to VTI's meager 50-150 thousand range.

That said, VTI usually represents a solid proxy for SPY with very close index coverage and risk, with a slightly better returns on slightly lower index expenses. Combined with its greater diversification which I believe should be considered in any index containing Google and you've got a good candidate for broadbased large-cap index coverage.

Disclaimer: I own both SPY and VTI in my personal portfolios.

The Next Central Bank Failure

As reported in EmergingMarkets (free registration required), IMF managing director Rodrigo de Rato warned that we're due for another central bank failure, and given emerging markets haven't fully differentiated against risk, pragmatic approaches to the markets is prudent.

So who's a likely candidate? de Rato suggests that while emerging Europe and Latin American reforms have made progress in reducing risk, sources quoted in the article suggest China's loans to Africa and India's export credits might be a place to look.

Some interesting work has been done by Bussiere and Fratzscher in their paper "Toward a New Early Warning System of Financial Crisis" (2002, European Central Bank Working Paper Series) on developing a forecasting model for an emerging market "Early Warning System."

They use six key indicators:
1. exchange-rate overvaluation (which has a strong weight in the crisis probability)
2. lending boom index
3. short term debt to lending reserves
4. cur account to GDP
5. contagion variables
6. growth

While the predictive power of the model is only enhanced to 73.7% (from previous models at 66.7%), and false alarms reduced from 50% to 44.1%, it would be interesting to see an implementation of the model and I'm curious about the extent of use it's seen since the model's discussion in 2002.

An interesting note by the authors is that in the 20-country sample, the only countries that never experienced a crisis are all emerging Europe nations: Hungary, Lithuania, Poland, Slovakia and Slovenia.

-Jamie

In Search of Dark Alpha

Much of the current focus of my ETF analysis has been been around an idea I've been referring to as "Dark Alpha." Alpha, according to the classic definition, is the extra return a security provides that is not explained by its correlation to beta (as defined by the correlation to the reference index; normally the S&P 500 but occasionally EEM is a little more of value in emerging market analysis).

So if the S&P goes up 1% and EWJ (iShares Japan) goes up .99%, S&P goes down .5% and EWJ goes down .498% and this tracking stays very constant, we have a pretty good definition of a positive beta close to 1.00 (as well as a good value for R-squared, which tells us statistically how strong that beta relationship is between the two per explaining changes in values).

Alpha seekers look for gains that occur on a particular security that are beyond that which the beta correlation predicts. If you think of the standard deviation fluctuation of the stock (the up and down movement over time as plotted on a graph), alpha seekers are looking for that extra unexplained boost on the peaks. It's like mountain climbing in a sense, looking for summits that are taller than the laws of physics might predict.

On the flip side of mountain climbing is spelunking - typically a place where people long in the market don't really enjoy being. It's the bottom part of the graph, below the opening line. On our stock chart, beta predicts a correlation that is the full standard deviation round trip. If the S&P 500 has a rather bad day and goes down a full standard deviation, your beta correlation should stay true. You should experience the same correlation to beta when things go down as they do when they go up.

However, just as the alpha summit seekers find "extra tall peaks" in some stocks, there are oddities on the other side of the chart, down in the caverns where bears reside. Akin to the concept of dark matter, deep in the caverns resides Dark Alpha. It's the unexplained non-loss that is not explained by beta. Technically, a non-loss is still a return, and one could argue that dark alpha really is alpha in that respect. However, there is a reason for splitting dark alpha apart from its cousin.

When stock markets go significantly under the line (say, at least 1 standard deviation), the go into stress. This is interesting to some in that markets have been observed to misbehave under stress. Specifically, there's the saying that "under stress, correlations approach 1.0." That's a clever way of saying your highly diversified, low correlating portfolio suddenly decides to act like a single stock on days where the market really does poorly.

But emerging market ETF analysis on "correlation under stress" shows this isn't always the case. Using a more aggressive stress model (using normalized z-scores < -2.0 for the index and then running a regression on various ETFs to the stressed index), there are some interesting results. Some markets immediately approach that 1.0, confirming our hunch. But others don't, and stay relatively unstressed.

What that means to investors is that some emerging market indexes provide much greater diversification, as well as potential resistance to high stress global events, which is of significant value. As many add emerging market exposure to their portfolio for this very reason, it's useful to test if this insurance policy is really worth anything at all, as well as identify which policies do a better job than others.

Of course, that data only points the direction of where to look further. My initial hunch is that we really have to think of what is in the ETF index once again (as my energy sensitivity analysis has shown - e.g. own an ETF like iShares MSCI Brazil [EWZ] that has more than 60% direct exposure to energy markets in its composition and you'll not surprisingly have an equity that behaves like an energy equity, not a "Brazil" equity whatever that would be).

My next steps include re-running the models under less stress ( Index daily Z-scores < -1.0) to see the strength of the correlation there, and then move into ETF decomposition to see if we can identify some sectors within that are showing the immunity to beta-induced stress. Already, global financial sector holdings show some interesting resistance. If that holds true, it may be some of those bank vaults in Singapore and Austria are holding secret deposits of dark alpha.

-jamie

Is this fuzzy enough for you?

Well, I've had some comments already on the name. Funny how not everybody "gets it" right away when you're so pleased with how clever your thinking is. Hell, I haven't seen that many confused faces since I tried explaining the Internet to Omaha's Fortune 500 back in 1993.

What Fuzzy Numbers really drives at is the realization that numbers indeed go somewhat fuzzy when we humans deal with them, and in a sense, they need to. Seeking endless precision may be useful in memorizing pi to 100,000 decimal places but other than that, it's at best a distraction and more possibly an indication of some serious issues with one's self-confidence.

Consider an experience I had more than fifteen years ago, as I started out my career as a lowly cost analyst for a smaller long distance company in Omaha. An early assignment thrown my way was to calculate the total damage caused by an international calling card fraud episode our company encountered. Back in the early 90s, this involved getting data off of switch tapes and either crunching it in Lotus 1-2-3 or the bootleg version of Excel I snuck in on Windows. I spent nearly a week crunching data, calculating rate tables based on least-cost routing terminations for tens of thousands of rather difficult to cost international minutes. At the conclusion, I came up with a precise $155,210.18 +/- $1.00. Steve M., my boss at the time, seemed more amused than impressed, especially at the level of accuracy. After he could no longer conceal the laughter, he explained that an estimate with +/- 25% would have been sufficient, I was horrified (that estimate would have probably taken no more than a few hours to work up).

Fortunately or not, I've had a few related experiences since then. Having moved into carrier network operations and engineering, a surragate for a more quantitative field (more on that at a later date), the desire for exceptional precision has continued to be a real factor. I've been fortunate to have several wise mentors who reminded me that the data really is only the beginning of the puzzle. Precision, in that respect, is only appropriate to the degree that it is required to reduce the error out enough from the model to make some sort of sense out of things. Hence... fuzzy numbers.

I think David Maister and Geoff Considine really summed it up in their article called "An Entrepreneurial Journey where they talk about the most important idea Geoff gained from reading "Managing the Professional Services Firm" is this:

“You are there to help, not to be right.” This single ‘Maisterism’ is one of the core ideas that Geoff repeats often to himself and when discussing professional services with others. People hire consultants because they want help with some issue. You don’t need to prove that you are smart — that is determined when they hire you.

Think about that one, especially if you find yourself defining the quality of your work to clients on the accuracy, precision and "rightness" of your designs. Geoff and David's realization has immensely helped me in my risk management work with client banks. Often for your client, fuzzy to you is the answer they need to address their problems, and that's what counts.

Welcome

Well, if the blog profile didn't scare you off already, welcome! Fuzzynumbers is where my discussions about emerging market models and related economic and technology aspects are thrown out for a little areopagitica (truth and falsehood beating each other over the head).

If you're a follower of financial markets, you may be aware of Omaha resident Warren Buffett and his firm Berkshire Hathaway. Warren's company has a unique "User's Manual" (which is required reading for those who invest in individual company stocks!). I'll present you with the highly condenced FuzzyNumbers version of that manual to kick things off around here:

1. What can you expect from FuzzyNumbers?

FuzzyNumbers is my forum for putting out ideas developed internally as well as observed externally and deserving of some attention, usually relating to capital markets, economics and related technological aspects. If you're interested in emerging markets, exchange traded funds, and really the whole gammit of the types of risk that tend to cause emerging markets catastrophies, FuzzyNumbers might be worth your time.

A lot of my focus is in risk management models for emerging equity markets. I closely follow Central Europe and India in analyst mode and attempt to appropriately cover the gammit of other emerging markets, albeit at a higher level. However, I hopefully won't bore you with excessive analyst detail and will keep things clear enough that it's useful to a general reader interested in the topic but not necessarily possessing the same expertise.

Better yet, I've found that coverage of market catastrophies tends to provide better entertainment value than the dry stuff finance and economics is usually known for - sort of like watching Amazing Sports Disasters videos. As I encourage my kids to learn from others misfortunes, the astute FuzzyNumbers reader will hopefully gain a similar return.

2. What's your bias?

As emerging market catastrophies are quite an interest that will be encountered here, I should profess a bit of bias in my approach to why these things tend to happen more frequently than some say they should. I'm quite partial to the endengenous model, which suggests that failure and catastrophy are built into the system rather than caused exclusively by external events (exogenous model).

Consider the recent plight of the hedge fund firm Amaranth in its multi-billion dollar natural gas market, or the exceptional collapse of Long-Term Capital Management. While some point to the absence of an active hurricane season causing Amaranth's leveraged position to become problematic, or the international currency devaluations that tripped up LTCM, a more careful analysis of these episodes indicates that much of the catastrophic risk was built into the market and the firms approach to it. How curious is it that the lack of a natural catastrophy e.g. hurricane caused a real one for Amaranth?

Thought of from an endegenous perspective, risk is inherent within the process (and is often magnified by our interaction with it, as LTCM and Amaranth can attest). The bad news is that this suggests we're unable to build perfect, risk-free models. The good news is that it provides for constant opportunity (as risk usually requires return), and hopefully if we recognize risk is inherent, we might take positions that expect and mitigate it.

3. What's the credibility of the information here?

My perspective is probably atypical to the extent of really classifying as an outlier in many respects to emerging markets analysis. I spent over 15-years of my career directly in the dot-com market, seeing aspects of capital formation and business operation that would probably violate most business theories tought. Responsible for starting the very first dot-com in the north-central United States (a Commercial Internet Exchange peering backbone Internet provider), I experienced an interesting world that ranged from predatory venture capital to penny stock scams. From raising capital in Geneva and Stockholm to dealing with Inacom executives with ulterior motives, I can certainly associate with the expression "swimming with sharks."

Following that adventure, worked with a business that developed a consumer electronics digital merchandising technology that was developed for use at Berkshire Hathaway-owned retailers. I moved from that adventure to overseeing a multinational voice and data network in emerging markets (Latin America, Spain and the Middle East) which helped kick off my expanded interest in emerging markets. The past few years, I've worked as an enterprise and information systems risk manager and analyst in the commercial bank industry the past few years and have to obligatory CISA and CISSP certifications.

4. What's my expected return out of this effort?

After too many years of living in an exceptionally wide standard deviation environment, I've migrated to a more focused finance and economics approach. Call it the self-discovery of high volatility and diminishing realized financial returns! While the education has been invaluable, the experience has certainly taught me that one who ignores risk is almost certain to fail.

FuzzyNumbers is my opportunity to share analysis and perspectives on risk, especially when it appears the firm or market has approached it in an unorthodox way (whether that's positive or not). The utility of this information to you (whether for informational or amusement purposes) and the process of attaining feedback and unique perspective on the analysis and models presented here is the return I'm seeking.

5. What do I expect of you?

If you find a discussion of interest or profoundly disagree, I'd really value your comments on the thread. If you're a fellow traveler in emerging market risk analysis, I'd very mcch like to hear from you as well.

6. What conflicts and disclaimers should be made?

I'll do my best to clearly disclose if I have any financial interest in any security or entity discussed here. I do not discuss issues respective to clients I work with, either through the information risk management firm I do consulting work through or my enterprise and financial risk management consulting firm, unless expressly released in writing by the client or represent in abstract forms that does not reveal the client.

With all that said, welcome and enjoy.

-jamie