Posted by Peter Orr on Sun, Jan 04, 2009 @ 10:15 AM
"The whole secret of mysticism is this: that man can understand everything by the help of what he does not understand. The morbid logician seeks to make everything lucid, and succeeds in making everything mysterious."
- G.K. Chesterton
Behavioral finance has been heralded as at once a new sunrise and false dawn in the annals of financial economics. However, behavioral finance has no unifying theory at this point though it has exposed a number of "cognitive illusions" which we human types tend to display when making financial decisions. And as often as those would claim that insights from behavioral finance sound the death knell for the efficient market hypothesis, others say it's impossible to determine whether the market is truly inefficient or that the market model being tested is wrong. Since behavioral finance offers no model of its own, it's impossible to test market efficiency under its finding. I wouldn't presume to add any real insight into this debate; I say let the debate rage on and a thousand more PhDs be granted. That said, I do question how or if the financial technology we surround ourselves with has been a contributor to our current situation…
Some behavioral finance findings relate to heuristic decision-making, the "rules of thumb" or educated guesses that we make in the face of complicated problems and uncertainty. For example, availability bias is the tendency towards overweighting information that is easily attained. Anchoring is the tendency towards extrapolating recent trends, possibly leading to an under-reaction to changing conditions. Overconfidence leads people towards over-estimating their predictive skills. Each of these three phenomena has been studied and documented as common in the human condition; even evolutionary psychologists have reasonable theories for some of these behaviors. But so what?
Consider these findings as they interrelate with our technology. The one nearly ubiquitous tool available to the masses in finance is the spreadsheet. I love spreadsheets. Spreadsheets can't be beat for certain purposes. However, I find that for measuring potential variability in a multi-factor risk setting, unenhanced spreadsheets display some pretty major shortcomings which I won't belabor here. Suffice it to say that in the absence of any other tool to more powerfully process information, if a spreadsheet is all that's available, an analyst will use a spreadsheet. People are forced to make a decision with what they've got, so often the only information that goes in is the stuff that can be reasonably quickly generated in a spreadsheet. Further, our natural inclination towards anchoring with recent data, as well as natural overconfidence in forecasts makes the spreadsheet the ideal medium for us to completely delude ourselves.
A MAD Example
Let me give you just one (of many) examples that frankly doesn't make any sense to me, particularly in this modern financial era. One liability related metric understandably deemed important by many analysts and certainly the rating agencies is MADS, or Maximum Annual Debt Service. It is supposed to represent the maximum of principal and interest payments that might be made by an issuer of debt over an annual fiscal cycle. It is one of those metrics that can be easily calculated in a spreadsheet by a user with only modest skills. However, this same user and likely worse, the audience for her/his analysis may be suffering badly from the cognitive issues described above.
The number of misunderstood, under-appreciated, and heroic assumptions that go into calculating MADS can be staggering. What assumption was used for calculating possible debt service on variable rate bonds or commercial paper? How about the performance of hedges such as interest rate swaps? Any basis variability? How about the likelihood of debt acceleration? Or perhaps a liquidity crunch which leads to either expensive or unavailable letters or lines of credit? What happens to debt service and MADS then? What does the "maximum" in MADS mean when all of the market assumptions that go into it are based upon some 10 or 20 year average, whose sole redeeming feature is that it's easily entered in a spreadsheet?
And MADS is an important number because it often is the denominator for statistics like debt service coverage ratios which are relied upon by investors, rating agencies, and even bond trustees. These are legally required calculations, and yet the amount of time and energy that goes into understanding their potential variability is often next to nothing.
IMHO, this is low hanging fruit which must be changed if we're going to improve disclosure, increase the value of our information and the density of its content, and ultimately enable people to make better decisions. What am I missing? What do you think?
Posted by Peter Orr on Tue, Dec 23, 2008 @ 10:03 PM
"What you risk reveals what you value."
- Jeanette Winterson 
I never had the opportunity to meet Fischer Black, the canonical first "quant" (though he was far more than that), but we did exchange emails in July, 1995. It started when I was supposed to be studying for a course in measure/probability at the University of Minnesota library. However in the frequent and sometimes fervent desire to avoid what I should've been doing, I found myself reading what I recall was a Journal of Derivatives article by Dr. Black. In this post, I describe a regrettable shortcoming among finance practitioners today related to that email exchange.
In the article, he was mentioning something about an adjustable rate mortgage pool with a more stable value, and hence, lower risk than a fixed rate pool. At the time I was also working as a financial advisor to states and local governments and from that experience, I knew that there were at least certain economic actors that didn't view variable or adjustable rate instruments as less risky than fixed rate versions. This was particularly so when those securities were liabilities. So I emailed Dr. Black and as cogently as possible tried to ask my clarifying question about risk. I wondered whether or not a homeowner would think of an adjustable rate mortgage in that pool as less risky than a fixed rate alternative, particularly in an inflationary economy.
He gave a fairly lengthy response which, to be frank, didn't fully compute at the time. But towards the end he said, "I think in purely present value terms." That got me wondering, should we all be thinking in purely present value terms, all the time? Is the variability in present value the only *right* way to think about risk? I'm guessing many of you might wholeheartedly agree, to the point that even asking the question is heretical.
Risk is undoubtedly a curious animal. Any creature that exhibits non-linearity the way risk does, where 1+1 might equal anything between 0 and 2, is naturally unsettling to the uninitiated. But isn't risk, akin to beauty, appropriately viewed in the eye of the beholder? The trivial example of variable rate bonds I think illustrates the point.
The value of (theoretically continuously resetting) variable rate bonds, no matter the interest rate environment, is par. To most quants, if there's no price movement in a security or portfolio than they must be riskless. That is, quants traditionally if not religiously tie the concept of risk to the potential price variation of an instrument portfolio. Certain GAAP/accounting guidelines reinforce this perspective which happens to be a hotly debated topic these days. The folks at RiskMetrics call the places where price volatility reign "financial environments." These are places like banks, mutual funds, insurance companies; not coincidentally where lots of quants have jobs.
Contrast this view with those of a company CFO or treasurer who's managing one or more series of variable rate bonds or other floating rate securities. In this context the bonds are liabilities (or assets with a minus sign). That the prices of these securities are stable to the investor is little consolation to the CFO whose bonds are resetting at 15%. In this financial economy, that particular CFO is likely very happy with her/his fixed-rate obligations despite the fact that the quant would label the fixed-rate bonds as the risky securities. The CFO has been trained cash is king; that the value of your variable rate bonds stayed at par from time of issuance all the way to today matters little in front of the bankruptcy judge. And bankruptcy is unfortunately what happens if nominal interest payments to debt holders could no longer be satisfied. These settings are corporate environments, where earnings and cash flow are driving considerations.
In short, I'd have to respectfully disagree with Dr. Black if he intimated that we all should think about risk only in present value terms. In our political economy, nominal cash flow variability often drives real risk that should be understood and considered. The unfortunate truth is that much analytic focus has been placed on problems that exist only in financial environments, largely because of the widespread adherence among quants to the same view expressed by Dr. Black. Not nearly as many tools are available to those trying to navigate corporate environments.
Does this mean that corporate environments don't have challenging cash flow or earnings problems to solve? Are these problems somehow unworthy of high quality, data-driven analytics? I think this question is answered well in a recent Sep08 Harvard Business Review article. Its contents are ones with which I totally agree. What do you think?