I would, however, suggest that we are living in an era when you don’t have to know a single thing about the law to know how the Supreme Court will rule on a case. Occam’s razor suggests that you simply ask yourself if it benefits business over workers, the powerful over the powerless, or Republicans over Democrats. If the answer is ‘yes’, that is how the Supreme Court will rule. I don’t know if this was always the case. Lawyers I know adamantly suggest that there was once upon a time a less ideological, more ‘law-focused’ Supreme Court. But it’s long past time we think of the Supreme Court as a ‘court’ in its proper, legitimating meaning of ‘arbitrating and interpreting the law of the land’, and instead just consider them one of many political players/institutions in the US landscape of other political players/institutions.

In other words, the ‘decisions’, ‘opinions’, ‘reasoning’, ‘precedence’, or ‘interpretation’ are all meaningless. If you refer to these things, I think that makes you something of a sucker. Instead, just draw a straight line between who benefits and how the court will decide.

I’m not bitter about this, genuinely, but I think legal commentary is essentially worthless here.

And third, you will see at least one fantasti-magical example of lateral thinking (in part 7.

The setup: the adventurers have found themselves on a nether plane. They discovered a semi-docile hell-cow, wandering about, chewing on some volcanic rock. The hell-cow delivered them to the mouth of a castle, where they spend their time attacking the baddies. One of the characters (Mike Krahulik, who does the art behind Penny Arcade), sees a minion running away with a chest of jewels. He initially thinks about trying to cast a spell to control the beast. But then he does something very different. Instead of casting a control spell on the beast, he decides to cast a prestidigitation spell on the minion running away, making him appear to be volcanic rock, hell-cow’s favorite food.

The description doesn’t do it justice. Take a watch from about 7:44 to about 11:38.

I’ve been thinking a lot about the prestidigitation play, it connects with some of my interests in automated trading, algorithms, and data mining. With regard to financial markets, as Felix Salmon reports, we are living in an computer-driven, algorithm world (“We may be able to slow it down, but we can never contain, control, or comprehend it. It’s the machines’ market now; we just trade in it”). But it is more than this, I think. Data-driven analysis, the strategy manifestation of quantification, is a force to be reckoned with.

But data-driven analysis can also be stupid. That is to say, data-driven analysis – what Hans Peter Luhn dubbed “business intelligence” in 1958 – has become more intelligence in the ‘fact gathering’ sense than intelligence in the lateral thinking sense.

This sets the context for speaking about this post on business intelligence and human-centric analysis, but as my brain power is working at 35% nowadays, I’m separating this into a couple/few posts before I can make a point.

We were pre-qualified for a loan; with two professional incomes, good credit and enough cash for a 20 percent down payment, that would not be our problem. Yet two mortgage companies turned us down. The first did so after its investors – big banks with household names – rejected our application. The second mortgage company’s internal underwriters also rejected us. Their reasons were the same: The home, a customized modular house of internationally acclaimed design, built in 1989, is . . . round.

Being “unusual” or “unique,” it was deemed “not marketable.” Despite its evident worth and multiple independent appraisals, the lenders said they could not assign a value to the house because there were no comparable properties. And, with no “value,” there was insufficient collateral for a loan.

This is a kind of obvious example of how different qualities matter to different constituencies, but also that some interests are able to assert themselves a little bit more than others. If they can be believed, it was investors in two mortgage companies “big banks with household names” who turned their application down.

There is something banal here, of course. The models require data, and a small number of like houses make the risk too volatile. And when they say things like “The mortgage industry apparently only wants us to buy what everyone else has (or had),” it makes me think these two authors are kind of assholes who are deliberately taking potshots at what is an obviously more complicated issue than what “the mortgage industry” wants. Institutions are desperately seeking ways to manage their risks; in the face of massive criticisms over decades of old-fashioned, face-to-face treatment of clients, the industry moved to more formal risk models; the quantitative and data turn in finance meant that these models have more than a skosh of seemingly inflexible, rules-based tint to them.

What is interesting, however, is the inability for individual discretion to intervene. Though of course, I suspect that within hours/days/weeks of this article being written, the poor professionals were able to purchase their dream house, because of – what? – individual discretion. In the mortgage industry, for good and bad (and bad), evaluating individual level risk is hard, and in absence of the ability to sort through a client’s biography, instead the client is reduced to a case. And then the circular house becomes a problem.

So, Iran is now making ‘reluctant concessions’ with regard to the farce of election they just held.

I wouldn’t push too hard on this point, but it’s the reason why neo-insitutional folks push back against the myth-versus-real construction. Here, the myth actually makes changes to the real more viable.

If you have thoughts, we’d love to hear them, but we hope you’ll be at least a little kind – this is one of those situations where your self-identity as brutally honest should not trump your self-identity as gracious.

And the links to the videos we reference: Fashion File: Making an Hermes Bag, and Contemporary Art Preview

Art and Markets 1: Selling Crafts and Art from Peter Levin on Vimeo.

But when are ambitious, symbolic statements going to be decoupled from on-the-ground changes, and when are ambitious, symbolic statements going to be the impetus for on-the-ground changes? Absent a wave to ‘well-situated activists’ or somesuch, do we really have an answer here?

Make-a-meeting

From CoreFiling’s insight blog: “It won’t be very long before it is those documents – the bar-coded financial disclosures – that will be the primary materials consumed by financial market systems to help analysts and investors make decisions about the best way to invest. This is vastly more sophisticated than today’s processes that rely on slow and inaccurate re-keying of a subset of the financial information published by companies.”

This is commensuration more than just standardization, since the tags are designed to be specific to a particular business enough so that everyone is not required to give the same information, yet the tags are standardized enough that everyone is required to give information that can be made comparable. The pitch for companies (other than, because otherwise we’ll fine you and take away your business license) is that XBRL will make their financial reporting less costly, less prone to error, and ultimately more efficient.

Personally, I think this is a flat out misrepresentation of what’s going on here. XBRL helps one group of people orders of magnitude more than anyone else: investors. And the trade-off between increased government efficiency and business streamlining of compliance data on the one hand, and increased ability for data-gatherers for banks, hedge funds, and the investor class is totally totally off the charts. What this will end up doing is: 1) creating a standard way for companies to report financials; 2) creating some increased efficiency for government entities to keep tabs on the finances of these organizations; and 3) create a massive additional datastream for financial services and investment firms to work with. If you think it is a challenge for public firms to resist making short-term decisions based on financial analysts’ quarterly reports of earnings now, wait until this information is directly readable by quant trading models.

This would be an amazing dissertation topic. I would track: a) the creation of the standard; b) the adoption of the standard around the world; c) how XBRL is being incorporated into financial modeling; d) the before-and-after effects of XBRL on market prices for firms; and e) qualitatively, what gets excised from XBRL, or rather, what remains incommensurable about firms, governments, etc.

UBMatrix
XBRL’s main site
US SEC’s ‘Interactive Data Viewers’
Microsoft uses XBRL
US GAAP XBRL Taxonomy (GAAP is the accounting standard in the US)
CoreFiling

What matters in life is the equation probability × consequence. This point might appear to be simple, but its consequences are not.

Suppose that you are deriving probabilities of future occurrences from the data, assuming that the past is representative of the future. An event can be an earthquake, a market crash, a spurt in inflation, hurricane damage in an area, a flood, crops destroyed by a disease, people affected in an epidemic, destruction caused by terrorism, etc. Note the following: the severity of the event, will be in almost all cases inversely proportional to its frequency: the ten-year flood will be more frequent than the 100 year flood – and the 100 year flood will be more devastating.

Now comes word that some number (actually up to 5 now) of undersea cables have been cut, knocking a wide area of the Middle East off the internet, particularly the route between Europe and Egypt, and from there to the rest of the Middle East.

But where is the 100 year flood? What appears to have happened is a connected series of accidents and snafus, including possibly the weather, an anchor dragging along the sea floor, or who knows what. Mysterious. What I would contend, drawing from org theory, is that what is more dangerous than a 100 year flood is a sequence of preventable, unforeseen errors. That is, it is the disruption of the routine more than a freakish activity that is most likely to create accidents and crises. The routine fire in a particularly bad location, a minor earthquake in an unexpected place, a sequence of coupled organizational routines that lead one-to-another into disaster. It’s not that you shouldn’t be looking for the next giant storm that’s inevitably coming down the pike, but more problematic are the breaks in the caulk around the tub that floods the electrical box, that shorts the grid. Or a failure in the bathrooms at the airport.

Read your Saul Alinsky, and get in the game.

The good old days of the domination of the first generation quant models, where you simply show up with three concepts – value, momentum, and discipline – are over. But, even more critically and, perhaps like career and business risk, out at the limits of arbitrage, is this need for judgmental overrides on rare macro events. Quants like to show off their discipline by marching off the cliff in rows (it is said, I hope apocryphally, that Shaka, the great Zulu Chief, marched an impi, or regiment, off a cliff to impress European observers and I hope it did). Well, in real life it would be nice to stop at the edge and say “I don’t like the look of this, perhaps my model missed something.” The extremely difficult objective is to maintain the advantages of quant discipline 95% or so of the time and hand over to a human being when you reach the edge of the cliff. You can imagine the problems in making this kind of phase change. But only by slowly overcoming this problem and integrating this hybrid approach into the DNA of the investment process can one aspire to being very effective investors in the long run.

His point is that the benefits to quantitative investing are mostly in the fatter part of the outlier events curve. Or, in the language of March and Simon, quants are better at exploitation that exploration. Qualitative investors (stock pickers, in Grantham’s language) by contrast are potentially more helpful when the models depart from reality. An ideal world would have a hybrid model of quals and quants.

But that almost never works in practice. In practice, a qual firm uses quants as showcases to show their ‘balance’, while quants use quals to demonstrate their creative flexibilities. The underlying problem is that the two styles at their best represent different (and incompatible) views of the world. If you believe that human behavioral frailty gets in the way of seeing financial facts for what they are, it makes it difficult to envision a world where a stock picker can accurately tell you when you’re running off a cliff. By contrast, quals are simply limited by human cognitive capacity, combined with all sorts of cumulative social contexts, to never be able ‘really’ know why they are right or wrong. Leaving money on the table is also a form of running off a cliff. Just a different cliff.

Or rather, in Grantham’s story, there is simply no way to tell which 5% of the time is going to be the time when quals are going to be invaluable.

There might be an answer is abandoning the (theoretical) notion of maximizing efficiency, in favor of a more Type I versus Type II error view of the world. A Type I error is a false positive, in this context to believe there is risk where there actually is none. This is the ‘leave money on the table’ problem – if you cut back on the amount of risk you take, effectively you are underperforming the ‘economically efficient’ horizon. Type II errors are false negatives, to believe there is no risk where there actually is some. This results in many more ‘Whoa, we should have seen this coming!’ kinds of mistakes, I think.

In short, there is a collective action problem at work, insofar as we are collectively trained to believe that maximization is desirable. I would guess that 95%+ of the problems of the financial system are not due to institutions shooting for returns of 3% over prime. It’s when we build a system based on 20% over prime returns that we always seem to create incentives to blow up.