A rant or down the slippery slope we go:
Defenders of free markets should also embrace free and free-flow of information. This means that they should not be against privacy laws - after all perfect information is one element of perfect competition and competition is the fulcrum of free markets. Libertarians who argue for free markets and competition should thus work to increase the aggregate information in the trading sphere. For instance, they should reveal how much they earn - increasing the efficiency of the labor markets that they are in. They should release their web browsing habits, thus allowing marketers to compete in offering them the best price for various services. In so far as monitoring is most efficiently done by the government, they should work hard to advocate not only for more government monitoring, but also the release of all statistics related to this monitoring.
After all, what is free markets without perfect information? Oligopoly?
Monday, April 5, 2010
Market segmentation
While googling around before our Easton visit I came across this link which described a mall close to the hotel we were staying. I was impressed with the demographic information that it provided for prospective tenants - mainly income and household counts within 3, 5, and 10 mile radius. My guess is the data came from Census.
It did remind me however, of another interesting bit of data that I came across while working on a project. This was the PRIZM Claritas market segmentation data. The household categories (by address/zipcode, I think) were quite entertaining to read.
If you use this link you can enter your zip code and find out who you are. The full list can be found here and the categories read like this: Upper Crust, Blue Blood Estates, Bohemian Mix, Money and Brains, Movers and Shakers, American Dreams (established urban immigrant families), Gray Power (affluent retirees in sunbelt cities), God's Country (executive exurban families), New Empty Nests (upscale suburban fringe couples), Young Digerati (tech-savvy young singles and couples), Shotguns and Pickups (rural blue-collar workers and families), City Startups, Middleburg Managers, Mobility Blues, New Beginnings, and Up-and-Comers.
It can also provide the following information - Money and Brains cluster is described as those with "High incomes, advanced degrees and sophisticated tastes to match their credentials." They tend to shop at Nordstrom, support the arts, read Business Week, listen to all-news radio, and drive a Jaguar. With this type of information, marketers can target their product to those who are most likely to buy.
I peeled the above from About.com. Wikipedia also provides a full list.
It did remind me however, of another interesting bit of data that I came across while working on a project. This was the PRIZM Claritas market segmentation data. The household categories (by address/zipcode, I think) were quite entertaining to read.
If you use this link you can enter your zip code and find out who you are. The full list can be found here and the categories read like this: Upper Crust, Blue Blood Estates, Bohemian Mix, Money and Brains, Movers and Shakers, American Dreams (established urban immigrant families), Gray Power (affluent retirees in sunbelt cities), God's Country (executive exurban families), New Empty Nests (upscale suburban fringe couples), Young Digerati (tech-savvy young singles and couples), Shotguns and Pickups (rural blue-collar workers and families), City Startups, Middleburg Managers, Mobility Blues, New Beginnings, and Up-and-Comers.
It can also provide the following information - Money and Brains cluster is described as those with "High incomes, advanced degrees and sophisticated tastes to match their credentials." They tend to shop at Nordstrom, support the arts, read Business Week, listen to all-news radio, and drive a Jaguar. With this type of information, marketers can target their product to those who are most likely to buy.
I peeled the above from About.com. Wikipedia also provides a full list.
Visiting Easton
We were in Easton, PA a couple of weeks ago during Spring Break. We stayed one night at the Towne Place Suites which was really nice. Getting to the hotel via Route 22 seemed to take longer than necessary. Also, Rt. 22 was much busier than I-78.
We visited the Crayola Factory the next day. The demonstration of how crayons are made was surprising in that it hasn't changed much from the one we saw on Mr. Rogers. The intensity of the labor suprised me then and the fact that these jobs have moved offshore is not surprising. What is surprising is that the returns on investing in technology to save some jobs is so low that there has literally been no innovation in the manufacturing process in 30 years.
Easton reminded me a little of Waterville, ME where I went to college. Lafayette College is totally separated from the town and although the building where Crayola is located has revived the downtown somewhat it still looks a little bit depressed. What it needs is a supermarket or grocery store, I think.
The National Canal Museum was also fun. Upon entering, the kids got wooden boats about 12-15 inches long and moved them along a model canal/locks complete with rushing water. There were also hands on exhibits where they got to work with pulleys and experienced what it felt like to pull on a rudder to steer a canal boat. All in all we spent about 4 hours there. K2 had lunch and the McDonalds downstairs while M and K1 went across the street to a Dunkin' Donuts.
We visited the Crayola Factory the next day. The demonstration of how crayons are made was surprising in that it hasn't changed much from the one we saw on Mr. Rogers. The intensity of the labor suprised me then and the fact that these jobs have moved offshore is not surprising. What is surprising is that the returns on investing in technology to save some jobs is so low that there has literally been no innovation in the manufacturing process in 30 years.
Easton reminded me a little of Waterville, ME where I went to college. Lafayette College is totally separated from the town and although the building where Crayola is located has revived the downtown somewhat it still looks a little bit depressed. What it needs is a supermarket or grocery store, I think.
The National Canal Museum was also fun. Upon entering, the kids got wooden boats about 12-15 inches long and moved them along a model canal/locks complete with rushing water. There were also hands on exhibits where they got to work with pulleys and experienced what it felt like to pull on a rudder to steer a canal boat. All in all we spent about 4 hours there. K2 had lunch and the McDonalds downstairs while M and K1 went across the street to a Dunkin' Donuts.
Friday, April 2, 2010
Bear books
Caught up with some reading on the collapse of Bear Stearns:
1. And then the Roof Caved In by David Faber
2. Street Fighters by Kate Kelly
3. Bear Trap by Bill Bamber
4. Fool's Gold by Gillian Tett
5. House of Cards by William Cohan
The most remarkable thing that struck me in reading about the collapse of Bear Stearns from all the books was that NOTHING really happened that precipitated the collapse. Nothing in the sense of macroeconomic news or event or anything else besides an apparent and sudden refusal by Bear's banks to rollover its short term paper. From this perspective, its hard to shake the sense that there was some kind of conspiracy (by other investment and commercial bank) to force Bear's failure.
One surprising discovery from reading these books (esp. Kate Kelly's version of events) was how close Hank Paulson came to becoming the dictator that I had wanted him to be. It was clear that it was he who set the low ball price of $2 for the sale of Bear Stearns (subsequently adjusted to $10) and it was clear that there was great desire on his part to punish Bear Stearns and its shareholders as well as to set the tone that there would be consequences from a bailout. Thus, it changed my opinion of Paulson - though it is hard to tell whether he was part of the conspiracy (as a Goldman alumni) to force Bear's collapse.
Another discovery was that the much maligned Jimmy Cayne held a lot of Bear Stearns stock - his paper wealth fell from 1 billion to 200 million. (One third of Bear stock was employee owned.) This points to the failure of stock ownership to align incentives. In Mr. Cayne's case, it also shows that when you have a billion dollars, losing a several hundred million is really no big deal, you still have a couple of hundred million when all's said and done and besides he had already taken a few hundred million out by the time of the collapse.
Gillian Tett's book made me appreciate the phrase "super-senior tranche". These are the toxic assets that nobody wants to touch - the triple A rated stuff that were held by Bear. The irony was that because it was triple A, it could only offer low returns and Bear (and other investment banks) wasn't able to offload it.
Of the books listed above, Street Fighters was definitely the most gripping, with more detail (than perhaps the average reader might want) provided by House of Cards. Bear Trap was disappointing - as a purported insider's view it offered nothing - no naming and shaming - just a philosophical wandering and wondering of the author. David Faber's book was a real surprise - it provided a lot of anecdotes on the start and the end of the subprime crisis - yet like Giant Pool of Money it did not provide the most juicy detail - who are these investors that flooded the market with liquidity. Ben Bernanke claims that it is caused by sovereign wealth funds which provided a global savings glut. If this is indeed the case then we should see some of these funds being wiped out. In fact I see no evidence of this - which still leaves me wondering who or what the culprit is. Here is an earlier blog post on Faber's book.
Gillian Tett's book was also revealing - being told from the side of JP Morgan and the band of derivatives traders who created the credit derivatives including the woman who built the financial weapon of mass destruction Blythe Masters. Again, I looked to this book as being the most likely to identify the source of liquidity and was only left with a very sneaky suspicion that the source of the increased liquidity were none other than the investment and commercial banks themselves. CDS were invented as a way to get around Basel capital requirements - freeing up capital. This freed up capital is the increase in liquidity. The volume of the liquidity from this capital is too small, you say. Yes, initially. But coupled with euphoria and exuberance which lead to higher asset prices and increased leverage, this initial pool of freed up capital became huge.
The reason the subprime crisis has only (initially) affected the financial sector and not SWFs is because they were the ones who generated the liquidity which led to the boom (and hence the apparent increase in working capital) and then the bust. The banks were making up the alphabet soup of derivatives and selling them to one another reaping huge fees and profits (and bonuses) at the same time. As Michael Lewis points out about Iceland, the secret to a bubble really is nothing more than getting two people to agree about the higher asset price (emphasis mine):
A handful of guys in Iceland, who had no experience of finance, were taking out tens of billions of dollars in short-term loans from abroad. They were then re-lending this money to themselves and their friends to buy assets—the banks, soccer teams, etc. Since the entire world’s assets were rising—thanks in part to people like these Icelandic lunatics paying crazy prices for them—they appeared to be making money. Yet another hedge-fund manager explained Icelandic banking to me this way: You have a dog, and I have a cat. We agree that they are each worth a billion dollars. You sell me the dog for a billion, and I sell you the cat for a billion. Now we are no longer pet owners, but Icelandic banks, with a billion dollars in new assets. “They created fake capital by trading assets amongst themselves at inflated values,” says a London hedge-fund manager. “This was how the banks and investment companies grew and grew. But they were lightweights in the international markets.”
So would I conclude that these securities that the financial sector were trading amongst themselves constituted fake capital? Well, yeah.
1. And then the Roof Caved In by David Faber
2. Street Fighters by Kate Kelly
3. Bear Trap by Bill Bamber
4. Fool's Gold by Gillian Tett
5. House of Cards by William Cohan
The most remarkable thing that struck me in reading about the collapse of Bear Stearns from all the books was that NOTHING really happened that precipitated the collapse. Nothing in the sense of macroeconomic news or event or anything else besides an apparent and sudden refusal by Bear's banks to rollover its short term paper. From this perspective, its hard to shake the sense that there was some kind of conspiracy (by other investment and commercial bank) to force Bear's failure.
One surprising discovery from reading these books (esp. Kate Kelly's version of events) was how close Hank Paulson came to becoming the dictator that I had wanted him to be. It was clear that it was he who set the low ball price of $2 for the sale of Bear Stearns (subsequently adjusted to $10) and it was clear that there was great desire on his part to punish Bear Stearns and its shareholders as well as to set the tone that there would be consequences from a bailout. Thus, it changed my opinion of Paulson - though it is hard to tell whether he was part of the conspiracy (as a Goldman alumni) to force Bear's collapse.
Another discovery was that the much maligned Jimmy Cayne held a lot of Bear Stearns stock - his paper wealth fell from 1 billion to 200 million. (One third of Bear stock was employee owned.) This points to the failure of stock ownership to align incentives. In Mr. Cayne's case, it also shows that when you have a billion dollars, losing a several hundred million is really no big deal, you still have a couple of hundred million when all's said and done and besides he had already taken a few hundred million out by the time of the collapse.
Gillian Tett's book made me appreciate the phrase "super-senior tranche". These are the toxic assets that nobody wants to touch - the triple A rated stuff that were held by Bear. The irony was that because it was triple A, it could only offer low returns and Bear (and other investment banks) wasn't able to offload it.
Of the books listed above, Street Fighters was definitely the most gripping, with more detail (than perhaps the average reader might want) provided by House of Cards. Bear Trap was disappointing - as a purported insider's view it offered nothing - no naming and shaming - just a philosophical wandering and wondering of the author. David Faber's book was a real surprise - it provided a lot of anecdotes on the start and the end of the subprime crisis - yet like Giant Pool of Money it did not provide the most juicy detail - who are these investors that flooded the market with liquidity. Ben Bernanke claims that it is caused by sovereign wealth funds which provided a global savings glut. If this is indeed the case then we should see some of these funds being wiped out. In fact I see no evidence of this - which still leaves me wondering who or what the culprit is. Here is an earlier blog post on Faber's book.
Gillian Tett's book was also revealing - being told from the side of JP Morgan and the band of derivatives traders who created the credit derivatives including the woman who built the financial weapon of mass destruction Blythe Masters. Again, I looked to this book as being the most likely to identify the source of liquidity and was only left with a very sneaky suspicion that the source of the increased liquidity were none other than the investment and commercial banks themselves. CDS were invented as a way to get around Basel capital requirements - freeing up capital. This freed up capital is the increase in liquidity. The volume of the liquidity from this capital is too small, you say. Yes, initially. But coupled with euphoria and exuberance which lead to higher asset prices and increased leverage, this initial pool of freed up capital became huge.
The reason the subprime crisis has only (initially) affected the financial sector and not SWFs is because they were the ones who generated the liquidity which led to the boom (and hence the apparent increase in working capital) and then the bust. The banks were making up the alphabet soup of derivatives and selling them to one another reaping huge fees and profits (and bonuses) at the same time. As Michael Lewis points out about Iceland, the secret to a bubble really is nothing more than getting two people to agree about the higher asset price (emphasis mine):
A handful of guys in Iceland, who had no experience of finance, were taking out tens of billions of dollars in short-term loans from abroad. They were then re-lending this money to themselves and their friends to buy assets—the banks, soccer teams, etc. Since the entire world’s assets were rising—thanks in part to people like these Icelandic lunatics paying crazy prices for them—they appeared to be making money. Yet another hedge-fund manager explained Icelandic banking to me this way: You have a dog, and I have a cat. We agree that they are each worth a billion dollars. You sell me the dog for a billion, and I sell you the cat for a billion. Now we are no longer pet owners, but Icelandic banks, with a billion dollars in new assets. “They created fake capital by trading assets amongst themselves at inflated values,” says a London hedge-fund manager. “This was how the banks and investment companies grew and grew. But they were lightweights in the international markets.”
So would I conclude that these securities that the financial sector were trading amongst themselves constituted fake capital? Well, yeah.
Thursday, April 1, 2010
Greenspan does not do it with models
Or maybe he does. In any case this speech (circa 2008) is a wonderful clarification of what we can and cannot do with models:
The essential problem is that our models – both risk models and econometric models – as complex as they have become, are still too simple to capture the full array of governing variables that drive global economic reality. A model, of necessity, is an abstraction from the full detail of the real world. ... The most credible explanation of why risk management based on state-of-the-art statistical models can perform so poorly is that the underlying data used to estimate a model’s structure are drawn generally from both periods of euphoria and periods of fear, that is, from regimes with importantly different dynamics. ... The contraction phase of credit and business cycles, driven by fear, have historically been far shorter and far more abrupt than the expansion phase, which is driven by a slow but cumulative build-up of euphoria. ... Negative correlations among asset classes, so evident during an expansion, can collapse as all asset prices fall together, undermining the strategy of improving risk/reward trade-offs through diversification.
But these models do not fully capture what I believe has been, to date, only a peripheral addendum to business-cycle and financial modelling – the innate human responses that result in swings between euphoria and fear that repeat themselves generation after generation with little evidence of a learning curve. Asset-price bubbles build and burst today as they have since the early 18th century, when modern competitive markets evolved. To be sure, we tend to label such behavioural responses as non-rational. But forecasters’ concerns should be not whether human response is rational or irrational, only that it is observable and systematic.
This, to me, is the large missing “explanatory variable” in both risk-management and macroeconometric models. Current practice is to introduce notions of “animal spirits”, as John Maynard Keynes put it, through “add factors”. That is, we arbitrarily change the outcome of our model’s equations. Add-factoring, however, is an implicit recognition that models, as we currently employ them, are structurally deficient; it does not sufficiently address the problem of the missing variable.
We will never be able to anticipate all discontinuities in financial markets. Discontinuities are, of necessity, a surprise. Anticipated events are arbitraged away. But if, as I strongly suspect, periods of euphoria are very difficult to suppress as they build, they will not collapse until the speculative fever breaks on its own. Paradoxically, to the extent risk management succeeds in identifying such episodes, it can prolong and enlarge the period of euphoria. But risk management can never reach perfection. It will eventually fail and a disturbing reality will be laid bare, prompting an unexpected and sharp discontinuous response.
The essential problem is that our models – both risk models and econometric models – as complex as they have become, are still too simple to capture the full array of governing variables that drive global economic reality. A model, of necessity, is an abstraction from the full detail of the real world. ... The most credible explanation of why risk management based on state-of-the-art statistical models can perform so poorly is that the underlying data used to estimate a model’s structure are drawn generally from both periods of euphoria and periods of fear, that is, from regimes with importantly different dynamics. ... The contraction phase of credit and business cycles, driven by fear, have historically been far shorter and far more abrupt than the expansion phase, which is driven by a slow but cumulative build-up of euphoria. ... Negative correlations among asset classes, so evident during an expansion, can collapse as all asset prices fall together, undermining the strategy of improving risk/reward trade-offs through diversification.
But these models do not fully capture what I believe has been, to date, only a peripheral addendum to business-cycle and financial modelling – the innate human responses that result in swings between euphoria and fear that repeat themselves generation after generation with little evidence of a learning curve. Asset-price bubbles build and burst today as they have since the early 18th century, when modern competitive markets evolved. To be sure, we tend to label such behavioural responses as non-rational. But forecasters’ concerns should be not whether human response is rational or irrational, only that it is observable and systematic.
This, to me, is the large missing “explanatory variable” in both risk-management and macroeconometric models. Current practice is to introduce notions of “animal spirits”, as John Maynard Keynes put it, through “add factors”. That is, we arbitrarily change the outcome of our model’s equations. Add-factoring, however, is an implicit recognition that models, as we currently employ them, are structurally deficient; it does not sufficiently address the problem of the missing variable.
We will never be able to anticipate all discontinuities in financial markets. Discontinuities are, of necessity, a surprise. Anticipated events are arbitraged away. But if, as I strongly suspect, periods of euphoria are very difficult to suppress as they build, they will not collapse until the speculative fever breaks on its own. Paradoxically, to the extent risk management succeeds in identifying such episodes, it can prolong and enlarge the period of euphoria. But risk management can never reach perfection. It will eventually fail and a disturbing reality will be laid bare, prompting an unexpected and sharp discontinuous response.
Math puzzle
K1's grade were given this optional math challenge in addition to their regular homework. I wasn't able to solve it but it was fun nevertheless.
I was a little disappointed that the solution was somewhat more of trial and error rather than an algebra problem. This was given out when they were doing algebra.
I was a little disappointed that the solution was somewhat more of trial and error rather than an algebra problem. This was given out when they were doing algebra.
Two by John Barrow
1. The World Within the World (TWWTW)
2. Theories of Everything (TOE)
Of the two TWWTW was more enjoyable, thought provoking and revealing than I had anticipated. It spanned philosophy, science, physics, cosmology and religion and from the Greeks to the present (circa 1990).
It revealed that physicists such as Maxwell, Einstein, and Newton were more religious than I had thought. (Of course, Einstein was already known by his remark to Bohr that he did not believed that God played dice.) It was thought provoking in that it asked questions that I never really thought much about:
Why are the Laws of Nature mathematical? Are there Laws of Nature? Does mathematics (differential equations) describe the laws of nature because the laws are mathematical or is nature inherently mathematical? What is accuracy and error?
One might think that the answer ... is increased accuracy of observation. The more accurate our present knowledge of the world, so the more reliable will be our future predictions. Unfortunately, increased accuracy does not really help us, because the uncertainty about the future grows so fast that it very easily overcomes our paltry attempts to improve our specification of the present (pg. 276)
To the scientist the term [error] means two other things. The first is straightforward: the limiting accuracy to which a quantity can be measured. A simple example is experimental error ... [which] is not terribly interesting, but it is obviously one of the goals ... to make it as small as possible. ... We have already seen that the Heisenberg Uncertainty Principle ... ensures that there are inevitable errors associated with the measurement of all quantities even if the measuring instruments are perfect ... This strange limitation arises becase the very process of observation is inseparable from the state being measured. Perfect knowledge of the Universe is impossible because the act of knowng influences the Universe in an unknowable way. It is as if, by the time we record its state, it has changed slightly.
... The second form of error ... [is] more serious in its consequences because one can never be certain that it has even been identified, let alone minimized or eradicated. This species of error we call a 'selection effect' or 'systematic error'.... Experimental arrangements and observational procedures have built-in propensities to gather certain types o facts more readily than others. In order to be sure that you are observing what you think you are observing, it is always necessary to have some theoretical understanding of the wider spectrum of phenomena that could be biasing your observations. [e.g. only stars of a certain brightness can be measured]
(pgs. 339-340)
TOE was less satisfatory - it seemed more of a rehash of TWWTW and combined it with chaos theory and sensitivity of things to initial conditions and hence read more as a critique then a description that more entertaining in TWWTW.
Both books are sprinkled with delightful quotes, some of which I extract here:
From TOE:
If everything on Earth were rational, nothing would happen. FYODOR DOSTOYEVSKY
From TWWTW:
As far as the laws of mathematics refer to reality they are not certain; as far as they are certain, they do not refer to reality. ALBERT EINSTEIN
Physics is mathematical not because we know so much about the physical world, but because we know so little: it is only its mathematical properties that we can discover. BERTRAND RUSSELL
2. Theories of Everything (TOE)
Of the two TWWTW was more enjoyable, thought provoking and revealing than I had anticipated. It spanned philosophy, science, physics, cosmology and religion and from the Greeks to the present (circa 1990).
It revealed that physicists such as Maxwell, Einstein, and Newton were more religious than I had thought. (Of course, Einstein was already known by his remark to Bohr that he did not believed that God played dice.) It was thought provoking in that it asked questions that I never really thought much about:
Why are the Laws of Nature mathematical? Are there Laws of Nature? Does mathematics (differential equations) describe the laws of nature because the laws are mathematical or is nature inherently mathematical? What is accuracy and error?
One might think that the answer ... is increased accuracy of observation. The more accurate our present knowledge of the world, so the more reliable will be our future predictions. Unfortunately, increased accuracy does not really help us, because the uncertainty about the future grows so fast that it very easily overcomes our paltry attempts to improve our specification of the present (pg. 276)
To the scientist the term [error] means two other things. The first is straightforward: the limiting accuracy to which a quantity can be measured. A simple example is experimental error ... [which] is not terribly interesting, but it is obviously one of the goals ... to make it as small as possible. ... We have already seen that the Heisenberg Uncertainty Principle ... ensures that there are inevitable errors associated with the measurement of all quantities even if the measuring instruments are perfect ... This strange limitation arises becase the very process of observation is inseparable from the state being measured. Perfect knowledge of the Universe is impossible because the act of knowng influences the Universe in an unknowable way. It is as if, by the time we record its state, it has changed slightly.
... The second form of error ... [is] more serious in its consequences because one can never be certain that it has even been identified, let alone minimized or eradicated. This species of error we call a 'selection effect' or 'systematic error'.... Experimental arrangements and observational procedures have built-in propensities to gather certain types o facts more readily than others. In order to be sure that you are observing what you think you are observing, it is always necessary to have some theoretical understanding of the wider spectrum of phenomena that could be biasing your observations. [e.g. only stars of a certain brightness can be measured]
(pgs. 339-340)
TOE was less satisfatory - it seemed more of a rehash of TWWTW and combined it with chaos theory and sensitivity of things to initial conditions and hence read more as a critique then a description that more entertaining in TWWTW.
Both books are sprinkled with delightful quotes, some of which I extract here:
From TOE:
If everything on Earth were rational, nothing would happen. FYODOR DOSTOYEVSKY
From TWWTW:
As far as the laws of mathematics refer to reality they are not certain; as far as they are certain, they do not refer to reality. ALBERT EINSTEIN
Physics is mathematical not because we know so much about the physical world, but because we know so little: it is only its mathematical properties that we can discover. BERTRAND RUSSELL
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