I came across an article on-line from the New York Times, written by Op-Ed Contributor Richard Dooling, "The Rise of the Machines",
Dooling mentions at the beginning of the article that Warren Buffett called derivatives "weapons of mass destruction". I guess time will tell. The article was published on October 11, 2008. Nearly two months later, things continue to combust.
"Somehow the genius quants — the best and brightest geeks Wall Street firms could buy — fed $1 trillion in subprime mortgage debt into their supercomputers, added some derivatives, massaged the arrangements with computer algorithms and — poof! — created $62 trillion in imaginary wealth. It’s not much of a stretch to imagine that all of that imaginary wealth is locked up somewhere inside the computers, and that we humans, led by the silverback males of the financial world, Ben Bernanke and Henry Paulson, are frantically beseeching the monolith for answers. Or maybe we are lost in space, with Dave the astronaut pleading, “Open the bank vault doors, Hal." '
Richard Dooling is the author of "Rapture of the Geeks: When AI Outsmarts IQ"
I've assembled a few related articles that focus on the role of the quant and some additional information that might assist in our understanding of what has been unfolding during the current economic recession:
Quants Gone Wild - The Subprime Crisis (3/27/08; A.W. Bodine and C.J. Nagel)
"The “best & brightest” quantitative analysts on Wall Street became so technologically advanced that many of the principals running investment firms simply didn’t understand the arcane risk models their “quants” developed – and sadly neither did the quants. We recall the comment made during a recent presentation at Concordia College by Don Gogel, President and CEO of Clayton, Dubilier and Rice. He noted that this “toxic cocktail” was something that even the “mixologists themselves didn’t understand” let alone those trading in them. Bryant Urstadt writing in MIT’s Technology Review in December 2007 also notes, “The more quants learn, the farther away a unified theory of finance seems. Human behavior, as manifest in financial markets, simply resists quantification, at least for now.” We should here also do homage to the investing approach of the sage Warren Buffet—that he does not invest in anything he doesn’t understand. Investment houses should note this simple truth."
On Becoming a Quant (pdf) May 2008; Mark Joshi
Hiring the Next Generation of Quants, Finance Tech, 3/31/2006; Ivy Schmerken
""An MBA does not cut it because operating in today's markets requires more quantitative skills than a typical MBA can offer," contends Linda Kreitzman, director of the Masters in Financial Engineering (MFE) program at the Haas School of Business at the University of California at Berkeley. "Trading is getting more complex, especially in structured products," she adds, citing as examples fixed income, mortgage-backed securities and asset-backed securities, as well as credit and equity derivatives and volatility trading. ""
Here is an article, written by Tom Davenport, in the Discussion Leader, Havard Business Publishing, that offers a a few ideas for solutions:
10 Principles of the New Business Intelligence
"I've argued for a while that organizations need to increase their focus on decision-making. In particular, they need to think again about the relationship between information and decision-making."
Showing posts with label decision-making. Show all posts
Showing posts with label decision-making. Show all posts
Saturday, December 6, 2008
Wednesday, October 8, 2008
Truthiness, quants, software, glitches, experts, and steps towards solutions...
Here is an example of something I think touches upon the usefulness of the "truthiness" concept.
Although the following article was written in 2003, it holds up well in 2008. The seeds of truthiness were planted well before Stephen Colbert came up with the word:
What the Quants Don't Learn in College
(Emanual Derman, Risk Magazine-Trends July 2003/Volume 16/No7)
"The only universally applicable law is that of approximate similarity, which states that the best estimate of the unknown market value of a security is the price of another security that's closely similar to it. You need to find (or invent) a model to establish the similarity between two securities by demonstrating the equivalence of their future payouts under a wide range of circumstances....
Suspend disbelief
Although all you have is the limited power of this simple law, you must take your model of similarity seriously. Temporarily, like a fiction reader, you must suspend disbelief in your model. Then, when it's complete, remind yourself that economics and valuation involve the behaviour of people, and think hard about what could go wrong...
On Wall Street, no-one knows what the correct model is, but they go ahead and price and trade anyhow. It's a bit like the trial in Alice in Wonderland...Academics often over emphasise models, but much of the success of a model depends on software engineering....You need live market data, historical time series, databases, input screens and calibration. As a result, for every financial engineer who works on a model you may need three or four more software engineers to make it usable.."
I wonder if any Wall Street companies hired additional software engineers. From what has transpired over the past months, if they did, they were not the right software engineers!
UPDATE: Please read the comments to this post for clarification of Calyx software errors by a representative of the company. The passage below is my original post.
To take a closer look at how software problems might have played a small part in the current situation, I chose to browse the Calyx Software website. Calyx provides software that is used by Freddie Mac and Fannie Mae. There is a treasure trove of information about the quality of this companies mortgage processing software on its support pages.
From my armchair analysis of the types of errors reported, it seems that it is not difficult for mortgage brokers to unknowingly make errors when determining a potential borrower's risk. The Freddie Mac Loan Prospector looks like it was rolled out before important errors were discovered. In my opinion, it was designed without the capacity to prevent critical errors.
Because the Calyx support page lists many of the problems as common, it is likely that the Freddie Mac Loan Prospector was designed with a lower level of error prevention than expected for this sort of transaction. To fix this problem after the fact, the support pages offer help solutions, but some of the solutions are quite complex.
Common Problems with the Freddie Mac Loan Prospector
Second home is not included in ratios
Credit Agency Missing from the Freddie Mac Loan Prospector
Credit Agency or Lender is Missing from Point
Little things that waste time
Experts depend on accurate, reliable data in order to make effective decisions, but our current systems did not do the job. Even so, it is worth keeping track of what the experts have to say about the crisis:
Must-watch video
If you have about an hour, watch this discussion between Charlie Rose, Floyd Norris, Mohamed El-Erian, Gretchen Morgenson, and Nouriel Roubini, which aired at the time of the Fannie Mae and Freddie Mac "bailout" decision.
According to Floyd Norris, "The senior managers of the banks assumed that the whizzes under them had their financial models which proved that they did not have any value at risk, and had nothing to worry about, and they believed all of this. And now what they believed looks like nonsense, and you wonder why they did. And that left those banks very exposed... In a lot of cases, they thought they were making money, but they really weren't."
The one thing that is clear to me is that our current economic models no longer function. We can't put the blame on the quants, or the politicians, or the greedy Wall Street leaders. We can't put the blame on new homeowners with low-incomes, or pressured lenders.
Right now, there is a high level of uncertainty, and we do not have anything tangible that ensures that things will be OK. This problem can not be solved quickly. We need better models that can support effective economic decision-making on Wall Street, Main Street, and everywhere in between.
The problem of preventing future economic disasters won't be solved by politicians, government officials, economists, and Wall Street leaders. The general public is strongly against the rescue bailout. It simply is too difficult to trust those we've blamed.
My idea for a collaborative interactive time-line is just one small step (see sidebar).
We need to stretch our thinking and cast a wide net. This will require an interdisciplinary approach, and include people from a variety of disciplines, who are untainted by monetary scandals and have innovative minds, who care about future generations, and who believe strongly that in a democratic society, all citizens must have access to accurate, understandable information in order to make effective decisions - in all aspects of their lives.
Who might these people be? University researchers, practitioners in the workplace, graduate students, soccer moms, grandpas...with experience in areas such as finance, psychology, history, geography, urban planning, business, economics, banking, sociology, computer science, human-computer interaction, information visualization, graphic arts, & communications.
It is up to everyone to wake up and take action in some way.
Although the following article was written in 2003, it holds up well in 2008. The seeds of truthiness were planted well before Stephen Colbert came up with the word:
What the Quants Don't Learn in College
(Emanual Derman, Risk Magazine-Trends July 2003/Volume 16/No7)
"The only universally applicable law is that of approximate similarity, which states that the best estimate of the unknown market value of a security is the price of another security that's closely similar to it. You need to find (or invent) a model to establish the similarity between two securities by demonstrating the equivalence of their future payouts under a wide range of circumstances....
Suspend disbelief
Although all you have is the limited power of this simple law, you must take your model of similarity seriously. Temporarily, like a fiction reader, you must suspend disbelief in your model. Then, when it's complete, remind yourself that economics and valuation involve the behaviour of people, and think hard about what could go wrong...
On Wall Street, no-one knows what the correct model is, but they go ahead and price and trade anyhow. It's a bit like the trial in Alice in Wonderland...Academics often over emphasise models, but much of the success of a model depends on software engineering....You need live market data, historical time series, databases, input screens and calibration. As a result, for every financial engineer who works on a model you may need three or four more software engineers to make it usable.."
I wonder if any Wall Street companies hired additional software engineers. From what has transpired over the past months, if they did, they were not the right software engineers!
UPDATE: Please read the comments to this post for clarification of Calyx software errors by a representative of the company. The passage below is my original post.
To take a closer look at how software problems might have played a small part in the current situation, I chose to browse the Calyx Software website. Calyx provides software that is used by Freddie Mac and Fannie Mae. There is a treasure trove of information about the quality of this companies mortgage processing software on its support pages.
From my armchair analysis of the types of errors reported, it seems that it is not difficult for mortgage brokers to unknowingly make errors when determining a potential borrower's risk. The Freddie Mac Loan Prospector looks like it was rolled out before important errors were discovered. In my opinion, it was designed without the capacity to prevent critical errors.
Because the Calyx support page lists many of the problems as common, it is likely that the Freddie Mac Loan Prospector was designed with a lower level of error prevention than expected for this sort of transaction. To fix this problem after the fact, the support pages offer help solutions, but some of the solutions are quite complex.
Common Problems with the Freddie Mac Loan Prospector
Second home is not included in ratios
Credit Agency Missing from the Freddie Mac Loan Prospector
Credit Agency or Lender is Missing from Point
Little things that waste time
Experts depend on accurate, reliable data in order to make effective decisions, but our current systems did not do the job. Even so, it is worth keeping track of what the experts have to say about the crisis:
Must-watch video
If you have about an hour, watch this discussion between Charlie Rose, Floyd Norris, Mohamed El-Erian, Gretchen Morgenson, and Nouriel Roubini, which aired at the time of the Fannie Mae and Freddie Mac "bailout" decision.
According to Floyd Norris, "The senior managers of the banks assumed that the whizzes under them had their financial models which proved that they did not have any value at risk, and had nothing to worry about, and they believed all of this. And now what they believed looks like nonsense, and you wonder why they did. And that left those banks very exposed... In a lot of cases, they thought they were making money, but they really weren't."
The one thing that is clear to me is that our current economic models no longer function. We can't put the blame on the quants, or the politicians, or the greedy Wall Street leaders. We can't put the blame on new homeowners with low-incomes, or pressured lenders.
Right now, there is a high level of uncertainty, and we do not have anything tangible that ensures that things will be OK. This problem can not be solved quickly. We need better models that can support effective economic decision-making on Wall Street, Main Street, and everywhere in between.
The problem of preventing future economic disasters won't be solved by politicians, government officials, economists, and Wall Street leaders. The general public is strongly against the rescue bailout. It simply is too difficult to trust those we've blamed.
My idea for a collaborative interactive time-line is just one small step (see sidebar).
We need to stretch our thinking and cast a wide net. This will require an interdisciplinary approach, and include people from a variety of disciplines, who are untainted by monetary scandals and have innovative minds, who care about future generations, and who believe strongly that in a democratic society, all citizens must have access to accurate, understandable information in order to make effective decisions - in all aspects of their lives.
Who might these people be? University researchers, practitioners in the workplace, graduate students, soccer moms, grandpas...with experience in areas such as finance, psychology, history, geography, urban planning, business, economics, banking, sociology, computer science, human-computer interaction, information visualization, graphic arts, & communications.
It is up to everyone to wake up and take action in some way.
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