Quants: The Alchemists of Wall Street

A focus on the exceptions that prove the rule

By Benoit Mandelbrot and Nassim Taleb

Published: March 23 2006 16:40 | Last updated: March 23 2006 16:40

Conventional studies of uncertainty, whether in statistics, economics, finance or social science, have largely stayed close to the so-called “bell curve”, a symmetrical graph that represents a probability distribution. Used to great effect to describe errors in astronomical measurement by the 19th-century mathematician Carl Friedrich Gauss, the bell curve, or Gaussian model, has since pervaded our business and scientific culture, and terms like sigma, variance, standard deviation, correlation, R-square and the Sharpe ratio are all directly linked to it.

If you read a mutual fund prospectus, or a hedge fund’s exposure, the odds are that it will supply you, among other information, with some quantitative summary claiming to measure “risk”. That measure will be based on one of the above buzzwords that derive from the bell curve and its kin.

Such measures of future uncertainty satisfy our ingrained desire to “simplify” by squeezing into one single number matters that are too rich to be described by it. In addition, they cater to psychological biases and our tendency to understate uncertainty in order to provide an illusion of understanding the world.

The bell curve has been presented as “normal” for almost two centuries, despite its flaws being obvious to any practitioner with empirical sense. Granted, it has been tinkered with, using such methods as complementary “jumps”, stress testing, regime switching or the elaborate methods known as GARCH, but while they represent a good effort, they fail to address the bell curve’s fundamental flaws.

The problem is that measures of uncertainty using the bell curve simply disregard the possibility of sharp jumps or discontinuities and, therefore, have no meaning or consequence. Using them is like focusing on the grass and missing out on the (gigantic) trees. In fact, while the occasional and unpredictable large deviations are rare, they cannot be dismissed as “outliers” because, cumulatively, their impact in the long term is so dramatic.

The traditional Gaussian way of looking at the world begins by focusing on the ordinary, and then deals with exceptions or so-called outliers as ancillaries. But there is also a second way, which takes the exceptional as a starting point and deals with the ordinary in a subordinate manner – simply because that “ordinary” is less consequential.

These two models correspond to two mutually exclusive types of randomness: mild or Gaussian on the one hand, and wild, fractal or “scalable power laws” on the other. Measurements that exhibit mild randomness are suitable for treatment by the bell curve or Gaussian models, whereas those that are susceptible to wild randomness can only be expressed accurately using a fractal scale. The good news, especially for practitioners, is that the fractal model is both intuitively and computationally simpler than the Gaussian, which makes us wonder why it was not implemented before.

Let us first turn to an illustration of mild randomness. Assume that you round up 1,000 people at random among the general population and bring them into a stadium. Then, add the heaviest person you can think of to that sample. Even assuming he weighs 300kg, more than three times the average, he will rarely represent more than a very small fraction of the entire population (say, 0.5 per cent). Similarly, in the car insurance business, no single accident will put a dent on a company’s annual income. These two examples both follow the “Law of Large Numbers”, which implies that the average of a random sample is likely to be close to the mean of the whole population.

In a population that follows a mild type of randomness, one single observation, such as a very heavy person, may seem impressive by itself but will not disproportionately impact the aggregate or total. A randomness that disappears under averaging is trivial and harmless. You can diversify it away by having a large sample.

There are specific measurements where the bell curve approach works very well, such as weight, height, calories consumed, death by heart attacks or performance of a gambler at a casino. An individual that is a few million miles tall is not biologically possible, but an exception of equivalent scale cannot be ruled out with a different sort of variable, as we will see next.

Wild randomness

What is wild randomness? Simply put, it is an environment in which a single observation or a particular number can impact the total in a disproportionate way. The bell curve has “thin tails” in the sense that large events are considered possible but far too rare to be consequential. But many fundamental quantities follow distributions that have “fat tails” – namely, a higher probability of extreme values that can have a significant impact on the total.

One can safely disregard the odds of running into someone several miles tall, or someone who weighs several million kilogrammes, but similar excessive observations can never be ruled out in other areas of life.

Having already considered the weight of 1,000 people assembled for the previous experiment, let us instead consider wealth. Add to the crowd of 1,000 the wealthiest person to be found on the planet – Bill Gates, the founder of Microsoft. Assuming that his net worth is close to $80bn, how much would he represent of the total wealth? 99.9 per cent? Indeed, all the others would represent no more than the variation of his personal portfolio over the past few seconds. For someone’s weight to represent such a share, he would need to weigh 30m kg.

Try it again with book sales. Line up a collection of 1,000 authors. Then, add the most read person alive, JK Rowling, the author of the Harry Potter series. With sales of several hundred million books, she would dwarf the remaining 1,000 authors who would collectively have only a few hundred thousand readers.

So, while weight, height and calorie consumption are Gaussian, wealth is not. Nor are income, market returns, size of hedge funds, returns in the financial markets, number of deaths in wars or casualties in terrorist attacks. Almost all man-made variables are wild. Furthermore, physical science continues to discover more and more examples of wild uncertainty, such as the intensity of earthquakes, hurricanes or tsunamis.

Economic life displays numerous examples of wild uncertainty. For example, during the 1920s, the German currency moved from three to a dollar to 4bn to the dollar in a few years. And veteran currency traders still remember when, as late as the 1990s, short-term interest rates jumped by several thousand per cent.

We live in a world of extreme concentration where the winner takes all. Consider, for example, how Google grabs much of internet traffic, how Microsoft represents the bulk of PC software sales, how 1 per cent of the US population earns close to 90 times the bottom 20 per cent or how half the capitalisation of the market (at least 10,000 listed companies) is concentrated in less than 100 corporations.

Taken together, these facts should be enough to demonstrate that it is the so-called “outlier” and not the regular that we need to model. For instance, a very small number of days accounts for the bulk of the stock market changes: just ten trading days represent 63 per cent of the returns of the past 50 years (see graph below).

Let us now return to the Gaussian for a closer look at its tails. The “sigma” is defined as a “standard” deviation away from the average, which could be around 0.7 to 1 per cent in a stock market or 8 to 10 cm for height. The probabilities of exceeding multiples of sigma are obtained by a complex mathematical formula. Using this formula, one finds the following values:

Probability of exceeding:

0 sigmas: 1 in 2 times

1 sigma: 1 in 6.3 times

2 sigmas: 1 in 44 times

3 sigmas: 1 in 740 times

4 sigmas: 1 in 32,000 times

5 sigmas: 1 in 3,500,000 times

6 sigmas: 1 in 1,000,000,000 times

7 sigmas: 1 in 780,000,000,000 times

8 sigmas: 1 in 1,600,000,000,000,000 times

9 sigmas: 1 in 8,900,000,000,000,000,000 times

10 sigmas: 1 in 130,000,000,000,000,000,000, 000 times

and, skipping a bit:

20 sigmas: 1 in 36,000,000,000,000,000,000, 000,000,000,000,000,000,000,000,000,000,000,000,
000,000,000,000,000,000,000,000,000,000,000 times

Soon, after about 22 sigmas, one hits a “googol”, which is 1 with 100 zeroes behind it. With measurements such as height and weight, this remote probability makes sense, as it would require a deviation from the average of more than 2m. The same cannot be said variables such as financial markets. For example, a level described as a 22 sigma has been exceeded with the stock market crashes of 1987 or the interest rate moves of 1992.

of variables such as financial markets. For example, a level described as a 22 sigma has been exceeded with the stock market crashes of 1987 and the interest rate moves of 1992.

The key here is to note how the frequencies in the preceding list drop very rapidly, in an accelerating way. The ratio is not invariant with respect to scale.

Let us now look more closely at a fractal, or scalable, distribution using the example of wealth. We find that the odds of encountering a millionaire in Europe are as follows:

Richer than 1 million: 1 in 62.5

Richer than 2 million: 1 in 250

Richer than 4 million: 1 in 1,000

Richer than 8 million: 1 in 4,000

Richer than 16 million: 1 in 16,000

Richer than 32 million: 1 in 64,000

Richer than 320 million: 1 in 6,400,000

This is simply a fractal law with a “tail exponent”, or “alpha”, of two, which means that when the number is doubled, the incidence goes down by the square of that number – in this case four. If you look at the ratio of the moves, you will notice that this ratio is invariant with respect to scale.

If the “alpha” were one, the incidence would decline by half when the number is doubled. This would produce a “flatter” distribution (fatter tails), whereby a greater contribution to the total comes from the low probability events.

Richer than 1 million: 1 in 62.5

Richer than 2 million: 1 in 125

Richer than 4 million: 1 in 250

Richer than 8 million: 1 in 500

Richer than 16 million: 1 in 1,000

We have used the example of wealth here, but the same “fractal” scale can be used for stock market returns and many other variables. Indeed, this fractal approach can prove to be an extremely robust method to identify a portfolio’s vulnerability to severe risks. Traditional “stress testing” is usually done by selecting an arbitrary number of “worst-case scenarios” from past data. It assumes that whenever one has seen in the past a large move of, say, 10 per cent, one can conclude that a fluctuation of this magnitude would be the worst one can expect for the future. This method forgets that crashes happen without antecedents. Before the crash of 1987, stress testing would not have allowed for a 22 per cent move.

Using a fractal method, it is easy to extrapolate multiple projected scenarios. If your worst-case scenario from the past data was, say, a move of –5 per cent and, if you assume that it happens once every two years, then, with an “alpha” of two, you can consider that a –10 per cent move happens every eight years and add such a possibility to your simulation. Using this model, a –15 per cent move would happen every 16 years, and so forth. This will give you a much clearer idea of your risks by expressing them as a series of possibilities.

You can also change the alpha to generate additional scenarios – lowering it means increasing the probabilities of large deviations and increasing it means reducing them. What would such a method reveal? It would certainly do what “sigma” cannot do, which is to show how some portfolios are more robust than others to an entire spectrum of extreme risks. It can also show how some portfolios can benefit inordinately from wild uncertainty.

Despite the shortcomings of the bell curve, reliance on it is accelerating, and widening the gap between reality and standard tools of measurement. The consensus seems to be that any number is better than no number – even if it is wrong. Finance academia is too entrenched in the paradigm to stop calling it “an acceptable approximation”.

Any attempts to refine the tools of modern portfolio theory by relaxing the bell curve assumptions, or by “fudging” and adding the occasional “jumps” will not be sufficient. We live in a world primarily driven by random jumps, and tools designed for random walks address the wrong problem. It would be like tinkering with models of gases in an attempt to characterise them as solids and call them “a good approximation”.

While scalable laws do not yet yield precise recipes, they have become an alternative way to view the world, and a methodology where large deviation and stressful events dominate the analysis instead of the other way around. We do not know of a more robust manner for decision-making in an uncertain world.

AUTHOR INFORMATION

Benoit Mandelbrot is Sterling professor emeritus of mathematical sciences at Yale University. He is the author of “Fractals and Scaling in Finance” (Springer-Verlag, 1999) and, with Richard L Hudson, of “The (Mis)Behaviour of Markets” (Profile, 2005).

Nassim Nicholas Taleb is a veteran derivatives trader and Dean’s professor in the sciences of uncertainty at the University of Massachusetts, Amherst. He is also the author of “Fooled by Randomness” (Random House, 2005) and “The Black Swan” (forthcoming).

Copyright The Financial Times Limited 2010. You may share using our article tools. Please don't cut articles from FT.com and redistribute by email or post to the web.

Michael Lewis’s ‘The Big Short’? Read the Harvard Thesis Instead!

By Peter Lattman

Deal Journal has yet to read “The Big Short,” Michael Lewis’s yarn on the financial crisis that hit stores today. We did, however, read his acknowledgments, where Lewis praises “A.K. Barnett-Hart, a Harvard undergraduate who had just written a thesis about the market for subprime mortgage-backed CDOs that remains more interesting than any single piece of Wall Street research on the subject.”

A.K. Barnett-Hart

While unsure if we can stomach yet another book on the crisis, a killer thesis on the topic? Now that piqued our curiosity. We tracked down Barnett-Hart, a 24-year-old financial analyst at a large New York investment bank. She met us for coffee last week to discuss her thesis, “The Story of the CDO Market Meltdown: An Empirical Analysis.” Handed in a year ago this week at the depths of the market collapse, the paper was awarded summa cum laude and won virtually every thesis honor, including the Harvard Hoopes Prize for outstanding scholarly work.

Last October, Barnett-Hart, already pulling all-nighters at the bank (we agreed to not name her employer), received a call from Lewis, who had heard about her thesis from a Harvard doctoral student. Lewis was blown away.

“It was a classic example of the innocent going to Wall Street and asking the right questions,” said Mr. Lewis, who in his 20s wrote “Liar’s Poker,” considered a defining book on Wall Street culture. “Her thesis shows there were ways to discover things that everyone should have wanted to know. That it took a 22-year-old Harvard student to find them out is just outrageous.”

Barnett-Hart says she wasn’t the most obvious candidate to produce such scholarship. She grew up in Boulder, Colo., the daughter of a physics professor and full-time homemaker. A gifted violinist, Barnett-Hart deferred admission at Harvard to attend Juilliard, where she was accepted into a program studying the violin under Itzhak Perlman. After a year, she headed to Cambridge, Mass., for a broader education. There, with vague designs on being pre-Med, she randomly took “Ec 10,” the legendary introductory economics course taught by Martin Feldstein.

“I thought maybe this would help me, like, learn to manage my money or something,” said Barnett-Hart, digging into a granola parfait at Le Pain Quotidien. She enjoyed how the subject mixed current events with history, got an A (natch) and declared economics her concentration.

Barnett-Hart’s interest in CDOs stemmed from a summer job at an investment bank in the summer of 2008 between junior and senior years. During a rotation on the mortgage securitization desk, she noticed everyone was in a complete panic. “These CDOs had contaminated everything,” she said. “The stock market was collapsing and these securities were affecting the broader economy. At that moment I became obsessed and decided I wanted to write about the financial crisis.”

Back at Harvard, against the backdrop of the financial system’s near-total collapse, Barnett-Hart approached professors with an idea of writing a thesis about CDOs and their role in the crisis. “Everyone discouraged me because they said I’d never be able to find the data,” she said. “I was urged to do something more narrow, more focused, more knowable. That made me more determined.”

She emailed scores of Harvard alumni. One pointed her toward LehmanLive, a comprehensive database on CDOs. She received scores of other data leads. She began putting together charts and visuals, holding off on analysis until she began to see patterns–how Merrill Lynch and Citigroup were the top originators, how collateral became heavily concentrated in subprime mortgages and other CDOs, how the credit ratings procedures were flawed, etc.

“If you just randomly start regressing everything, you can end up doing an unlimited amount of regressions,” she said, rolling her eyes. She says nearly all the work was in the research; once completed, she jammed out the paper in a couple of weeks.

“It’s an incredibly impressive piece of work,” said Jeremy Stein, a Harvard economics professor who included the thesis on a reading list for a course he’s teaching this semester on the financial crisis. “She pulled together an enormous amount of information in a way that’s both intelligent and accessible.”

Barnett-Hart’s thesis is highly critical of Wall Street and “their irresponsible underwriting practices.” So how is it that she can work for the very institutions that helped create the notorious CDOs she wrote about?

“After writing my thesis, it became clear to me that the culture at these investment banks needed to change and that incentives needed to be realigned to reward more than just short-term profit seeking,” she wrote in an email. “And how would Wall Street ever change, I thought, if the people that work there do not change? What these banks needed is for outsiders to come in with a fresh perspective, question the way business was done, and bring a new appreciation for the true purpose of an investment bank - providing necessary financial services, not creating unnecessary products to bolster their own profits.”

Ah, the innocence of youth.

http://blogs.wsj.com/deals/2010/03/15/michael-lewiss-the-big-short-read-the-harvard-thesis-instead/

Obama turns Castro, not only in content but now also in form.

Obama's 17-minute, 2500-word response to woman's claim of being 'over-taxed'

by Anne E. Kornblut

CHARLOTTE - Even by President Obama's loquacious standards, an answer he gave here on health care Friday was a doozy.

Toward the end of a question-and-answer session with workers at an advanced battery technology manufacturer, a woman named Doris stood to ask the president whether it was a "wise decision to add more taxes to us with the health care" package.

"We are over-taxed as it is," Doris said bluntly.

Obama started out feisty. "Well, let's talk about that, because this is an area where there's been just a whole lot of misinformation, and I'm going to have to work hard over the next several months to clean up a lot of the misapprehensions that people have," the president said.

He then spent the next 17 minutes and 12 seconds lulling the crowd into a daze. His discursive answer - more than 2,500 words long -- wandered from topic to topic, including commentary on the deficit, pay-as-you-go rules passed by Congress, Congressional Budget Office reports on Medicare waste, COBRA coverage, the Recovery Act and Federal Medical Assistance Percentages (he referred to this last item by its inside-the-Beltway name, "F-Map"). He talked about the notion of eliminating foreign aid (not worth it, he said). He invoked Warren Buffett, earmarks and the payroll tax that funds Medicare (referring to it, in fluent Washington lingo, as "FICA").

Always fond of lists, Obama ticked off his approach to health care -- twice. "Number one is that we are the only -- we have been, up until last week, the only advanced country that allows 50 million of its citizens to not have any health insurance," he said.

A few minutes later he got to the next point, which seemed awfully similar to the first. "Number two, you don't know who might end up being in that situation," he said, then carried on explaining further still.

"Point number three is that the way insurance companies have been operating, even if you've got health insurance you don't always know what you got, because what has been increasingly the practice is that if you're not lucky enough to work for a big company that is a big pool, that essentially is almost a self-insurer, then what's happening is, is you're going out on the marketplace, you may be buying insurance, you think you're covered, but then when you get sick they decide to drop the insurance right when you need it," Obama continued, winding on with the answer.

Halfway through, an audience member on the riser yawned.

But Obama wasn't finished. He had a "final point," before starting again with another list -- of three points.

"What we said is, number one, we'll have the basic principle that everybody gets coverage," he said, before launching into the next two points, for a grand total of seven.

His wandering approach might not matter if Obama weren't being billed as the chief salesman of the health-care overhaul. Public opinion on the bill remains divided, and Democratic officials are planning to send Obama into the country to persuade wary citizens that it will work for them in the long run.

It was not evident that he changed any minds at Friday's event. The audience sat politely, but people in the back of the room began to wander off.

Even Obama seemed to recognize that he had gone on too long. He apologized -- in keeping with the spirit of the moment, not once, but twice. "Boy, that was a long answer. I'm sorry," he said, drawing nervous laughter that sounded somewhat like relief as he wrapped up.

But, he said: "I hope I answered your question."

By Anne E. Kornblut | April 2, 2010; 3:01 PM ET

How to ruin a child

By George Will

http://www.JewishWorldReview.com | Memo to that Massachusetts school where children in physical education classes jump rope without using ropes: Get some ropes. And you — you are about 85 percent of all parents — who are constantly telling your children how intelligent they are: Do your children a favor and pipe down.

These are nuggets from "NurtureShock: New Thinking About Children" by Po Bronson and Ashley Merryman. It is another book to torment modern parents who are determined to bring to bear on their offspring the accumulated science of child-rearing. Modern parents want to nurture so skillfully that Mother Nature will gasp in admiration at the marvels their parenting produces from the soft clay of children.

Those Massachusetts children are jumping rope without ropes because of a self-esteem obsession. The assumption is that thinking highly of oneself is a prerequisite for high achievement. That is why some children's soccer teams stopped counting goals (think of the damaged psyches of children who rarely scored) and shower trophies on everyone. No child at that Massachusetts school suffers damaged self-esteem by tripping on the jump rope.

But the theory that praise, self-esteem and accomplishment increase in tandem is false. Children incessantly praised for their intelligence (often by parents who are really praising themselves) often underrate the importance of effort. Children who open their lunchboxes and find mothers' handwritten notes telling them how amazingly bright they are tend to falter when they encounter academic difficulties. Also, Bronson and Merryman say that overpraised children are prone to cheating because they have not developed strategies for coping with failure.

"We put our children in high-pressure environments," Bronson and Merryman write, "seeking out the best schools we can find, then we use the constant praise to soften the intensity of those environments." But children excessively praised for their intelligence become risk-adverse in order to preserve their reputations. Instead, Bronson and Merryman say, praise effort ("I like how you keep trying"): It is a variable children can control.

They often cannot control cars. In 1999, a Johns Hopkins University study found that some school districts that abolished driver's education courses experienced a 27 percent decrease in auto accidents among 16- and 17-year-olds. Odd.

Not really. Bronson and Merryman say driver's ed teaches the rules of the road and mechanics of driving, but teenagers are in fatal crashes at twice the rate of other drivers because of poor decisions, not poor skills. The wiring in the frontal lobe of the teenage brain is not fully formed. Driver's ed courses make getting a license easy, thereby increasing the supply of young drivers who actually have holes in their heads.

Their unfinished heads should spend more time on pillows. Only 5 percent of high school seniors get eight hours of sleep a night. Children get a hour less than they did 30 years ago, which subtracts IQ points and adds body weight.

Until age 21, the circuitry of a child's brain is being completed. Bronson and Merryman report research on grade schoolers showing that "the performance gap caused by an hour's difference in sleep was bigger than the gap between a normal fourth-grader and a normal sixth-grader." In high school, there is a steep decline in sleep hours, and a striking correlation of sleep and grades.

Tired children have trouble retaining learning "because neurons lose their plasticity, becoming incapable of forming the new synaptic connections necessary to encode a memory. . . . The more you learned during the day, the more you need to sleep that night."

The school day starts too early because that is convenient for parents and teachers. Awakened at dawn, teenage brains are still releasing melatonin, which makes them sleepy. This is one reason young adults are responsible for half of the 100,000 annual "fall asleep" automobile crashes. When Edina, Minn., changed its high school start from 7:25 a.m. to 8:30 a.m., math/verbal SAT scores rose substantially.

Furthermore, sleep loss increases the hormone that stimulates hunger and decreases the one that suppresses appetite. Hence the correlation between less sleep and more obesity.

Bronson and Merryman slay a slew of myths. But perhaps the soundest advice for parents is: Lighten up. People have been raising children for approximately as long as there have been people. Only recently — about five minutes ago, relative to the long-running human comedy — have parents been driving themselves to distraction by taking too seriously the idea that "as the twig is bent the tree's inclined." Twigs are not limitlessly bendable; trees will be what they will be.

Walgreens: no new Medicaid patients as of April 16

By Janet I. Tu

Seattle Times staff reporter

Effective April 16, Walgreens drugstores across the state won't take any new Medicaid patients, saying that filling their prescriptions is a money-losing proposition — the latest development in an ongoing dispute over Medicaid reimbursement.

The company, which operates 121 stores in the state, will continue filling Medicaid prescriptions for current patients.

In a news release, Walgreens said its decision to not take new Medicaid patients stemmed from a "continued reduction in reimbursement" under the state's Medicaid program, which reimburses it at less than the break-even point for 95 percent of brand-name medications dispensed to Medicaid patents.

Walgreens follows Bartell Drugs, which stopped taking new Medicaid patients last month at all 57 of its stores in Washington, though it still fills Medicaid prescriptions for existing customers at all but 15 of those stores.

Doug Porter, the state's director of Medicaid, said Medicaid recipients should be able to readily find another pharmacy because "we have many more pharmacy providers in our network than we need" for the state's 1 million Medicaid clients.

He said those who can't can contact the state's Medical Assistance Customer Service Center at 1-800-562-3022 for help in locating one.

Along with Walgreens and Bartell, the Ritzville Drug Company in Adams County announced in November that it would stop participating in Medicaid.

Fred Meyer and Safeway said their pharmacies would continue to serve existing Medicaid patients and to take new ones, though both expressed concern that the reimbursement rate is too low for pharmacies to make a profit.

The amount private insurers and Medicaid pay pharmacies for prescriptions isn't the actual cost of those drugs but rather is based on what's called the drug's estimated average wholesale price. But that figure is more like the sticker price on a car than its actual wholesale cost.

Washington was reimbursing pharmacies 86 percent of a drug's average wholesale price until July, when it began paying them just 84 percent. While pharmacies weren't happy about the reimbursement reduction, the Department of Social and Health Services said that move was expected to save the state about $10 million.

Then in September came another blow. The average wholesale price is calculated by a private company, which was accused in a Massachusetts lawsuit of fraudulently inflating its figures. The company did not admit wrongdoing but agreed in a court settlement to ratchet its figures down by about 4 percent.

That agreement took effect in September — and prompted a lawsuit by a group of pharmacies and trade associations that said Washington state didn't follow federal law in setting its reimbursement rate, and that that rate is too low. The lawsuit is pending.

"Washington state Medicaid is now reimbursing pharmacies less than their cost of participation," said Jeff Rochon, CEO of the Washington State Pharmacy Association.

Pharmacies that continue to fill Medicaid prescriptions at the current state reimbursement rate are "at risk of putting themselves out of business altogether," he said.

Information from Seattle Times archives was used in this report.

Warren Buffett and Bill Gates: Keeping America Great

Q&A with Thomas Sowell

Thomas Sowell was interviewed at Stanford University about politics, his books and columns, and his views as a conservative African-American. He talked about the role of government and public institutions, the state of American intellectualism, and public discourse in the digital age. This interview was conducted in 2005.

Michael Lewis promotes his new book "The Big Short"

Michael Lewis

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By the way, please compare this video of Michael Lewis talking about what happened with the stock market in 2007-2008 with the interview that Jim Cramer gave Stewart couple of months before here

CBS 60 Minutes Part I:




CBS 60 Minutes Part II:

Jim Cramer on "Daily Show"

Jim Cramer Extended Interview Pt. 1

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Jim Cramer Extended Interview Pt. 2

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Jim Cramer Extended Interview Pt. 3

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