S&P downgrades US

I particularly like Paul Krugman’s comment on S&P

More broadly, the rating agencies have never given us any reason to take their judgments about national solvency seriously. It’s true that defaulting nations were generally downgraded before the event. But in such cases the rating agencies were just following the markets, which had already turned on these problem debtors.

And in those rare cases where rating agencies have downgraded countries that, like America now, still had the confidence of investors, they have consistently been wrong. Consider, in particular, the case of Japan, which S.& P. downgraded back in 2002. Well, nine years later Japan is still able to borrow freely and cheaply. As of Friday, in fact, the interest rate on Japanese 10-year bonds was just 1 percent.

So there is no reason to take Friday’s downgrade of America seriously. These are the last people whose judgment we should trust.



Tidbits from Superfreakonomics that illustrate the unifying theme of the book: People respond to incentives.

César Martinelli and Susan W. Parker, two economists who analyzed the data from more than 100,000 Oportunidades clients, found that applicants routinely underreported certain items, including cars, trucks, video recorders, satellite TVs, and washing machines. This shouldn’t surprise anyone. People hoping to get welfare benefits have an incentive to make it sound like they are poorer than they truly are. But as Martinelli and Parker discovered, applicants overreported other items: indoor plumbing, running water, a gas stove, and a concrete floor. Why on earth would welfare applicants say they had these essentials when they didn’t? Martinelli and Parker attribute it to embarrassment. Even people who are poor enough to need welfare apparently don’t want to admit to a welfare clerk that they have a dirt floor or live without a toilet.



Holding off death by even a single day can sometimes be worth millions of dollars. Consider the estate tax, which is imposed on the taxable estate of a person upon his or her death. In the United States, the rate in recent years was 45 percent, with an exemption for the first $2 million. In 2009, however, the exemption jumped to $3.5 million—which meant that the heirs of a rich, dying parent had about 1.5 million reasons to console themselves if said parent died on the first day of 2009 rather than the last day of 2008. With this incentive, it’s not hard to imagine such heirs giving their parent the best medical care money could buy, at least through the end of the year. Indeed, two Australian scholars found that when their nation abolished its inheritance tax in 1979, a disproportionately high number of people died in the week after the abolition as compared with the week before.


Sobering, isn’t it?

Golden Balls: Split or Steal?

A variant of the prisoner’s dilemma:

So this was the payoff matrix they faced:

Stealing is a weakly dominant strategy: regardless of whether Sarah splits or steals, stealing always gives Steve a payoff that’s at least as good as splitting. Orange and red indicate the three Nash equilibria, where neither player has an incentive to unilaterally change his or her action. What’s most interesting however, is the strategy of both stealing. In fact, “both get nothing” isn’t exactly representative of the payoff to the loser. After all, if you were Steve, wouldn’t you feel a little consoled if you managed to thwart her plans? At the same time, wouldn’t you be much more upset if you simply allowed her to get away like that?

Some payoff clearly needs to be added for revenge. Indeed this is an important finding in the ultimatum game, where people offered significantly less than a 50-50 split typically choose to punish the other person by rejecting the entire sum. Not economically rational, since getting a little is better than getting nothing; but this experiment shows the importance of emotions in decision making.

Steve wasn’t rational by any measure, but maybe he did it on the (mistaken?) belief that many wouldn’t be able to walk away with that kind of guilt. In her defense, Sarah can truly claim to be a rational economic agent.

The Black Swan: The Impact of the Highly Improbable

This is my attempt to review the first edition of Nassim Nicholas Taleb’s The Black Swan: The Impact of the Highly Improbable.

I want to start with Taleb’s tone in the book: This is not trivial, because whether or not you can finish the book depends quite a bit on whether you can stomach his… idiosyncratic writing style. I’m accustomed to reading his style of writing having read his earlier book, Fooled by Randomness: The Hidden Role of Chance in Markets and in Life. But a reader new to Taleb should be warned: Taleb is rude. He loathes most economists, mathematicians, and statisticians – and he does not mince his words. Whether or not his dislike for them and their ideas is founded is beyond my ability to evaluate. He may have a point, and a very good one at that, but his no-holds-barred attacks on many people (again, I don’t know if they deserve it. Maybe, but his rants are often overboard and unprofessional*) and his very poorly veiled attempts to portray himself as a humble (definitely not) and deep thinker (definitely so) can come across as annoying.

On to the real stuff, I’d like to make a quick summary of his ideas; a very risky business because I don’t want to misrepresent any ideas, but I shall attempt to do so because the ideas are valuable.

The Black Swan Problem

The title is The Black Swan because of what he calls the Black Swan Problem, otherwise known as Hume’s problem of induction (after David Hume, the 18th century philosopher): Can we be certain that all swans are white simply based on the fact that all the swans we have seen are white? We may be tempted to make that inference if all our lives we’ve only seen white swans, but all it takes is one black swan to prove us wrong.

This asymmetry underlies his argument that much of the forecasting we see in financial markets is useless. If we accept that knowledge by induction is flawed, then statistical techniques like regression analysis, which involves extrapolating data points to forecast the future, has limited use. Having seen so many white swans, we naturally assume that the swans we see in the future will also be white. But when we bank our entire fortune on it – as many banks did in the recent subprime mortgage crisis – we may, one day, be in for a surprise.

Capitalizing on the asymmetry

When I first heard the idea of the black swan problem, my first thought was, yes Taleb is right, but if we constantly fear the occurrence of the black swan, aren’t we letting go of what can reasonably work with, i.e. swans are most likely to be white? In other words, should we just do nothing?

No. Taleb has a way around it: limit our downside risk as far as possible, and maximize our exposure to unforeseen benefits, or serendipity. A good example of exposing ourselves to positive consequences is Pascal’s wager. Blaise Pascal said that in deciding whether or not to believe in God, one should choose the latter because he has everything to gain if God exists and nothing to lose if God does not.

In the financial world, derivatives serve the purpose of exposing the buyer to positive consequences (or negative consequences if you happen to be the seller). The seller of a call option gives the buyer the right to buy a share at the exercise price, and the seller of a put option gives the buyer the right to sell a share back to him at the exercise price.

So suppose I have a stock worth $20 now. Thinking that stock prices are likely to fall since the economy doesn’t look too good, I want to make a quick buck by selling an American call option with an exercise price of $25. I do this for $2. This gives the buyer the right but not the obligation to buy my stock for $25 at any time before expiration, say in 6 months’ time. 6 months later, if my stock price indeed falls, then I have nothing to fear: I’ve made an easy profit on the sale of the call option. But if the price of the stock rises beyond $25, then the buyer can exercise the option and buy the stock from me for only $25.  What is the most he can lose? $2. But what he can earn is virtually unlimited – limited only by the final price of the stock. By buying out-of-money call and put options, he can only “bleed” slowly to death, but cannot “blow up” because of a sudden unexpected event such as a financial crisis. During unexpected events favorable to him, his profits are immense.

The Gaussian Distribution – Great Intellectual Fraud (GIF)?

Taleb makes one final claim in this book: that the ubiquitous bell curve is an intellectual fraud when employed in areas such as finance. Here, he makes the distinction between Mediocristan and Extremistan. Mediocristan is the land in which variables are “mediocre”, i.e. they fluctuate mildly around a certain average, and outliers are rare – if they exist, they do not significantly change the aggregate. Such variables include physical quantities such as height, weight, and IQ, which follow a Gaussian distribution. Extremistan on the other hand, is the land where variables can be extreme, and outliers can exert a massive influence. Examples include wealth (Bill Gates can greatly distort the distribution of wealth), use of words in the vocabulary (see Zipf’s law), and they do not follow the Gaussian.

So why Great Intellectual Fraud? Harsh words, and very unfair, but Taleb makes it clear within the book that the Gaussian distribution is rightly used in places where we are looking for a Yes/No answer. For example, statistical testing in psychology uses the bell curve appropriately. But when it comes to financial markets, creating sophisticated models based on the Gaussian is akin to living in your own world because the models simply don’t fit the facts.

Instead, empirical findings by the late Benoit Mandelbrot (who developed the Mandelbrot Set used in fractal geometry and Chaos Theory) showed that stock market returns exhibit memory effects, an observation which goes against one fundamental assumption of the Gaussian distribution – that of independence between trials. With that, Mandelbrot came up with the idea that stock returns exhibit fractal or wild randomness, instead of mild and controllable randomness.

Whether or not stock returns follow a Gaussian distribution makes a world of a difference, because sophisticated models taught in finance are all largely based on the Gaussian, which does not adequately measure the probability of extreme events. In fact, the Gaussian tells us that the probability that we observe a deviation from the mean decreases exponentially as this deviation increases. For example, the probability of observing a four-sigma event, or one that is four standard deviations from the mean, is 1 in 32,000. The corresponding probabilities for five- and six-sigma events are 1 in 3.5 million and 1 in a billion respectively: an exponential increase.

Indeed, if we believe that stock returns are normally distributed and consequently apply models such as the capital asset pricing model (CAPM), we would greatly underestimate the true risk of extreme events. On the other hand, fractal randomness attributes more accurate probabilities to extreme events, an issue discussed in greater depth in Mandelbrot’s book, The (Mis)Behavior of Markets: A Fractal View of Risk, Ruin, and Reward.


I’ve only presented the main ideas that jumped out at me when I was reading the book. There are some other valuable ones pertaining to how we process information and make decisions, but again, these are better discussed in books on cognitive psychology and behavioral economics. This is a book that will change the way you view the world, and in particular, the role of randomness in our lives. If you can stomach his arrogant tone (and that is a big If), this is a great read.

*Taleb has on a few occasions traded public attacks with Myron Scholes (of the Black-Scholes formula, which Taleb condemns). After Long-Term Capital Management blew up, he commented that Scholes would be better off doing sudoku in a retirement home instead of giving advice on risk management.

How far will behavioral economics bring us?

Not very far, according to George Loewenstein, one of the field’s pioneers.

But the field has its limits. As policymakers use it to devise programs, it’s becoming clear that behavioral economics is being asked to solve problems it wasn’t meant to address. Indeed, it seems in some cases that behavioral economics is being used as a political expedient, allowing policymakers to avoid painful but more effective solutions rooted in traditional economics.

Behavioral economics should complement, not substitute for, more substantive economic interventions. If traditional economics suggests that we should have a larger price difference between sugar-free and sugared drinks, behavioral economics could suggest whether consumers would respond better to a subsidy on unsweetened drinks or a tax on sugary drinks.

But that’s the most it can do. For all of its insights, behavioral economics alone is not a viable alternative to the kinds of far-reaching policies we need to tackle our nation’s challenges.

Loewenstein’s article is motivated by his observation that every week, we see books and articles that talk about how irrational decision making can have implications on our life. Indeed, apart from some exceptional ones, many books and articles simply repeat what everyone already knows about how irrational we really are. The book review I spotted on Amazon probably describes better some of the books out there:

This book is a feature length article expanded into a book. After the first 30 pages, I felt like the dead horse was being kicked, and kicked, and kicked, and kicked… and it was dead. I get it… People make bad decisions, and have bad beliefs they cling to. Enough already. I tried reading every tenth page, and it was just the same stuff.

Of course we must remember that behavioral economics is a relatively new – and fertile – field. At this point of time however, I think what we require more is the application of behavioral economics. Richard Thaler and Cass Suntein’s Nudge does a great job on proposing how its findings can be applied in public policy to achieve what they call “libertarian paternalism”. Libertarian paternalism refers to fulfilling the demands of citizens to have freedom of choice (hence libertarian), yet subtly nudging people towards making choices that are good for them (hence paternalism).

An example would be the urinals below – by the way Terminal 3 of Changi Airport has toilets with urinals that have flies etched on them – through the design they prompt males to aim better, reducing spillage.

It would be wonderful if we could see more books like this instead of those that simply repeat the same old experiments that reveal human irrationality.

We might go even further if behavioral economics starts integrating formally with macroeconomics. At present, financial mathematics is receiving plenty of flak all around for its contribution to the financial crisis. Economyths: Ten Ways Economics Gets it Wrong put me to sleep every single time I read it, but its main premise is that economics has hit a brick wall because of how economists have incorporated mathematics based on unrealistic assumptions to make the field more rigorous, like a science.

Nassim Taleb’s Fooled by Randomness very severely denigrates the field of financial mathematics, calling quants and traders alike arrogant in their belief that risk can be systematically managed. For that matter, Taleb harshly criticizes the entire field of economics – the proof being his mockery of the Nobel Prize in Economics – save for the more realistic behavioral economics. In How Markets Fail*, John Cassidy calls traditional economics “utopian economics”, and behavioral economics “reality-based economics.

All these make me wonder about the future of economics and finance. While debates still rage on in these fields, what are we as students going to be taught? Are we going to continue to rely on such theories as the efficient markets hypothesis and capital asset pricing model? How much of the behavioral approach should we be exposed to?  One thing’s for sure: all this uncertainty makes for a very exciting time to learn finance.

*How Markets Fail is a very, very illuminating read of an overview of economics.


Two interesting things I learnt about learning today:

1. Why shouldn’t passersby praise, pet, or feed a service dog while he is working?

Service dogs learn by operant conditioning, a form of associative learning in which the consequences of a behavior changes the frequency of the behavior’s occurrence. For example, a child may be rewarded with ice cream (consequence) for completing his homework (behavior). This encourages him to continue being diligent in his work. Providing rewards while the dog is working may interfere with his training.

2. Where did the idea for the Homing Pigeon in Worms come from?

B.F. Skinner, an American psychologist famous for his work on Behaviorism, tested the concept of a pigeon-guided missile during World War II. A pigeon in the warhead would operate the flaps on the missile and guide it home by pecking at an image of a target.  How could this work? When the missile was in flight, the pigeon pecked the moving image on a screen, receiving a reward of food to keep the designated target in the center of the screen. This reward produced corrective signals to keep the missile on course. It worked, but it was never put into practice. Once again, the principle of operant conditioning.

In economics, this would simply be known as incentive theory.