How my IIMB Class explains the 2008 financial crisis

I have a policy of not enforcing attendance in my IIMB class. My view is that it’s better to have a small class of dedicated students rather than a large class of students who don’t want to be there. One of the upsides of this policy is that there has been no in-class sleeping. Almost. I caught one guy sleeping last week, in what was session 16 (out of 20). Considering that my classes are between 8 and 9:30 am on Mondays and Tuesdays, I like to take credit for it.

I also like to take credit for the fact that despite not enforcing attendance, attendance has been healthy. There have usually been between 40 and 50 students in each class (yes, I count, when I’ve bamboozled them with a question and the class has gone all quiet), skewed towards the latter number. Considering that there are 60 students registered for the course, this translates to a pretty healthy percentage. So perhaps I’ve been doing something right.

The interesting thing to note is that where there are about 45 people in each class, it’s never the same set of 45. I don’t think there’s a single student who’s attended all of my classes. However, people appear and disappear in a kind of random uncoordinated fashion, and the class attendance has remained in the forties, until last week that is. This had conditioned me into expecting a rather large class each time I climbed up that long flight of stairs to get into class.

While there were many causes of the 2008 financial crisis, one of the prime reasons shit hit the fan then was that CDOs (collateralised debt obligations) blew up. CDOs were an (at one point in time) innovative way of repackaging receivables (home loans or auto loans or credit card bills) so as to create a set of instruments of varying credit ratings.

To explain it in the simplest way, let’s say I’ve lent money to a 100 people and each owes me a rupee each month. So I expect to get a hundred rupees each month. Now I carve it up into tranches and let’s say I promise Alice the “first 60 rupees” I receive each month. In return she pays me a fee. Bob will get the “next 20 rupees”, again for a fee. Note that if fewer than 60 people pay me this month, Bob gets nothing. Let’s say Eve gets the next 10 rupees, so in case less than 80 people pay up, Eve gets nothing. So this is very risky, and Eve pays much less for her tranche than Bob pays for his which is in turn much less than what Alice pays for hers. The last 10 rupees is so risky that no one will buy it and so I hold it.

Let’s assume that about 85 to 90 people have been paying on their loans each month. Not the same people, but different, like in my class. Both Alice and Bob are getting paid in full each month, and the return is pretty impressive considering the high ratings of the instruments they hold (yes these tranches got rated, and the best tranche (Alice’s) would typically get AAA, or as good as government bonds). So Alice and Bob make a fortune. Until the shit hits the fan that is.

The factor that led to healthy attendance in my IIMB class and what kept Alice and Bob getting supernormal returns was the same – “correlation”. The basic assumption in CDO markets was that home loans were uncorrelated – my default had nothing to do with your default. So both of us defaulting together is unlikely. When between 10 and 15 people are defaulting each month, that 40 (or even 20) people will default together in a given month has very low probability. Which is what kept Alice and Bob happy. It was similar in my IIMB class – the reason I bunk is uncorrelated to the reason you bunk, so lack of correlation in bunking means there is a healthy attendance in my class each day.

The problem in both cases, as you might have guessed, is that correlations started moving from zero to one. On Sunday and Monday night this week, they had “club selections” on IIMB campus. Basically IIMB has this fraud concept called clubs (which do nothing), which recruiters value for reasons I don’t know, and so students take them seriously. And each year’s officebearers are appointed by the previous year’s officebearers, and thus you have interviews. And so these interviews went on till late on Monday morning. People were tired, and some decided to bunk due to that. Suddenly, there was correlation in bunking! And attendance plummeted. Yesterday there were 10 people in class. Today perhaps 12. Having got used to a class of 45, I got a bit psyched out! Not much damage was done, though.

The damage was much greater in the other case. In 2008, the Federal Reserve raised rates, thanks to which banks increased rates on home loans. The worst borrowers defaulted, because of which home prices fell, which is when shit truly hit the fan. The fall in home prices meant that many homes were now worth less than the debt outstanding on them, so it became rational for homeowners to default on their loans. This meant that defaults were now getting correlated! And so rather than 85 people paying in a month, maybe 45 people paid. Bob got wiped out. Alice lost heavily, too.

This was not all. Other people had bet on how much Alice would get paid. And when she didn’t get paid in full, these people lost a lot of money. And then they defaulted. And it set off a cascade. No one was willing to trade with anyone any more. Lehman brothers couldn’t even put a value on the so-called “toxic assets” they held. The whole system collapsed.

It is uncanny how two disparate events such as people bunking my class and the 2008 financial crisis are correlated. And there – correlation rears its ugly head once again!


Correlations: In Traffic, Mortgages and Everything Else

Getting caught in rather heavy early morning traffic while on my way to a meeting today made me think of the concept of correlation. This was driven by the fact that I noticed a higher proportion of cars than usual this morning. It had rained early this morning, and more people were taking out their cars as a precautionary measure, I reasoned.

Assume you are the facilities manager at a company which is going to move to a new campus. You need to decide how many parking slots to purchase at the new location. You know that all your employees possess both a two wheeler and a car, and use either to travel to work. Car parking space is much more expensive than two wheeler parking space, so you want to optimize on costs. How will you decide how many parking spaces to purchase?

You will correctly reason that not everyone brings their car every day. For a variety of reasons, people might choose to travel to work by scooter. You decide to use data to make your decision on parking space. For three months, you go down to the basement (of the old campus) and count the number of cars, and you diligently tabulate them. At the end of the three months, you calculate that on an average (median), thirty people bring their cars to work every day. You calculate that on ninety five percent of the days there were forty or fewer cars in the basement, and on no occasion did the total number of cars in the basement cross forty five.

So you decide to purchase forty car parking spaces in the new facility. It is not the same set of people who bring their cars to work every day. In fact, each employee has brought his/her car to the workplace at least once in the last three months. What you are betting on here, however, is correlation, You assume that the reason Alice brings her car to office is not related to the reason Bob brings his car to office. To put it statistically, you assume that Alice bringing her car and Bob bringing his car are independent events. Whether Alice brings her car or not has no bearing on Bob’s decision to bring his car, and vice versa. And you know that even on the odd day when more than forty people bring their cars, there are not more than forty five cars, and you can somehow “adjust” with your neighbours to borrow the additional slots for that day. You get a certificate from the CEO for optimizing on the cost of parking space.

And then one rainy morning things go horribly wrong. Your phone doesn’t stop ringing. Angry staffers are calling you complaining that they have no place to park. Given the heavy rains that morning, none of the staffers have wanted to risk getting wet in the rain, and have all decided to bring their cars. Never before have they faced a problem parking so they are all confident that there will be no problem parking once they get to work, only to realize there is not enough parking space. Over a hundred employees have driven to work, and there are only forty slots to park.

The problem here, as you might discover, is that of correlation. You had assumed that Alice’s reason to get her car was uncorrelated to Bob’s decision. What you had not accounted for was the possibility that there could be an exogenous event that could suddenly drive the correlation from zero to one, thus upsetting all your calculations!

This is analogous to what happened during the Financial Crisis of 2008. Normally, Alice defaulting on her home loan is not correlated with Bob defaulting on his. So you take a thousand such loans, all seemingly uncorrelated with each other and put them in a bundle, assuming that 99% of the time not more than five loans will default. You then slice this bundle into tranches, get some of them rated AAA, and sell them on to investors (and keep some for yourself). All this while, you have assumed that the loans are uncorrelated. In fact, the independence was a key assumption in your expectation of the number of loans that will default and in your highest tranche getting a AAA rating.

Now, for reasons beyond your control and understanding, house prices drop. Soon it becomes possible for home owners to willfully default on their loans – the value of the debt now exceeds the value of their home. With one such exogenous event, correlations suddenly rise. Fifty loans in your pool of thousand default (a 1 in gazillion event according to your calculations that assumed zero correlation). Your AAA tranche is forced to pay out less than full value. The lower tranches get wiped out. This and a thousand similar bundles of loans set off what ultimately became the Financial Crisis of 2008.

The point of this post is that you need to be careful about assuming correlations. It is to illustrate that sometimes an exogenous event can upset your calculations of correlations. And when you go wrong with your correlations – especially those among a large number of variables, you can get hurt real bad.

I’ll leave you with a thought: assuming you live in a primarily two wheeler city (like Bangalore, where I live), what will happen to the traffic on a day when 10% more people than usual get out their cars?

On age and experience and respecting elders

A lot of commentary about the financial crisis of 2008 spoke about there not being anyone around who had experienced the Great Depression of the 1930s. The American Economy was largely stable till the end of the 1970s, they had argued, because the memory of the Depression was fresh in the minds of most policy-makers, and they made sure not to repeat similar mistakes. With that cohort retiring, and dying, however, in the 1990s and 2000s there emerged a bunch of policy makers with absolutely no recollection of the depression (in the 1990s, most policy makers would have been born in the 1940s or later). And so they did not hedge themselves and the economy against the kind of risks that had brought America down to its knees in the 1930s.

Now, think back to a society which was far less networked than ours is, and there was little writing (“no writing” would take us too far back in time, but think of a time when it was fairly expensive to write and store written material). This meant, that there were no books, and little to understand and experience apart from what one directly experienced. For example, one would never know what a storm is if one had never directly experienced it. One wouldn’t know how to light a fire if one had never seen a fire being lit. You get the drift. Back in those days when societies were hardly networked and there wasn’t much writing, there was only one way in which one could have learnt things – by having experienced it.

I suspect that this whole concept of elders having to be unconditionally respected had its advent in one such age. Back then, the older you were, the more you had experienced (naturally!), and hence the more you knew! There was no other way in which one could accumulate knowledge or understanding. In places like India, even education didn’t help, for “education” back in those days consisted of little more than learning the scriptures by rote, and didn’t teach much in terms of real knowledge. So taking the advice of elders naturally meant taking the advice of someone who knew more. It is natural to assume that these people who knew more than the ones around were respected.

With the advent of books, and later (post Gutenburg) the advent of cheap books, all this began to change. It became possible for people to know without having experienced. It became possible for people to get more networked, and the direct impact of both of these was that it became possible to know more without having really experienced it. In this day of highly networked societies and wikipedia, it is even possible to know everything about something without even pretending to have experienced it (attend some high school seminars and you’ll know what I’m talking about). There is no connection at all now between age and how much you know.

Culture, however, doesn’t adapt itself so quickly. It didn’t help that “elders”, whose position as the “most knowledgeable” was being threatened thanks to writing and networking, were also the people in power. In any case, the real reason of respect for elders had probably been lost, so it was easier for them to extend their reign. And so it continues to extend.

Older people nowadays fail to recognize that younger people might know more than them, and get offended if the younger people tend to argue with them. Yes, experience is still a great teacher, but the correlation between experience and knowledge has long since been broken. As the pupils sang at the beginning of the Vishnuvardhan starrer Guru Shishyaru (the teacher and the pupils), “doDDavarellaa jaaNaralla, chikkavarellaa kONaralla, gurugaLu hELida maatugaLantoo endoo nijavallaa” (elders are not wise, youngsters are not buffaloes, what the teacher says is never true).

PS: As I was writing this, it struck me that this whole “respect for elders” paradigm is more prevalent in societies (such as India) where education was largely religious. Societies where education was more secular don’t seem to have this paradigm.

The Lingaraj Effect and Financial Regulation

Lingaraj was a driver who used to work for my father. He had a unique way of dealing with traffic jams on two-lane roads without a divider down the middle. He would instinctively swing the ambassador into the right lane – meant for traffic in the opposite direction (the jam ahead meant there was little traffic flow in that direction).

I remember both my father and I abusing him (Lingaraj) for this method which would only make the jam worse. However, he would persist. And we soon found that he wasn’t unique in his methods. It is the favoured method of most Bangalore drivers. Thus, whenever there is a minor jam somewhere, thousands of Lingarajs clog the “return lane” in all directions, and end up making it worse.

The funny thing about Lingaraj’s method was that it was “too big to fail”. Having switched to the right lane, we would progress much faster (till the site of the jam, of course) than our law-abiding brethren stuck in the left lane. There, someone who had taken responsibility of clearing the jam (not necessarily a cop) would realize that a necessary condition to clear the jam was to get our ambassador out of the right lane. And we would be given passage to shift to the left lane, and past the jam site, much ahead of those suckers who stuck to the law.

For drivers like Lingaraj, moving to the right lane in the wake of a jam is seen as “arbitrage”. And a necessary condition for it to be an arbitrage is that the offending vehicle is “too big to fail”, as I mentioned earlier. And given that in Bangalore, measures like traffic tickets sent by post aren’t that effective, this continues to be an arbitrage, and hence you still see so many drivers use this “method”.

While stuck in a traffic jam like that one last weekend (I was driving, and I consider myself socially responsible so stuck to the left lane), I realized how similar this was to the financial crisis of three years ago.

Traders noticed an “arbitrage” that didn’t really exist (namely, some AAA rated bonds traded at higher yields than other AAA rated bonds) and proceeded to trade on it. When they got into trouble the regulators realized that they had to be bailed out in order to clear the larger mess. The resemblance is uncanny.

So what should the regulators have done? Basically, drivers should’ve been prevented from getting to the right lane in the first place. Then there would have been no requirement to bail them out. In some places, this is done by installing road dividers, but in my experience I’ve seen that doesn’t help, too. People use whatever gaps are available in the divider to go to the right lane, and contribute to the jam.

The only option I can think of is some variation of postal tickets – having bailed out the drivers for going to the right lane, they need to be made to pay for it. Yeah, postal tickets (sending tickets by post for traffic violations) may not be effective, but that seems like the best we can do to regulate this problem. The upshot is that once we figure out how to solve this problem on the road, we can extend the solution to financial regulation, too!

Big Management and Big Picture

One common shortcoming that top management in a lot of companies is accused of is that they give too much attention to details (i.e. sometimes they micromanage), and they are unable to see the big picture.

For example, if you think about the financial crisis of 2007-08, people kept making stupid bets about the mortgage market because they didn’t look at mortgages in the overall context of economy. They looked at their models, made sure they “converged” to a zillion digits, the math was perfect, etc. And priced. And conveniently forgot some of the “big assumptions”.

I think this has to do with the typical promotion procedures in corporations, and an assumption that people who are good at one kind of stuff will continue to be good at other kinds of stuff.

For example, in the early part of your career, in order to move up the “corporate ladder”, it’s important to show your skills at being able to give attention to detail, to be able to see the “little picture”, be careful and precise, and so on. For these are the kind of skills that makes one successful in the lower-level jobs.

Now, my hypothesis is that being good at details and being good at seeing the big picture are at best orthogonal, and at worst negatively correlated. I base this hypothesis on some initial reading on stuff like Attention Deficit & Hyperactivity Disorder and related topics.

So, when you promote people based on their ability to be good at details (which is required at lower levels of the job), you will end up with a top and middle management full of people who are excellent at details, and whose ability in seeing the big picture is at best questionable. Explains well, right?

I don’t know what can be done to rectify this. Promotion is too important to take away as an incentive for good performance at junior levels. Some organizations do institute procedures where for higher promotions you also need to show skills that show your big picture skills. But these are only for people who have already reached middle management, which is people who are good at details, which means that a large part of those who started at the bottom, and who are “big picture people” would have already fallen at the wayside by then.

Does my hypothesis make sense? If it does, what do you think needs to be done to get big picture thinkers at the top?



Addition to the Model Makers Oath

Paul Wilmott and Emanuel Derman, in an article in Business Week a couple of years back (at the height of the financial crisis) came up with a model-makers oath. It goes:

• I will remember that I didn’t make the world and that it doesn’t satisfy my equations.

• Though I will use models boldly to estimate value, I will not be overly impressed by mathematics.

• I will never sacrifice reality for elegance without explaining why I have done so. Nor will I give the people who use my model false comfort about its accuracy. Instead, I will make explicit its assumptions and oversights.

• I understand that my work may have enormous effects on society and the economy, many of them beyond my comprehension.

While I like this, and try to abide by it, I want to add another point to the oath:

As a quant, it is part of my responsibility that my fellow-quants don’t misuse quantitative models in finance and bring disrepute to my profession. It is my responsibility that I’ll put in my best efforts to be on the lookout for deviant behavour on the part of other quants, and try my best to ensure that they too adhere to these principles.

Go read the full article in the link above (by Wilmott and Derman). It’s a great read. And coming back to the additional point I’ve suggested here, I’m not sure I’ve drafted it concisely enough. Help in editing and making it more concise and precise is welcome.


The Aftermath

Baada collaborated on the research leading up to his post. I hereby acknowledge his contribution and condemn his laziness for not blogging it himself.

One of the major problems of the financial crisis that has been happening for about two years now is that investment bankers, as a profession, stand discredited. Before this, they used to claim to be on the top of the intellectual ladder. And now, thanks to a handful (more than a handful; but still a small proportion) of phenomenally stupid investment bankers, the entire community stands discredited. Not just that, they have left the community of quants, of people who can be good at structuring, of finance people, of statisticians, all discredited. You say “all you need to do is to get a few ibankers into these jobs” and you’ll have people come at you like a pack of hounds, waving Mint and saying “look at the damage these buggers have caused, and you think they can solve this problem”.

So Baada and I were talking about cricket the other day. About how thanks to the demands of television, flat pitches are being prepared everywhere. Which is leading to tame and boring draws. Which has led to domestic cricket being effectively reduced to a one-innings game. Which has led to massive fourth innings run chases. Which has led to bowlers break down once every couple of seasons. And so forth.

The argument put forth in favour of flat pitches is that in order to maximise television revenues, you need the game to last five days. Excellent argument, and Baada and I agreed to it. But the friggin’ point is that if you have  a boring game, no one is going to watch it. If you have a game that is most likely to end up as a draw, it will have no audience. Advertisers would be paying through their nose for near-zilch viewership.

In the medium term, things should even out. Advertisers will realize that due to the boring nature of Test cricket, no one will watch it anyway, and will back away. Ad rates will fall. And TV rights bids will fall consequently. And the boards will understand their folly and take steps to make cricket interetsing again. (there is also the danger that boards will use this to say that no one watches Test cricket anymore and scraps it altogether). However, advertisers should not be so passive and wait for things to even out.

Given a large number of statistics, playing conditions, day of week seasonality and all such stuff, it shouldn’t be hard for the smart advertiser to figure out which are going to be his most profitable slots. And bid specifically for those. If one smart advertiser does that, then that advertiser stands to gain against other advertisers who will end up paying more money for less profitable slots. And so all advertisers will become smart. Now, the channels will stop seeing uniform demand patterns for their various advertising slots. They will now need to acquire smartness in order to combat the smart advertisers. This way, smartness will prevail in the system.

I’m sure that once something like this happens, natural balance will get restored. It will take much less time for TV channels to realize that three-day Tests on bowling pitches can get them greater revenues compared to runfests played over five days. And they won’t take much time to communicate the same to the boards who will then restore Test cricket back to glory.

The problem with a lot of advertising people is that they see themselves as “creative people” because of which they assume they don’t need to know and use maths. And they don’t do the smart calculations I described earlier. As for the brand managers, it is likely that a lot of them decided to pursue marketing because they either didn’t like quant or found themselves weak at quant. Apart from a few simple excel models, they too are likely to shun the kind of smartness required here.

So where are the white knights who can save the version of the gentleman’s game played in whites? Not currently in the ad agencies. Most likely not in the marketing departments. They are all out there. A few months ago, they were employed. Earning very good salaries, and grand bonuses. Earning amounts of money unaffordable to most advertising and marketing companies. Thanks to the financial meltdown, they are available now. Looking for a fresh challenge.

This is the best time for you to infuse quant to your business. You won’t get the kind of quant supply in the market that you are seeing now. Even if the financial industry doesn’t recover (in any case it will never go back to 2007 levels), supply side factors should ensure lower supply. Do that little experiment now. Acknowledge that numbers can do a lot of good for your business. Understand what structuring is all about, and estimate the kind of impact a good structurer can have on your revenues. Make that little bit effort and I’m sure you’ll get convinced. Go make that offer. An offer these ex-ibankers can’t refuse in the current circumstances at least.

PS: When I refer to investment banking, I also include the “outside-the-wall” side of the business (called “markets”; “sales and trading”; “securities” and various other names). In fact, I mostly talk about the outside-the-wall business, not having had any exposure inside the wall.