It is a similar case with travel agents. Vacation markets (flights, hotels, etc.) are nowhere as liquid as financial markets, and will never be. Sometimes, when you are booking holidays to a strange place, you know little about it, and hence commission a travel agent to find you a place to stay there. Given that you know little about that place, the agent can charge you hefty commissions, and make a nice spread. Of course, nowadays such opportunities are diminishing for agents, as you have websites such as Agoda which allow you to book hotels directly. Now, at one place you can compare the prices of different hotels, and have better information compared to what the agents traditionally offer you. The spread is on the downswing, I must think.

Then, don’t you think package tours are very similar to structured products? Structured products are nothing but a package of several risks packaged together. By acting as a counterparty on a structured product, a bank (even now ) can afford to charge fairly hefty fees. Structured products are illiquid,  and there is no publicly available “market price”, so it is easy for banks to make themselves good spreads on such products. However, all it takes to defeat this is an intelligent customer. All the customer needs to do is to try and understand the risks himself, and start “unbundling” them. Once he unbundles the risks, he can now trade each of them independently, on more liquid markets, and get a much better price than what bankers will offer him. The catch here is that he’ll need to put in that effort in unbundling.

It’s the same with package tours. Given the bundles, it is easy for the agents to make higher spreads. However, if you as a customer simply unbundle the package (hotels, transport, food, etc.), you can find out the price of each (available on sites like agoda and elsewhere) and find out for yourself the spread that the agent is making. And then you compare the agent’s premium with the “cost” of making all the bookings yourself and make an informed choice.

Apart from communication, among the greatest boons of the internet has to do with dismantling middleman monopolies. It is incredible how much use a little information can be of!

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!

People like models. People like models because it gives them a feeling of being in control. When you observe a completely random phenomenon, financial or otherwise, it causes a feeling of unease. You feel uncomfortable that there is something that is beyond the realm of your understanding, which is inherently uncontrollable. And so, in order to get a better handle of what is happening, you resort to a model.

The basic feature of models is that they need not be exact. They need not be precise. They are basically a broad representation of what is actually happening, in a form that is easily understood. As I explained above, the objective is to describe and understand something that we weren’t able to fundamentally comprehend.

All this is okay but the problem starts when we ignore the assumptions that were made while building the model, and instead treat the model as completely representative of the phenomenon it is supposed to represent. While this may allow us to build on these models using easily tractable and precise mathematics, what this leads to is that a lot of the information that went into the initial formulation is lost.

Mathematicians are known for their affinity towards precision and rigour. They like to have things precisely defined, and measurable. You are likely to find them going into a tizzy when faced with something “grey”, or something not precisely measurable. Faced with a problem, the first thing the mathematician will want to do is to define it precisely, and eliminate as much of the greyness as possible. What they ideally like is a model.

From the point of view of the mathematician, with his fondness for precision, it makes complete sense to assume that the model is precise and complete. This allows them to bringing all their beautiful math without dealing with ugly “greyness”. Actual phenomena are now irrelevant.The model reigns supreme.

Now you can imagine what happens when you put a bunch of mathematically minded people on this kind of a problem. And maybe even create an organization full of them. I guess it is not hard to guess what happens here – with a bunch of similar thinking people, their thinking becomes the orthodoxy. Their thinking becomes fact. Models reign supreme. The actual phenomenon becomes a four-letter word. And this kind of thinking gets propagated.

Soon the people fail to  see beyond the models. They refuse to accept that the phenomenon cannot obey their models. The model, they think, should drive the phenomenon, rather than the other way around. The tails wagging the dog, basically.

I’m not going into the specifics here, but this might give you an idea as to why the financial crisis happened. This might give you an insight into why obvious mistakes were made, even when the incentives were loaded in favour of the bankers getting it right. This might give you an insight as to why internal models in Moody’s even assumed that housing prices can never decrease.

I think there is a lot more that can be explained due to this love for models and ignorance of phenomena. I’ll leave them as an exercise to the reader.

Apart from commenting about the content of this post, I also want your feedback on how I write when I write with pencil-on-paper, rather than on a computer.

So while scores of older scientists and economists and policymakers lament the “loss” of so many bright minds to science, has anyone at all considered the reverse possibility? Of the impact that Wall Street has had on grad schools in the US?

One thing you need to face is that there are not a lot of academic jobs going around. The number of people finishing with PhDs each year is far more than the number of academic jobs that open up each year. I’m mostly talking about “assistant professor” kind of jobs here, and assuming that becoming a post-doc just delays your entry into the job market rather than removing you from the market altogether.

In certain fields such as engineering, there are plenty of jobs in the industry for PhDs who don’t get academic jobs, for whatever reason. Given this, it is “cheaper” to do a PhD in these subjects, since it is very likely that you will end up with a “good job”. Hence, there is more incentive to do a PhD in subjects like this, and universities usually never have a problem in finding suitable candidates for their PhD programs. However, there is no such cushion in the pure sciences (math/physics). There are few “industry employers” who take on the slack after all the academic positions have been filled up. And that is where Wall Street steps in.

The presence of Wall street jobs offers a good backstop to potential Math and Physics PhD candidates. If they aren’t able to do the research that they so cherish, they needn’t despair since there exists a career path which will enable them to make lots of money. And knowing the existence of this career option means more people will be willing to take the risk of doing a PhD in these subjects (since the worst case isn’t so bad now). Which in turn enhances the candidate pool available to grad schools.

So even if you were to believe that complex derivatives are financial “weapons of mass destruction”, there is reason for them to exist, to encourage the financial sector to pick up PhDs. For if PhDs were kept out of these jobs, it is real academic research in “real subjects” such as the pure sciences that will suffer. By picking up PhDs in large numbers, the financial sector is making its own little contribution to research in pure sciences.

So after I’d written that, I started thinking – so if IPO managers turn out to be devious/incompetent, like LinkedIn’s bankers have, how can a company really trust them to raise the amount of money they want? What is the guarantee that the banker will price the company at the appropriate price?

One way of doing that is to get the views of a larger section of people before the IPO price is set. How would you achieve that? By having a little IPO. Let me explain.

You want to raise money for expansion, or whatever, but you don’t need all the money now. However, you are also concerned about dilution of your stake, so would like to price the IPO appropriately. So why don’t you take advantage of the fact that you don’t need all the money now, and do it in stages?

You do a small IPO up front, with the sole purpose of getting listed on the country’s big exchanges. After that the discovery of the value of your company will fall into the hands of a larger set of people – all the stock market participants. And now that the market’s willingness to pay is established, you can do a follow on offer in due course of time, and raise the money you want.

However, I don’t know any company that has followed this route, so I don’t know if there’s any flaw with this plan. I know that if you do a small IPO you can’t get the big bankers to carry you, but knowing that some big bankers don’t really take care of you (for whatever reason) it’s not unreasonable to ditch them and go with smaller guys.

What do you think of this plan?

The IPO pricing issue is in the news again now, with LinkedIn listing at close to 100% appreciation of its IPO price. The IPO was sold to investors at $45 a share, and within minutes of listing it was trading at close to $90. I haven’t really followed the trajectory of the stock after that, but assume it’s still closer to $90 than to $45.

Unlike in the Makemytrip case (maybe that got ignored since it’s an Indian company and not many commentators know about it), the LinkedIn IPO has got a lot of footage among both the mainstream media and the blogosphere. There have been views on both sides – that the i-banks shafted LinkedIn, and that this appreciation is only part of the price discovery mechanism, so it’s fair.

One of my favourite financial commentators Felix Salmon has written a rather large piece on this, in which he quotes some of the other prominent commentators also. After giving a summary of all the views, Salmon says that LinkedIn investors haven’t really lost out too much due to the way the IPO has been priced (I’ve reproduced a quote here but I’d encourage you to go read Salmon’s article in full):

But the fact is that if I own 1% of LinkedIn, and I just saw the company getting valued on the stock market at a valuation of $9 billion or so, then I’m just ecstatic that my stake is worth $90 million, and that I haven’t sold any shares below that level. The main interest that I have in an IPO like this is as a price-discovery mechanism, rather than as a cash-raising mechanism. As TED says, LinkedIn has no particular need for any cash at all, let alone $300 million; if it had an extra $200 million in the bank, earning some fraction of 1% per annum, that wouldn’t increase the value of my stake by any measurable amount, because it wouldn’t affect the share price at all.

Now, let us look at this in another way. Currently Salmon seems to be looking at it from the point of view of the client going up to the bank and saying “I want to sell 100,000 shares in my company. Sell it at the best price you can”. Intuitively, this is not how things are supposed to work. At least, if the client is sensible, he would rather go the bank and say “I want to raise 5 million dollars. Raise it by diluting my current shareholders by as little as possible”.

Now you can see why the existing shareholders can be shafted. Suppose I owned one share of LinkedIn, out of a total 100 shares outstanding. Suppose I wanted to raise 9000 rupees. The banker valued the current value at $4500, and thus priced the IPO at $45 a share, thus making me end up with 1/300 of the company.

However, in hindsight, we know that the broad market values the company at $90 a share, implying that before the IPO the company was worth $9000. If the banker had realized this, he would have sold only 100 fresh shares of the company, rather than 200. The balance sheet would have looked exactly the same as it does now, with the difference that I would have owned 1/200 of the company then, rather than 1/300 now!

1/200 and 1/300 seem like small numbers without much difference, but if you understand that the total value of LinkedIn is $9 billion (approx) and if you think about pre-IPO shareholders who held much larger stakes, you know who has been shafted.

I’m not passing a comment here on whether the bankers were devious or incompetent, but I guess in terms of clients wanting to give them future business, both are enough grounds for disqualification.

• 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.

Based on some past pattern recognition, I realized that the guy in the suit was peddling the said stock (he was a research analyst) and asking people to buy it. According to him, the stock price would reach 1200 (I have no clue what company this is and how much it trades for now). However, there were two important pieces of information he didn’t give me, because of which I’ll probably never take advice from him or someone else of his ilk.

Firstly, he doesn’t tell me when the stock price will reach 1200. For example, if it is 1150 today, and it is expected to reach 1200 in 12 years, I’d probably be better off putting my money in the bank, and watching it grow risk-free. Even if the current price were lower, I would want a date by which the stock is supposed to reach the target price. Good finance implies tenure matching, so I should invest accordingly. If the stock is expected to give good returns in a year, then I should put only that money into it which I would want to invest for around that much time. And so forth.

Then he doesn’t tell me how long it will stay at 1200. I’m not an active investor. I might check prices of stocks that I own maybe once in a week (I currently don’t own any stock). So it’s of no use to me if the price hits 1200 some time during some intraday trade. i would want the price to remain at 1200 or higher for a longer period so that I can get out.

Thirdly and most importantly, he doesn’t tell me anything about volatility. He doesn’t give me any statistics. He doesn’t tell me if 1200 is the expected value of the stock, or the median, or the maximum, or minimum, at whatever point of time (we’ve discussed this time bit before). He doesn’t tell me what are the chances that I’ll get that 1200 that he professes. He doesn’t tell me what I can expect out of the stock if things don’t go well. And as a quant, I refuse to touch anything that doesn’t come attached with a distribution.

Life in general becomes so much better when you realize and recognize volatility (maybe I’ll save that for another discourse). It helps you set your expectations accordingly; it helps you plan for situations you may not have thought of; most importantly it allows you to recognize the value of options (not talking about financial options here; talking of everyday life situations). And so forth.

So that is yet another reason I don’t generally watch business TV. I have absolutely no use for their stock prediction and tips. And I think you too need to take these tips and predictions with a bit of salt. And not spend a fortune buying expensive reports. Just use your head. Use common sense. Recognize volatility. And risk. And you’ll do well.

We had returned to Leh that afternoon after spending the previous day at Nubra valley, some hundred and fifty kilometres to the north of Leh. On the way back to Leh, we had been informed by the driver of a car passing the other way that there had been a cloudburst in Leh and hundreds of people had died. There were hardly any armymen at Khardung-La; on the way to Nubra the previous day, the place had been teeming with armymen and tourists.

Everything in Leh was closed; we were told everyone had gone to help out with rescue operations. Thankfully we found we had a booking at a hotel and checked in and quickly booked a ticket on the first flight the following morning. The evening was spent playing cards and watching news on some horrible Hindi channels (the hotel didn’t have any English channels). I was on the terrace, talking to Pinky over the phone. And I saw people in the street walking down towards a nearby hill.

Soon there were more people. And even more. All of them carrying some sort of luggage, like they were running away from something. Soon the street was filled with people running towards the hill. It was as if the whole town was running towards the hill. I went in and informed the others, who checked up with the hotel staff who instructed us too to proceed to the hill.

A couple of hours earlier we had found out that our hotel building had been built of mud, like all other buildings in Leh. Leh is earthquake-prone but it hardly rains there so mud houses are the norm. Given the floods of the previous night we had already been apprehensive about spending the night at the hotel. And now when we heard stories that some canal had burst and the street where our hotel was would get flooded we panicked. Picking up our bare essential belongings (basically the “hand luggage”) we followed the town down the road and up the hill, and settled in a reasonably comfortable place there.

I must have spent some three hours on the hill. Some friends spent double the time there, apprehensive of getting back to the hotel. While I was there I got conflicting news. Some people were saying that the floods had not hit our part of Leh. Others said it was only a matter of time and the entire area would be flooded with water. At times we worried if we were high enough on the hill, at other times we contemplated descending. It was crazy.

While on the hill, frantically trying to calm myself down, I thought this was just like the global financial crisis of 2008. The problem in 2008 after Lehman crashed was that nobody trusted anybody any more (coincidence: Lehman’s ticker on NYSE was “LEH”). So if I don’t trust you I don’t trade with you. The lack of trustworthy sources of information meant that nobody knew which financial institutions were in what state of health. So everyone just assumed the worst and refused to trade. It was only after the government (some sort of credible player, essentially) stepped in (TARP, discount window, etc.) that people began trusting each other and the markets calmed down presently.

It was similar on the hill. There were no credible sources of info. Nobody knew what was happening, and given the extreme risks involved (in the worst case we could have  been washed away, either by the rain on the hill (there wasn’t any when I was up there) or by floods on the street). People would go up the hill, and down the hill. Looking at them, others would try glean information (I decided it was safe enough to descend when most of the hill emptied; wisdom of crowds fundaes). But then there was distortion throughout the system. It was like all of us were playing one big game of Chinese Whispers.

It must be mentioned here that following the previous night’s cloudburst and floods there was a sense of panic all over town (just like there was in the financial markets back in 2008) so it was easy to spread rumours. The only way to have controlled damage was to have some credible sources (like say some armymen in uniform) to come and let us know what was happening. But then there were parts of town significantly worse affected compared to us so there was no help coming our way. And we continued to panic. And play chinese whispers.

The three hours I spent on the hill are probably the scariest of my life. Even now, thinking about that gives me the jitters. I’m happy I’m here, sitting at home at my ancient teak-wood desk in front of my laptop, telling you the story.

PS: the title of this post derives its name from a Beatles song of a similar name and has no other connotations

MakeMyTrip Opens Up 57% Post-IPO; May Be Year’s Best Deal

It doesn’t, to me. How in the world is the IPO successful if it has opened 57% higher in the first hour (it ended the first day 90% higher than the IPO price)? To rephrase, from whose point of view has the IPO been the “best deal”?

What this headline tells me is that makemytrip has been well and truly shafted. If the stock has nearly doubled on the first day, all it means is that MMYT raised just about half the cash from the IPO as it could have raised. If not anything else, the IPO has been a spectacular failure from the company’s point of view.

The US has a screwed up system for IPOs. Unlike in India where there is a 100% book-building process where there is effectively an auction to determine the IPO price (though within a band) in the US it is all the responsibility of the bank in charge of the IPO to distribute stock (as far as I understand). Which is why working in Equity Capital Markets groups in investment banks is so much more work there than it is here – you need to go around to potential investors hawking the stock and convincing them to invest, etc.

Now, the bank usually gets paid a percentage of the total money raised in the IPO so it is in their incentive to set the price as high as they can (and the fact that they are underwriting means they can’t get too greedy and set a price no one will buy at). Or so it is designed.

The problem arises because the firm that is IPOing is not the only client of the bank. Potential investors in the IPO are most likely to be clients of other divisions of the bank (say, sales and trading). By giving these investors a “good price” on the IPO (i.e. by setting the IPO price too low), the bank hopes to make up for the commission it loses by way of business that the investors give to other divisions of the bank. If most of the IPO buyers are clients of the bank’s sales and trading division (it’s almost always the case) then what all these clients together gain by a low IPO price far outweighs the bank’s lost commission.

It is probably because of this nexus that Google decided to not raise money in a conventional way but instead go through an auction (it made big news back then, but then that’s how things always happen in India so we have a reason to be proud). Unfortunately they were able to do it only because they are google and other companies have failed to successfully raise money by that process.

The nexus between investment banks and investors in IPOs remains and unless there are enough companies that want to do a Google, it won’t be a profitable option to IPO in the US. Which makes it even more intriguing that MMYT chose to raise funds in the US and not here in India.