The (missing) Desk Quants of Main Street

A long time ago, I’d written about my experience as a Quant at an investment bank, and about how banks like mine were sitting on a pile of risk that could blow up any time soon.

There were two problems as I had documented then. Firstly, most quants I interacted with seemed to be solving maths problems rather than finance problems, not bothering if their models would stand the test of markets. Secondly, there was an element of groupthink, as quant teams were largely homogeneous and it was hard to progress while holding contrarian views.

Six years on, there has been no blowup, and in some sense banks are actually doing well (I mean, they’ve declined compared to the time just before the 2008 financial crisis but haven’t done that badly). There have been no real quant disasters (yes I know the Gaussian Copula gained infamy during the 2008 crisis, but I’m talking about a period after that crisis).

There can be many explanations regarding how banks have not had any quant blow-ups despite quants solving for math problems and all thinking alike, but the one I’m partial to is the presence of a “middle layer”.

Most of the quants I interacted with were “core” in the sense that they were not attached to any sales or trading desks. Banks also typically had a large cadre of “desk quants” who are directly associated with trading teams, and who build models and help with day-to-day risk management, pricing, etc.

Since these desk quants work closely with the business, they turn out to be much more pragmatic than the core quants – they have a good understanding of the market and use the models more as guiding principles than as rules. On the other hand, they bring the benefits of quantitative models (and work of the core quants) into day-to-day business.

Back during the financial crisis, I’d jokingly predicted that other industries should hire quants who were now surplus to Wall Street. Around the same time, DJ Patil et al came up with the concept of the “data scientist” and called it the “sexiest job of the 21st century”.

And so other industries started getting their own share of quants, or “data scientists” as they were now called. Nowadays its fashionable even for small companies for whom data is not critical for business to have a data science team. Being in this profession now (I loathe calling myself a “data scientist” – prefer to say “quant” or “analytics”), I’ve come across quite a few of those.

The problem I see with “data science” on “Main Street” (this phrase gained currency during the financial crisis as the opposite of Wall Street, in that it referred to “normal” businesses) is that it lacks the cadre of desk quants. Most data scientists are highly technical people who don’t necessarily have an understanding of the business they operate in.

Thanks to that, what I’ve noticed is that in most cases there is a chasm between the data scientists and the business, since they are unable to talk in a common language. As I’m prone to saying, this can go two ways – the business guys can either assume that the data science guys are geniuses and take their word for the gospel, or the business guys can totally disregard the data scientists as people who do some esoteric math and don’t really understand the world. In either case, value added is suboptimal.

It is not hard to understand why “Main Street” doesn’t have a cadre of desk quants – it’s because of the way the data science industry has evolved. Quant at investment banks has evolved over a long period of time – the Black-Scholes equation was proposed in the early 1970s. So the quants were first recruited to directly work with the traders, and core quants (at the banks that have them) were a later addition when banks realised that some quant functions could be centralised.

On the other hand, the whole “data science” growth has been rather sudden. The volume of data, cheap incrementally available cloud storage, easy processing and the popularity of the phrase “data science” have all increased well-at-a-faster rate in the last decade or so, and so companies have scrambled to set up data teams. There has simply been no time to train people who get both the business and data – and the data scientists exist like addendums that are either worshipped or ignored.

Investment banks, scientific research and cows

I’ve commented earlier on this blog about how investment banks indirectly fund scientific research – by offering careers to people with PhDs in pure sciences such as maths and physics.

The problem with a large number of disciplines is that the only career opportunity available to someone with a PhD is a career in academia. Given that faculty positions are hard to come by, this can result in a drop in number of people who want to do a PhD in that subject, which has the further effect of diminishing research in that subject.

Investment banks, by hiring people with pure science PhDs, have offered a safety net for people who haven’t been able to get a job in academia, as a consequence of which more people are willing to do PhDs in these subjects. This increases competition and overall improves the quality of research in these topics.

Beef is like investment banks to the dairy industry. I recall an article (can’t recall the source and link to it, though) which talked about V Kurien of Amul going to a meeting called by the Union government on banning cow slaughter. Kurien talked about his mandate from his cooperative being that everything was okay as long as cow slaughter wasn’t banned – for that would kill the dairy industry.

Prima facie (use of latin phrase on this block – check)  this might sound like a far-fetched analogy (research to cows). However, cow slaughter has an important (positive) role to play in encouraging the dairy industry.

When you buy a cow, you aren’t sure how good it is in providing milk, until you’ve put it through a few cycles of childbirth and milking. If after purchase it turns out that the cow is incapable of producing as much milk as you were promised, it turns out to be a dud investment – like getting a PhD in a field with few non-academic opportunities and not being able to get a faculty position.

When cow slaughter is permitted, however, you can at least sell the cow for its meat (when it is still healthy and fat) and hope to recover at least a part of the (rather hefty) investment on it. This provides some kind of a “safety net” for dairy farmers and encourages them to invest in more cows, and that results in increasing milk production and a healthier dairy industry.

This is not all. Legal slaughter means that there is a positive “terminal value” that can be extracted from cows at the end of their milking lives. Money can also be made off the male calves (cruel humans have made the dairy industry one-to-many. Semen from stud bulls is used to impregnate lots of cows, and most bulls never get to fuck) which would otherwise have negative value.

A ban on killing cows implies a removal of these safety nets. Investing in cows becomes a much more risky business. And lesser farmers will invest in that. To the detriment of the dairy industry.

There are already reports that following the ban on cow slaughter in Maharashtra last year, demand for cows is going down as farmers are turning to the more politically pliable buffaloes.

Similarly, with the investment banking industry seeing a downturn and the demand for “quants” going down, it is likely that the quality of input to graduate programs in pure science might go down – though it may be reasonable to expect Silicon Valley to offer a bailout in this case. Cows have no such luck, though.

I don’t know what to name this bias

So yet again I’m at that point in my life when I’m pondering about my career, pulling up my socks and asking myself uncomfortable questions. I’m asking myself what it is I really want to do, what it is that I really enjoy, what is the best way I can monetize my skills and the like. I’ve been pondering between radically different alternatives – from staying on in Wall Street to becoming a hippie; from becoming a professor to starting a company. I’ve been thoroughly confused and have been talking to a number of people about this.

The one common strand I extract from my conversations with all these people is that most people give you advice that is aligned with what they are doing. When I talk to the prof, he talks to me about becoming a prof, and about why I’m suited for it. When I talk to the corporate whore, he tries to convince me that there’s no way out from corporate whoredom and that I must simply embrace and accept it. When I ask the hippie, he thinks it’s no big deal if I keep switching jobs, and that I’m being dishonest with myself continuing to do something I don’t enjoy. And the entrepreneur tries his best to push me into becoming an entrepreneur.

Given my thoroughly confused state of mind, all this has been mostly adding to the confusion, but now that I’ve managed to extract this common strand, I been able to add the appropriate amount of spices to all the advice I’ve received, and making more sense of it. While I continue to figure out what’s the best course of action for me, I wonder what it is that makes people want other people to be like them.

I must mention that this is not a recent phenomenon. Back when I was in college, I remember talking to a senior who went into consulting, and he convinced me that I should do that, too. The banker talked about how banking is perfect for my skills. Till I was in 10th standard, I had no clue about the existence of IIT until a rocket scientist uncle told me about it, and about how going there would be the best thing I could do.

Of all the people who have given me career advice, perhaps the only person who didn’t clearly show this kind of bias was my father. He was an accountant, and he used to work as a regulator. And right from the beginning he made it clear to me that I should neither become an accountant nor should I work for the government.

And I’m trying to think of what kind of advice I dish out. Perhaps because I don’t have one clear “career axis”, I don’t really show this kind of a bias. Or maybe it’s hereditary.

On Learning At Home

Recently, India has enacted this Right To Education Law, one of whose provisions dictates that schools must reserve at least 25% of seats for kids from economically backward communities. This post will be tangential and will not be trying to examine the merits and demerits of the law.

So earlier this week, the Wall Street Journal published a long (and pretty good) analysis of the impact of the law (it was published in India in Mint). While I might discuss the rest of the article in another post, the paragraph that caught my eye was this one:

Sumit’s father and many of the poorer parents are troubled by the fact that their own limited literacy prevents them from helping. Some wealthy parents, meanwhile, chafe at the slowed pace of learning. They have suggested segregating the poor kids.

Made me wonder how much primary learning actually happens in school, and how much happens at home. Looking back at my own childhood, I learnt most of my “concepts” at home, and before any subject was taught in school I was well prepared for it. In fact, I would be so ahead of my class that I’d frequently get bored, and would think that my classmates were dumb because they weren’t able to keep pace with me.

My parents were no “tiger parents“. And I wasn’t a particularly industrious child. Of course, there would be times when my parents would make me recite tables of two-digit numbers as I traveled wedged between them on our Bajaj Priya, but never forced me to study (until maybe till there were a few months left for the IIT-JEE). And still, somehow, they managed to teach me everything at home. And that proved to be a massive advantage over kids that were encountering the concepts for the first time in school.

Of course, as I went to advanced classes, there was only so much they could teach me at home (since we were going beyond the basic fundamentals here, and there was only so much they could remember), but the head start that I got in primary school was, I think, really useful in my being a topper for most of my schooling, with there being a significant positive feedback.

So what do you think? How much do you think parents actually contribute to their kids’ learning in early age? Is there a positive correlation of kids doing well in school with whether their parents are well-informed, have time for kids and can teach well? If there does exist significant correlation, what are the policy implications of it? Does it defeat the purpose of reservations in school?

Successful IPOs

Check out this article in the Wall Street Journal. Read the headline. Does this sound right to you?

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.