Axes of diversity

Companies and educational institutions, especially those that have a global footprint and a reputation to protect, make a big deal about diversity policies. It is almost impossible to sit through a recruitment or admissions talk by one such entity without a mention to their diversity policies, which they are proud of.

And they have good reasons to have a diverse workforce. It has been shown, for example, that diversity leads to better decision-making and overall better performance. Having a diverse workforce brings together people with different backgrounds, and since backgrounds influence opinion, a more diverse team is more likely to have more diversity of opinion which results in better decision making. And so forth.

The problem, however, is that it is not easy to simultaneously achieve diversity on all possible axes. Let’s say that we have defined a number of axes, and are looking to recruit an incoming MBA class. If we want diversity on each of these axes, selection of each candidate is going to rule out a large number of other candidates and we will need a really large pool to choose from. In other words, it is akin to the eight queens problem (where you have to place eight queens on a chessboard such that no two of them are on the same row, column or diagonal). For those of you not familiar with chess, think of it like a Sudoku puzzle.

Since the pool of candidates large enough to achieve diversity on all axes is simply not feasible, firms and schools choose to prioritise certain axes over others, and seek to achieve diversity in these chosen axes. And since they can arbitrarily choose axes that they can prioritise, the incentive is to pick out those axes where diversity is most visible.

And so when you go to a global organisation or school that preaches diversity, you will notice that they indeed have a very diverse workforce/student body in terms of gender, race, and nationality, which are fairly visible dimensions. Beyond this, the choice of dimensions to impose diversity on is a matter of discretion. So you have organisations which seek diversity in sexual orientation. Others seek diversity in age profile. Yet others in educational backgrounds. And so forth.

The result of prioritising more “visible” dimensions to ensure diversity is that organisations end up becoming horribly similar in the “sacrificed dimensions”. Check out this excerpt from Peter Thiel’s Zero to One, for example, on the founding members of paypal:

The early PayPal team worked well together because we were all the same kind of nerd. We all loved science fiction: Cryptonomicon was required reading, and we preferred the capitalist Star Wars to the communist Star Trek

Now, remember that this was a fairly diverse team when it came to ethnicity, nationality and sexuality. But in a less visible dimension, the team was not diverse at all. And Thiel mentions it in his book as if it’s a good thing that they all thought so similarly.

On a similar note, I once worked for an organisation that made great shakes of its diversity policy, and the organisation was pretty diverse in terms pretty much every visible axis of diversity. And the seminars (some compulsory) they organised helped me significantly broaden my outlook on issues such as race or sexual orientation. But when it came to work, the (fairly large) team was horribly similar. Quoting from an earlier blogpost (a bit ranty, I admit):

First, a large number of guys building models come from similar backgrounds, so they think similarly. Because so many people think similarly, the rest train themselves to think similarly (or else get nudged out, by whatever means). So you have massive organizations full of massively talented brilliant minds which all think similarly! Who is to ask the uncomfortable questions?

So essentially because you had a large organisation of people from basically similar educational backgrounds (masters and PhDs in similar subjects), their way of thinking became dominant, and others were forced to conform, leading to groupthink, which might have potentially led to mishaps (but didn’t, at least not in my time).

And what of the Ivy League schools that again pride themselves on (visible forms of) diversity? Here is an excerpt from William Deresiewicz’s excellent 2008 essay:

Elite schools pride themselves on their diversity, but that diversity is almost entirely a matter of ethnicity and race. With respect to class, these schools are largely—indeed increasingly—homogeneous. Visit any elite campus in our great nation and you can thrill to the heartwarming spectacle of the children of white businesspeople and professionals studying and playing alongside the children of black, Asian, and Latino businesspeople and professionals. At the same time, because these schools tend to cultivate liberal attitudes, they leave their students in the paradoxical position of wanting to advocate on behalf of the working class while being unable to hold a simple conversation with anyone in it.

So the next time you want to make your organisation diverse, think of which axes you want diversity on. If you are public-minded and want to brag about your diversity, the obvious way to go would be to be diverse on visible axes, but that leaves other issues. On the other hand you could put together a team of people that look the same but think different!

It’s entirely up to you!

Should you have an analytics team?

In an earlier post a couple of weeks back, I had talked about the importance of business people knowing numbers and numbers people knowing business, and had put in a small advertisement for my consulting services by mentioning that I know both business and numbers and work at their cusp. In this post, I take that further and analyze if it makes sense to have a dedicated analytics team.

Following the data boom, most companies have decided (rightly) that they need to do something to take advantage of all the data that they have and have created dedicated analytics teams. These teams, normally staffed with people from a quantitative or statistical background, with perhaps a few MBAs, is in charge of taking care of all the data the company has along with doing some rudimentary analysis. The question is if having such dedicated teams is effective or if it is better to have numbers-enabled people across the firm.

Having an analytics team makes sense from the point of view of economies of scale. People who are conversant with numbers are hard to come by, and when you find some, it makes sense to put them together and get them to work exclusively on numerical problems. That also ensures collaboration and knowledge sharing and that can have positive externalities.

Then, there is the data aspect. Anyone doing business analytics within a firm needs access to data from all over the firm, and if the firm doesn’t have a centralized data warehouse which houses all its data, one task of each analytics person would be to get together the data that they need for their analysis. Here again, the economies of scale of having an integrated analytics team work. The job of putting together data from multiple parts of the firm is not solved multiple times, and thus the analysts can spend more time on analyzing rather than collecting data.

So far so good. However, writing a while back I had explained that investment banks’ policies of having exclusive quant teams have doomed them to long-term failure. My contention there (including an insider view) was that an exclusive quant team whose only job is to model and which doesn’t have a view of the market can quickly get insular, and can lead to groupthink. People are more likely to solve for problems as defined by their models rather than problems posed by the market. This, I had mentioned can soon lead to a disconnect between the bank’s models and the markets, and ultimately lead to trading losses.

Extending that argument, it works the same way with non-banking firms as well. When you put together a group of numbers people and call them the analytics group, and only give them the job of building models rather than looking at actual business issues, they are likely to get similarly insular and opaque. While initially they might do well, soon they start getting disconnected from the actual business the firm is doing, and soon fall in love with their models. Soon, like the quants at big investment banks, they too will start solving for their models rather than for the actual business, and that prevents the rest of the firm from getting the best out of them.

Then there is the jargon. You say “I fitted a multinomial logistic regression and it gave me a p-value of 0.05 so this model is correct”, the business manager without much clue of numbers can be bulldozed into submission. By talking a language which most of the firm understands you are obscuring yourself, which leads to two responses from the rest. Either they deem the analytics team to be incapable (since they fail to talk the language of business, in which case the purpose of existence of the analytics team may be lost), or they assume the analytics team to be fundamentally superior (thanks to the obscurity in the language), in which case there is the risk of incorrect and possibly inappropriate models being adopted.

I can think of several solutions for this – but irrespective of what solution you ultimately adopt – whether you go completely centralized or completely distributed or a hybrid like above – the key step in getting the best out of your analytics is to have your senior and senior-middle management team conversant with numbers. By that I don’t mean that they all go for a course in statistics. What I mean is that your middle and senior management should know how to solve problems using numbers. When they see data, they should have the ability to ask the right kind of questions. Irrespective of how the analytics team is placed, as long as you ask them the right kind of questions, you are likely to benefit from their work (assuming basic levels of competence of course). This way, they can remain conversant with the analytics people, and a middle ground can be established so that insights from numbers can actually flow into business.

So here is the plug for this post – shortly I’ll be launching short (1-day) workshops for middle and senior level managers in analytics. Keep watching this space 🙂

The Quants

Since investment bank bashing seems to be in fashion nowadays, let me add my two naya paise to the fire. I exited a large investment bank in September 2011, after having worked for a little over two years there. I used to work as a quant, spending most of my time building pricing and execution models. I was a bit of an anomaly there, since I had an MBA degree. What was also unusual was that I had previously spent time as a salesperson in an investment bank . Most other people in the quant organization came from a heavily technical background, with the most popular degrees being PhDs in Physics and Maths, and had no experience or interest in the business side of things at the bank.

You might wonder what PhDs in Physics and Maths do at investment banks. I used to wonder the same before I joined. Yes, there are some tough mathematical puzzles to be solved in the course of devising pricing and execution algorithms (part of the work that us quants did), which probably kept them interested. However, the one activity for which these pure science PhDs were prized for, and which they spent most of their time doing, was C++ coding. Yeah, you read that right. These guys could write mean algorithms – I don’t know if even Computer Science graduates (and there were plenty of those) could write as clean (and quick) C++ code as these guys.

While most banks stress heavily on diversity, and makes considerable efforts (in the form of recruitment, affiliation groups, etc.) to ensure a diverse workplace, it is not enough to prevent a large portion of quants coming from a similar kind of background. And when you put large numbers of Physics and Math PhDs together, it is inevitable that there is some degree of groupthink. You have the mavericks like me who like to model things differently, but if everyone else in your organization thinks one way, who do you go to in order to push your idea? You stop dropping your own ideas and start thinking like everyone else does. And you become yet another cog in the big quant wheel.

The biggest problem with hardcore Math people working on trading strategies is that they do not seek to solve a business problem through their work – they seek to solve a math problem, which they will strive to do as elegantly and correctly as it is possible. It doesn’t matter to the quants if the assumption of asset prices being lognormal is widely off the mark. In fact, they don’t care how the models behave. All they care about is about their formulae and results being correct – GIVEN the model of the market. I remember once spending a significant amount of time (maybe a couple of weeks) looking for bugs in my pricing logic because prices from two methods didn’t match up to the required precision of twelve decimal places (or was it fourteen? I’ve forgotten). And this after making the not-very-accurate assumption that asset prices are log normal. The proverb that says, “measure with a micrometer, mark with a chalk, cut with an axe”, is quite apt to describe the priorities of most quants.

Before I joined the firm, I used to wonder how bankers can be so stupid to make the kind of obvious silly errors (like assuming that housing prices cannot go down) that led to the global financial crisis of 2008. Two years at the firm, however, made me realize why these things happen. In fact, the bigger surprise, after the two years there, was about why such gross mistakes don’t occur more regularly. I think I’ve already talked about the culprits earlier in the post, but I should repeat myself.

First, a large number of guys building models come from similar backgrounds, so they think similarly. Because so many people think similarly, the rest train themselves to think similarly (or else get nudged out, by whatever means). So you have massive organizations full of massively talented brilliant minds which all think similarly! Who is to ask the uncomfortable questions? Next, who has time to ask the uncomfortable questions? Every one, from Partner downwards, has significant amount of “day to day work” to take care of every day. Bankers are driven hard (in that sense, and in that they are mostly brilliant, they do deserve the money they make), and everyone has a full plate (if you don’t it is an indication that you may not have a plate any more). There is little scope for strategic thinking. Again, remember that in an organization full of people who think similarly, people who have got promoted and made it to the top are likely to be those that think best along that particular axis. While it is the top management of the firm that is supposed to be responsible for the “big” strategic decisions, the kind of attention to details (which Math/Physics PhDs are rich in) that takes them to the top doesn’t leave them enough bandwidth for such thinking.

And so shit happens. Anyone who had the ability to think differently has either been “converted” to the conventional way of thinking, or is playing around with big bucks at some tiny hedge fund somewhere – because he found that it wasn’t possible to grow significantly in a place where most people think different to the way he thinks, and no one has the patience for his thinking.

This is the real failure in investment banking (markets) culture that has led to innumerable crises. The screwing over of clients and loss of “culture” in terms of ethics is a problem that has existed for a long time, and nothing new, contrary to what Greg Smith (formerly of Goldman Sachs) has written. The real failure of banking culture is this promotion of one-dimensional in-line-with-the-party thought, and the curbs against thinking and acting contrary to popular (in the firm) wisdom. It is this failure of culture that has led to the large negative shocks to the economy in the years gone by, and it is these shocks that have led common people to lose money rather than one off acts by banks where they don’t necessarily act in the interest of clients. And irrespective of how many Business Standards Committees and Risk Committees banks constitute, it is unlikely that this risk is going to go away any time soon. And I can’t think of a regulatory cure against this.

The Necktie Index

I’m currently reading Roger Lowenstein’s When Genius Failed – about the rise and fall of the hedge fund LTCM. So when LTCM was in trouble, the employees there came up with a measure called the “necktie index”. I’m not able to find a good link to it, and unfortunately physical books don’t offer an efficient “Ctrl+F” option so I’ll have to paraphrase and put it here.

The necktie index states that the more senior officers of the company wear neckties, and the more the meetings they attend, the more trouble the company is in.

I think this concept is generally true, and applicable more widely and to all companies. The more the number of employees wear neckties (compared to normal business days), the more the trouble the company is in. The indexing to “normal business days” is important because different companies have different normal dress codes, so normalization is required.

On a related note, I read somewhere that sometime in the beginning of this decade, when most other investment banks had a business casual dress policy, Lehman Brothers insisted that all its employees wear suits and ties to office. And you know what happened to the firm.

Now UBS has released a 43 page dress code, insisting its employees wear ties, among other things. It probably gives you an indication of where the company is headed.

On a less related note, I used to work for a startup hedge fund whose first office was a room inside the office of a fairly large BPO/KPO company in Gurgaon. And every week, “inspirational quotes” from the founders of the BPO/KPO would go up on the walls, along with their photos. And this was fairly well correlated with the decline of the stock price of that company.

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.