May a thousand market structures bloom

In my commentary on SEBI’s proposal to change the regulations of Indian securities markets in order to allow new kinds of market structures, I had mentioned that SEBI should simply enable exchanges to apply whatever market structures they wanted to apply, and let market participants sort out, through competition and pricing, what makes most sense for them.

This way, different stock exchanges in India can pick and choose their favoured form of regulation, and the market (and market participants) can decide which form of regulation they prefer. So you might have the Bombay Stock Exchange (BSE) going with order randomisation, while the National Stock Exchange (NSE) might use batch auctions. And individual participants might migrate to the platform of their choice.

Now, Matt Levine, who has been commenting on market structures for a long time now, makes a similar case in his essay on the Chicago Stock Exchange’s newly introduced “speed bump”:

A thousand — or at least a dozen — market structures can bloom, each subtly optimized for a different type of trader. It’s an innovative and competitive market, in which each exchange can figure out what sorts of traders it wants to favor, and then optimize its speed bumps to cater to¬†those traders.

Maybe I should now accuse Levine of “borrowing” my ideas without credit! ūüėõ


Regulating HFT in India

The Securities and Exchange Board of India (SEBI) has set a cat among the HFT (High Frequency Trading) pigeons by proposing seven measures to curb the impact of HFT and improve “real liquidity” in the stock markets.

The big problem with HFT is that algorithms tend to cancel lots of orders – there might be a signal to place an order, and even before the market has digested that order, the order might get cancelled. This results in an illusion of liquidity, while the constant placing and removal of liquidity fucks with the minds of the other algorithms and market participants.

There has been a fair amount of research worldwide, and SEBI seems to have drawn from all of them to propose as many as seven measures – a minimum resting time between HFT orders, matching orders through frequent batch auctions rather than through the order book, introducing random delays (IEX style) for orders, randomising the order queue periodically, capping order-to-trade ratio, creating separate queues for orders from co-located servers (used by HFT algorithms) and review provision of the tick-by-tick data feed.

While the proposal seems sound and well researched (in fact, too well researched, picking up just about any proposal to regulate stock markets), the problem is that there are so many proposals, which are all pairwise mutually incompatible.

As the inimitable Matt Levine commented,

If you run batch auctions and introduce random delays and reshuffle the queue constantly, you are basically replacing your matching engine with a randomizer. You might as well just hold a lottery for who gets which stocks, instead of a market.

My opinion this is that SEBI shouldn’t mandate how each exchange should match its orders. Instead, SEBI should simply enable individual exchanges to regulate the markets in a way they see fit. So in my opinion, it is possible that all the above proposals go through (though I’m personally uncomfortable with some of them such as queue randomisation), but rather than mandating exchanges pick all of them, SEBI simply allows them to use zero or more of them.

This way, different stock exchanges in India can pick and choose their favoured form of regulation, and the market (and market participants) can decide which form of regulation they prefer. So you might have the Bombay Stock Exchange (BSE) going with order randomisation, while the National Stock Exchange (NSE) might use batch auctions. And individual participants might migrate to the platform of their choice.

The problem with this, of course, is that there are only two stock exchanges of note in India, and it is unclear if the depth in the Indian equities market will permit too many more. This might lead to limited competition between bad methods (the worst case scenario), leading to horrible market inefficiencies and the scaremongers’ pet threat¬†of trading shifting to exchanges in Singapore or Dubai actually coming true!

The other problem with different exchanges having different mechanisms is that large institutions and banks might find it difficult to build systems that can trade accurately on all exchanges, and arbitrage opportunities across exchanges might exist for longer than they do now, leading to market inefficiency.

Then again, it’s interesting to see how a “let exchanges do what they want” approach might work. In the United States, there is a new exchange called the Intercontinental Exchange (IEX) that places “speed bumps” over incoming orders, thus reducing the advantage of HFTs. IEX started only recently, after major objections from incumbents who alleged they were making markets less fair.

With IEX having started, however, other exchanges are responding in their own ways to make the markets “fairer” to investors. NASDAQ, which had vehemently opposed IEX’s application, has now filed a proposal to reward orders by investors who wait for at least once second before cancelling them.

Surely, large institutions won’t like it if this proposal goes through, but this gives you a flavour of what competition can do! We’ll have to wait and see what SEBI does now.

Liquidity and the Trump Trade

The United States Treasury department has floated a new idea to improve liquidity in the market for treasury bonds, which has been a concern ever since the Volcker Rule came into place.

The basic problem with liquidity in the bond market is that there are a large number of similar instruments trading, which leads to a fragmented market. This is a consequence of the issuer (the US Treasury in this case) issuing a new bond every time they wish to borrow more money, and with durations being long, many bonds are in the market at the same time.

The proposed solution, which commentators have dubbed the “Trump Trade” (thanks to the Republican Presidential candidate’s penchant for restructuring debt of¬†his companies), involves the treasury buying back bonds before they have run their full course. These bonds bought back will be paid for by newly issued 10-year bonds.

The idea here is that periodic retirement of old illiquid bonds and their replacement by a new “consolidated” bond can help aggregate the market and boost liquidity. This is not all. As the FT ($) reports,

The US Treasury would then buy older, less liquid and therefore cheaper debt across the market, which could in theory then be reissued at a lower yield. In recent months, yields on older issues have risen more than those for recently sold debt, suggesting a deterioration in liquidity.

This implies that because these “off the run” treasuries are less liquid, they are necessarily cheaper, and this “Trump Trade” is thus a win. This, however, is not necessarily the case. Illiquidity need not always imply lower price – it is more likely that it leads to wider spreads.

Trading an illiquid instrument implies that you need to pay a higher transaction cost. The “illiquidity discount” that many bonds see is because people are loathe to holding them (given the transaction cost), and thus less people are willing to buy them.

When the treasury wants to buy back such instruments, however, it is suddenly a seller’s market – since a large number of bonds need to be bought back to take it off the market, sellers can command a higher spread over the “mid price”.

Matt Levine of Bloomberg View has a nice take on the “IPO pop” which I’ve written about on this blog several times (here, here, here and here). He sees it as the “market impact cost” of trying to sell a large number of securities on the market at a particular instant.

Instead the typical trade of selling, say, $1 million of a bond with $1 billion outstanding, and paying around 0.3 percent ($3,000) for liquidity, you want to sell, say, $1 billion worth of a bond with¬†zero¬†bonds¬†outstanding. That is: You want to issue a brand-new bond, and sell all of it in one day. What sort of bid-ask spread should you pay? First principles would tell you that if selling a few bonds from a large bond issue¬†costs 0.3 percent, then selling 100 or 1,000 times as many bonds — especially brand-new bonds — should cost … I mean, not 100 or maybe even 10 times as much, but¬†more, anyway. No?

Taking an off-the-run bond off the market is reverse of this trade – instead of selling, you are buying a large number of bonds at the same time. And that results in a market impact cost, and you need to pay a significant bid-ask spread. So rather than buying the illiquid bond for cheap, the US Treasury will actually have to pay a premium to retire such bonds.

In other words, the Trump Trade is unlikely to really work out too well – the transaction costs of the scheme are going to defeat it. Instead, I second John Cochrane’s idea of issuing perpetual bonds and then buying them back periodically.

These securities pay $1 coupon forever. Buy these back, not on a regular schedule, but when (!) the day of surpluses comes that the government wants to pay down the debt. Then there is one issue, with market depth in the trillions, and the whole on the run vs. off the run phenomenon disappears.

People don’t worry¬†enough about liquidity when they are trying to solve other liquidity worries, it seems!


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 Рcheck)  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.

Matt Levine describes my business idea

When I was leaving the big bank I was working for (I keep forgetting whether this blog is anonymous or not, but considering that I’ve now mentioned it on my LinkedIn profile (and had people congratulate me “on the new job”), I suppose it’s not anonymous any more) in 2011, I didn’t bother looking for a new job.

I was going into business, I declared. The philosophy (that’s a word I’ve learnt to use in this context by talking to Venture Capitalists) was that while Quant in investment banking was already fairly saturated, there was virgin territory in other industries, and I’d use my bank-honed quant skills to improve the level of reasoning in these other industries.

Since then things have more or less gone well. I’ve worked in several sectors, and done a lot of interesting work. While a lot of it has been fairly challenging, very little of it has technically been of a level that would be considered challenging by an investment banking quant. And all this is by design.

I’ve long admired Matt Levine for the way in which he clearly explains fairly complicated finance stuff in his daily newsletter¬†(that you can get delivered to your inbox for free),¬†¬†and more or less talking about finance in an entertaining model. I’ve sometimes mentioned that I’ve wanted to grow up to be like him, to write like him, to analyse like him and all that.

And I find that in yesterday’s newsletter he clearly encapsulates the idea with which I started off when I quit banking in 2011. He writes:

A good trick is, find an industry where the words “Monte Carlo model” make you sound brilliant and mysterious, then go to town.

This is exactly what I set out to do in 2011, and have continued to do since then. And you’d be amazed to find the number of industries where “Monte Carlo model” makes you sound brilliant and mysterious.

Considering the difficulties I’ve occasionally had in communicating to people what exactly I do, I think I should adopt Levine’s line to describe my work. I clearly can’t go wrong that way.


Darwin Awards in Investment Banking

Some 20 analysts from Goldman Sachs and 10 from JP Morgan have been dismissed after it emerged that they were cheating during some mandatory tests during their analyst training program.

As the article says, it is not unusual for bankers to assist each other when it comes to tests in mandatory training and compliance, since they are seen as being time consuming and repetitive.

In that sense, that these guys copied or helped each other is not news. What matters, though, is that they got caught in the process. And that is unacceptable for a banker.

If you look at how investment banking has been shaped over the last decade or so, there have apparently been several people who have fudged stuff – from financial results to key rates to benchmarks, and gotten away with it because they haven’t got caught. And they continue to remain successful bankers.

So in the banking culture, fudging is okay, but getting caught isn’t. By getting caught fudging in tests during their training program, these analysts have betrayed the one skill that is necessary for being a successful banker, and for this reason they have been rightly weeded out.

It’s like the Darwin awards, except that for these guys it is only the end of their careers in banking.

Revisiting IPOs

I’ve written several times (here, here and here) that the IPO pop is unfair to existing shareholders¬†since they end up selling the stock cheaper than necessary. Responses I’ve received to this (not all on the blog comments) have mostly been illogical and innumerate, talking about how the pop “increases the value of the entrepreneurs’ holdings”, and that the existing shareholder¬†“should be happy that the value has gone up” rather than wondering why he sold his shares at the low value.

Thinking about this in the context of the impending Cafe Coffee Day IPO, I realised that a pop is necessary (though not maybe to the extent of the MakeMyTrip and LinkedIn pops), because investors need some incentive to invest in the IPO rather than buying the stock in the secondary market after listing.

Secondary markets have superior price discovery compared to primary markets since the former have several (close to infinite) attempts at price discovery, while the latter have only one attempt. Also, prices in the secondary market change “slowly” (compared to the price difference between primary and secondary market), so even if someone has invested at a price they later have dissonance with, they can reverse the investment without incurring a high cost.

For this reason, if you want to invest in a company and want to know that you are paying a “fair price”, investing in secondary markets is superior to investing in primary markets. In other words, you need a higher incentive in order to buy in primary markets. And this incentive is provided to you in the form of the IPO pop.

In other words, the IPO pop is an incentive paid to the IPO buyer in exchange for investing at a time when the price discovery is in a sense incomplete and cannot be particularly trusted.¬†Rather than pricing the IPO at what bankers and bookbuilders think is the “fair price”, they will price it at a discount, which offers IPO investors insurance against the bankers having made a mistake in their pricing of the IPO.

And how much to underprice it (relative to any “fair price” that the bankers have discovered) is a function of how sure the bankers are about the fair price they have arrived at. The greater their confidence in such a price, the smaller the pop they need to offer (again, this is in theory since investors need not know what fair price bankers have arrived at).

The examples I took while arguing that the IPO pop is unfair to existing shareholders were MakeMyTrip and LinkedIn, both pioneers in some sense. LinkedIn was the first major social network to go public, much before Facebook or Twitter, and thus there was uncertainty about its valuation, and it gave a big pop.

MakeMyTrip was a travel booking site from India listing on NASDAQ, and despite other travel sites already being public, the fact that it was from an “emerging market” possibly added to its uncertainty, and the resulting high pop.

So I admit it. I was wrong on this topic of IPO pops. They do make sense, but from a risk perspective. Nothing about “wealth of existing shareholders increases after the pop”.