IPOs and right to match

Long time readers of the blog might know that I’m not a big fan of the IPO pop. I’ve traditionally belonged to the party (led by Bill Gurley) that says that a big IPO pop is akin to “leaving money on the table” for the company.

And so as my party has grown, the IPO process itself has also changed. Way back in 2004, Google allocated shares using a simple Dutch auction. Facebook pushed its bankers hard enough on the IPO price that the IPO “pop” in that case was negative. Spotify and Slack and a few other companies went public in a direct listing. Nowadays you have SPACs. It’s all very interesting stuff for anyone interested in market design.

Over the last few years, though, Matt Levine has been trying hard (and sort of succeeding), in getting to move me to the side that says IPO pops are okay. His first compelling argument was the demand-supply (and market depth) one – in an IPO there is a large offload of shares, and so an IPO buyer can expect to get a discount on the shares. Another is that since the IPO is the first time the stock will be traded, buyers in the IPO are taking risk, and need to be compensated for it in the form of a lower price. Fair enough again.

Matt has outdone himself in his latest newsletter on the topic, where he talks about the IPOs of Roblox and Coupang. About Roblox, he wrote:

I mean, I’ll tell you the answer[1]: Roblox sold stock to venture capitalists at $45, and then it traded up in public markets to $70. In a traditional initial public offering, a company sells stock to mutual funds at $45, and then it trades up in public markets to $70. Venture capitalists are not happy when mutual funds get underpriced stock: It dilutes existing shareholders and “leaves money on the table.” Venture capitalists are of course perfectly happy when venture capitalists get underpriced stock; that’s the business they are in.

This served the purpose of moving me more to his side.

This blogpost, however, is about the Coupang IPO.

All normal enough. But here’s the unusual thing about Coupang. Apparently, of the hundreds of investors who put in orders to buy shares in the IPO—many of whom did roadshow meetings and put in work to understand the company and come up with a price—fewer than 100 were allocated any shares, with most of those shares going to about 25 accounts handpicked by Coupang. Coupang apparently kept tight control over the allocation, choosing its investors itself rather than deferring to its underwriters (led by Goldman Sachs Group Inc.). Now those favored investors—investors favored by Coupang, not investors favored by Goldman—will benefit from the IPO pop. Everyone else, who put in the work and decided they wanted to own Coupang, will have to buy in the aftermarket, from those initial investors, and pay up to do so.

Obviously Coupang has left money on the table, but who cares? Coupang underpriced its IPO, but the beneficiaries of the underpricing are the existing investors that it wanted to benefit.

Basically Coupang announced an IPO at a $27-30 price range. It did a roadshow to gauge investor demand. Demand was strong. And then the price range was upped to $31-34. Demand was strong once again. And then, instead of letting its banker Goldman Sachs price the IPO at 34, and allocate the shares to who Goldman thought would make the best investors, Coupang went to its existing investors and told them “we have a bunch of investors willing to buy our stock at $34. What do you think?”

And the existing investors, finding validation, said “Oh, in that case we can pay $35 for it”. In IPL auction parlance, Coupang’s existing investors basically had a “right to match option”. All the other potential investors were asked, and then the existing investors were “more equal” than the others.

The stock duly popped.

Now, right to match in an IPO might be an interesting structure, but I highly doubt that it will sustain. Basically banks won’t like it. Put yourself in Goldman’s shoes for a moment.

They have done all the hard work of pricing the IPO and taking it to potential clients and doing all the paperwork, and at the end of it, their buy side clients are a mostly pissed of bunch – they’ve again done all the hard work of deciding whether the IPO was worth it and then told that they were cut out of the deal.

The least Goldman’s buy side clients would have wanted is the right to match Coupang’s original investors’ offer ($35). Having done all the hard work, and then being forced to buy the stock (if at all) at the popped price of $49, they will be a totally miffed lot. And they would have conveyed their displeasure to Goldman.

One thing about IPOs is that the companies selling the stock play a one-time game, while the bankers (and IPO investors) play a repeated game, participating in IPOs regularly. And because of this, the incentive structure of IPOs is that bankers tend to favour buy side clients than sell side, and so the big pop. And so bankers will not regularly want to do things that will piss off the buy side, such as offering “right to match” to the selling company’s chosen investors.

So will we see more such IPOs?

My take is that inspired by Coupang, some more companies might insist on a right to match while selling their shares in an IPO. And this right to match will piss off the buy side, who will push back against the bankers and demand a right to match for themselves.

And what happens when both sides (company’s favourite investors and bank’s favourite investors) insist on a mutual right to match? We get an auction of course.

I don’t think anyone will have that much of a problem if IPO share allocation gets resolved by a Dutch auction, like Google did way back in 2004.

Monetising volatility

I’m catching up on old newsletters now – a combination of job and taking my email off what is now my daughter’s iPad means I have a considerable backlog – and I found this gem in Matt Levine’s newsletter from two weeks back  ($; Bloomberg).

“it comes from monetizing volatility, that great yet under-appreciated resource.”

He is talking about equity derivatives, and says that this is “not such a good explanation”. While it may not be such a good explanation when it comes to equity derivatives itself, I think it has tremendous potential outside of finance.

I’m reminded of the first time I was working in the logistics industry (back in 2007). I had what I had thought was a stellar idea, which was basically based on monetising volatility, but given that I was in a company full of logistics and technology and operations research people, and no other derivatives people, I had a hard time convincing anyone of that idea.

My way of “monetising volatility” was rather simple – charge people cancellation fees. In the part of the logistics industry I was working in back then, this was (surprisingly, to me) a particularly novel idea. So how does cancellation fees equate to monetising volatility?

Again it’s due to “unbundling”. Let’s say you purchase a train ticket using advance reservation. You are basically buying two things – the OPTION to travel on that particular day using that particular train, sitting on that particular seat, and the cost of the travel itself.

The genius of the airline industry following the deregulation in the US in the 1980s was that these two costs could be separated. The genius was that charging separately for the travel itself and the option to travel, you can offer the travel itself at a much lower price. Think of the cancellation charge as as the “option premium” for exercising the option to travel.

And you can come up with options with different strike prices, and depending upon the strike price, the value of the option itself changes. Since it is the option to travel, it is like a call option, and so higher the strike price (the price you pay for the travel itself), the lower the price of the option.

This way, you can come up with a repertoire of strike-option combinations – the more you’re willing to pay for cancellation (option premium), the lower the price of the travel itself will be. This is why, for example, the cheapest airline tickets are those that come with close to zero refund on cancellation (though I’ve argued that bringing refunds all the way to zero is not a good idea).

Since there is uncertainty in whether you can travel at all (there are zillions of reasons why you might want to “cancel tickets”), this is basically about monetising this uncertainty or (in finance terms) “monetising volatility”. Rather than the old (regulated) world where cancellation fees were low and travel charges were high (option itself was not monetised), monetising the options (which is basically a price on volatility) meant that airlines could make more money, AND customers could travel cheaper.

It’s like money was being created out of thin air. And that was because we monetised volatility.

I had the same idea for another part of the business, but unfortunately we couldn’t monetise that. My idea was simple – if you charge cancellation fees, our demand will become more predictable (since people won’t chumma book), and this means we will be able to offer a discount. And offering a discount would mean more people would buy this more predictable demand, and in the immortal jargon of Silicon Valley, “a flywheel would be set in motion”.

The idea didn’t fly. Maybe I was too junior. Maybe people were suspicious of my brief background in banking. Maybe most people around me had “too much domain knowledge”. So the idea of charging for cancellation in an industry that traditionally didn’t charge for cancellation didn’t fly at all.

Anyway all of that is history.

Now that I’m back in the industry, it remains to be seen if I can come up with such “brilliant” ideas again.

People are worried about marriage market liquidity

Every time we have a sort of financial crisis that has something to do with settlement, and collaterals, and weird instruments, people start questioning why more instruments are not traded on exchanges. They cite the example of equities, which world over are exchange traded, centrally settled, and whose markets function rather efficiently.

After the 2008 Financial Crisis, for example, there was a move to take Credit Default Swaps (CDS) to exchanges, rather than letting the market go over the counter (OTC). Every few years, ideas are floated about trading bonds on exchanges (rather than OTC, like they are now), and the blame falls on “greedy bankers who don’t want to let go of control”.

There is an excellent podcast by Bloomberg Odd Lots where Chris White, a former Goldman Sachs banker, talks about how the equity markets went electronic in the 1970s with NASDAQ, and how the “big bang” in the UK markets propelled equities into electronic trading everywhere.

A lot of these ideas have also been discussed in my book on market design

In any case, I think I have the perfect explanation of why bond trading on exchanges hasn’t really taken off. To understand this, let’s look at another market that I discussed extensively in my book – the market for relationships (that chapter has been extracted in Mint).

The market for relationships is in the news thanks to this Netflix documentary called Indian Matchmaking. I started watching it on a whim on Saturday night, and I got so addicted to it that yesterday I postponed my work to late night so that I could finish the show instead.

Marriage can be thought of as a sale of “50% of the rest of your life“, paid for by 50% of the rest of someone else’s life.

There are two ways you can go about it – either “over the counter” (finding a partner by yourself) or “exchange traded” (said exchange could be anything from newspaper classifieds to Tinder to Shaadi.com). Brokers are frequently used in the OTC market – either parents or friends (who set you up) or priests.

The general rule of markets is that the more bespoke (or “weird” or “unusual”) an instrument is, the better the likelihood of finding a match in the OTC markets than on exchanges. The reason is simple – for an exchange to exist, the commodity being traded needs to be a commodity.

Read any literature on agricultural markets, for example, and they all talk about “assaying” and “grading” the commodities. The basic idea is that all goods being traded on a marketplace are close enough substitutes of each other that they can be interchanged for each other.

Equity shares, by definition, are commodities. Equity and index derivatives are commodities as well, easy enough to define. Commodities are, by definition, commodities. Bond futures are commodities, since they can be standardised on a small number of axes. We’ll come to bonds in a bit.

Coming back to relationship markets, the “exchanges” work best if you have very few idiosyncrasies, and can be defined fairly well in terms of a small number of variables. It also helps you to find a partner quicker in case many others in the market have similar attributes as you, which means that the market for “your type of people” becomes “liquid” (this is a recurring theme in my book).

However, in case you are either not easily describable by commonly used variables, or in case there are few others like you in the market, exchanges are likely to work less well for you. Either of these conditions makes you “illiquid”, and it is not a great idea to list an illiquid asset on an exchange.

When you list an illiquid asset on an exchange, unless you are extremely lucky, it is likely to sit there for a long time without being traded (think about “bespoke exchanges” like eBay here, where commodification is not necessary). The longer the asset sits on an exchange, the greater the likelihood that people who come across the asset on the exchange think that “something is wrong with it”.

So if you’re listing it on an exchange, its value will decay exponentially, and unless you are able to trade soon after you have listed it, you are unlikely to get much value for it.

In that sense, if you are “illiquid” for whatever reason (can’t be easily described, or belong to a type that few others in the market belong to), exchanges are not for you. And if you think about each of the characters in Indian Matchmaking who come to Sima aunty, they are illiquid in one way or another.

  • Aparna has entered the market at 34, and few other women of her age are in the market. Hence illiquid.
  • Nadia belongs to a small ethnicity, Indian-Guyanese-American, which makes her illiquid.
  • Pradhyuman has quirky interests (jewelry and fashion), which his parents are trying to suppress as they pass him off a liquid “rich Maadu boy”. Quirky interests mean he’s not easily describable. Hence illiquid.
  • Vyasar, by Indian-American standards, doesn’t have a great job. So not too many others like him. Illiquid, even before you take his family situation into account.
  • Ankita is professionally ambitious. Few of those women in the Indian arranged marriage market. Illiquid.
  • Rupam is divorced with a child. Might be liquid by conventional American markets, but illiquid in an Indian context. And she is, rather inexplicably, going the Indian way despite being American.
  • Akshay is possibly the most liquid (characterless except for an overly-dominating mom), and maybe that’s why he’s shown getting engaged.

All of these people will be wasting themselves listing themselves on exchanges. And so they come to a matchmaker. Now, Sima Auntie is both a broker and a clearinghouse (refer to Chapter 3 of my book 😛). She helps find matches for people, but only matches within her own inventory (though she decided Ankita has no matches at all in her own inventory, so connected her with another broker-clearinghouse).

This makes it hard – first of all you have illiquid assets, and you are trying to fulfil them within limited inventory. This is why she is repeatedly showing saying that her candidates need to “compromise” (something that seems to have triggered a lot of viewers). By compromise, she is saying that these people are so illiquid that in case they need to get a deal in her little exchange, they need to be willing to accept an “illiquidity discount” in order to get a trade. 

Back to bonds, why is trading them on an exchange so difficult? Because each bond is so idiosyncratic. There is the issuer, the exact date of expiry and the coupon, and occasionally some weird derivatives tacked on. The likelihood that you might find someone quickly enough to take the other side of such a deal is minuscule, so if you were to list your bond on an exchange, its value would drop significantly (by being continuously listed) before you could find a counterparty.

Hence, people trade this uncertain discount to a certain discount, by trading their bonds with market makers (investment banks) who are willing to take the other side of the deal immediately.

Unfortunately, market making is not a viable strategy when it comes to relationship markets. So what do you do if you can either be not defined easily in a few parameters, or if there are few others like you in the arranged  marriage market? You basically go Over The Counter. Ditch the market and find someone for yourself, or ask people you know to set you up. Or hire a matrimonial advisor who will tell you what to do.

If this doesn’t convince you on why matchmakers are important, then may be you should read what my other half has to say. If she’s the better half or not, you figure.

More On Direct Listings

Regular long-time readers of this blog might know that I’m not a big fan of IPO pops (I’ve written about them at least four times so far: one, two, three and four). You can think of this as Number Five, though this is specifically about Direct Listings.

In case you don’t have patience to click through and read my posts, what is the big deal about direct listings? And what is the problem with traditional IPOs? To put it simply, companies looking to raise capital through IPOs are playing a one-time game (you only do an IPO once), while companies that are investing in them are playing a repeated game (they participate in pretty much every IPO that comes on the market – ok may be not WeWork).

This means that investment banks, which stand between the buyer and the seller in such cases, have an incentive to structure the deal to favour the (repeated) buyers, and they price the IPO conservatively. This means that when the company actually lists on the market, it usually does so at a price higher than the IPO price, resulting in a quick win for the IPO investors.

This is injurious for the original investors in the company (founders, VCs, employees) since they are “leaving money on the table”. A pop of 10-20% is considered fair game (a price for the uncertainty on how the market will react to the IPO), but when MakeMyTrip lists 60% higher, or Beyond Meat lists 160% up, it is a significant loss to the early shareholders.

Over the last few months (possibly after the Beyond Meat IPO), Silicon Valley has woken up to this problem of the IPO pop, and suggested that the middleman (equity capital markets divisions of investment banks) be disintermediated from the IPO process. And their vehicle of choice for disintermediation is the direct listing.

A direct listing is what it is. Rather than raising fresh capital from the market, the company picks an auspicious date and declares that on that date its stock will list on the exchanges. The opening auction in the exchange on that day sets what is effectively the IPO price, and the company is public just like that.

Spotify was among the first well-known companies in recent times to do a direct listing, when it went public in 2018. Earlier this year, Slack did a direct listing as well. Here is Benchmark Capital’s Bill Gurley (a venture capitalist) on the benefits of a direct listing.

Direct Listing is all well and good when a company doesn’t have to raise capital. The question is how do you go public while at the same time raising capital (which is what a traditional IPO does)? Slack and Spotify were able to do the direct listing because they didn’t want capital from the IPOs – they just wanted to offer liquidity to their investors.

The New York Stock Exchange thinks it can be done, and has proposed a product where companies can use the opening daily auction to price the new shares being offered. There are issues, of course, about things like supply of shares, lock-ups, price support and so on, but the NYSE thinks this can be done.

NYSE’s President Stacey Cunningham recently appeared on the a16z podcast (again run by a VC, notice!) and spoke eloquently about the benefits of direct listing.

The SEC (stock regulator in the US) isn’t very happy with the proposal, and rejected it. Traditional bankers are not happy with the NYSE’s proposal, either, and continue to find problems with it (my main source of this angst is Matt Levine, who is a former ECM Banker and who thus has solid reasons as to why ECM Bankers should exist). In any case, the NYSE has refiled its proposal.

So what is the deal with direct listings?

In a way, you can think about them as a way to simply disintermediate the market. The ECM Banker, after all, is a middleman who stands between the buyer (IPO investor) and seller (company raising capital), helping them come up with a smooth deal, for a fee. The process has been set for about 40 years now, and has become so stable that the sellers think it has become unfair to them. And so there is the backlash.

Until now, the sellers were all independent entities with their own set of investors, and so they were unable to coordinate and express their displeasure with the IPO process. The buyers, on the other hand, play the game repeatedly, and can thus coordinate among themselves and with the middlemen to give themselves a sweet deal.

The development in this decade is that the same set of VC investors invest in a large number of go-to-public companies, and so suddenly you have sellers who are present across deals, and that has changed the game in a sense. And so direct listings are on every tech or investing podcast.

Among the things I wrote in my book (which came out a bit over two years ago) is that one important role that middlemen play is to reduce uncertainty and volatility in the market.

One concern with direct listings is that there can be a wide variation in the valuations by different players in the market, and the opening auction is not an efficient enough process to resolves all these variations. The thing with the Spotify and Slack listings was that there was a broad consensus on the valuation of these companies (more in line with public company valuations), a set of investors who wanted to get in and a set of investors who wanted to get out. And so it all went smoothly.

But what do you do with something like WeWork? The problem with private market valuations is that with players like SoftBank, they can be well divorced from market realities. In WeWork’s case, the range of IPO valuations that came up differed by an order of magnitude. And that kind of difference is not usually reconcilable in one normal opening auction (imagine a bid of 8 billion and an ask of 69 billion, and other numbers somewhere in between) without massive volatility going forward. In that sense, the attempted traditional IPO did a good job of understanding demand and supply and just declaring “no deal”. “No deal” is usually not an option when you do a direct listing.

OK I’ve written a lot I know (this is already 2X the length of my usual blog posts), so what do I really think about IPOs? I think all this talk about direct listings will shift the market ever so slightly in favour of the sellers. Companies will follow a mixed strategy – well known companies (consumer brands, mostly) with stable valuations will go for direct listings. Less well known companies, or those with unstable valuations will go for IPOs.

And in the latter case, I predict that we will move closer to a Dutch auction (like what Google did) among the investors rather than the manual allocation process that ECM bankers indulge in nowadays. It will have the benefit of large blocks being traded at time zero, at a price considered fair by everyone, and hopefully low volatility.

Monetising the side bets

If you were to read Matt Levine’s excellent newsletter regularly, you might hypothesize that the market for Credit Default Swaps (CDS) is dying. Every other day, we see news of either engineered defaults (companies being asked to default by CDS holders in exchange for cheap loans in the next round), transfer of liability from one legal entity to another (parent to subsidiary or vice versa), “orphaning” of CDSs (where on group company pays off debt belonging to another) and so on.

So what was once a mostly straightforward instrument (I pay you a regular stream of money, and you pay me a lumpsum if the specified company defaults) has now become an overly legal product. From what seemed like a clever way to hedge out the default risk of a loan (or a basket of loans), CDSs have become an over-lawyered product of careful clauses and letters and spirits, where traders try to manipulate the market they are betting on (if stuff like orphaning or engineered default were to happen in sports, punters would get arrested for match-fixing).

One way to think of it is that it was a product that got too clever, and now people are figuring out a way to set that right and the market will soon disappear. If you were to follow this view, you would thin that ordinary credit traders (well, most credit traders work for large banks or hedge funds, so not sure this category exists) will stop trading CDSs and the market will die.

Another way to think about it is that these over-legalistic implications of CDSs are a way by the issuer of the debt to make money off all the side bets that happen on that debt. You can think about this in terms of horse racing.

Horse breeding is largely funded by revenues from bets. Every time there is a race, there is heavy betting (this is legal in most countries), and a part of the “rent” that the house collects from these bets is shared with the owners of the horses (in the form of prizes and participation fees). And this revenue stream (from side bets on which horse is better, essentially) completely funds horse rearing.

CDSs were a product invented to help holders of debt to transfer credit risk to other players who could hedge the risk better (by diversifying the risk, owning opposite exposures, etc.). However, over time they got so popular that on several debt instruments, the amount of CDSs outstanding is a large multiple of the total value of the debt itself.

This is a problem as we saw during the 2008 financial crisis, as this rapidly amplified the impacts of mortgage defaults. Moreover, the market in CDSs has no impact whatsoever on the companies that issued the debt  – they can see what the market thinks of their creditworthiness but have no way to profit from these side bets.

And that is where engineered defaults come in – they present a way for debt issuers to actually profit from all the side bets. By striking a deal with CDS owners, they are able to transfer some of the benefits of their own defaults to cheaper rates in the next round of funding. Even orphaning of debt and transferring between group companies are done in consultation with CDS holders – people the company ordinarily should have nothing to do with.

The market for CDS is very different from ordinary sports betting markets – there are no “unsophisticated players”, so it is unclear if anyone can be punished for match fixing. The best way to look at all the turmoil in the CDS market can thus be looked at in the same way as horse rearing – an activity being funded by “side bets”.

Advertising Agencies: From Brokers to Dealers

The Ken, where I bought a year long subscription today, has a brilliant piece on the ad agency business (paywalled) in India. More specifically, the piece is on pricing in the industry and how it is moving from a commissions only basis to a more mixed model.

Advertising agencies perform a dual role for their clients. Apart from advising them on advertising strategy and helping them create the campaigns, they are also in charge of execution and buying the advertising slots – either in print or television or hoardings (we’ll leave online out since the structure there is more complicated).

As far as the latter business (acquisition of slots to place the ad – commonly known as “buying”) is concerned, typically agencies have operated on a commission basis. The fees charged has been to the extent of about 2.5% of the value of the inventory bought.

In financial markets parlance, advertising agencies have traditionally operated as brokers, buying inventory on behalf of their clients and then charging a fee for it. The thrust of Ashish Mishra’s piece in ate Ken is that agencies are moving away from this model – and instead becoming what is known in financial markets as “dealers”.

Dealers, also known as market makers, make their money by taking the other side of the trade from the client. So if a client wants to buy IBM stock, the dealer is always available to sell it to her.

The dealer makes money by buying low and selling high – buying from people who want to sell and selling to people who want to buy. Their income is in the spread, and it is risky business, since they bear the risk of not being able to offload inventory they have had to buy. They hedge this risk by pricing – the harder they think it is to offload inventory, the wider they set the spreads.

Similarly, going by the Ken story, what ad agencies are nowadays doing is to buy inventory from media companies, and then selling it on to the clients, and making money on the spread. And clients aren’t taking too well to this new situation, subjecting the dealers ad agencies to audits.

From a market design perspective, there is nothing wrong in what the ad agencies are doing. The problem is due to their transition from brokers to dealers, and their clients not coming to terms with the fact that dealers don’t normally have a fiduciary responsibility towards their clients (unlike brokers who represent their clients). There are also local monopoly issues.

The main service that a dealer performs is to take the other side of the trade. The usual mechanism is that the dealer quotes the prices (both buy and sell) and then the client has the option to trade. If the client feels the dealer is ripping her off, she has a chance to not do the deal.

And in this kind of a situation, the price at which the dealer obtained the inventory is moot – all that matters to the deal is the price that the dealer is willing to sell to the client at, and the price that competing dealers might be charging.

So when clients of ad agencies demand that they get the inventory at the same price at which the agencies got it from the media, they are effectively asking for “retail goods at wholesale rates” and refusing to respect the risk that the dealers might have taken in acquiring the inventories (remember the ad agencies run the risk of inventories going unsold if they price them too high).

The reason for the little turmoil in the ad agency industry is that it is an industry in transition – where the agencies are moving from being brokers to being dealers, and clients are in the process of coming to terms with it.

And from one quote in the article (paywalled, again), it seems like the industry might as well move completely to a dealer model from the current broker model.

Clients who are aware are now questioning the point of paying a commission to an agency. “The client’s rationale is that is that it is my money that is being spent. And on that you are already making money as rebate, discount, incentive and reselling inventory to me at a margin, so why do I need to pay you any agency commissions? Some clients have lost trust in their agencies owing to lack of transparency,” says Sodhani.

Finally, there is the issue of monopoly. Dealers work best when there is competition – the clients need to have an option to walk away from the dealers’ exorbitant prices. And this is a bit problematic in the advertising world since agencies act as their clients’ brokers elsewhere in the chain – planning, creating ads, etc.

However the financial industry has dealt with this problem where most large banks function as both brokers and dealers. It’s only a matter of time before the advertising world goes down that path as well.

PS: you can read more about brokers and dealers and marketplaces and platforms in my book Between the Buyer and the Seller

Suckers still exist

Matt Levine’s latest newsletter describes a sucker of a trade:

 

  1. You give Citigroup Inc. $1,000, when Amazon.com’s stock is at $1,339.60.
  2. At the end of each quarter for the next three years, Citi looks at Amazon’s stock price. If it’s at or below $1,339.60, Citi sends you $25 and the trade continues. If it’s above $1,339.60, Citi sends you back your $1,000 and the trade is over.
  3. At the end of the three years, Citi looks at Amazon’s stock price. If it’s above $1,004.70 (75 percent of the initial stock price), then Citi sends you $1,025 and the trade is over. But if it’s below $1,004.70, you eat the full amount of the loss: For instance, if Amazon’s stock price is $803.80 (60 percent of the initial stock price), then you lose 40 percent of your money, and get back only $600. Citi keeps the rest. (You get to keep all the premiums, though.)

Anyone with half a brain should know that this is not a great trade.

For starters, it gives the client (usually a hedge fund or a pension fund or someone who represents rich guys) a small limited upside (of 10% per year for three years), while giving unlimited downside if Amazon lost over 25% in 3 years.

Then, the trade has a “knock out” (gets unwound with Citigroup paying back the client the principal) clause, with the strike price of the knockout being exactly the Amazon share price on the day the contract came into force. And given that Amazon has been on a strong bull run for a while now, it seems like a strange price at which to put a knock out clause. In other words, there is a high probability that the trade gets “knocked out” soon after it comes into existence, with the client having paid up all the transaction costs (3.5% of the principal in fees).

Despite this being such a shitty deal, Levine reports that Citigroup sold $16.3 million worth of these “notes”. While that is not a large amount, it is significant that nearly ten years after the financial crisis, there are still suckers out there, whom clever salespersons in investment banks can con into buying such shitty notes. It seems institutional memory is short (or these clients are located in states in the US where marijuana is legal).

I mean, who even buys structured notes nowadays?

PS: Speaking of suckers, I recently got to know of the existence of a school in Mumbai named “Our Lady of Perpetual Succour“. Splendid.

A one in billion trillion event

It seems like capital markets quants have given up on the lognormal model for good, for nobody described Facebook’s stock price drop last Thursday as a “one in a billion trillion event”. For that is the approximate probability of it happening, if we were to assume a lognormal model of the market.

Created using Quantmod package. Data from Yahoo.

Without loss of generality, we will use 90 days trailing data to calculate the mean and volatility of stock returns. As of last Thursday (the day of the fall), the daily mean returns for FB was 0.204%, or an annualised return of 51.5% (as you can see, very impressive!). The daily volatility in the stock (using a 90-day lookback period again) was 1.98%, or an annualised volatility of 31.4% . While it is a tad on the higher side, it is okay considering the annual return of 51.5%.

Now, traditional quantitative finance models have all used a lognormal distribution to represent stock prices, which implies that the distribution of stock price returns is normal. Under such an assumption, the likelihood of a 18.9% drop in the value of Facebook (which is what we saw on Thursday) is very small indeed.

In fact, to be precise, when the stock is returning 0.204% per day with a vol of 1.98% per day, the an 18.9% drop is a 9.7 sigma event. In other words, if the distribution of returns were to be normal, Thursday’s drop is 9 sigmas away from normal. Remember that most quality control systems (admittedly in industrial settings, where faults are indeed governed by a nearly normal distribution) are set for a six sigma limit.

Another way to look at Thursday’s 9.7 sigma event is that again under the normal distribution, the likelihood of seeing this kind of a fall in a day is $math ~10^{-21}$. Or one in a billion trillion. In terms of the number of trading days required for such a fall to arrive at random, it is of the order of a billion billion years, which is an order of magnitude higher than the age of the universe!

In fact, when the 1987 stock market crash (black monday) happened, this was the defence the quants gave for losing their banks’ money – that it was an incredibly improbable event. Now, my reading of the papers nowadays is sketchy, and I mostly consume news via twitter, but I haven’t heard a single such defence from quants who lost money in the Facebook crash. In fact, I haven’t come across too many stories of people who lost money in the crash.

Maybe it’s the power of diversification, and maybe indexing, because of which Facebook is now only a small portion of people’s portfolios. A 20% drop in a stock that is even 10% of your portfolio erodes your wealth by 2%, which is tolerable. What possibly caused traders to jump out of windows on Black Monday was that it was a secular drop in the US market then.

Or maybe it’s that the lessons learnt from Black Monday have been internalised, and included in models 30 years hence (remember that concepts such as volatility smiles and skews, and stochastic volatility, were introduced in the wake of the 1987 crash).

That a 20% drop in one of the five biggest stocks in the United States didn’t make for “human stories” or stories about “one in a billion billion event” is itself a story! Or maybe my reading of the papers is heavily biased!

PostScript

Even after the spectacular drop, the Facebook stock at the time of this update is trading at 168.25, a level last seen exactly 3 months ago – on 26th April, following the last quarter results of Facebook. That barely 3 months’ worth of earnings have been wiped out by such a massive crash suggests that the only people to have lost from the crash are traders who wrote out of the money puts.

Bankers predicting football

So the Football World Cup season is upon us, and this means that investment banking analysts are again engaging in the pointless exercise of trying to predict who will win the World Cup. And the funny thing this time is that thanks to MiFiD 2 regulations, which prevent banking analysts from giving out reports for free, these reports aren’t in the public domain.

That means we’ve to rely on media reports of these reports, or on people tweeting insights from them. For example, the New York Times has summarised the banks’ predictions on the winner. And this scatter plot from Goldman Sachs will go straight into my next presentation on spurious correlations:

Different banks have taken different approaches to predict who will win the tournament. UBS has still gone for a classic Monte Carlo simulation  approach, but Goldman Sachs has gone one ahead and used “four different methods in artificial intelligence” to predict (for the third consecutive time) that Brazil will win the tournament.

In fact, Goldman also uses a Monte Carlo simulation, as Business Insider reports.

The firm used machine learning to run 200,000 models, mining data on team and individual player attributes, to help forecast specific match scores. Goldman then simulated 1 million possible variations of the tournament in order to calculate the probability of advancement for each squad.

But an insider in Goldman with access to the report tells me that they don’t use the phrase itself in the report. Maybe it’s a suggestion that “data scientists” have taken over the investment research division at the expense of quants.

I’m also surprised with the reporting on Goldman’s predictions. Everyone simply reports that “Goldman predicts that Brazil will win”, but surely (based on the model they’ve used), that prediction has been made with a certain probability? A better way of reporting would’ve been to say “Goldman predicts Brazil most likely to win, with X% probability” (and the bank’s bets desk in the UK could have placed some money on it).

ING went rather simple with their forecasts – simply took players’ transfer values, and summed them up by teams, and concluded that Spain is most likely to win because their squad is the “most valued”. Now, I have two major questions about this approach – firstly, it ignores the “correlation term” (remember the famous England conundrum of the noughties of fitting  Gerrard and Lampard into the same eleven?), and assumes a set of strong players is a strong team. Secondly, have they accounted for inflation? And if so, how have they accounted for inflation? Player valuation (about which I have a chapter in my book) has simply gone through the roof in the last year, with Mo Salah at £35 million being considered a “bargain buy”.

Nomura also seems to have taken a similar approach, though they have in some ways accounted for the correlation term by including “team momentum” as a factor!

Anyway, I look forward to the football! That it is live on BBC and ITV means I get to watch the tournament from the comfort of my home (a luxury in England!). Also being in England means all matches are at a sane time, so I can watch more of this World Cup than the last one.

 

A banker’s apology

Whenever there is a massive stock market crash, like the one in 1987, or the crisis in 2008, it is common for investment banking quants to talk about how it was a “1 in zillion years” event. This is on account of their models that typically assume that stock prices are lognormal, and that stock price movement is Markovian (today’s movement is uncorrelated with tomorrow’s).

In fact, a cursory look at recent data shows that what models show to be a one in zillion years event actually happens every few years, or decades. In other words, while quant models do pretty well in the average case, they have thin “tails” – they underestimate the likelihood of extreme events, leading to building up risk in the situation.

When I decided to end my (brief) career as an investment banking quant in 2011, I wanted to take the methods that I’d learnt into other industries. While “data science” might have become a thing in the intervening years, there is still a lot for conventional industry to learn from banking in terms of using maths for management decision-making. And this makes me believe I’m still in business.

And like my former colleagues in investment banking quant, I’m not immune to the fat tail problem as well – replicating solutions from one domain into another can replicate the problems as well.

For a while now I’ve been building what I think is a fairly innovative way to represent a cricket match. Basically you look at how the balance of play shifts as the game goes along. So the representation is a line graph that shows where the balance of play was at different points of time in the game.

This way, you have a visualisation that at one shot tells you how the game “flowed”. Consider, for example, last night’s game between Mumbai Indians and Chennai Super Kings. This is what the game looks like in my representation.

What this shows is that Mumbai Indians got a small advantage midway through the innings (after a short blast by Ishan Kishan), which they held through their innings. The game was steady for about 5 overs of the CSK chase, when some tight overs created pressure that resulted in Suresh Raina getting out.

Soon, Ambati Rayudu and MS Dhoni followed him to the pavilion, and MI were in control, with CSK losing 6 wickets in the course of 10 overs. When they lost Mark Wood in the 17th Over, Mumbai Indians were almost surely winners – my system reckoning that 48 to win in 21 balls was near-impossible.

And then Bravo got into the act, putting on 39 in 10 balls with Imran Tahir watching at the other end (including taking 20 off a Mitchell McClenaghan over, and 20 again off a Jasprit Bumrah over at the end of which Bravo got out). And then a one-legged Jadhav came, hobbled for 3 balls and then finished off the game.

Now, while the shape of the curve in the above curve is representative of what happened in the game, I think it went too close to the axes. 48 off 21 with 2 wickets in hand is not easy, but it’s not a 1% probability event (as my graph depicts).

And looking into my model, I realise I’ve made the familiar banker’s mistake – of assuming independence and Markovian property. I calculate the probability of a team winning using a method called “backward induction” (that I’d learnt during my time as an investment banking quant). It’s the same system that the WASP system to evaluate odds (invented by a few Kiwi scientists) uses, and as I’d pointed out in the past, WASP has the thin tails problem as well.

As Seamus Hogan, one of the inventors of WASP, had pointed out in a comment on that post, one way of solving this thin tails issue is to control for the pitch or  regime, and I’ve incorporated that as well (using a Bayesian system to “learn” the nature of the pitch as the game goes on). Yet, I see I struggle with fat tails.

I seriously need to find a way to take into account serial correlation into my models!

That said, I must say I’m fairly kicked about the system I’ve built. Do let me know what you think of this!