Category: Pricing

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.

Gym pricing

In a weird sort of way, this is a blog-length expansion of a flippant thought I put out as a tweet.

Back to topic – gym memberships are a bundle. They bundle together the ability to use the gym over a long contiguous block of time. It doesn’t matter whether you want to go once a week or every day, in most gyms you have no choice but to buy the full bundle.

In some gyms (such as the one I was a member of before the lockdown started), there was more than the opportunity to use the equipment that was thrown into the bundle – the gym conducted lots of group classes every day. The option to join one of these classes (or maybe more – I never tried) was also bundled into the membership. Similarly, in an earlier gym I was a member of, the membership came bundled with the option to use squash courts, and use the gym bar.

The bundling made sense – cognitively it was easy on the members. The advantage of bundling is that marginal costs are kept at zero, which means mental accounting becomes far easier. Should I go to the gym today? I only need to think about whether I have the time and want the exercise. The decision is not complicated by money that I might have to spend. Similarly, should I join the class or just lift weights? Again depends upon mood and not on whether I need to pay anything for anything.

In any case, the pandemic and lockdown completely ruined the bundle. A lot of the options that were part of the bundle were forced to expire un-exercised since the gym was mandated to be closed (it’s unclear if they’re giving us any extensions of memberships once they restart this week).

Moreover, once the gyms restart (while they have been allowed to start on Wednesday, so far there’s been no communication from my gym on when they’re actually starting), they are likely to want to ensure some sort of social distancing. This means that the sort of bundles that they would sell earlier will be very hard to sustain.

Earlier, the bundle had both the option to attend the rather crowded 6:30 am class or the rather empty 9:30 am class. There was no differential pricing, and for good reason – mental costs were kept low. Now, in case the gym decides that the number of people per class needs to be capped (mgiht have to do that to ensure social distancing), the bundle will become unworkable.

It will be as if the members who can only attend the rather crowded 6:30 am class and no other class are part of the same chit fund, betting against each other so that they can attend their favourite class. From the gym’s point of view, this is not workable.

While gyms worldwide have for long benefited from extreme bundling (with massive discounts for long-term contracts), with the understanding that people won’t utilise a large portion of that bundle, the post-pandemic era that restricts the number of people who can attend the gym at the same time might cause this model to unravel.

It will be interesting to see how the gym pricing models evolve. I liked this model that a gym my wife briefly attended follows – which was like the mobile phone plans of olden days. For a fixed sum, you would be entitled to a certain number of classes that had to be utilised in a certain number of days (eg. 6 classes in a month). And then you would have to book online to book a class and exercise each of these options.

Then again, a lot of gyms belong to what I call the “passion economy” – people who are in business because they are passionate about something rather than because they are good at business. So I don’t know how rational they will be with their pricing.

The World After Overbooking

Why do you think you usually have to wait so much to see a doctor, even when you have an appointment? It is because doctors routinely overbook.

You can think of a doctor’s appointment as being a free option. You call up, give your patient number, and are assigned a slot when the doctor sees you. If you choose to see the doctor at that time, you get the doctor’s services, and then pay for the service. If you choose to not turn up, the doctor’s time in that slot is essentially wasted, since there is nobody else to see then. The doctor doesn’t get compensated for this as well.

In order to not waste their time, thus, doctors routinely overbook patients. If the average patient takes fifteen minutes to see, they give appointments once every ten minutes, in the hope of building up a buffer so that their time is not wasted. This way they protect their incomes, and customers pay for this in terms of long waiting hours.

Now, in the aftermath of the covid crisis, this will need to change. People won’t want to spend long hours in a closed waiting room with scores of other sick people. In an ideal world, doctors will want to not let two of their patients even see each other, since that could mean increased disease transmission.

In the inimitable words of Ravishastri, “something’s got to give”.

One way could be for doctors to simply up their fees and give out appointments at intervals that better reflect the time taken per patient. The problem with this is that there are reputation costs to upping fee per patient, and doctors simply aren’t conditioned to unexpected breaks between patients. Moreover, lower number of slots might mean appointments not being available for several days together, and higher cancellations as well, both problems that doctors want to avoid.

As someone with a background in financial derivatives, there is one obvious thing to tackle – the free option being given to patients in terms of the appointment. What if you were to charge people for making appointments?

Now, taking credit card details at the time of booking is not efficient. However, assuming that most patients a doctor sees are “repeat patients”, just keeping track of who didn’t turn up for appointments can be used to charge them extra on the next visit (this needs to have been made clear in advance, at the time of making the appointment).

My take is that even if this appointment booking cost is trivial (say 5% of the session fee), people are bound to take the appointments more seriously. And when people take their appointments more seriously, the amount of buffer built in by doctors in their schedules can be reduced. Which means they can give out appointments at more realistic intervals. Which also means their income overall is protected, while still maintaining social distancing among patients.

I remember modelling this way back when I was working in air cargo pricing. There again, free options abound. I remember building this model that showed that charging a nominal fee for the options could result in a much lower fee for charging the actual cargo. A sort of win-win for customers and airlines alike. Needless to say, I was the only ex-derivatives guy around and it proved to be a really hard sell everywhere.

However, the concept remains. When options that have hitherto been free get monetised, it will lead to a win-win situation and significantly superior experience for all parties involved. The only caveat is that the option pricing should be implemented in a manner with as little friction as possible, else transaction costs can overwhelm the efficiency gains.

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

Why Real Estate Prices are High

World over, high housing prices seem to be a problem. They’ve always been an issue in India. They are an issue in the US, where millennials are not able to afford houses to live in. In the UK as well, rising housing prices mean that today’s young are unable to buy up houses. The global phenomenon that is driving all this is the drive towards increasingly large cities.

Going by first principles, there are two major components that determine the cost of a house (note that I said cost and not price) – the cost of the land and the cost of construction. It can be safely assumed that the latter hasn’t increased at a rate dramatically higher than inflation over the years.

Yes, there are bubbles and busts in prices of commodities such as steel and cement. Houses nowadays are being built largely to better specifications and quality than earlier homes. In places like the US, modern houses are  bigger. But all this is balanced by technological innovation which makes stuff cheaper. So on an average, the increase in construction costs over the years is not dramatic.

That implies that the massive increase in price of housing the world over is driven by  increasing costs of land. Some scaremongers will try to tell you that this is due to there being too many human beings in the world, and we are soon headed for a Malthusian collapse. However, the land needed for housing is small, compared to say agriculture, so regular transfer of land from agriculture to housing should take care of this. So why are land prices increasing so much?

It has to do with the distribution. During most of the 20th century, manufacturing being the base of the economy meant that a lot of smaller cities and towns flourished. These cities and towns were either located conveniently enough to tap raw materials or markets for industrial goods, or were helped by the fact that land requirements for industries meant that big cities would get expensive very soon for industries, driving development to smaller cities and towns.

As the share of populations in manufacturing falls, and more people move into services, the larger cities gain at the expense of smaller cities and towns. This means the distribution of demand has changed massively over the last 30 years or so. Rather than demand being more or less uniform over cities, nowadays most of the housing demand is spread over a few small cities.

And these cities aren’t able to keep up. Supply in some cities such as San Francisco and Mumbai, are constrained by regulations on how much can be built. Other cities such as Bangalore or Houston have expanded radially, but housing in the far suburbs is much less attractive than closer to town (due to increased transport costs), and there is only so much supply in “convenient areas” of towns.

This changing pattern of urbanisation is leading to rapid increase in the prices of housing in places that people want to live in. And so millennials are being priced out, unable to buy homes. The distribution of jobs across cities means they don’t have the luxury of “settling down” in smaller cities and towns where housing is still affordable. And until the larger cities hit their limits of growth and businesses start moving to smaller cities (thus creating newer hubs), this housing shortage will exist.

 

Revenue management and transaction costs

So I just sent off a letter to India. To be precise, it is a document I had to sign and send to my accountant there – who sends regular “letters” any more?

The process at the post office (which, in my suburb, is located inside a large bookstore) was simple. In the first screen of the touch screen kiosk, there was an option for “worldwide < 20 grams”. A conveniently placed scale told me my letter weighed 18 grams, and one touch and one touch of my debit card later, I had my stamp. Within a minute, my letter was in the letterbox.

The story of how we pay the same amount for sending mail over large areas (“worldwide” in my case today) is interesting. Earlier, mail rates were based on distance, but as new roads kept being built in the 19th century America, and distances kept changing, figuring out how much to charge for a letter became “expensive”. A bright fellow figured out that the cost (in terms of time) of figuring out how much to charge for mail was of the same order of magnitude as the cost of the mail itself. And so the flat rate scheme for mail, that is prevalent worldwide today, was born.

Putting it in technical terms, transaction costs trumped price discrimination in this case. Price discrimination is the art (yes, it’s an art) of charging different amounts to different people based on their differential willingness to pay. Uber surge pricing is one example (I have a chapter in my book on this). Airline fares are another common example.

Until the late 18th century (well after mail prices had gone “flat”), price discrimination was rather common everywhere, a concept I have devoted a chapter to in the book. In fact, the initial motivation for fixed price retail was religious – Quakers, who owned many departmental stores in the US North-East, thought “all men are created equal before God” and so it was incorrect to charge different amounts to different people.

Soon other benefits of fixed prices became apparent (faster billing; less training for staff; in fact it was fixed prices that permitted the now prevalent supermarket format), and it took off. The concept is the same as stamps – the transaction cost of figuring out how much to charge whom is higher than the additional revenue you can make with such price differentiation (not counting possible loss of reputation, and fairness issues). Price discrimination at the shop is now confined to high value high margin businesses such as cars.

And it works in other high gross margin businesses such as airlines, hotels and telecom. These are all businesses with high fixed costs and low marginal costs for the suppliers. Low marginal costs has meant that price discrimination ha been termed as “revenue management” in the airline industry.

During the launch function of my book last year, I got asked if Uber’s practice of personalising fares for passengers is fair (I had given a long lecture on how Uber’s surge pricing is a necessary component of keeping average prices low and boosting liquidity in the taxi market). I had answered that a marketplace needs to ensure that its pricing is perceived as being “fair”, else they might lose customers to competitors. But what if all players in a market practice extreme price discrimination?

Thinking about it, transaction costs will take care of price discrimination before businesses and marketplaces start thinking of fairness. Beyond a point (the point varies by industry), the marginal revenues from price discrimination will fall below the transaction cost of executing this discrimination. And that poses a natural limit to how much price discrimination a business can practice.

British retail strategy

Right under where I currently live, there’s a Waitrose. Next door, there’s a Tesco Express. And a little down the road, there’s a Sainsbury Local. The day I got here, a week ago, I drove myself nuts trying to figure out which of these stores is the cheapest.

And after one week of random primary research, I think I have the classic economist’s answer – it depends. On what I’m looking to buy that is.

Each of these chains has built a reputation of sourcing excellent products and selling them to customers at a cheap price. The only thing is that each of them does it on a different kind of products. So there is a set of products that Tesco is easily the cheapest at, but the chain compensates for this by selling other products for a higher rate. It is similar with the other chains.

Some research I read a year or two back showed that while Amazon was easily the cheapest retailer in the US for big-ticket purchases, their prices for other less price-sensitive items was not as competitive. In other words, Amazon let go of the margin on high-publicity goods, and made up for it on goods where customers didn’t notice as much.

It’s the same with British retailers – each of their claims of being the cheapest is true, but that applies only to a section of the products. And by sacrificing the margin on these products, they manage to attract a sufficient number of customers to their stores, who also buy other stuff that is not as competitively priced!

Now, it is possible for an intelligent customer to conduct deep research and figure out the cheapest shop for each stock keeping unit. The lack of quick patterns of who is cheap for what, however, means that the cost of such research and visiting multiple shops usually far exceeds the benefits of buying everything from the cheapest source.

I must mention that this approach may not apply in online retail where at the point of browsing a customer is not “stuck” to any particular shop (unlike in offline where a customer is at a physical store location while browsing).

Variable pricing need not be boring at all!

Who do you subsidise?

One basic rule of pricing is that it is impossible for all buyers to have the same consumer surplus (the difference between what a buyer values the item at and what he paid). This is because each buyer values the item differently, and is thus willing to pay a different price for it. People who value the item more end up having a higher consumer surplus than those who value it less (and are still able to afford it).

Dynamic pricing systems (such as what we commonly see for air travel and hotels) try to price such that such a surplus is the same for all consumers, and equal to zero, but they never reach this ideal. While the variation in consumer surplus under such systems is lower, it is impossible for it to come to zero for all, or even a reasonable share of, customers.

So what effectively happens is that customers with a lower consumer surplus end up subsidising those with a higher consumer surplus. If the former customers didn’t exist, for example, the clearing price would’ve been higher, resulting in a lower consumer surplus for those who currently have a higher consumer surplus.

Sometimes the high surplus customer and the low surplus customer need not be different people – it could be the same person at different times. When I’m pressed for time, for example, my willingness to pay for a taxi is really high, and I’m highly likely to gain a significant consumer surplus by taking a standard taxi or ride-hailing marketplace ride then. At a more leisurely time, travelling on a route with plenty of bus service, I’d be willing to pay less, resulting in a lower consumer surplus. It is important to note, however, that my low surplus journey resulted in a further subsidy to my higher surplus journey.

When it comes to markets with network effects (whether direct, such as telecommunications, or indirect, like any two-sided marketplace), this surplus transfer effect is further exacerbated – not only do low-surplus customers subsidise high-surplus customers by keeping clearing price low, but network effects mean that by becoming customers they also add direct value to the high surplus customers.

So when you are pleasantly surprised to find that Uber is priced low, the low price is partly because of other customers who are paying close to their willingness to pay for the service. When you pay an amount close to the value you place on the service, you are in turn subsidising another customer whose willingness to pay is much higher.

This transfer of consumer surplus can be seen as an instance of bundling, but from the seller’s side. Since a seller cannot discriminate effectively among customers (even with dynamic pricing algorithms such as Uber’s surge pricing), the high-surplus customers come bundled with the low-surplus customers. And from the seller’s perspective, this bundling is optimal (see this post by Chris Dixon on why bundling works, and invert it).

So the reason I thought up this post is that there has been some uncertainty about ride-hailing marketplaces in Bangalore recently. First, drivers went on strike alleging that they weren’t being paid fairly by the marketplaces. Then, a regulator decided to take the rulebook too literally and banned pooled rides. As i write this, a bunch of young women I know are having a party, and it’s likely that they’ll need these ride-hailing services for getting home.

Given late night transport options in Bangalore, and the fact that the city sleeps early, their willingness to pay for a safe ride home will be high. If markets work normally, they’re guaranteed a high consumer surplus. And this will be made possible by someone, somewhere else, who stretched their budget to be able to afford an Uber ride.

Think about it!

Cross-posted at RQ

More football structuring

I’ve commented earlier on innovative structuring of football player contracts, with call options and put options and all other exotic options being involved. Now I see another interesting transfer structure, this time in the contract of Juventus (and Spain) striker Alvaro Morata.

In 2014, Real Madrid sold Morata to Juventus for a transfer fee of €20 million, but the sale had a “buy back clause”. Embedded in the sale was an option for Real Madrid to buy back Morata at any time for €30 million, and now it seems like they’re exercising it!

While this might be based on Morata’s performances (both for Juventus and Spain) in the last couple of years, the interesting thing about the buyback is that Real Madrid are unlikely to keep hold of Morata. Instead, talk is that they plan to sell him on, with PSG and Manchester United being interested in the forward.

Effectively the deal is something like “as long as Morata’s perceived market value is  < €30M, Juventus can keep him, but once his perceived market value goes up, all the upside goes to Real Madrid”. The downside (in case Morata regressed as a player and his market value went below €20M), of course, remained with Juventus. To put it simply, Madrid is exercising its call option on the player.

While loan agreements have earlier had clauses such as “right but obligation to make deal permanent” or “obligation but not right to make deal permanent”, this is the first time I’m seeing an actual transfer deal with this kind of a clause, which is being exercised. So why did Juventus and Real Madrid hammer out such a complicated-looking structure?

For Juventus, the simple answer is that the option they wrote reduced the cost of buying the player. While they have given up on significant upside in writing this call option, this is what perhaps made the purchase possible for them, and in some ways, it’s worked out by giving them two more Scudetti.

The answer is less clear from Real Madrid’s perspective. Clearly, the fact that they got a call option meant that they believed there was a significant chance of Morata improving significantly. At the point of time of sale (2014), however, he was surplus to their requirements and they believed sending him elsewhere would help in this significant improvement.

It is possible that the market in 2014 wasn’t willing to bear the price implied by Real Madrid’s expectation of Morata’s improvement, but was only willing to pay based on his then abilities and form. In other words, while Morata’s current abilities were fairly valued, his future abilities were grossly undervalued.

And Madrid did the smart thing by unbundling the current and future values, by structuring a deal that included a call option!

Again, this is only my speculation of how it would have turned out, but it’s indeed fascinating. Given how global financial markets are performing nowadays, it seems like structuring of football deals is now far more interesting than structuring financial derivatives! But then the market is illiquid!

Valuing Global Fashion Group

Yesterday, in Mint, I wrote about ratchets in option valuation (a pet topic of mine), and gave alternate valuations of different Indian “unicorns” by accounting for the downside protection clauses that come with startup investment.

Money quote:

This implies that a share of the company held by [investors] includes a long put option, while a share of the company held by earlier investors includes a short put option (since they have implicitly written this option). In other words, a share held by the new investors is worth much more than a share held by earlier investors.

Now comes news that Global Fashion Group (that includes Jabong and a few other fashion houses started by Rocket Internet) has raised money at a “down round”. This gives me a good opportunity to put my theory to practice.

GFG has now raised $339M for a headline valuation of $1.13 billion. In its earlier round, it had raised $169M for a headline valuation of $3.5 billion. Let us look at a hypothetical employee of GFG who owned 0.1% of the company before the previous round of investment, and see what these shares are worth now.

Absence of ratchets

GFG had a “pre-money” valuation of $3.33 billion, and 0.1% of that would have been worth $3.33 million. As of that round of investment, existing investors had 95% stake in the company, so our friend’s share of the company would have come down to 0.095% (95% of 0.1%).

The new round shows a pre-money valuation of $791 million, and so our friend’s stock would be worth $750,000 after the latest round of valuation. This is a comedown from the previous valuation, but is still significant enough.

Presence of ratchets

Let’s assume that the previous round of investment into GFG came with a full ratchet (we’ll look at other downside protection instruments later). This would mean that its investors in that round would have to be compensated for the drop in valuation.

Investors in the previous round put in $169M for a headline valuation of $3.33Bn. The condition of the full ratchet is that is that if this round’s pre-money valuation were to be less than last round’s post-money valuation, the monetary value of last round’s investors has to be the same.

So despite this round showing a pre-money valuation of only $791M, last round’s investors would claim that $169M of that belongs to them (the way this is achieved in an accounting context is that the ratio in which their preferred shares convert to common shares changes). So the earlier investors (who came before last round) see the value of their shares go down to a paltry $622M. From owning 95% of the company, the down-round means they only own 79% now. And that is before the new round has come in.

Investors in the new round have put in $339M for a headline valuation of $1.13Bn, giving them a round 30% stake. Earlier investors have a 70% stake, of which investors who came before the previous round (which includes employees like our friend) have a 79% stake, giving them a net stake of 55%.

Coming back to our friend, remember that he owned 0.1% of the shares before the last round of investment. The ratchet means that he owns 0.1% of 55% of the company’s current headline valuation. This values his shares at $622,000.

But not so fast – since this assumes that the latest round of investment has no ratchets. If we need to take into consideration that this round has a full ratchet as well, the option formula I used in the Mint piece says that GFG is now worth $760M, far lower than the $1.13Bn headline valuation.

This implies that the stock held by investors prior to this round is now worth only $421M ($760M – $339M). Investors prior to the last round held 79% of these shares, so their stake is worth $331M now. Our friend held 0.1% of that, so his stake is only worth $331,000.

In other words, if both the previous and current rounds of investment in GFG came with a full ratchet protection, the shares held by ordinary investors such as our friend would have lost 56% of its value on account of optionality alone! Notwithstanding the fact that the remaining shares are held in a company whose value is on the downswing!

Then again, downside protection for investors could have come by other means, which were less harsh than full ratchet. Nevertheless, this can help illustrate how much of founders’ and employees’ shareholder value can be destroyed using ratchets!