## Brahmastra

Sometimes we overdo “option value”. We do things that have a small possibility of a big upside, and big possibility of no or very minimal downside, in the belief that “nothing can go wrong in trying”.

My father used to term this “pulling a mountain with a string”, with the reasoning being that if you actually manage to pull, then you have moved a mountain. If not, all that you have lost is a string.

There is one kind of situation, however, where I think we might overindex on option value – these are what I call “one shot events” or “brahmastras”.

Going into a little bit of mythology, there is the story of the Brahmastra in the Mahabharata. Famously, Karna possesses it. It is an incredibly powerful weapon with the feature (or bug, rather) that it can be used only once. Karna would have set it aside to use on Arjuna, but the Pandavas decide to send Ghatotkacha to create havoc during the night fight when Karna is forced to use up his brahmastra on Ghatotkacha – meaning he didn’t have access to it in his battle with Arjuna, where he (Karna) ultimately got killed.

Because the Brahmastra could be used only once, Karna wanted to maximise the impact of the weapon. His initial plan was to use it on what he thought might be a decisive battle with Arjuna. The Pandavas’ counterplan was to force him to use it earlier.

Actually, thinking about it – the Brahmastra can be thought of as another kind of option. The problem here being one of optimal exercise. Actually, there is a very stud paper written by economist Avinash Dixit on this topic – regarding Elaine’s sponges.

Read the whole paper. It is surely worth it. To quickly summarise, Elaine has a limited number of “contraceptive sponges”, and wants to maximise her “utility” of using them. When a guy comes along, she needs to decide whether it is worth expending a sponge on him. Dixit derives a nice equation to determine a function for this.

Basically, Brahmastra occurs when you have only one sponge left, and you need to use it at an “optimal time”. There is another problem in economics  called the “secretary problem” (nothing to do with secretary birds) that deals with this.

Recently I’ve been thinking – these kind of Brahmastra / sponge / secretary problems are important to solve when you are thinking of talking to someone.

Let’s say you have what you think is a studmax application of GenAI and want to talk to VCs about it. If you go too early, the VC will only see a half-baked version of your idea, and even if you go to them later once you have fully formed it, the half-baked idea you had showed them will influence them enough to discount your later fully formed idea.

And if you go too late, the idea may not be that studmax any more, and the VC might dismiss it. So it’s a problem of “optimal exercise” (note that this is an issue only with American options, not European).

It is similar with asking someone out (or so I think – I’ve been out of this business for 14 years now). You approach them “too early” (before they know you), they will dismiss you then and then forever. You approach too late and the option would have expired.

In the world of finance, we focus too much on the PRICE of options and (based on my now limited knowledge) too little on optimal expiry of the said options. In the real world, the latter is also important.

## 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.

## VC Funding, Ratchets and Optionality

A bug (some call it a “feature”) of taking money from VCs is that it comes in with short optionality. VCs try to protect their investments by introducing “ratchets” which protect them against the reduction in valuation of the investee in later rounds.

As you might expect, valuation guru Aswath Damodaran has a nice post out on how to value these ratchets, and how to figure out a company’s “true valuation” after accounting for the ratchets.

A few months back, I’d mentioned only half in jest that I want to get into the business of advising startups on optionality and helping them value investment offers rationally after pricing in the ratchets, so that their “true valuation” gets maximised.

In a conversation yesterday, however, I figured that this wouldn’t be a great business, and startups wouldn’t want to hire someone like me for valuing the optionality in VC investments. In fact, they wouldn’t want to hire anyone for valuing this optionality.

There are two reasons for this. Firstly, startups want to show the highest valuation possible, even if it comes embedded with a short put option. A better valuation gives them bigger press, which has some advertising effect for sales, hiring and future valuations. A larger number always has a larger impact than a smaller number.

Then, startup founders tend to be an incredibly optimistic bunch of people, who are especially bullish about their own company. If they don’t believe enough in the possible success of their idea, they wouldn’t be running their company. As a consequence, they tend to overestimate the probability of their success and underestimate the probability of even a small decrease in future valuation. In fact, the probability of them estimating the latter probability at zero is non-zero.

So as the founders see it, the probability of these put options coming into the money is near-zero. It’s almost like they’re playing a Queen of Hearts strategy. The implicit option premium they get as part of their valuation they see as “free money”, and want to grab it. The strikes and structures don’t matter.

I have no advice left to offer them. But I have some advice for you – given that startups hardly care about optionality, make use of it and write yourself a fat put option in the investment you make. But then this is an illiquid market and there is reputation risk of your option expiring in the money. So tough one there!

## Minimum Support Prices

In India, we have this concept of “Minimum Support Price” for agricultural commodities. It is basically an unlimited put option written by the Government in order to protect farmers against not getting “appropriate remuneration” for their produce. In that sense it can be thought of as an implicit subsidy towards agriculture. There is merit in the argument in favour of such a measure – agriculture is a fundamentally high risk business and in the absence of such safety nets, not enough people might take the risk to sow a particular crop, leading to shortages.

On the other hand, it can be distortionary too. If the MSP is set too high, it can lead to a glut in that particular crop in that year, at the cost of other crops, leading to shortages in the latter. Hence, it is a tool that is necessary but one that should be used with care.

Now, the MSP has to be set in advance – so the MSP for the 2013-14 season has already been set.  This is again a risky move but a necessary move – farmers need to know the minimum amount they can get for each crop before they make their sowing decision.

The figure on shows the Compounded Annual Growth Rate (CAGR) in the MSP of a few important agricultural commodities between 2007-08 and 2013-14. Notice that the CAGR is lowest for crops such as wheat or rice, and high for crops such as Tur Dal or Moong Dal. Under the current Public Distribution System (PDS), families below the poverty line get rice and wheat at subsidized rates, but not pulses. Note that I’m only mentioning facts and not trying to suggest any causation here.

Interestingly, the MSP for coarse grains such as Ragi and Bajra has also grown significantly faster than that of rice or wheat. Also note that prices of cotton and jute have grown rather slowly over the period of consideration.

Now, while this tells us by how much prices have changed in the last six years, it is also pertinent to see how the prices have changed – did the price rise consistently over the last 5-6 years or were there some discontinuities? The next figure tries to address this issue.

The figure on the left here charts the actual year by year growth in the Minimum Support price of the crops under consideration. To me, two things jump out from this graph – apart from sugarcane, there was a steep increase in the minimum support prices of all commodities between 2007-8 and 2008-9. You might want to be reminded that India went to polls in the summer of 2009 and Maharashtra, a prime sugarcane growing state, went to polls in the winter of the same year. Again, I don’t want to claim any causation.

Then, from 2009 to 2012, minimum support prices of these commodities remained largely constant – perhaps compensating for the large jump from 2008 to 09? And then again there was a spike from 2012 to 2013. There is no such jump from 2013 to 2014, though. Note that the nation goes to the polls in 2014.

Tur and Moong dal, however, have seen a rather secular increase in prices in the last five-six years.

How the proposed Food Security Bill will affect the MSP is left as an exercise to the reader. Comments are open.

PS: Data that I’ve used for this post is available at the website of the Commission for Agricultural Costs and Prices.