Ratings revisited

Sometimes I get a bit narcissistic, and check how my book is doing. I log on to the seller portal to see how many copies have been sold. I go to the Amazon page and see what are the other books that people who have bought my book are buying (on the US store it’s Ray Dalio’s Principles, as of now. On the UK and India stores, Sidin’s Bombay Fever is the beer to my book’s diapers).

And then I check if there are new reviews of my book. When friends write them, they notify me, so it’s easy to track. What I discover when I visit my Amazon page are the reviews written by people I don’t know. And so far, most of them have been good.

So today was one of those narcissistic days, and I was initially a bit disappointed to see a new four-star review. I started wondering what this person found wrong with my book. And then I read through the review and found it to be wholly positive.

A quick conversation with the wife followed, and she pointed out that this reviewer perhaps reserves five stars for the exceptional. And then my mind went back to this topic that I’d blogged about way back in 2015 – about rating systems.

The “4.8” score that Amazon gives as an average of all the ratings on my book so far is a rather crude measure – since one reviewer’s 4* rating might differ significantly from another reviewer’s.

For example, my “default rating” for a book might be 5/5, with 4/5 reserved for books I don’t like and 3/5 for atrocious books. On the other hand, you might use the “full scale” and use 3/5 as your average rating, giving 4 for books you really like and very rarely giving a 5.

By simply taking an arithmetic average of ratings, it is possible to overstate the quality of a product that has for whatever reason been rated mostly by people with high default ratings (such a correlation is plausible). Similarly a low average rating for a product might mask the fact that it was rated by people who inherently give low ratings.

As I argue in the penultimate chapter of my book (or maybe the chapter before that – it’s been a while since I finished it), one way that platforms foster transactions is by increasing information flow between the buyer and the seller (this is one thing I’ve gotten good at – plugging my book’s name in random sentences), and one way to do this is by sharing reviews and ratings.

From this perspective, for a platform’s judgment on a product or seller (usually it’s the seller, but for products such as AirBnb, information about buyers also matters) to be credible, it is important that they be aggregated in the right manner.

One way to do this is to use some kind of a Z-score (relative to other ratings that the rater has given) and then come up with a normalised rating. But then this needs to be readjusted for the quality of the other items that this rater has rated. So you can think of some kind of a Singular Value Decomposition you can perform on ratings to find out the “true value” of a product (ok this is an achievement – using a linear algebra reference given how badly I suck in the topic).

I mean – it need not be THAT complicated, but the basic point is that it is important that platforms aggregate ratings in the right manner in order to convey accurate information about counterparties.

Freelancing and transaction costs

In the six years of running my own consulting business, I’d forgotten about an essential part that you need to endure as part of a job – piecemeal work. It is fairly often when you’re working for someone else that you get work that is so tiny or insignificant that you can hardly take ownership of it. The best strategy for dealing with it is to quietly get it over with and hope you won’t get such stuff again.

However, sometimes you can get caught in a rut of continuously getting this kind of work, and start wondering what you actually signed up for. And this is one thing I hadn’t expected to encounter when I got back to full time working earlier this year.

Thinking about why I never had to encounter such stuff during my consulting life, I realised there’s a fairly simple explanation – transaction costs.

Being a consultant is high transaction cost business. Every time you need to take on a new piece of work, you need to go through the charade of negotiating specifics with the client, pricing and drawing up a contract. All put together, the effort is not insignificant.

Moreover, in the line of work that I used to do, there was this massive overhead cost of understanding, cleaning and getting comfortable with the client’s data  – the effort involved in that meant that after a particular point in time I stopped taking work that wasn’t chunky enough. For a while I started refusing such work, but then got smarter and started pricing myself out of such work (though some clients were generous enough to meet that price to get their little tasks done – effectively I’d passed on the transaction costs to them).

The downside of this, of course, was that there was a fair amount of money I could have made taking up small works which I didn’t since the transaction cost was too high – this can be thought of as potential lost revenues. The upside was that whatever work I did was of high quality and (hopefully) made a big impact on the client’s business.

In the nature of the firm, Ronald Coase wrote that the purpose of the corporation was that transaction cost of dealing with co-workers can be eliminated. But then, I realise that sometimes this transaction cost can also be a good thing!

Oh, and obligatory plug here – my book Between the buyer and the seller deals with transaction costs, among other things. It’s available for sale (both in print and digital) on Amazon.


The (missing) Desk Quants of Main Street

A long time ago, I’d written about my experience as a Quant at an investment bank, and about how banks like mine were sitting on a pile of risk that could blow up any time soon.

There were two problems as I had documented then. Firstly, most quants I interacted with seemed to be solving maths problems rather than finance problems, not bothering if their models would stand the test of markets. Secondly, there was an element of groupthink, as quant teams were largely homogeneous and it was hard to progress while holding contrarian views.

Six years on, there has been no blowup, and in some sense banks are actually doing well (I mean, they’ve declined compared to the time just before the 2008 financial crisis but haven’t done that badly). There have been no real quant disasters (yes I know the Gaussian Copula gained infamy during the 2008 crisis, but I’m talking about a period after that crisis).

There can be many explanations regarding how banks have not had any quant blow-ups despite quants solving for math problems and all thinking alike, but the one I’m partial to is the presence of a “middle layer”.

Most of the quants I interacted with were “core” in the sense that they were not attached to any sales or trading desks. Banks also typically had a large cadre of “desk quants” who are directly associated with trading teams, and who build models and help with day-to-day risk management, pricing, etc.

Since these desk quants work closely with the business, they turn out to be much more pragmatic than the core quants – they have a good understanding of the market and use the models more as guiding principles than as rules. On the other hand, they bring the benefits of quantitative models (and work of the core quants) into day-to-day business.

Back during the financial crisis, I’d jokingly predicted that other industries should hire quants who were now surplus to Wall Street. Around the same time, DJ Patil et al came up with the concept of the “data scientist” and called it the “sexiest job of the 21st century”.

And so other industries started getting their own share of quants, or “data scientists” as they were now called. Nowadays its fashionable even for small companies for whom data is not critical for business to have a data science team. Being in this profession now (I loathe calling myself a “data scientist” – prefer to say “quant” or “analytics”), I’ve come across quite a few of those.

The problem I see with “data science” on “Main Street” (this phrase gained currency during the financial crisis as the opposite of Wall Street, in that it referred to “normal” businesses) is that it lacks the cadre of desk quants. Most data scientists are highly technical people who don’t necessarily have an understanding of the business they operate in.

Thanks to that, what I’ve noticed is that in most cases there is a chasm between the data scientists and the business, since they are unable to talk in a common language. As I’m prone to saying, this can go two ways – the business guys can either assume that the data science guys are geniuses and take their word for the gospel, or the business guys can totally disregard the data scientists as people who do some esoteric math and don’t really understand the world. In either case, value added is suboptimal.

It is not hard to understand why “Main Street” doesn’t have a cadre of desk quants – it’s because of the way the data science industry has evolved. Quant at investment banks has evolved over a long period of time – the Black-Scholes equation was proposed in the early 1970s. So the quants were first recruited to directly work with the traders, and core quants (at the banks that have them) were a later addition when banks realised that some quant functions could be centralised.

On the other hand, the whole “data science” growth has been rather sudden. The volume of data, cheap incrementally available cloud storage, easy processing and the popularity of the phrase “data science” have all increased well-at-a-faster rate in the last decade or so, and so companies have scrambled to set up data teams. There has simply been no time to train people who get both the business and data – and the data scientists exist like addendums that are either worshipped or ignored.

Firecrackers and the Hindu religion

There was massive controversy in India last month when the Supreme Court banned the sale of firecrackers in and around Delhi, in an ostensible Move to cut pollution.

As one might expect, the move drew heavy criticism on the grounds that the court was ruling against a fundamental tenet of Hindu religion. In return, other people pointed out that bursting firecrackers on the occasion of Deepavali is a rather recent tradition, and thus has nothing to do with the “fundamental tenets of Hinduism”.

As it happened, the ban continued to stay, though reports say that both noise and air pollution levels in Delhi were unaffected by the ban. Here’s my humble attempt to argue that why modern traditions such as bursting firecrackers is important to religion,

As I’ve mentioned several times on this blog, religion in general and festivals in particular are memes, in the traditional Richard Dawkins sense of the term.

Religion and festivals are basically ideas that compete in an ideas marketplace, and people propagate the ideas that they like the most. In one sense people like what they find useful – which is why imagined orders such as democracy or public limited companies continue to propagate and thrive.

At a more personal level, though, people will choose to associate with and propagate ideas that they simply like, and at a very basic level, enjoy. In other words, the more fun people find a concept, the more heavily they’ll adopt and propagate it.

Hence religions evolve, and (in what can be seen as parallels to mutation), pick up ideas from outside that can make them more fun. So the American Christians picked up and appropriated thanksgiving from the red Indians. Even further back Christianity picked up and amalgamated the idea of Christmas. Hare Krishna people picked up wild dancing. Bombay people picked up Ganesha processions. And so on.

By incorporating fun practices from outside, religions make themselves fitter, as they open up leeway’s for new recruits (such as kids). Short of coercion, without fun practices there’s little chance that religion can pick up new recruits.

Crackers on Deepavali, or colours on Holi, are aspects that have come into the hindu religion that have made it more fun. That theee aspects make the religion more fun mean that it’s easier to co-opt new recruits, especially the young kind. This makes the meme that is the hindu religion fitter.

So it doesn’t matter how ancient a practice is – as long as it’s fun, and increases the memetic fitness of a religion, it remains a fundamental part of the religion.

Without firecrackers the idea of Deepavali might lose its identity and people might stop celebrating it. And it being one of Hinduism’s most celebrated festivals, a weakening Deepavali meme leads to a weakening hindu meme.

So the banning of firecrackers in Delhi on the occasion of Deepavali was indeed injurious to the hindu religion.

Just keep in mind that culture (using memes) evolves much much faster than organisms (which use genes)!

Lessons from poker party

In the past I’ve drawn lessons from contract bridge on this blog – notably, I’d described a strategy called “queen of hearts” in order to maximise chances of winning in a game that is terribly uncertain. Now it’s been years since I played bridge, or any card game for that matter. So when I got invited for a poker party over the weekend, I jumped at the invitation.

This was only the second time ever that I’d played poker in a room – I’ve mostly played online where there are no monetary stakes and you see people go all in on every hand with weak cards. And it was a large table, with at least 10 players being involved in each hand.

A couple of pertinent observations (reasonable return for the £10 I lost that night).

Firstly a windfall can make you complacent. I’m usually a conservative player, bidding aggressively only when I know that I have good chances of winning. I haven’t played enough to have mugged up all the probabilities – that probably offers an edge to my opponents. But I have a reasonable idea of what constitutes a good hand and bid accordingly.

My big drawdown happened in the hand immediately after I’d won big. After an hour or so of bleeding money, I’d suddenly more than broken even. That meant that in my next hand, I bid a bit more aggressively than I would have for what I had. For a while I managed to stay rational (after the flop I knew I had a 1/6 chance of winning big, and having mugged up the Kelly Criterion on my way to the party, bid accordingly).

And when the turn wasn’t to my liking I should’ve just gotten out – the (approx) percentages didn’t make sense any more. But I simply kept at it, falling for the sunk cost fallacy (what I’d put in thus far in the hand). I lost some 30 chips in that one hand, of which at least 21 came at the turn and the river. Without the high of having won the previous hand, I would’ve played more rationally and lost only 9. After all the lectures I’ve given on logic, correlation-causation and the sunk cost fallacy, I’m sad I lost so badly because of the last one.

The second big insight is that poverty leads to suboptimal decisions. Now, this is a well-studied topic in economics but I got to experience it first hand during the session. This was later on in the night, as I was bleeding money (and was down to about 20 chips).

I got pocket aces (a pair of aces in hand) – something I should’ve bid aggressively with. But with the first 3 open cards falling far away from the face cards and being uncorrelated, I wasn’t sure of the total strength of my hand (mugging up probabilities would’ve helped for sure!). So when I had to put in 10 chips to stay in the hand, I baulked, and folded.

Given the play on the table thus far, it was definitely a risk worth taking, and with more in the bank, I would have. But poverty and the Kelly Criterion meant that the number of chips that I was able to invest in the arguably strong hand was limited, and that limited my opportunity to profit from the game.

It is no surprise that the rest of the night petered out for me as my funds dwindled and my ability to play diminished. Maybe I should’ve bought in more when I was down to 20 chips – but then given my ability relative to the rest of the table, that would’ve been good money after bad.

Auctions of distressed assets

Bloomberg Quint reports that several prominent steel makers are in the fray for the troubled Essar Steel’s assets. Interestingly, the list of interested parties includes the promoters of Essar Steel themselves. 

The trouble with selling troubled assets or bankrupt companies is that it is hard to put a value on them. Cash flows and liabilities are uncertain, as is the value of the residual assets that the company can keep at the end of the bankruptcy process. As a result of the uncertainty, both buyers and sellers are likely to slap on a big margin to their price expectations – so that even if they were to end up overpaying (or get underpaid), there is a reasonable margin of error.

Consequently, several auctions for assets of bankrupt companies fail (an auction is always a good mechanism to sell such assets since it brings together several buyers in a competitive process and the seller – usually a court-appointed bankruptcy manager – can extract the maximum possible value). Sellers slap on a big margin of error on their asking price and set a high reserve price. Buyers go conservative in their bids and possibly bid too low.

As we have seen with the attempted auctions of the properties of Vijay Mallya (promoter of the now bankrupt Kingfisher Airlines) and Subroto Roy Sahara (promoter of the eponymous Sahara Group), such auctions regularly fail. It is the uncertainty of the value of assets that dooms the auctions to failure.

What sets apart the Essar Steel bankruptcy process is that while the company might be bankrupt, the promoters (the Ruia brothers) are not. And having run the company (albeit to the ground), they possess valuable information on the value of assets that remain with the company. And in the bankruptcy process, where neither other buyers nor sellers have adequate information, this information can prove invaluable.

When I first saw the report on Essar’s asset sale, I was reminded of the market for footballers that I talk about in my book Between the buyer and the seller. That market, too, suffers from wide bid-ask spreads on account of difficulty in valuation.

Like distressed companies, the market for footballers also sees few buyers and sellers. And what we see there is that deals usually happen at either end of the bid-ask spectrum – if the selling club is more desperate to sell, the deal happens at an absurdly low price, and if the buying club wants the deal more badly, they pay a high price for it.

I’ve recorded a podcast on football markets with Amit Varma, for the Seen and the unseen podcast.

Coming back to distressed companies, it is well known that the seller (usually a consortium of banks or their representatives) wants to sell, and is usually the more desperate party. Consequently, we can expect the deal to happen close to the bid price. A few auctions might fail in case the sellers set their expectations too high (all buyers bid low since value is uncertain), but that will only make the seller more desperate, which will bring down the price at which the deal happens.

So don’t be surprised if the Ruias do manage to buy Essar Steel, and if they manage to do that at a price that seems absurdly low! The price will be low because there are few buyers and sellers and the seller is the more desperate party. And the Ruias will win the auction, because their inside information of the company they used to run will enable them to make a much better bid.


London’s 7D

In classes 11 and 12 i had to travel every day from Jayanagar to indiranagar to get to school. There was a direct bus that took me from just behind my house to Just behind my school. This was 7D. But despite my mother’s insistence that I take that, I seldom did. For it took such a circuitous route that it would take ages.

I’m sure that someone has done a survey of bangalores most convoluted bus routes, and if so, 7D would fall close to the top there (the only bus that I imagine could beat 7D is 201).

So rather than take 7D I’d take one of the many buses bound to Shivajinagar and get off at Richmond circle, from where I’d get 138 to take me right behind school (or the double decker 131 to take me 10 mins walk away in the other direction). The changeover at Richmond circle was rather simple (no walking involved) and this process would help me save at least 15 minutes each way every day.

Now I’ve figured that the London Underground has its own 7D, except for the fact that the route is not circuitous – it’s simply slow. I live in Ealing and my office is near Victoria so the most direct way for me to travel is to take the district line. It takes 35 minutes and runs once every 10 minutes (the line splits in two places to frequency to Ealing is low).

On most days I don’t travel directly from home to work since I drop Berry to her Nursery on the way. So taking the district line straight from Home to work is never an option.

Yesterday I was ill and so my wife took Berry to her Nursery. So I travelled directly to work. And for the first time ever since I joined this office I took the district line on the way to Office.

I reached Ealing broadway at 8:02 and Just about caught the 8:03 train. The train rolled into Victoria at 8:40 and I was in Office at 8:45.

Today once again I was traveling directly from home to work, and reached Ealing broadway station a few seconds later than yesterday, just missing the train I’d caught yesterday. I had the option to wait 10 minutes for the next district line train or using what seemed like a convoluted route. I chose the latter.

I took a great western railway train to Paddington, where I walked for about 5-7 minutes to the bakerloo line and got it. I got off the bakerloo five stops later at oxford circus where I changed to the Victoria line, and got off two stops later at Victoria. The time was 8:35!

In other words I’d left later than I had yesterday, changed trains twice (one involving a long walk) and still reached five minutes earlier. And all the time traveling in trains far less crowded than an early morning district line train headed to the city!

I hereby christen the district line as London’s 7D. Except that the route isn’t anywhere circuitous!