Author: SK

Relative pricing revisited

Yesterday I bought a pair of jeans. Normally it wouldn’t be a spectacular event (though one of my first blogposts was about a pair of jeans), but regular squatting has meant that I’ve been tearing through jeans well-at-a-faster-rate, and also that it’s been hard to find jeans that fit me well.

Basically, I have a well-above-average thigh and a well-below-average arse for my waist size, and that makes it hard to find readymade pants that fit well. As a consequence I’ve hardly bought trousers in the last 2-3 years, though I’ve been losing many pairs to the tear in this period of time.

And so when I found a pair of jeans that fit me comfortably yesterday I wasn’t too concerned about paying a record price for it (about 1.8 times the maximum I’d ever paid for a pair in the past). In fact, I’d seen another pair that fit well a few minutes earlier (and it was a much fancier brand), but it was well above budget (3 times as expensive as my historically costliest ever pair), and so I moved on (more importantly, it came with a button fly, and I’d find that rather inconvenient).

Jeans having been bought, we went off to a restaurant at the mall for lunch, at the end of which the wife pointed out that the money we paid for the lunch was more than the difference in prices between the two pairs of jeans. And that if only we would avoid eating out when it’s avoidable, we could spend on getting ourselves much more fancier clothes without feeling guilty.

I’ve written about relative prices in the past, especially about the Big Mac Index, and how it doesn’t make sense because of differential liquidity. After moving to London, I’m yet to come to terms with the fact that relative prices of goods here is vastly different from that back home; and that I haven’t adjusted my lifestyle accordingly leading to inefficient spending and a possible strain on lifestyle.

Food, for example, is much more expensive here than in India (we’ll use official exchange rates for the purpose of this post). The average coffee costs £2.5 (INR 225), which is about 10 times the price of an average coffee in Bangalore (I’m talking about a good quick cup of coffee here, so ignoring the chains which are basically table rentals). The average weekday takeaway lunch costs £6 (INR 540), which is again 10X what it costs in Bangalore.

Semi-fancy meals (a leisurely meal at a sit down restaurant with a drink, perhaps) are relatively less costly here, costing about £25-30 per head compared to INR 1200-1500 in Bangalore, a ratio of about 2X. A beer at a pub costs about the same, though cocktails here are much more expensive.

The alternative to eating out is, of course, eating in, and most “regular” ingredients such as vegetables and rice cost more here, though cheeses (which are relatively less liquid in India) are actually cheaper here. Milk costs about the same.

Controlling for quality, clothes cost about the same (or might even be less costly here when you go for slightly more fancy stuff). Electronics again cost about the same (they come through the same global supply chain). Contact lenses are more expensive here (though the ones I buy in India are manufactured in the UK!).

In my book, I have a chapter called “if you want to live like a Roman, live in Rome”. It’s about how different cities have different relative liquidity of goods. Similarly, different cities and countries have different relative prices, and long-term residents of these places evolve their spending to optimise for their given set of relative prices.

And when you move cities or countries, if you don’t change your lifestyle accordingly you might end up spending suboptimally, and get less welfare from life.

Once again this points out problems with international price indices being constructed based on a particular commodity, or set of commodities. For not only are different commodities differentially liquid (as I pointed out in my Mint piece linked above) in different places, but also the “standard consumption basket” also varies from city to city!

And if a Delhi-ite consumes lots of apples, and a Bangalorean consumes lots of oranges, you can’t make an apples-to-apples comparison in cost of living in these cities!

There’s no way out

One thing relationship gurus parrot often is the need for compromise. Conflicts are inevitable in a relationship, they say, and so sometimes you need to compromise. While sometimes it’s possible to hold back a thought or a statement, and prevent a situation from blowing up, at other times, there’s simply no way out.

Consider the situation last night, for example. The wife wanted to make Maggi for dinner. Now, unlike most Indians of my generation I don’t like Maggi, and I can at best tolerate it for a snack – definitely not for dinner. And so I definitely wasn’t pleased at the proposal that we have Maggi for dinner. The question was how I should react.

I had two choices – either to grudgingly accept, eat Maggi for dinner and feel unhappy about what I had for dinner, or to tell her I would make my own dinner, which would come at the cost of her getting upset that I wasn’t going to eat something she was going to lovingly prepare.

So one way I would feel unhappy, and the other way she would – so the moment she uttered the proposal to make Maggi, some kind of conflict or unhappiness was given.

That, however, doesn’t mean that it was her fault either – to propose that we eat Maggi, because the same tradeoff applies there as well. She could either propose we eat Maggi, with the result that either she’ll get upset or I’ll get unhappy, or she could hold back the thought and silently bottle it in. Both would incur a cost.

And it happens all the time – there are days when I want us to go out, knowing fully well that the wife likes to stay home. If I voice my desire to go out, it will lead to conflict – either I get upset that she refuses or she’ll grudgingly accompany me. If not, I’ll sulk silently at not having explored the option to go out. And it goes on.

How do you even resolve such differences? By backward induction, the conflict happens at the moment a thought is planted in your head – you could say that we should train ourselves that we don’t even get thoughts that could potentially lead to situations that potentially lead to conflicts, but isn’t that possibly taking things too far?

Anyway, last night we “monetised” our anguish, when I said she should go ahead and make Maggi, but only for herself and Berry (who also loves Maggi). I made myself a Halloumi sandwich. As it happened, all three of us woke up in the middle of the night feeling incredibly hungry!

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!

Mini me

Two years back when we were expecting, relatives would wonder if it would be a “mini Priyanka” or “mini Karthik”. This was their way of wondering whether it would be a girl or a boy. Having spent the first half of the pregnancy in Spain, we knew that it would be a girl, but in most cases refused to answer this “mini Priyanka/Karthik” question.

In hindsight, it’s a bit annoying – to assume that the kid is the mini version of the parent she shares her gender (or should I be saying “sex”, as a Brooklyn-based friend recently remarked) with. What makes people simply assume that a girl should be like her mother and a boy should be like his father, when it is clear that irrespective of sex (take that Brooklyn, I got it right) the kid receives the same number of chromosomes from each parent.

And as it happens, our specimen is a clear exhibit of being like the parent of the opposite sex. She might be a mini Priyanka in that she is a girl, but that and her Bambi eyes apart, she is uncannily like me in pretty much everything else. In fact, upon seeing her as a baby, her godmother remarked that “Karthik could have married an old shoe and still produced a child that looks exactly like this”.

The specimen in question

Save for her eyes, she looks nearly exactly the same way as I did at her age. Just like me, she’s outgoing, and likes to go aimlessly wandering (to go “on beat” as we would say in Kannada). For the large part, she likes the same kind of foods that I like (a notable exception is her affinity for Maggi). Just like me, she looks out for cashewnuts or peanuts in whatever food she is having.

This list is a long one, with the list of her similarities with her mother being much much shorter.

And on top of all this, she is also attached to me. She doesn’t let me get out of home without insisting that I take her along (I clearly remember doing this a lot to my father as well), while she happily says “bye” to her mother. When she wakes up, she starts screaming “appa” and “ka” (short for “kara” which is short for “Karthik”. it’s a nickname used mainly by my wife and one of my cousins). She calls out to me from the other end of the house in a way she’s never called out to her mother. And she doesn’t trouble me like she troubles her mother!

I had been told by several people that fatherhood can change you, but one thing I hadn’t bargained for was that it would make me more emotional. But then I guess having a little version of you who you can totally empathise with around can do that to you!

Lessons from Shoe Dog

I first came across Shoe Dog, Nike founder Phil Knight’s memoir, from this post on Tren Griffin’s blog. Soon enough, I saw the book pop up multiple times on my GoodReads, and when I got the Kindle sample last week, I noticed that all my friends on GoodReads had given it a five star rating.

So while I normally don’t read autobiographies (the only other one that I remember liking is Andrea Pirlo’s), the recommendations made this one hard to resist. And it was a brilliant read. Finished the book in two days.

It’s a story very well told, written in an engaging style that makes you sometimes wonder if it is a work of fiction. Knight has eschewed the boring details and focussed on the interesting, and impactful, stuff, and the book is full of stories about the early days of the firm (it basically “ends” with Nike going public).

What struck me about Nike’s story is the number of times it nearly went under (which is what possibly makes it such a great story). In the light of those challenges (lawsuits, supply issues, constant working capital troubles, leverage), it is a wonder that the company survived long enough to thrive! In that sense, while it makes sense to draw business lessons from Nike’s story, I sometimes wonder if it’s simply a case of survivorship bias.

For starters, for nearly twenty years after founding, Nike remained a closely held private company, with little outside investment. While the company was mostly profitable (except on one occasion when it broke up with a supplier), it was forever cash poor. Well into its fifteenth year of operations, Knight talks about the company “not having money” for stuff like advertising (for example).

Instead, the company relied on heavy leverage, borrowing as much as it could from any bank that would deal with it (which was basically all banks in Oregon – in the 1960s and 70s, there was no inter-state banking in the US). Several times, the company came close to running out of money, when banks refused to extend its credit. But then it survived.

Griffin’s review of the book shows all this as “learnings” – innovative sources of financing, high growth, dealing with crises, but to me it looks like a lot of bad financial management. Too little equity for too long, an obsession for control (finally Nike went public only when Knight figured he could issue dual class stock), high leverage and all that.

The other thing that struck me about Nike is that even in the late 70s, when the company was 15 years old, it seemed like a bunch of buddies of Knight running it – there wasn’t that much of professional management around, and this could again be attributed to the business being continuously short on cash. I guess times are different now, and equity financing is more available, and firms can start hiring professional managers early, but a 15 year old company being seemingly run in a chaotic manner seemed odd.

Finally, back in business school, we were told that when applying to companies such as Nike or Adidas, we should highlight whatever sporting achievements we might have on our CVs. That struck me as odd – what impact could having played cricket for my hostel wing possibly have on how I could sell shoes?

Reading the book, though, it seems like a culture issue. In several places in the book Knight talks about the firm being driven by a “passion for sport”, with the early employees all being sportsmen. Culture permeates, and you hire more people like you. There is this vague sense of brotherhood, among people who have played competitive sport, and that’s hard to permeate for non sportspersons. And the culture goes on. Whether this lack of diversity is good for the company is another matter!

English Premier League: Goal Difference to points correlation

So I was just looking down the English Premier League Table for the season, and I found that as I went down the list, the goal difference went lower. There’s nothing counterintuitive in this, but the degree of correlation seemed eerie.

So I downloaded the data and plotted a scatter-plot. And what do you have? A near-perfect regression. I even ran the regression and found a 96% R Square.

In other words, this EPL season has simply been all about scoring lots of goals and not letting in too many goals. It’s almost like the distribution of the goals itself doesn’t matter – apart from the relegation battle, that is!

PS: Look at the extent of Manchester City’s lead at the top. And what a scrap the relegation is!

A year of wiping arse near the Thames

So it’s been exactly one year and one day since we moved to London. Exactly one year ago (one day after we moved here), I wrote about why Brits talk so much about the weather.

The last one week has been among my most depressing in London. Between Tuesday and Friday, the only times I stepped out of home was to the store round the corner, for grocery shopping. The wife didn’t step out of home at all. The daughter accompanied me on one trip to the store. Between Tuesday evening and Saturday morning, there was a layer (or few) of snow on the ground, thanks to the Beast From The East.

This wasn’t the first time in life that I’d seen snow fall – that had occurred in early December when we were similarly snowed in one Sunday, and had run out of supplies.

This apart, another source of depression was the latitude – between early November and late January, it would get dark insanely early here – around 4pm or so. It would be especially cruel on weekends when we’d be home, to see it getting dark so early. I would take walks in the middle of work (I was working for a company then) to make sure I at least got to see some sun (or white clouds!).

Weather apart, one big insight about London after a year of living here is that it’s a massive sprawl. For example, I live in a 2-storey house, with a backyard at least 100 feet long. And this is typical of all the houses in my area. Roads curve around and have plenty of cul de sacs, giving most residential neighbourhood a suburban feel. Check out the satellite picture of my area here: 
Until I moved here last year, I had assumed that London is an “urban” and dense city, given what I’d seen in 2005 (when I’d stayed in South Kensington) and the fact that the city has great public transport and congestion charges. As it turns out, the neighbourhoods are really suburban and low density. Residential areas are really residential, and you need to go to your area’s “high street” if you need to shop.

In the suburbs, most people have cars, which they use fairly regularly – though not for commuting into the city. The area I live in, Ealing, for example, has brilliant public transport connections, but is fundamentally built for life with cars. We currently live in a 1880s house, but are soon moving to a more “urban” apartment in a building that used to be a pub.

London being a sprawl means that it takes a long time to get anywhere, unless you’re commuting directly in or out of town. Most tube connections are radial, which means that if you need to visit someone in another neighbourhood it can take a long time indeed. As a consequence, I’ve hardly met my friends here – with the one I’ve met most often it’s been at an average frequency of once in 2 months.

The other thing that’s intrigued me about London is the pubs – those in the middle of town are all mostly horribly crowded, while those in the suburbs are really nice and friendly. There’s this one place close to home where I go for my football matches, and where we once went for a Sunday roast (yes, pubs here offer baby high chairs!).

Other pubs in the area look inviting as well, and make me wonder why I don’t have “area friends” to go to them with!

Finally, coming to the title of this post, when we were house-hunting this time last year, one of the things I looked for was a house with a bidet or health faucet. We were told by the agents that such fixtures weren’t normal for rental housing in the UK. After we’d moved in, we asked our landlords if we could install a health faucet. Once again we got the same reply, and that we were free to install them as long as we took them away when we moved out.

So as it has happened, we haven’t really “washed arse in the Thames“!

 

Algorithmic curation

When I got my first smartphone (a Samsung Galaxy Note 2) in 2013, one of the first apps I installed on it was Flipboard. I’d seen the app while checking out some phones at either the Apple or Samsung retail outlets close to my home, and it seemed like a rather interesting idea.

For a long time, Flipboard was my go-to app to check the day’s news, as it conveniently categorised news into “tech”, “business” and “sport” and learnt about my preferences and fed me stuff I wanted. And then after some update, it suddenly stopped working – somehow it started serving too much stuff I didn’t want to read about, and when I tuned (by “following” and “unfollowing” topics) my feed, it progressively got worse.

I stopped using it some 2 years back, but out of curiosity started using it again recently. While it did throw up some nice articles, there is too much unwanted stuff in the app. More precisely, there’s a lot of “clickbaity” stuff (“10 things about Narendra Modi you would never want to know” and the like) in my feed, meaning I have to wade through a lot of such articles to find the occasional good ones.

(Aside: I dedicate about half a chapter to this phenomenon in my book. The technical term is “congestion”. I talk about it in the context of markets in relationships and real estate)

Flipboard is not the only one. I use this app called Pocket to bookmark long articles and read later. A couple of years back, Pocket started giving “recommendations” based on what I’d read and liked. Initially it was good, and mostly curated from what my “friends” on Pocket recommended. Now, increasingly I’m getting clickbaity stuff again.

I stopped using Facebook a long time before they recently redesigned their newsfeed (to give more weight to friends’ stuff than third party news), but I suspect that one of the reasons they made the change was the same – the feed was getting overwhelmed with clickbaity stuff, which people liked but didn’t really read.

Basically, there seems to be a widespread problem in a lot of automatically curated news feeds. To put it another way, the clickbaity websites seem to have done too well in terms of gaming whatever algorithms the likes of Facebook, Flipboard and Pocket use to build their automated recommendations.

And more worryingly, with all these curators starting to do badly around the same time (ok this is my empirical observation. Given few data points I might be wrong), it suggests that all automated curation algorithms use a very similar algorithm! And that can’t be a good thing.

The Anti-Two Pizza Rule

So Amazon supposedly has a “two pizza rule” to limit the size of meetings – the convention is that two pizzas should be sufficient to feed all participants in any meeting. While pizza is not necessarily served at most meetings, the rule effectively implies that a meeting can’t have more than seven or eight people.

The point of the rule is not hard to see – a meeting that has too many people will inevitably have people who are not contributing, and it’s a waste of their time. Limiting meeting size also means cutting total time employees spend in meetings, meaning they can get more shit done.

While this is indeed a noble “rule” in a corporate setting, it just doesn’t work for parties. In fact, after having analysed lots of parties I’ve either hosted or attended over the years, and after an especially disastrous party not so long ago (I’ve waited a random amount of time since that party before writing this so as to not offend the hosts), I hereby propose the “anti two pizza rule” for parties.

While five to eight people is a good number for a meeting, having enough people contributing but no deadweight, the range doesn’t do well at all for more social gatherings. The problem is that with this number, it is not clear if the gathering should remain in one group, or split into multiple groups.

When you have a “one pizza party” (5-6 people or less), you have one tight group (no pun intended) and assuming that people will get along with each other, you’re likely to have a good time.

When you have a “three pizza party” (more than 10 people), it’s intuitive for the gathering to breakup into multiple groups, and if things go well, these groups will be fluid and everyone will have a good time. Such a gathering also allows people to test waters with multiple co-attendees and then settle on the mini-group that they’ll end up spending most time with.

A two-pizza party (6-10 people), on the other hand, falls between the two stools. One group means there will be people left out of the conversation without respite. In such a small gathering, it is also not easy to break out of the main group and start your own group (again, seating arrangement matters). And so while some attendees (the “core group”) might end up having fun, the party doesn’t really work for most participating parties.

So, the next time you’re hosting a party, do yourself and your guests a favour and ensure that you don’t end up with between 6 and 10 people at the party. Either less or more is fine!

You might want to read this other post I’ve written on coordinating guest lists for birthday parties.

FaceTime Baby

My nephew Samvit, born in 2011, doesn’t talk much on the phone. It’s possibly because he didn’t talk much on the phone as a baby, but I’ve never been able to have a decent phone conversation with him (we get along really well when we meet, though). He talks a couple of lines and hands over the phone to his mother and runs off. If it’s a video call, he appears, says hi and disappears.

Berry (born in 2016), on the other hand, seems to have in a way “leapfrogged” the phone. We moved to London when she was five and a half months old, and since then we’ve kept in touch with my in-laws and other relatives primarily through video chat (FaceTime etc.). And so Berry has gotten used to seeing all these people on video, and has become extremely comfortable with the medium.

For example, when we were returning from our last Bangalore trip in December, we were worried that Berry would miss her grandparents tremendously. As it turned out, we landed in London and video called my in-laws, and Berry was babbling away as if there was no change in scene!

Berry has gotten so used to video calling that she doesn’t seem to get the “normal” voice call. Sure enough, she loves picking up the phone and holding it against her ear and saying “hello” and making pretend conversations (apparently she learnt this at her day care). But give her a phone and ask her to talk, and she goes quiet unless there’s another person appearing on screen.

Like there’s this one aunt of mine who is so tech-phobic that she doesn’t use video calls. And every time I call her she wants to hear Berry speak, except that Berry won’t speak because there is nobody on the screen! I’m now trying to figure out how to get this aunt to get comfortable with video calling just so that Berry can talk to her!

 

In that sense, Berry is a “video call” native. And I wouldn’t be surprised if it turns out that she’ll find it really hard to get comfortable with audio calls later on in life.

I’ll turn into one uncle now and say “kids nowadays… “