Tag: index

Mata Amrita in the time of Covid-19

You remember the Mata Amrita Index? I’d first defined it in early 2009, and it is broadly defined as “the likelihood that you will hug a randomly chosen friend or acquaintance you meet”. There is a bilateral version as well, which is defined as “the likelihood that a given pair of people will hug each other when they meet”.

I’ve revisited this concept several times on this blog. Once, I had wondered how you can go about “changing your MAI” with someone. On another occasion I had tried to add a quality dimension to the index, to account for the “quality of hugs”. But indices in general don’t do well when you try to complicate them too much.

In any case, I’ve been wondering how people’s MAI will evolve given the covid-19 crisis. I also wonder how the quality-adjusted MAI will evolve.

For one, Mumbai Mirror reports that Mata Amrita (in whose honour the index has been named) herself has been badly affected by the crisis.

“Like everywhere in the world, life in Kerala and the ashram have changed,” says the ‘hugging saint’, Mata Amritanandamayi, known to her devotees as ‘Amma’, over email. “This is the first time in more than 45 years that there has been no darshan.”

The crisis automatically means that we will, to the extent possible, try to avoid physical contact with other people. When shaking hands itself is frowned upon, hugs are out of the question. However, there will be people outside your immediate family with whom you would have developed a high bilateral MAI. How do you deal with them once you start meeting them again?

My guess is that the bilateral MAI will get sharply partitioned, and “collapse” (in a Schrödingerian sense). For people with whom you’ve had a high historical MAI, and where the historical quality has also been high, you are likely to take a “hell with the virus” approach and continue the (high quality) hugs.

Among other things these also tend to be the people you trust very well (why would you hug someone tightly if you don’t trust them?), and also there aren’t likely to be very many of them.

At the other end, anyone for whom historical bilateral MAI is not close to 1, or with whom the historical quality of hugs hasn’t been great, you’ll simply eschew the hug, going all the way to the namaste, maybe.

So all these “polite hugs” will disappear (which isn’t a bad thing at all, in my opinion). People will also feel less queasy about rejecting a hug – now they have a very good reason to do so.

The other thing is that you need a sort of “trust jump” with someone to get to a point where your MAI jumps from 0 to 1. The old progression (which was never a continuous progression) from handshake to side hug to quick hug to full hug is not going to be valid any more, as you need to directly jump from a zero MAI to a high quality one MAI.

Finally, what will happen of Mata Amrita herself? Is the dip in her “darshan” a temporary impact or a permanent impact? I suspect it’s the former?

Understanding Stock Market Returns

Earlier today I had a short conversation on Twitter with financial markets guru Deepak Mohoni, one of whose claims to fame is that he coined the word “Sensex”. I was asking him of the rationale behind the markets going up 2% today and he said there was none.

While I’ve always “got it” that small movements in the stock market are basically noise, and even included in my lectures that it is futile to fine a “reason” behind every market behaviour (the worst being of the sort of “markets up 0.1% on global cues”), I had always considered a 2% intra-day move as a fairly significant move, and one that was unlikely to be “noise”.

In this context, Mohoni’s comment was fairly interesting. And then I realised that maybe I shouldn’t be looking at it as a 2% move (which is already one level superior to “Nifty up 162 points”), but put it in context of historical market returns. In other words, to understand whether this is indeed a spectacular move in the market, I should set it against earlier market moves of the same order of magnitude.

This is where it stops being a science and starts becoming an art. The first thing I did was to check the likelihood of a 2% upward move in the market this calendar year (a convenient look-back period). There has only been one such move this year – when the markets went up 2.6% on the 15th of January.

Then I looked back a longer period, all the way back to 2007. Suddenly, it seems like the likelihood of a 2% upward move in this time period is almost 8%! And from that perspective this move is no longer spectacular.

So maybe we should describe stock market moves as some kind of a probability, using a percentile? Something like “today’s stock market move was a top 1%ile  event” or “today’s market move was between 55th and 60th percentile, going by this year’s data”?

The problem there, however, is that market behaviour is different at different points in time. For example, check out how the volatility of the Nifty (as defined by a 100-day trailing standard deviation) has varied in the last few years:

Niftysd

As you can see, markets nowadays are very different from markets in 2009, or even in 2013-14. A 2% move today might be spectacular, but the same move in 2013-14 may not have been! So comparing absolute returns is also not a right metric – it needs to be set in context of how markets are behaving. A good way to do that is to normalise returns by 100-day trailing volatility (defined by standard deviation) (I know we are assuming normality here).

The 100-day trailing SD as of today is 0.96%, so today’s 2% move, which initially appears spectacular is actually a “2 sigma event”. In January 2009, on the other hand, where volatility was about 3.3% , today’s move would have been a 0.6 sigma event!

Based on this, I’m coming up with a hierarchy for sophistication in dealing with market movements.

  1. Absolute movement : “Sensex up 300 points today”.
  2. Returns: “Sensex up 2% today”
  3. Percentile score of absolute return: “Sensex up 3%. It’s a 99 %ile movement”
  4. Percentile score of relative return: “Sensex up 2-sigma. Never moved 2-sigma in last 100 days”

What do you think?

Who should the IPL franchises retain?

I have a proprietary algorithm for evaluating cricket matches. This algorithm analyzes matches ball-by-ball and then computes the “impact” of each player on the game, in terms of both batting and bowling.

I’ve been intending to do this for a while now but I finally got down to calculating the impact of different players in the past editions of the IPL, and who it makes sense for franchises to retain (incidentally, today is the last day for franchises to announce to the IPL who the players are who they are going to retain).

Let us go franchise by franchise and see who the best players are. The numbers in the brackets represent the impact of each player according to my proprietary system.

1. Chennai Super Kings

By a long way, their two best players are MS Dhoni (3.53) and Ravindra Jadeja (3.46). Interestingly, the primary reason for the latter’s high score is his batting  (2.86)- he has been bowling well, too (0.6), but it is his batting that has had significant impact.

These two are followed some distance behind by Raina (2.02) and the now retired Mike Hussey (1.75). Ashwin is some way behind at 0.7 (his bowling is at 1 and batting at 0.33; the algorithm tends to unfairly penalize bowlers for their batting abilities, or the lack of it).

Chennai have already made their decision on who to retain. They are going to retain Dhoni, Jadeja, Raina, Ashwin and Dwayne Bravo. The last is a bit of a puzzle, at -1.09. His batting has been excellent – he has contributed 1.52 but his bowling has been utter crap at 2.61. CSK would do well to use him as a batsman only

2. Delhi Daredevils

This is a team that has performed rather badly in the last bunch of IPLs, so they might be expected to dispense with some players. Virender Sehwag (3.14), though, has performed exceptionally in the rot, though this season’s domestic performance (or the lack of it) might go against him. Next is the injury-prone Irfan Pathan (1.72). Shahbaz Nadeem is a surprise package at 1.56. I wouldn’t expect them to retain anyone.

Umesh Yadav (-1.77) and Mahela Jayawardene (-2.33) have been especially poor performers

3. Kings XI Punjab

Another franchise that didn’t do particularly well in the last set of IPLs. David Miller (2.05) was their standout performer, followed by Gurkeerat Singh (1.24), Shaun Marsh (1.11) and Praveen Kumar (1.02). The latter two are highly injury prone and they may not want to part with a large part of their budget for the yet uncapped Gurkeerat. So if you expect them to retain any players, it would only be Miller.

At the other end, Parvinder Awana (-1.92) has been the standout performer.

4. Kolkata Knight Riders

Sunil Narine (4.48) and Gautam Gambhir (4.22) tower over the rest. Following them are Shakib al Hasan (1.63) and Iqbal Abdulla (1.13). One would expect them to hold on to the first two (Narine and Gambhir) and try to use their trump card to match a price for Shakib.

Jacques Kallis performed particularly badly (-2.81) and is unlikely to be retained.

5. Mumbai Indians

If you were to rank all players in descending order of impact, the standout player across teams would be Harbhajan Singh (5.04; 3.64 bowling, 1.41 batting). Despite his axing from the national team, one would expect him to be retained by the franchise. He is followed some way behind by Lasith Malinga (2.01), Kieron Pollard (1.97; with 3.05 in batting and – 1.09 in bowling) and Rohit Sharma (1.74). One would expect all of those three to be retained. Dinesh Karthik at 1.31 might also be retained, for they will only need to give up Rs. 4 Crore from their salary cap  to get him.

6. Rajasthan Royals

If one goes by the gossip, the Royals are expected to retain a large number of players. They are the “moneyball” team of the IPL. They don’t spend too much on salary but try to get otherwise undervalued players to play for them.

Brad Hodge (1.91) has been their star performer but his age might go against him – they might prefer to match him using their trump card. They are expected to retain Shane Watson (1.55 with 3.83 batting and -2.28 bowling), though. Stuart Binny at 1.34 is also a good bet to be retained.

Interestingly, the system shows a negative impact for the otherwise highly rated Sanju Samson (-0.17)! He is, however, another player they might retain.

7. Royal Challengers Bangalore

The Royal Challengers have already made their decision – they will retain Chris Gayle (4.93; with 6.51 batting and -1.58 bowling), AB de Villiers (3.12) and Virat Kohli (1.95 with 2.22 batting and -0.27 bowling). The one highly rated player they are not retaining is Zaheer Khan (3.69). Khan has been exceptional considering that his partners in the RCB pace attack are Vinay Kumar (-3.59), RP Singh (-2.83) and Abhimanyu Mithun (-1.69).

Their only other highly rated bowler is Murali Kartik (1.05). They will need to completely rebuild their bowling attack in order to compete this IPL

8. Sunrisers Hyderabad

Dale Steyn (3.43) is the standout performer and they would do well to retain him. The next best is Shikhar Dhawan, who is some distance away at 0.72. Given the paucity of quality Indian players, though, they might end up retaining Dhawan also.

I’m willing to share the full results of my analysis. Do reach out to me if you want to play around with it and I’ll send it to you. And let me know what you think of these ratings.