randomness – Pertinent Observations

Average skill and peak skill

One way to describe how complex a job is is to measure the “average level of skill” and “peak level of skill” required to do the job. The more complex the job is, the larger this difference is. And sometimes, the frequency at which the peak level of skill is required can determine the quality of people you can expect to attract to the job.

Let us start with one extreme – the classic case of someone turning screws in a Ford factory. The design has been done so perfectly and the assembly line so optimised that the level of skill required by this worker each day is identical. All he/she (much more likely a he) has to do is to show up at the job, stand in the assembly line, and turn the specific screw in every single car (or part thereof) that passes his way.

The delta between the complexity of the average day and the “toughest day” is likely to be very low in this kind of job, given the amount of optimisation already put in place by the engineers at the factory.

Consider a maintenance engineer (let’s say at an oil pipeline) on the other hand. On most days, the complexity required of the job is very close to zero, for there is nothing much to do. The engineer just needs to show up and potter around and make a usual round of checks and all izz well.

On a day when there is an issue however, things are completely different – the engineer now needs to identify the source of the issue, figure out how to fix it and then actually put in the fix. Each of this is an insanely complex process requiring insane skill. This maintenance engineer needs to be prepared for this kind of occasional complexity, and despite the banality of most of his days on the job, maintain the requisite skill to do the job on these peak days.

In fact, if you think of it, a lot of “knowledge” jobs, which are supposed to be quite complex, actually don’t require a very high level of skill on most days. Yet, most of these jobs tend to employ people at a far higher skill level than what is required on most days, and this is because of the level of skill required on “peak days” (however you define “peak”).

The challenge in these cases, though, is to keep these high skilled people excited and motivated enough when the job on most days requires pretty low skill. Some industries, such as oil and gas, resolve this issue by paying well and giving good “benefits” – so even an engineer who might get bored by the lack of work on most days stays on to be able to contribute in times when there is a problem.

The other way to do this is in terms of the frequency of high skill days – if you can somehow engineer your organisation such that the high skilled people have a reasonable frequency of days when high skills are required, then they might find more motivation. For example, you might create an “internal consulting” team of some kind – they are tasked with performing a high skill task across different teams in the org. Each time this particular high skill task is required, the internal consulting team is called for. This way, this team can be kept motivated and (more importantly, perhaps) other teams can be staffed at a lower average skill level (since they can get help on high peak days).

I’m reminded of my first ever real taste of professional life – an internship in an investment bank in London in 2005. That was the classic “high variance in skills” job. Having been tested on fairly extreme maths and logic before I got hired, I found that most of my days were spent just keying in numbers in to an Excel sheet to call a macro someone else had written to price swaps (interest rate derivatives).

And being fairly young and immature, I decided this job is not worth it for me, and did not take up the full time offer they made me. And off I went on a rather futile “tour” to figure out what kind of job has sufficient high skill work to keep me interested. And then left it all to start my own consultancy (where others would ONLY call me when there was work of my specialty; else I could chill).

With the benefit of hindsight (and having worked in a somewhat similar job later in life), though, I had completely missed the “skill gap” (delta between peak and average skill days) in my internship, and thus not appreciated why I had been hired for it. Also, that I spent barely two months in the internship meant I didn’t have sufficient data to know the frequency of “interesting days”.

And this is why – most of your time might be spent in writing some fairly ordinary code, but you will still be required to know how to reverse a red-black tree.

Most of your time might be spent in writing SQL queries or pulling some averages, but on the odd day you might need to know that a chi square test is the best way to test your current hypothesis.

Most of your time might be spent in managing people and making sure the metrics are alright, but on the odd day you might have to redesign the process at the facility that you are in charge of.

In most complex jobs, the average day is NOT similar to the most complex day by any means. And thus the average day is NOT representative of the job. The next time someone I’m interviewing asks me what my “average day looks like”, I’ll maybe point that person to this post!

Discoverability and chaos

Last weekend (4-5 Feb) I visited Blossom Book House on Church Street (the “second branch” (above Cafe Matteo), to be precise). I bought a total of six books that day, of which four I was explicitly looking for (including two of Tufte’s books). So only two books were “discovered” in the hour or so I spent there.

This weekend (11-12 Feb) I walked a little further down Church Street (both times I had parked on Brigade Road), and with wife and daughter in tow, to Bookworm. The main reason for going to Bookworm this weekend is that daughter, based on a limited data points she has about both shops, declared that “Bookworm has a much better collection of Geronimo Stilton books, so I want to go there”.

This time there were no books I had intended to buy, but I still came back with half a dozen books for myself – all “discovered”. Daughter got a half dozen of Geronimos. I might have spent more time there and got more books for myself, except that the daughter had finished her binge in 10 minutes and was now desperate to go home and read; and the wife got bored after some 10-20 minutes of browsing and finding one book. “This place is too chaotic”, she said.

To be fair, I’ve been to Blossom many many more times than I’ve been to Bookworm (visits to the latter are still in single digits for me). Having been there so many times, the Blossom layout is incredibly familiar to me. I know that I start with the section right in front of the billing counter that has the bestsellers. Then straight down to the publisher-wise shelves. And so on and so forth.

My pattern of browsing at Blossom has got so ritualised that I know that there are specific sections of the store where I can discover new books (being a big user of a Kindle, I don’t really fancy very old books now). And so if I discover something there, great, else my browsing very quickly comes to a halt.

At Bookworm, though, I haven’t yet figured out the patterns in terms of how they place their books. Yes, I agree with my wife that it is “more random”, but in terms of discoverability, this increased randomness is a feature for me, not a bug! Not knowing what books to expect where, I’m frequently pleasantly surprised. And that leads to more purchases.

That said, the chaos means that if I go to the bookstore with a list of things to buy, the likelihood of finding them will be very very low (that said, both shops have incredibly helpful shopkeepers who will find you any book that you want and which is in stock at the store).

Now I’m thinking about this in the context of e-commerce. If randomness is what drives discoverability, maybe one bug of e-commerce is that it is too organised. You search for something specific, and you get that. You search for something vague, and the cost of going through all the results to find something you like is very high.

As for my books, my first task is to finish most of the books I got these weekends. And I’ll continue to play it random, and patronise both these shops.

Dhoni and Japan

Back in MS Dhoni’s heyday, CSK fans would rave about his strategy that they called as “taking it deep”. The idea was that while chasing a target, Dhoni would initially bat steadily, getting sort of close but increasing the required run rate. And then when it seemed to be getting out of hand, he would start belting, taking the bowlers by surprise and his team to victory.

This happened many times to be recognised by fans as a consistent strategy. Initially it didn’t make sense to me – why was it that he would purposely decrease the average chances of his team’s victory so that he could take them to a heroic chase?

But then, thinking about it, the strategy seems fair – he would never do this in a comfortable chase (where the chase was “in the money”). This would happen only in steep (out of the money) chases. And his idea of “taking it deep” was in terms of increasing the volatility.

Everyone knows that when your option is out of the money, volatility is good for you. Which means an increase in volatility will increase the value of the option.

And that is exactly what Dhoni would do. Keep wickets and let the required rate increase, which would basically increase volatility. And then rely on “mental strength” and “functioning under pressure” to win. It didn’t always succeed, of course (and that it didn’t always fail meant Dhoni wouldn’t come off badly when it failed). However, it was a very good gamble.

We see this kind of a gamble often in chess as well. When a player has a slightly inferior position, he/she decides to increase chances by “mixing it up a bit”. Usually that involves a piece or an exchange sacrifice, in the hope of complicating the position, or creating an imbalance. This, once again, increases volatility, which means increases the chances for the player with the slightly inferior position.

And in the ongoing World Cup, we have seen Japan follow this kind of strategy in football as well. It worked well in games against Germany and Spain, which were a priori better teams than Japan.

In both games, Japan started with a conservative lineup, hoping to keep it tight in the first half and go into half time either level or only one goal behind. And then at half time, they would bring on a couple of fast and tricky players – Ritsu Doan and Kaoru Mitoma. Basically increasing the volatility against an already tired opposition.

And then these high volatility players would do their bit, and as it happened in both games, Japan came back from 0-1 at half time to win 2-1. Basically, having “taken the game deep”, they would go helter skelter (I was conscious to not say “hara kiri” here, since it wasn’t really suicidal). And hit the opposition quickly, and on the break.

Surprisingly, they didn’t follow the same strategy against Croatia, in the pre-quarterfinal, where Doan started the game, and Mitoma came on only in the 64th minute. Maybe they reasoned that Croatia weren’t that much better than them, and so the option wasn’t out of the money enough to increase volatility through the game. As it happened, the game went to penalties (basically deeper than Japan’s usual strategy) where Croatia prevailed.

The difference between Dhoni and Japan is that in Japan’s case, the players who increase the volatility and those who then take advantage are different. In Dhoni’s case, he performs both functions – he first bats steadily to increase vol, and then goes bonkers himself!

Hot hands in safaris

We entered Serengeti around 12:30 pm on Saturday, having stopped briefly at the entrance gate to have lunch packed for us by our hotel in Karatu. Around 1 pm, our guide asked us to put the roof up, so we could stand and get a 360 degree view. “This is the cheetah region”, he told us.

For the next hour or so we just kept going round and round. We went off the main path towards some rocks. Some other jeeps had done the same. None of us had any luck.

By 2 pm we had seen nothing. Absolutely nothing. For a place like Serengeti, that takes some talent, given the overall density of animals there. We hadn’t even seen a zebra, or a wildebeest. Maybe a few gazelles (I could never figure out how to tell between Thomson’s and Grant’s through the trip, despite seeing tonnes of both on the trip). “This is not even the level of what we saw in Tarangire yesterday”, we were thinking.

And then things started to happen. First there was a herd of zebras. On Friday we had missed an opportunity to take a video of a zebra crossing the road (literally a “zebra crossing”, get it?). And now we had a whole herd of zebras crossing the road in front of us. This time we didn’t miss the opportunity (though there was no Spice Telecom).

And then we saw a herd of buffaloes. And then a bunch of hippos in a pool. We asked our guide to take us closer to them, and he said “oh don’t worry about hippos. Tomorrow I’ll take you to a hippo pool with over a fifty hippos”. And sped off in the opposite direction. There was a pack of lions fallen asleep under a tree, with the carcass of a wildebeest they had just eaten next to them (I posted that photo the other day).

This was around 3 pm. By 4 pm, we had seen a large herd of wildebeest and zebra on their great annual migration. And then seen a cheetah sitting on a termite hill, also watching the migration. And yet another pool with some 50 hippos lazing in it. It was absolutely surreal.

It was as if we had had a “hot hand” for an hour, with tremendous sightings after a rather barren first half of the afternoon. We were to have another similar “hot hand” on Monday morning, on our way out from the park. Again in the course of half an hour (when we were driving rather fast, with the roof down, trying to exit the park ASAP) we saw a massive herd of elephants, a mother and baby cheetah, a pack of lions and a single massive male lion right next to the road.

If you are the sort who sees lots of patterns, it is possibly easy to conclude that “hot hands” are a thing in wildlife. That when you have one good sighting, it is likely to be followed by a few other good sightings. However, based on a total of four days of safaris on this trip, I strongly believe that here at least hot hands are a fallacy.

But first a digression. The issue of “hot hands” has been a long-standing one in basketball. First some statisticians found that the hot hand truly exists – that NBA (or was it NCAA?) players who have made a few baskets in succession are more likely to score off their next shot. Then, other statisticians found some holes in the argument and said that it was simply a statistical oddity. And yet again (if i remember correctly) yet another group of statisticians showed that with careful analysis, the hot hand actually exists. This was rationalised as “when someone has scored a few consecutive baskets, their confidence is higher, which improves the chances of scoring off the next attempt”.

So if a hot hand exists, it is more to do with the competence and confidence of the person who is executing the activity.

In wildlife, though, it doesn’t work that way. While it is up to us (and our guides) to spot the animals, that you have spotted something doesn’t make it more likely to spot something else (in fact, false positives in spotting can go up when you are feeling overconfident). Possibly the only correlation between consecutive spottings is that guides of various jeeps are in constant conversation on the radio, and news of spottings get shared. So if a bunch of jeeps have independently spotted stuff close to each other, all the jeeps will get to see all these stuffs (no pun intended), getting a “hot hand”.

That apart, there is no statistical reason in a safari to have a “hot hand”.

Rather, what is more likely is selection bias. When we see a bunch of spottings close to one another, we think it is because we have a “hot hand”. However, when we are seeing animals only sporadically (like we did on Sunday, not counting the zillions of wildebeest and zebra migrating), we don’t really register that we are “not having a hot hand”.

It is as if you are playing a game of coin tosses, where you register all the heads but simply ignore the tails, and theorise about clumping of heads. When a low probability event happens (multiple sightings in an hour, for example), it registers better in our heads, and we can sometimes tend to overrepresent them in our memories. The higher probability (or “lower information content”) events we simply ignore! And so we assume that events are more impactful on average than they actually are.

Ok now i’m off on a ramble (this took a while to write – including making that gif among other things) – but Nassim Taleb talks about it this in one of his early Incerto books (FBR or Black Swan – that if you only go by newspaper reports, you are likely to think that lower average crime cities are more violent, since more crimes get reported there).

And going off on yet another ramble – hot hands can be a thing where the element of luck is relatively small. Wildlife spotting has a huge amount of luck involved, and so even with the best of skills there is only so much of a hot hand you can produce.

So yeah – there is no hot hand in wildlife safaris.

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.

Uncertainty and Anxiety

A lot of parenting books talk about the value of consistency in parenting – when you are consistent with your approach with something, the theory goes, the child knows what to expect, and so is less anxious about what will happen.

It is not just about children – when something is more deterministic, you can “take it for granted” more. And that means less anxiety about it.

From another realm, prices of options always have “positive vega” – the higher the market volatility, the more the price of the option. Thinking about it another way, the more the uncertainty, the more people are willing to pay to hedge against it. In other words, higher uncertainty means more anxiety.

However, sometimes the equation can get flipped. Let us take the case of water supply in my apartment. We have both a tap water connection and a borewell, so historically, water supply has been fairly consistent. For the longest time, we didn’t bother thinking about the pressure of water in the taps.

And then one day in the beginning of this year the water suddenly stopped. We had an inkling of it that morning as the water in the taps inexplicably slowed down, and so stored a couple of buckets until it ground to a complete halt later that day.

It turned out that our water pump, which is way deep inside the earth (near the water table) was broken, so it took a day to fix.

Following that, we have become more cognisant of the water pressure in the pipes. If the water pressure goes down for a bit, the memory of the day when the motor conked is fresh, and we start worrying that the water will suddenly stop. I’ve panicked at least a couple of times wondering if the water will stop.

However, after this happened a few times over the last few months I’m more comfortable. I now know that fluctuation of water pressure in the tap is variable. When I’m showering at the same time as my downstairs neighbour (I’m guessing), the water pressure will be lower. Sometimes the level of water in the tank is just above the level required for the pump to switch on. Then again the pressure is lower. And so forth.

In other words, observing a moderate level of uncertainty has actually made me more comfortable now and reduced my anxiety – within some limits, I know that some fluctuation is “normal”. This uncertainty is more than what I observed earlier, so in other words, increased (perceived) uncertainty has actually reduced anxiety.

One way I think of it is in terms of hidden risks – when you see moderate fluctuations, you know that fluctuations exist and that you don’t need to get stressed around them. So your anxiety is lower. However, if you’ve gone a very long time with no fluctuation at all, then you are concerned that there are hidden risks that you have not experienced yet.

So when the water pressure in the taps has been completely consistent, then any deviation is a very strong (Bayesian) sign that something is wrong. And that increases anxiety.

Arzoos

Founders, once they have a successful exit, tend to treat themselves as Gods.

Investors bow to them, and possibly recruit them into their investment teams. Startups flock to them, in the hope that they might use their recently gained wealth to invest in these companies. Having produced one successful exit, people assume that these people have “cracked the startup game”.

And so even if they have started humbly after their exit, all this adulation, and the perceived to potentially make or break a company by pulling out their chequebooks, goes to their head and the successful exit founders start treating themselves as Gods. And they believe that their one successful exit, which might have come for whatever reason (including a healthy dose of luck), makes them an authority to speak on pretty much any topic under the sun.

Now, I’m not grudging their money. There would have been something in the companies that they built, including timing or luck, even, that makes these people deserving of all the money they’ve made. What irritates me is their attitude of “knowing the mantra to be successful”, which allows them to comment on pretty much any issue or company, thinking people will take them seriously.

Recently I’ve come up with a word to represent all these one-time-successful founders who then flounder while dispensing advice – “Arzoos”.

The name of course alludes to Arzoo.com, which Sabeer Bhatia started after selling Hotmail to Microsoft. He had made a massive exit, and was one of the poster children of the dotcom boom (before the bust), especially in his native India. Except that the next company he started (Arzoo) sank without a trace to the extent that nobody even knows (or remembers) what the company did.

There is a huge dose of luck involved in making a small company successful, and that someone had a good exit doesn’t necessarily mean that they are great businessmen. As a corollary, that someone’s startup failed doesn’t make them bad businessmen.

Then again, it is part of human nature that we attribute all our successes to skill, and all our failures to bad luck!

Randomness and sample size

I have had a strange relationship with volleyball, as I’ve documented here. Unlike in most other sports I’ve played, I was a rather defensive volleyball player, excelling in backline defence, setting and blocking, rather than spiking.

The one aspect of my game which was out of line with the rest of my volleyball, but in line with my play in most other sports I’ve played competitively, was my serve. I had a big booming serve, which at school level was mostly unreturnable.

The downside of having an unreturnable serve, though, is that you are likely to miss your serve more often than the rest – it might mean hitting it too long, or into the net, or wide. And like in one of the examples I’ve quoted in my earlier post, it might mean not getting a chance to serve at all, as the warm up serve gets returned or goes into the net.

So I was discussing my volleyball non-career with a friend who is now heavily involved in the game, and he thought that I had possibly been extremely unlucky. My own take on this is that given how little I played, it’s quite likely that things would have gone spectacularly wrong.

Changing domains a little bit, there was a time when I was building strategies for algorithmic trading, in a class known as “statistical arbitrage”. The deal there is that you have a small “edge” on each trade, but if you do a large enough number of trades, you will make money. As it happened, the guy I was working for then got spooked out after the first couple of trades went bad and shut down the strategy at a heavy loss.

Changing domains a little less this time, this is also the reason why you shouldn’t check your portfolio too often if you’re investing for the long term – in the short run, when there have been “fewer plays”, the chances of having a negative return are higher even if you’re in a mostly safe strategy, as I had illustrated in this blog post in 2008 (using the Livejournal URL since the table didn’t port well to wordpress).

And changing domains once again, the sheer number of “samples” is possibly one reason that the whole idea of quantification of sport and “SABRmetrics” first took hold in baseball. The Major League Baseball season is typically 162 games long (and this is before the playoffs), which means that any small edge will translate into results in the course of the league. A smaller league would mean fewer games and thus more randomness, and a higher chance that a “better play” wouldn’t work out.

This also explains why when “Moneyball” took off with the Oakland A’s in the 1990s, they focussed mainly on league performance and not performance in the playoffs – in the latter, there are simply not enough “samples” for a marginal advantage in team strength to necessarily have the impact in terms of results.

And this is the problem with newly appointed managers of elite football clubs in Europe “targeting the Champions League” – a knockout tournament of that format means that the best team need not always win. Targeting a national league, played out over at least 34 games in the season is a much better bet.

Finally, there is also the issue of variance. A higher variance in performance means that observations of a few instances of bad performance is not sufficient to conclude that the player is a bad performer – a great performance need not be too far away. For a player with less randomness in performance – a more steady player, if you will – a few bad performances will tell you that they are unlikely to come good. High risk high return players, on the other hand, need to be given a longer rope.

I’d put this in a different way in a blog a few years back, about Mitchell Johnson.

4/13: HM

I’m married to an absolute crackpot. Pinky can go so mental at times that it’s not even funny. She takes the concept of absurdity to a whole new level at times. She sometimes behaves so weird that I start wondering what I’ve married. And then she asks, “did you imagine I’d turn out like this when you married me?”. And I always reply “well, you’ve always been this mental”.

Let me do this in bullet points – she’s so mental that it’s difficult to structure this post in any other way.

She’s not normally that religious (maybe she was, but I’ve kinda drawn her away from it), but on some days she puts up a serious face and says, “Karu, don’t you think we should pray to God? Why have we become like this?”
There are things that get “stuck” in her head that she keeps repeating. Some times it’s the names of people which are unusual (oh, have I told you that she and her sister maintain a database of funny names?). Other times, it’s random words or phrases. Her latest obsession is with “responsible PIC” (I have no clue what that means)
Normally she’s an incredibly levelheaded and logical person, but at times she loses all signs of logic, and makes absurd connections. Like she thinks I have a loud voice because I was born in December! Go figure.
It’s incredibly hard to predict what might upset her (going by the above point, she sometimes rationalises this by saying all women are like this). Things that should normally upset her she doesn’t get upset by (that’s actually deeply upsetting for me), and she gets upset with things you’d have no clue has the potential to upset someone. Then again, I guess this bit of madness doesn’t make her stand apart so much.
She has a habit of saying something random completely disconnected to the ongoing conversation. OK I must mention here that this is not something that I’m particularly worried about, since I love “arbit conversations”. More on that in another post.
She displays a wide range of ages in terms of the way she acts. Within a particular domain, she can talk like she belongs to different age groups. Sometimes she sounds like she’s a middle aged lady. Other times her reasoning and advice makes her sound like a teenager. In that sense, age is just a number for that (so I don’t know why I’m writing this post series for her birthday!)
For a while she wouldn’t open the door when I rang the doorbell – I had to speak a passphrase she had come up with, and until I said that she wouldn’t oblige me

And the list goes on and continues to grow by the day – I won’t give away too much more about her, but all I’ll say is that these quirks make her a massively fun person, and I love her for all this!

Uncle’s son’s shorts are stuck!

1/13: Leaving home

2/13: Motherhood statements

3/13: Stockings

Portfolio communication

I just got a promotional message from my broker (ICICI Direct). The intention of the email is possibly to get me to log back on to the website and do some transactions – remember that the broker makes money when I transact, and buy-and-hold investors don’t make much money for them.

So the mail, which I’m sure has been crafted after getting some “data insight”, goes like this:

Here is a quick update on what is happening in the world of investments since you last visited your ICICIdirect.com investment account.

1.	Your total portfolio size is INR [xxxxxx]*
2.	Sensex moved up by 8.36% during this period^#
3.	To know more about the top performing stocks and mutual funds, click here.

While this information might be considered to be useful, it simply isn’t enough information to make me learn sufficiently about my portfolio to take any action.

It’s great to know what my portfolio value is, and what the Sensex moved by in this period (“since my last logon”). A simple additional piece of information would be how much my portfolio has gone up by in this period – to know how I’m performing relative to the market.

And right in my email, they could’ve suggested some mutual funds and stock portfolios that I should move my money to – and given me an easy way to click through to the website/app and trade into these new portfolios using a couple of clicks.

There’s so much that can be done in the field of personal finance, in terms of how brokers and advisors can help clients invest better. And a lot of it is simple formula-based, which means it can be automated and hence done at a fairly low cost.

But then as long as the amount of money brokers make is proportional to the amount the client trades, there will always be conflicts of interest.