finance – Page 2 – Pertinent Observations

A misspent career in finance

I spent three years doing finance – not counting any internships or consulting assignments. Between 2008 and 2009 I worked for one of India’s first High Frequency Trading firms. I worked as a quant, designing intra-day trading strategies based primarily on statistical arbitrage.

Then in 2009, I got an opportunity to work for the big daddy of them all in finance – the Giant Squid. Again I worked as a quant, designing strategies for selling off large blocks of shares, among others. I learnt a lot in my first year there, and for the first time I worked with a bunch of super-smart people. Had a lot of fun, went to New York, played around with data, figured that being good at math wasn’t the same as being good at data – which led me to my current “venture”.

But looking back, I think I mis-spent my career in finance. While quant is kinda sexy, and lets you do lots of cool stuff, I wasn’t anywhere close to the coolest stuff that my employers were doing. Check out this, for example, written by Matt Levine in relation to some tapes regarding Goldman Sachs and the Fed that were published yesterday:

The thing is:

Before this deal, Santander had received cash (from Qatar), and agreed to sell common shares (to Qatar), but wasn’t getting capital credit from its regulators.

After this deal, Santander had received cash (from Qatar), and agreed to sell common shares (to Qatar), and was getting capital credit from its regulators, and Goldman was floating around vaguely getting $40 million.

This is such brilliantly devious stuff. Essentially, every bad piece of regulation leads to a genius trade. You had Basel 2 that had lesser capital requirements for holding AAA bonds rather than holding mortgages, so banks had mortgages converted into Mortgage Backed Securities, a lot of which was rated AAA. In the 1980s, there were limits on how much the World Bank could borrow in Switzerland and Germany, but none on how much it could borrow in the United States. So it borrowed in the United States (at an astronomical interest rate – it was the era of Paul Volcker, remember) and promptly swapped out the loan with IBM, creating the concept of the interest rate swap in that period.

In fact, apart form the ATM (which Volcker famously termed as the last financial innovation that was useful to mankind, or something), most financial innovations that you have seen in the last few decades would have come about as a result of some stupid regulation somewhere.

Reading articles such as this one (the one by Levine quoted above) wants me to get back to finance. To get back to finance and work for one of the big boys there. And to be able to design these brilliantly devious trades that smack stupid regulations in the arse! Or maybe I should find myself a job as some kind of a “codebreaker” in a regulatory organisation where I try and find opportunities for arbitrage in any potentially stupid rules that they design (disclosure: I just finished reading Cryptonomicon).

Looking back, while my three years in finance taught me much, and have put me on course for my current career, I think I didn’t do the kind of finance that would give me the most kick. Maybe I’m not too old and I should give it another shot? I won’t rule that one out!

PS: back when I worked for the Giant Squid, a bond trader from Bombay had come down to give a talk. I asked him a question about regulatory arbitrage. He didn’t seem to know what that meant. At that point in time I lost all respect for him.

Provisioning for Non Performing Assets at Banks

K C Chakrabarty, a Deputy Governor at the Reserve Bank of India recently made a presentation on the credit quality at Indian banks (HT: Deepak Shenoy). In this presentation Dr. Chakrabarty talks about the deteriorating quality of credit in Indian banks, especially public sector banks.

What caught my eye as I went through the presentation, however, was this graph that he presented on “Gross” and “Net” NPAs (Non-Performing Assets). Now, every bank is required to “provision” for NPAs. If I’ve lent out Rs. 100 and I estimate that I can recover Rs. 98 out of this, I need to “provision” for the other Rs. 2 which I expect to become “bad assets”. Essentially even before there is the default of Rs. 2, you account for it in your books, so that when the default does occur, it won’t be a surprise to either you or your investors.

Now, NPAs are measured in two ways – gross and net. Gross NPAs is just the total assets that you’ve lent out that you cannot recover. Net NPAs are gross NPAs less provisioning – for example, if you expected that this year Rs. 2 out of Rs. 100 will not come back, and indeed you manage to collect Rs. 98, then your Net NPA is zero, since you’ve “provisioned” for the Rs. 2 of assets that went bad. If on the other hand, you’ve expected and provisioned for Rs. 2 out of Rs. 100 to be “bad”, and you manage to collect only Rs. 97, your “Net NPA” is Re. 1, since you now have Gross NPA of Rs. 3 of which only Rs. 2 had been provisioned for.

This graph is from Dr. Chakarabarty’s presentation, indicating the movement of total NPAs (across banks, gross and net) over the years:

Source: Presentation by K C Chakrabarty, RBI Dy. Gov. , via Capital Mind

What should strike you is that the net NPA number has always been strictly positive. What this means is that our banks, collectively, have never provisioned enough to offset the total quantity of loans that went bad. I’m not saying that they are not forecasting accurately enough – loan defaults are mighty hard to forecast and it is hard for the banks to get it right down to the last rupee. What I’m saying is that there seems to be a consistent bias in the forecast – banks are consistently under-forecasting the proportion of their assets that go bad, and are not provisioning enough for it. This has been a consistent trend over the years.

This fundamentally indicates a failure of regulation, on the part of both the bank regulator (RBI) and the stock market regulator (SEBI). That the banks are not provisioning enough means that they are misleading their investors by telling them that they are going to have lesser bad assets than actually are there (SEBI). That the banks are not provisioning enough also means that they are exposing themselves to a higher chance (small, but positive) of defaulting on their deposit holders (RBI).

How would this graph look like if the banks were provisioning properly?

The Gross NPA line would have remained where it is, for it doesn’t depend on provisioning. However, if the banks were provisioning adequately, the Net NPA line should have been hovering around zero, going both positive and negative, but mean-reverting to zero! This is because banks would periodically over and under-forecast their bad assets and provision accordingly, and then dynamically change the model. And so forth..

Read the full post by Deepak to understand more about our bank assets.

Why the rate of return on insurance is low

I’m currently doing this course on Asset Pricing at Coursera, offered by John Cochrane of the University of Chicago Booth School of Business. I’m about a fourth of the way into the course and the beauty of the course so far has been the integration of seemingly unrelated concepts. When I went to business school (IIM Bangalore) about a decade ago, I was separately taught concepts on utility functions, discount rates, CAPM, time series analysis and financial derivatives, but these were taught as independent concepts without anybody bothering to make the connections. The beauty of this course is that it introduces us to all these concepts, and then shows how they are all related.

The part that I want to dwell upon in this post is the relationship between discount factors and utility functions. According to one of the basic asset pricing formulae introduced and discussed as part of this course, the returns from an asset is a positive function of the correlation between the price of the asset and your expected consumption growth. Let me explain that further.

The basic concept is that one’s utility function is concave. If you were to plot consumption on the X axis and utility from consumption on the Y-axis, the curve would look like this:

In other words, let us say I give you a rupee. How much additional happiness would that give you? It depends on what you already have! If you started off with nothing, the additional happiness out of the rupee that I gave you would be large. However, if you already have a lot of money, then the happiness you would derive out of this additional rupee would be much lower. This is known in basic economics as the law of diminishing marginal utility, and is also sometimes called the “law of diminishing returns”.

So, let us say that tomorrow you will either have Rs. 80 or Rs. 120 (the reason for this difference in payoff doesn’t matter). Let us call these as states “A” and “B ” respectively. Now, suppose I’m a salesman and I offer you two products. Product X pays you Rs. 20 if you are in state A but nothing if you are in state B. Product Y pays you Rs. 20 if you are in state B and nothing if you are in state A. Assuming that you can end up in states A or B with equal probability, which product would you pay a higher price for?

The naive answer would be that you would be indifferent between the two products and would thus pay the same amount for both. However, rather than looking at just the payoffs, you should also look at the utility of the payoffs. Given the concave utility function, you would derive significantly higher happiness from the additional Rs. 20 when you are in State A rather than in State B (refer to appendix below). Hence, you would pay a premium for product X relative to product Y.

Now, from a purely monetary perspective, the payoffs from X and Y are equal. However, you are willing to pay more for product X than for product Y. Consequently, the expected returns from product X will be much lower than the expected returns from Y (define returns as $frac {payoff}{price} - 1$ . Hence, for the same payoff, the higher the price the lower the returns). Keep this in mind.

Now let us come to insurance. Let us take the example of car insurance. Most of the time this doesn’t pay off. However, when your car gets smashed, you are compensated for the amount you spend in getting it fixed. What should be your expected return from this product?

Notice that when your car gets smashed, you will need to spend money to get it repaired. So at the time of your car getting smashed, the amount of money (and consequently consumption) is going to be lower than usual. Hence, the marginal utility of the insurance payout is likely to be higher than the marginal utility of a similar payout at a point in time when your consumption is “normal”. This is like product X above – which gives you a payoff at a time when your consumption level is low! And remember that you were willing to expect lower returns from X. Similarly, you should be willing to expect a lower rate of return from the insurance product!

Technical Appendix

A standard utility function used in finance textbooks is parabolic. Let us assume that for a consumption of $C$ , the utility is $- (200-C)^2$ . The following table shows the utility at various levels of consumption:

Consumption Utility
80 (A) -14400
100 -10000
120 (B) -6400
140 -3600

Notice from the above table that getting the payoff of 20 when you are at A increases your utility by 4400, whereas when you are at B, the payoff of 20 increases your utility by only 2800. Hence, your utility from the payoff is much higher when you are at A than at B. Hence, you would pay a higher price for product X (which pays you when your consumption is low) than product Y (which pays you when your consumption is already high)

India: Disinvestment Receipts

Common wisdom is that disinvestment in India was on a high back in the days when Atal Behari Vajpayee was Prime Minister, when there was a dedicated Ministry of Disinvestment under Arun Shourie. The UPA, upon coming to power in 2004, disbanded this ministry and common wisdom is that disinvestment stopped as a result of that.

Here, we take a look at disinvestment in India over the years. Here is the total disinvestment amount by year:

You can see that there was a spike in disinvestment in 2003-04, which was Vajpayee’s last year as Prime Minister. You can also see that disinvestment ground to a halt in the first term of the UPA government – possibly as a result of the presence of the Left Front as part of the government. However, you may not have realized that in its second avatar the UPA government has taken up disinvestment with a vengeance, with the receipts in the last four years far exceeding the receipts during Vajpayee’s tenure as Prime Minister.

However, the picture becomes clear if we look at the method of disinvestment. Most disinvestment receipts in the 1990s and in the last five years have come through a sale of minority stakes in PSUs. The disinvestment receipts in the Vajpayee years, however, came mostly through majority stake sales and strategic sales. In other words, there has been no big bang disinvestment in the last ten years – the money the government has made is through quiet sales of minority stakes in PSUs. So one can say that big bang disinvestment has ground to a halt after Vajpayee’s tenure.

Exponential increase in uptake of IMPS

We had dealt with exponential increases on this blog once before. We revisit the topic, and this time this is in the context of the inter bank mobile payment system that came into place sometime last year. I’ve never used it so I’m not sure how it works, but going by the data put out by the National Payments Corporation of India, the volume of transactions is increasing at an exponential rate.

How do we determine this is an exponential rate? First, let us look at the time series of total volumes of transactions:

Source: http://www.npci.org.in/impsVolumes.aspx

Notice that after remaining flat for a couple of months (maybe even decreasing) the number of transactions has really taken off (March is probably an aberration – but given that it’s the month of financial closure the higher volumes can be expected). Increased exponentially, you say? How can we test that?

We can test that by using a logarithmic scale for the y-axis. Here is the same plot again, except that this time the Y-axis is logarithmic.

Notice that apart from the part with the aberration and the initial two months, the graph is now linear. In other words, we can describe this graph by a line of the form

log y = a + b x

or y = exp (a + bx)

Thus, exponential!

Coming back from the geekery, it is really good to note that IMPS has taken off. However, this should not be taken as proof of the fact that mobile payments are easy, for IMPS is anything but easy. New RBI Governor Raghuram Rajan has said in his inaugural speech that he hopes to make it simpler to make payments via mobile. Hopefully this will take off soon. Till then all we can do is to contribute to the exponential growth in the update of the IMPS!

Banking activity and economic activity

Out on Capitalmind, Deepak Shenoy has an excellent post on the penetration of banking services in India, where he points out that 30% of all bank deposits in India are in Mumbai and Delhi. I encourage you to read that post in full.

Having read that, I was interested to see the per capita figures and compare them across states. On a whim, I decided to compare that to per capita state GDP and this is what I got:

Data source: RBI website Note: Maharashtra, Delhi and Goa have been left out because they are outliers. Some other states (Chandigarh, Gujarat and Mizoram) have been left out since their latest GSDP figures are not available — Data source: RBI website
Note: Maharashtra, Delhi and Goa have been left out because they are outliers. Some other states (Chandigarh, Gujarat and Mizoram) have been left out since their latest GSDP figures are not available

While the direction of causality cannot be clearly established, this clearly shows that banking penetration is highly correlated with economic activity.

Addition to the Model Makers Oath

Paul Wilmott and Emanuel Derman, in an article in Business Week a couple of years back (at the height of the financial crisis) came up with a model-makers oath. It goes:

• I will remember that I didn’t make the world and that it doesn’t satisfy my equations.

• Though I will use models boldly to estimate value, I will not be overly impressed by mathematics.

• I will never sacrifice reality for elegance without explaining why I have done so. Nor will I give the people who use my model false comfort about its accuracy. Instead, I will make explicit its assumptions and oversights.

• I understand that my work may have enormous effects on society and the economy, many of them beyond my comprehension.

While I like this, and try to abide by it, I want to add another point to the oath:

As a quant, it is part of my responsibility that my fellow-quants don’t misuse quantitative models in finance and bring disrepute to my profession. It is my responsibility that I’ll put in my best efforts to be on the lookout for deviant behavour on the part of other quants, and try my best to ensure that they too adhere to these principles.

Go read the full article in the link above (by Wilmott and Derman). It’s a great read. And coming back to the additional point I’ve suggested here, I’m not sure I’ve drafted it concisely enough. Help in editing and making it more concise and precise is welcome.

The Trouble With Analyst Reports

The only time I watch CNBC is in the morning when I’m at the gym. For reasons not known to me, my floor in office lacks televisions (every other floor has them) and the last thing I want to do when I’m home is to watch TV, that too a business channel, hence the reservation for the gym. I don’t recollect what programme I was watching but there were some important looking people (they were in suits) talking and on the screen “Target 1200” flashed (TVs in my gym are muted).

Based on some past pattern recognition, I realized that the guy in the suit was peddling the said stock (he was a research analyst) and asking people to buy it. According to him, the stock price would reach 1200 (I have no clue what company this is and how much it trades for now). However, there were two important pieces of information he didn’t give me, because of which I’ll probably never take advice from him or someone else of his ilk.

Firstly, he doesn’t tell me when the stock price will reach 1200. For example, if it is 1150 today, and it is expected to reach 1200 in 12 years, I’d probably be better off putting my money in the bank, and watching it grow risk-free. Even if the current price were lower, I would want a date by which the stock is supposed to reach the target price. Good finance implies tenure matching, so I should invest accordingly. If the stock is expected to give good returns in a year, then I should put only that money into it which I would want to invest for around that much time. And so forth.

Then he doesn’t tell me how long it will stay at 1200. I’m not an active investor. I might check prices of stocks that I own maybe once in a week (I currently don’t own any stock). So it’s of no use to me if the price hits 1200 some time during some intraday trade. i would want the price to remain at 1200 or higher for a longer period so that I can get out.

Thirdly and most importantly, he doesn’t tell me anything about volatility. He doesn’t give me any statistics. He doesn’t tell me if 1200 is the expected value of the stock, or the median, or the maximum, or minimum, at whatever point of time (we’ve discussed this time bit before). He doesn’t tell me what are the chances that I’ll get that 1200 that he professes. He doesn’t tell me what I can expect out of the stock if things don’t go well. And as a quant, I refuse to touch anything that doesn’t come attached with a distribution.

Life in general becomes so much better when you realize and recognize volatility (maybe I’ll save that for another discourse). It helps you set your expectations accordingly; it helps you plan for situations you may not have thought of; most importantly it allows you to recognize the value of options (not talking about financial options here; talking of everyday life situations). And so forth.

So that is yet another reason I don’t generally watch business TV. I have absolutely no use for their stock prediction and tips. And I think you too need to take these tips and predictions with a bit of salt. And not spend a fortune buying expensive reports. Just use your head. Use common sense. Recognize volatility. And risk. And you’ll do well.

Liquidity

We live in an era of unprecedented liquidity. Think about the difference from just about ten years ago. Back then, there was a much larger amount of cash reserve that one had to keep in one’s home, or on one’s person. There were no ATMs. There were no credit cards. All purchases needed to be meticulously planned, and budgeted for.

Now, because we don’t need to carry as much hard cash, there is so much more money in the banking system. While that gives depositors the nominal daily interest rate (at some obscenely low rate), there is much more money available with the banks to lend out, which increases the total amount of economic activity by nearly the same amount.

Just think about it. It’s fantastic, the effect of modern finance. And I don’t disagree with Paul Volcker when he says that the most important contribution of modern finance has been the ATM.

PS: My apologies for the break in blogging. I was in and around Ladakh for a week (yes, I was there when the cloudburst happened) and there were some problems with my laptop when I returned because of which I wasn’t able to blog. Hopefully I’ll be able to get back to my one-post-a-day commitment. And I have lots of stories to tell (from my Leh trip) so hope to keep you people busy.

A View From the Other Side

For the first time ever, a few days bck, I was involved in looking at resumes for campus recruitment, and helping people in coming up with a shortlist. These were resumes from IIMB and we were looking to recruit for the summer internship. Feeling slightly jobless, I ended up taking more than my fair share of CVs to evaluate. Some pertinent observations

There was simply way too much information on peoples’ CVs. I found it stressful trying to hunt down pieces of information that would be relevant for the job that I was recruiting for. IIMB restricts CVs to one page, but even that, I felt, was too much. Considering I was doing some 30 CVs at a page a minute, I suppose you know how tough things can be!
The CVs were too boring. The standard format certainly didn’t help. And the same order that people followed -undergrad scores followed by workex followed by “positions of responsibility” etc. Gave me a headache!
People simply didn’t put in enough effort to make things stand out. IIMB people overdo the bolding thing (I’m also guilty of that), thus devaluing it. And these guys used no other methods to make things stand out. Even if they’d done something outstanding in their lives, one had to dig through the CV to find it..
There was way too much irrelevant info. In their effort to fill a page and fill some standard columns, people ended up writing really lame stuff. Like how they had led their wing football team in the intra-hostel tournament. Immense wtfness. Most times this ended up devaluing the CV
Most CVs were “standard”. It was clear that people didn’t make an effort to apply to us! Most people had sent us their “finance CV” but would you send the same CV for an accounting job as you will for a quant job? Ok yeah I understand this is summers, but if I see a CV with priorities elsewhere, I won’t shortlist them!
By putting in several rounds of resume checking and resume workshops, IIMB is doing a major disservice to recruiters. What we see are some average potential corporate whores, not the idiosyncracies of the candidates. Recruiting was so much more fun when I’d gone to IITM three years back. Such free-spirited CVs and all that! This one is too sanitised for comfort. Give me naughtyboy123@yahoo.com any day
People should realize that campus recruitment is different from applying laterally. In the latter, yours is one of the few CVs that the recruiter is looking at and can hence devote much more time going through the details. Unfortunately this luxury is not there when one has to shortlist 20 out of 180 or so, so you need to tailor your CVs better. You need to be more crisp and to the point, and really highlight your best stuff. And if possible, to try and break out of standard formatI admit my CV doesn’t look drastically different from the time it did when I was in campus (apart from half a page of workex that got added), but I think even there I would make sure I put a couple of strongly differentiating points right on top, and hopefully save the recruiter the trouble of going through the whole thing.
I think I’m repeating myself on this but people need to realize that recruiters don’t care at all about your extra-currics unless you’ve done something absolutely spectacular, or if there is some really strong thread running through that section. So you don’t need to write about all the certificates that you have in your file

The bottom line is that recruitment is a hard job, especially when you have to bring down a list of 200 to 20 in very quick time. So do what you can to make the recruiter’s job easy. Else he’ll just end up putting NED and pack you.