Category: investment banking

High Frequency Trading and Pricing Regulations

It all began with a tweet, moments ago. Degree Raju, a train travel attempter (I don’t know how often he manages to actually travel since he never seems to get tickets) tweeted this:

It is an apt analogy. The reason high frequency trading exists is that there is regulation on what the minimum bid-ask spread needs to be – it needs be at least 1 cent in the US, and at least 5 paise in India (if I’m not wrong). If the best bid (quote to purchase a stock) is at 49.95 and the best ask (quote to sell a stock) is at 50.00, there is nothing you can do to get ahead of the guy who has bid 49.95 – for regulations mean that you cannot bid 49.96!

The consequence of this is that if you want to offer the best bid, at a price close to 49.95, there is no option but for you to be the first person to have bid that amount! And so there is a race among all possible bidders, and in order to win the race you need to be fast, and so you co-locate your servers with the exchange, and so you (and your co-runners) indulge in what is called High Frequency Trading (this is a  rather simplified explanation, and it works).

Tatkal ticket booking has a similar pricing anomaly – the cancellation charges on Indian railways are fixed, and really low. Moreover, fares are static, and are not set according to demand and supply. More moreover, the Indian Railways suffers from chronic under-capacity. The result of all this together is that if you need to get a railway ticket, you should be the first person to put a bid (at a fixed price, of course) for that ticket, and so there is a race among all ticket-buyers!

In case the pricing of railway tickets was more flexible – either dynamic pricing according to demand, or higher cancellation charges (as I’ve noted here), this mad race (pun intended) to buy tatkal tickets would not be there. The way things are going I wouldn’t be surprised if agents want to get servers co-located with IRCTC servers so that they can procure tickets the fastest.

With HFT in stock prices, if only there were no limit on the minimum tick size – let’s say that a bid or an ask could just be any real number within a reasonable (say 6-digits?) precision, then in order to have the best bid, you need not be the fastest – you can compete on price!

Thus, HFT in stock markets and tatkal ticket booking are two good examples of situations where onerous regulations have led to a race to be the fastest.

And all this ties in with this old theory I have which says that the underlying reason for most financial innovation is stupid regulations. Swaps were invented because the World Bank could not borrow with floating (or was it fixed?) interest rates. CDOs became popular because AAA rated instruments required lower capital provisioning than home loans. Such examples are plentiful..

The Quants

Since investment bank bashing seems to be in fashion nowadays, let me add my two naya paise to the fire. I exited a large investment bank in September 2011, after having worked for a little over two years there. I used to work as a quant, spending most of my time building pricing and execution models. I was a bit of an anomaly there, since I had an MBA degree. What was also unusual was that I had previously spent time as a salesperson in an investment bank . Most other people in the quant organization came from a heavily technical background, with the most popular degrees being PhDs in Physics and Maths, and had no experience or interest in the business side of things at the bank.

You might wonder what PhDs in Physics and Maths do at investment banks. I used to wonder the same before I joined. Yes, there are some tough mathematical puzzles to be solved in the course of devising pricing and execution algorithms (part of the work that us quants did), which probably kept them interested. However, the one activity for which these pure science PhDs were prized for, and which they spent most of their time doing, was C++ coding. Yeah, you read that right. These guys could write mean algorithms – I don’t know if even Computer Science graduates (and there were plenty of those) could write as clean (and quick) C++ code as these guys.

While most banks stress heavily on diversity, and makes considerable efforts (in the form of recruitment, affiliation groups, etc.)  to ensure a diverse workplace, it is not enough to prevent a large portion of quants coming from a similar kind of background. And when you put large numbers of Physics and Math PhDs together, it is inevitable that there is some degree of groupthink. You have the mavericks like me who like to model things differently, but if everyone else in your organization thinks one way, who do you go to in order to push your idea? You stop dropping your own ideas and start thinking like everyone else does. And you become yet another cog in the big quant wheel.

The biggest problem with hardcore Math people working on trading strategies is that they do not seek to solve a business problem through their work – they seek to solve a math problem, which they will strive to do as elegantly and correctly as it is possible. It doesn’t matter to the quants if the assumption of asset prices being lognormal is widely off the mark. In fact, they don’t care how the models behave. All they care about is about their formulae and results being correct – GIVEN the model of the market. I remember once spending a significant amount of time (maybe a couple of weeks) looking for bugs in my pricing logic because prices from two methods didn’t match up to the required precision of twelve decimal places (or was it fourteen? I’ve forgotten). And this after making the not-very-accurate assumption that asset prices are log normal. The proverb that says, “measure with a micrometer, mark with a chalk, cut with an axe”, is quite apt to describe the priorities of most quants.

Before I joined the firm, I used to wonder how bankers can be so stupid to make the kind of obvious silly errors (like assuming that housing prices cannot go down) that led to the global financial crisis of 2008. Two years at the firm, however, made me realize why these things happen. In fact, the bigger surprise, after the two years there, was about why such gross mistakes don’t occur more regularly. I think I’ve already talked about the culprits earlier in the post, but I should repeat myself.

First, a large number of guys building models come from similar backgrounds, so they think similarly. Because so many people think similarly, the rest train themselves to think similarly (or else get nudged out, by whatever means). So you have massive organizations full of massively talented brilliant minds which all think similarly! Who is to ask the uncomfortable questions? Next, who has time to ask the uncomfortable questions? Every one, from Partner downwards, has significant amount of “day to day work” to take care of every day. Bankers are driven hard (in that sense, and in that they are mostly brilliant, they do deserve the money they make), and everyone has a full plate (if you don’t it is an indication that you may not have a plate any more). There is little scope for strategic thinking. Again, remember that in an organization full of people who think similarly, people who have got promoted and made it to the top are likely to be those that think best along that particular axis. While it is the top management of the firm that is supposed to be responsible for the “big” strategic decisions, the kind of attention to details (which Math/Physics PhDs are rich in) that takes them to the top doesn’t leave them enough bandwidth for such thinking.

And so shit happens. Anyone who had the ability to think differently has either been “converted” to the conventional way of thinking, or is playing around with big bucks at some tiny hedge fund somewhere – because he found that it wasn’t possible to grow significantly in a place where most people think different to the way he thinks, and no one has the patience for his thinking.

This is the real failure in investment banking (markets) culture that has led to innumerable crises. The screwing over of clients and loss of “culture” in terms of ethics is a problem that has existed for a long time, and nothing new, contrary to what Greg Smith (formerly of Goldman Sachs) has written. The real failure of banking culture is this promotion of one-dimensional in-line-with-the-party thought, and the curbs against thinking and acting contrary to popular (in the firm) wisdom. It is this failure of culture that has led to the large negative shocks to the economy in the years gone by, and it is these shocks that have led common people to lose money rather than one off acts by banks where they don’t necessarily act in the interest of clients. And irrespective of how many Business Standards Committees and Risk Committees banks constitute, it is unlikely that this risk is going to go away any time soon. And I can’t think of a regulatory cure against this.

Travel agents and investment bankers

The more I think about it, the more I’m convinced that travel agents perform a very similar role to investment bankers. In the olden days, not everyone had access to financial markets. In order to buy or sell stocks, one had to go through a brokerage company, who would be paid a hefty commission for his services. The markets weren’t that liquid, and they were definitely not transparent, so the brokers would make a killing on the spread. With the passage of time, advent of electronic trading and transparency in the markets brokers aren’t able to make the same spreads that they used to. Customers know the exact market price for the instruments they are trading, and this results in brokers not able to make too much out of these trades.

It is a similar case with travel agents. Vacation markets (flights, hotels, etc.) are nowhere as liquid as financial markets, and will never be. Sometimes, when you are booking holidays to a strange place, you know little about it, and hence commission a travel agent to find you a place to stay there. Given that you know little about that place, the agent can charge you hefty commissions, and make a nice spread. Of course, nowadays such opportunities are diminishing for agents, as you have websites such as Agoda which allow you to book hotels directly. Now, at one place you can compare the prices of different hotels, and have better information compared to what the agents traditionally offer you. The spread is on the downswing, I must think.

Then, don’t you think package tours are very similar to structured products? Structured products are nothing but a package of several risks packaged together. By acting as a counterparty on a structured product, a bank (even now ) can afford to charge fairly hefty fees. Structured products are illiquid,  and there is no publicly available “market price”, so it is easy for banks to make themselves good spreads on such products. However, all it takes to defeat this is an intelligent customer. All the customer needs to do is to try and understand the risks himself, and start “unbundling” them. Once he unbundles the risks, he can now trade each of them independently, on more liquid markets, and get a much better price than what bankers will offer him. The catch here is that he’ll need to put in that effort in unbundling.

It’s the same with package tours. Given the bundles, it is easy for the agents to make higher spreads. However, if you as a customer simply unbundle the package (hotels, transport, food, etc.), you can find out the price of each (available on sites like agoda and elsewhere) and find out for yourself the spread that the agent is making. And then you compare the agent’s premium with the “cost” of making all the bookings yourself and make an informed choice.

Apart from communication, among the greatest boons of the internet has to do with dismantling middleman monopolies. It is incredible how much use a little information can be of!

The Lingaraj Effect and Financial Regulation

Lingaraj was a driver who used to work for my father. He had a unique way of dealing with traffic jams on two-lane roads without a divider down the middle. He would instinctively swing the ambassador into the right lane – meant for traffic in the opposite direction (the jam ahead meant there was little traffic flow in that direction).

I remember both my father and I abusing him (Lingaraj) for this method which would only make the jam worse. However, he would persist. And we soon found that he wasn’t unique in his methods. It is the favoured method of most Bangalore drivers. Thus, whenever there is a minor jam somewhere, thousands of Lingarajs clog the “return lane” in all directions, and end up making it worse.

The funny thing about Lingaraj’s method was that it was “too big to fail”. Having switched to the right lane, we would progress much faster (till the site of the jam, of course) than our law-abiding brethren stuck in the left lane. There, someone who had taken responsibility of clearing the jam (not necessarily a cop) would realize that a necessary condition to clear the jam was to get our ambassador out of the right lane. And we would be given passage to shift to the left lane, and past the jam site, much ahead of those suckers who stuck to the law.

For drivers like Lingaraj, moving to the right lane in the wake of a jam is seen as “arbitrage”. And a necessary condition for it to be an arbitrage is that the offending vehicle is “too big to fail”, as I mentioned earlier. And given that in Bangalore, measures like traffic tickets sent by post aren’t that effective, this continues to be an arbitrage, and hence you still see so many drivers use this “method”.

While stuck in a traffic jam like that one last weekend (I was driving, and I consider myself socially responsible so stuck to the left lane), I realized how similar this was to the financial crisis of three years ago.

Traders noticed an “arbitrage” that didn’t really exist (namely, some AAA rated bonds traded at higher yields than other AAA rated bonds) and proceeded to trade on it. When they got into trouble the regulators realized that they had to be bailed out in order to clear the larger mess. The resemblance is uncanny.

So what should the regulators have done? Basically, drivers should’ve been prevented from getting to the right lane in the first place. Then there would have been no requirement to bail them out. In some places, this is done by installing road dividers, but in my experience I’ve seen that doesn’t help, too. People use whatever gaps are available in the divider to go to the right lane, and contribute to the jam.

The only option I can think of is some variation of postal tickets – having bailed out the drivers for going to the right lane, they need to be made to pay for it. Yeah, postal tickets (sending tickets by post for traffic violations) may not be effective, but that seems like the best we can do to regulate this problem. The upshot is that once we figure out how to solve this problem on the road, we can extend the solution to financial regulation, too!

Models

This is my first ever handwritten post. Wrote this using a Natraj 621 pencil in a notebook while involved in an otherwise painful activity for which I thankfully didn’t have to pay much attention to. I’m now typing it out verbatim from what I’d written. There might be inaccuracies because I have a lousy handwriting. I begin

People like models. People like models because it gives them a feeling of being in control. When you observe a completely random phenomenon, financial or otherwise, it causes a feeling of unease. You feel uncomfortable that there is something that is beyond the realm of your understanding, which is inherently uncontrollable. And so, in order to get a better handle of what is happening, you resort to a model.

The basic feature of models is that they need not be exact. They need not be precise. They are basically a broad representation of what is actually happening, in a form that is easily understood. As I explained above, the objective is to describe and understand something that we weren’t able to fundamentally comprehend.

All this is okay but the problem starts when we ignore the assumptions that were made while building the model, and instead treat the model as completely representative of the phenomenon it is supposed to represent. While this may allow us to build on these models using easily tractable and precise mathematics, what this leads to is that a lot of the information that went into the initial formulation is lost.

Mathematicians are known for their affinity towards precision and rigour. They like to have things precisely defined, and measurable. You are likely to find them going into a tizzy when faced with something “grey”, or something not precisely measurable. Faced with a problem, the first thing the mathematician will want to do is to define it precisely, and eliminate as much of the greyness as possible. What they ideally like is a model.

From the point of view of the mathematician, with his fondness for precision, it makes complete sense to assume that the model is precise and complete. This allows them to bringing all their beautiful math without dealing with ugly “greyness”. Actual phenomena are now irrelevant.The model reigns supreme.

Now you can imagine what happens when you put a bunch of mathematically minded people on this kind of a problem. And maybe even create an organization full of them. I guess it is not hard to guess what happens here – with a bunch of similar thinking people, their thinking becomes the orthodoxy. Their thinking becomes fact. Models reign supreme. The actual phenomenon becomes a four-letter word. And this kind of thinking gets propagated.

Soon the people fail to  see beyond the models. They refuse to accept that the phenomenon cannot obey their models. The model, they think, should drive the phenomenon, rather than the other way around. The tails wagging the dog, basically.

I’m not going into the specifics here, but this might give you an idea as to why the financial crisis happened. This might give you an insight into why obvious mistakes were made, even when the incentives were loaded in favour of the bankers getting it right. This might give you an insight as to why internal models in Moody’s even assumed that housing prices can never decrease.

I think there is a lot more that can be explained due to this love for models and ignorance of phenomena. I’ll leave them as an exercise to the reader.

Apart from commenting about the content of this post, I also want your feedback on how I write when I write with pencil-on-paper, rather than on a computer.

 


The Impact of Wall Street on Grad School

I don’t need to be an insider to tell you that Wall Street employs lots of PhDs. PhDs of various denominations, but mostly those with backgrounds in Math, Physics and Engineering are employed by various Wall Street firms by the thousand. I don’t think too many of them exactly work on the kind of stuff that they were doing in grad school, but certain general skills that they pick up and hone through their multiple years in grad school are found extremely useful by banks.

So while scores of older scientists and economists and policymakers lament the “loss” of so many bright minds to science, has anyone at all considered the reverse possibility? Of the impact that Wall Street has had on grad schools in the US?

One thing you need to face is that there are not a lot of academic jobs going around. The number of people finishing with PhDs each year is far more than the number of academic jobs that open up each year. I’m mostly talking about “assistant professor” kind of jobs here, and assuming that becoming a post-doc just delays your entry into the job market rather than removing you from the market altogether.

In certain fields such as engineering, there are plenty of jobs in the industry for PhDs who don’t get academic jobs, for whatever reason. Given this, it is “cheaper” to do a PhD in these subjects, since it is very likely that you will end up with a “good job”. Hence, there is more incentive to do a PhD in subjects like this, and universities usually never have a problem in finding suitable candidates for their PhD programs. However, there is no such cushion in the pure sciences (math/physics). There are few “industry employers” who take on the slack after all the academic positions have been filled up. And that is where Wall Street steps in.

The presence of Wall street jobs offers a good backstop to potential Math and Physics PhD candidates. If they aren’t able to do the research that they so cherish, they needn’t despair since there exists a career path which will enable them to make lots of money. And knowing the existence of this career option means more people will be willing to take the risk of doing a PhD in these subjects (since the worst case isn’t so bad now). Which in turn enhances the candidate pool available to grad schools.

So even if you were to believe that complex derivatives are financial “weapons of mass destruction”, there is reason for them to exist, to encourage the financial sector to pick up PhDs. For if PhDs were kept out of these jobs, it is real academic research in “real subjects” such as the pure sciences that will suffer. By picking up PhDs in large numbers, the financial sector is making its own little contribution to research in pure sciences.

Issuing in stages

I apologise for this morning’s post on IPOs. It was one of those posts I’d thought up in my head a long time ago, and got down to writing only today, because of which I wasn’t able to get the flow in writing.

So after I’d written that, I started thinking – so if IPO managers turn out to be devious/incompetent, like LinkedIn’s bankers have, how can a company really trust them to raise the amount of money they want? What is the guarantee that the banker will price the company at the appropriate price?

One way of doing that is to get the views of a larger section of people before the IPO price is set. How would you achieve that? By having a little IPO. Let me explain.

You want to raise money for expansion, or whatever, but you don’t need all the money now. However, you are also concerned about dilution of your stake, so would like to price the IPO appropriately. So why don’t you take advantage of the fact that you don’t need all the money now, and do it in stages?

You do a small IPO up front, with the sole purpose of getting listed on the country’s big exchanges. After that the discovery of the value of your company will fall into the hands of a larger set of people – all the stock market participants. And now that the market’s willingness to pay is established, you can do a follow on offer in due course of time, and raise the money you want.

However, I don’t know any company that has followed this route, so I don’t know if there’s any flaw with this plan. I know that if you do a small IPO you can’t get the big bankers to carry you, but knowing that some big bankers don’t really take care of you (for whatever reason) it’s not unreasonable to ditch them and go with smaller guys.

What do you think of this plan?

IPOs Revisited

I’ve commented earlier on this blog about investment bankers shafting companies that want to raise money from the market, by pricing the IPO too low. While a large share price appreciation on the day of listing might be “successful” from the point of view of the IPO investors, it’s anything but that from the point of view of the issuing companies.

The IPO pricing issue is in the news again now, with LinkedIn listing at close to 100% appreciation of its IPO price. The IPO was sold to investors at $45 a share, and within minutes of listing it was trading at close to $90. I haven’t really followed the trajectory of the stock after that, but assume it’s still closer to $90 than to $45.

Unlike in the Makemytrip case (maybe that got ignored since it’s an Indian company and not many commentators know about it), the LinkedIn IPO has got a lot of footage among both the mainstream media and the blogosphere. There have been views on both sides – that the i-banks shafted LinkedIn, and that this appreciation is only part of the price discovery mechanism, so it’s fair.

One of my favourite financial commentators Felix Salmon has written a rather large piece on this, in which he quotes some of the other prominent commentators also. After giving a summary of all the views, Salmon says that LinkedIn investors haven’t really lost out too much due to the way the IPO has been priced (I’ve reproduced a quote here but I’d encourage you to go read Salmon’s article in full):

But the fact is that if I own 1% of LinkedIn, and I just saw the company getting valued on the stock market at a valuation of $9 billion or so, then I’m just ecstatic that my stake is worth $90 million, and that I haven’t sold any shares below that level. The main interest that I have in an IPO like this is as a price-discovery mechanism, rather than as a cash-raising mechanism. As TED says, LinkedIn has no particular need for any cash at all, let alone $300 million; if it had an extra $200 million in the bank, earning some fraction of 1% per annum, that wouldn’t increase the value of my stake by any measurable amount, because it wouldn’t affect the share price at all.

Now, let us look at this in another way. Currently Salmon seems to be looking at it from the point of view of the client going up to the bank and saying “I want to sell 100,000 shares in my company. Sell it at the best price you can”. Intuitively, this is not how things are supposed to work. At least, if the client is sensible, he would rather go the bank and say “I want to raise 5 million dollars. Raise it by diluting my current shareholders by as little as possible”.

Now you can see why the existing shareholders can be shafted. Suppose I owned one share of LinkedIn, out of a total 100 shares outstanding. Suppose I wanted to raise 9000 rupees. The banker valued the current value at $4500, and thus priced the IPO at $45 a share, thus making me end up with 1/300 of the company.

However, in hindsight, we know that the broad market values the company at $90 a share, implying that before the IPO the company was worth $9000. If the banker had realized this, he would have sold only 100 fresh shares of the company, rather than 200. The balance sheet would have looked exactly the same as it does now, with the difference that I would have owned 1/200 of the company then, rather than 1/300 now!

1/200 and 1/300 seem like small numbers without much difference, but if you understand that the total value of LinkedIn is $9 billion (approx) and if you think about pre-IPO shareholders who held much larger stakes, you know who has been shafted.

I’m not passing a comment here on whether the bankers were devious or incompetent, but I guess in terms of clients wanting to give them future business, both are enough grounds for disqualification.

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.