Archive
Motivating transparency: what we could do about too big to fail
In this previous post, I promised a follow-up post about how we can devise a system in which large banks are actually motivated to be transparent about what is inside their portfolios. We have also discussed why the current system doesn’t work this way and that the banks have every reason to obfuscate their holdings, and in fact make loads of money by doing so. This makes appropriate external risk management difficult or impossible.
I have actually thought about this problem quite a bit since that post, and I (and a friend in finance) have come up with two quasi ideas, which hopefully together add up to be as good as one complete idea. The first comes under the category, “add stuff to what we have now”, whereas the second comes under the category, “initiate a new system which will over time replace the one we have”. Both of these systems rely on a good understanding of the underlying problem of the current system, namely the concept of “too big to fail.”
If you’re reading this and you have comments about either idea, please do comment. We are hoping for lots of feedback so we can improve the details.
Too Big to Fail
Recall that the way it works when hedge funds want to trade stuff: they have prime brokers, i.e. banks like Deutche and Goldman Sachs and Bank of America (see list of the biggies here). When the brokers don’t like the trade, or think it’s not sufficiently liquid, or think that the hedge fund may fail for any reason, they demand that the hedge funds post margin. That way if the bet goes sour there is a limited amount of risk that the brokerage could lose. As soon as a position starts to look riskier, which could happen because of recent volatility or lack of price transparency, the amount of margin that needs to be posted normally increases, putting pressure on the hedge fund to liquidate suspicious assets.
In other words, there is a real cost to hedge funds for trading in illiquid or complex securities, namely their cash is tied up in bank accounts with their brokers. This is not to say that they don’t take large risks, but there is a limit of how much risk they can take because of the “posting margin” system.
By contrast, big banks don’t post margins. They trade with hedge funds, of course, since hedge funds trade with them, but it’s the banks who demand margin, not the hedge funds (actually there’s a historical exception to this rule, namely Paulson’s hedge fund demanded margin from its brokers during the 2008 financial crisis).
This asymmetrical situation begs the question, why do hedge funds have to post margin but the big banks don’t? Two reasons: first, banks have access to Federal funds, and second, they are deemed to big to fail. [I admit I don’t know exactly why the access to Federal funds is granted to banks, nor do I understand exactly what the effect is. But I do think it’s a pertinent fact which is why I’ve included it here. Please do comment if you know more! Also note it may be a red herring since Goldman Sachs didn’t have access to Fed funds until the crisis.]
This “too big to fail” guarantee is a huge problem, which has only gotten more precise (since we’ve seen the bailout and now everyone knows the guarantee is there) and larger (because, in the end, the net result of all the 2008 crisis is fewer, larger banks) and about which absolutely nothing seems to be getting done. The disingenuous whining of greedy bankers like Jamie Dimon serves as a smokescreen for the fact that, if anything, banks are presumably waltzing into the next phase of their life with more power and fewer checks than they could have dreamed about in August 2008.
Idea #1: make banks post margins
“Too big to fail” means that it is assumed that the bank will be rescued by the government if it makes huge bad bets that threaten to bring them down. Two of the reasons the government can be counted on to bail out banks are first, that the deposits of normal Americans are at risk, which is discussed below in Idea #2, and second, that a bankruptcy would be catastrophically complicated, which we discuss here. One result of the guarantee is that hedge funds don’t bother demanding margins, which makes the banks riskier, which makes the “too big to fail” guarantee even worse.
What if the lawmakers enforced a symmetry of posted margins? We have to be precise, because actually there are different kinds of margins that traders are forced to post.
First, there’s the margin you post in the sense of “keep $x as a deposit for the position”, the thinking being that even if things go south, the broker could liquidate at something better than $x below current marked price in a hurry. This is the initial margin.
Next there’s the “your position lost $10 today, so you need to give me $10” (this is called variation margin). This is the most likely way to get margin called.
The idea here is to require brokers to post initial margin just as hedge funds do now. More precisely, the idea would be to let the two parties negotiate on the initial margin, which could be more for hedge funds since they may well be riskier, but then once it’s set to have complete symmetry of variation margin.
Occasionally, in risky environments, the initial margin of $x is increased, which causes a lot of unraveling, and possibly cascading waves of problems which set off a panic. We’d need to have rules about how often this can happen to avoid the “symmetric of variation margin” rule from being bypassed with lots of initial margin modifications. The symmetry aspect should keep the margin contracts from allowing this to happen too often.
The overall goal would be to devise a system that would:
- Encourage the posting and calling of (variation) margins,
- Encourage sufficient sizing of initial margin,
- Encourage early calls and liquidating if there is doubt that a variation margin call could be met, and
- Simplify the bankruptcy rules on ownership of assets, especially for illiquid or complex assets.
The initial margin can be thought of as the dollar amount a price could move by between a margin call and it being paid. It should not be thought of as an asset for either party (and therefore the accounting of the various margins should be carefully considered, but I’m no accounting expert), and certainly should not be able to be recycled to buy more stuff, i.e. add to ones leverage, or offered towards capital requirements. Moreover, if it is indeed symmetric, that would mean if a bank claims to only need to post n dollars in initial margin, then the hedge fund can turn around and use that same number for that same trade, at least up to an understood discount.
As for bankruptcy, we should start with the following. When a margin call is made by one side and it isn’t met, the person making the call:
- keeps ALL the margin,
- gets the security, and
- is a (super-senior level of seniority) claimaint to the variation margin they posted with the counterparty.
Moreover, rules 1 and 2 above do not go into a bankruptcy filing if one occurs (in particular, if the security is a swap, it’s just torn up). This is a key point since that means the bankruptcy is simplified and at the same time the security is back in liquid hands. All over, this setup, or one like it, encourage hedge funds to margin call frequently (banks already do that), which is a good thing, and as described above is a further incentive to invest in liquid, non-complex securities, which in the end creates transparency.
The above idea doesn’t deal directly with desired property 2, and may well cause margins to be lower. One possibility to encourage margins to be of sufficient size would be to allow either party to “put” the security in question on to the other party at a cost of giving up the initial margin posted.
Idea #2: grow a separate system of utility deposit banks
Besides incredibly complicated bankruptcy filings with infinitely many counterparties, one of the major reasons those banks really are too big to fail is that they hold deposits, and the government doesn’t want people to worry that their life savings are at risk, causing a run on the banks and chaos. Another way to get around this, at least eventually, is to create new “utility banks” at the state level which do not trade securities (beyond very basic one like interest rate swaps and treasuries), don’t take large risks, and have FDIC guarantees on savings.
In order to get consumers to switch to banks like this, the government should intentionally create incentives for people to transfer their deposits from “too big to fail” banks to these utility banks. A list of incentives could start with reasonable, transparent fees, and the eventual loss of FDIC insurance guarantee at non-utility banks. Then people who want to stay with risk-taking banks can do so knowing that, as long as bankruptcy laws eventually get simplified, the “too big to fail” guaranteed will in fact be gone.
Moreover, another layer of separation between depositors and utility banks should be the requirement that, even with the restricted kinds of trades allowed for utility banks, they should be done in separate corporate entities (since banks are always a mishmash of many companies anyway).
This idea is not new, and can be seen for example in this article. In fact it is incredibly obvious: admit that what we have now is a guarantee for a get-out-of-jail card for greedy bankers, and transfer that guarantee to a banking system that we’ve created to be boring, along the lines of the post office.
Bank accounting link
I wanted to share this link with you; it is both interesting and relevant to another post I’m working on (a follow up to this one) that will describe two ideas I’m contemplating regarding how to systematically change the way big banks are motivated to behave in the presence of the “too big to fail” guarantee.
Its goal is to describe how banks will behave in a given situation with a mortgage, but the thought process generalizes quite well to how banks behave in general, and in particular how accounting considerations trump utility to the depositors and even the long-term shareholders. It also explains, to those of us who were wondering, why Obama’s mortgage modification plan was never going to work.
Short Post!
I’ve been told my posts are intimidatingly long, what with the twitter generation’s sound byte attention span. Normally I’d say, screw that! It’s because my ideas are so freaking nuanced they can’t be condensed to under a paragraph without losing their essence!
But today I acquiesce; here’s a short post containing at most one idea.
Namely, I’ve been getting pretty strong reactions online and offline regarding my post about whether an academic math job is a crappy job. I just want to set the record straight: I’m not even saying it’s a crappy job, I’m simply talking about someone else’s essay which describes it that way. But moreover, even if I were saying that, I would only be saying it’s crappy (which I’m not) compared to other jobs that very very smart mathy people could get. Obviously in the grand scheme of things it’s a very good job- safe working conditions, regular hours, well-respected, etc., and many people in this world have far crappier jobs and would love a job with those conditions. But relative to other jobs that math people could be getting, it may not be the best.
Many professors of math (you know who you are) have this weird narrow world view, that they feed their students, which goes something like, “if you want to be a success, you should be exactly like me (which is to say, an academic)”. So anyone who gets educated in a math department is apt to run into all these people who define success as getting tenure in an academic math department, and they just don’t know about or consider other kinds of gigs. It would be nice if there was a way to get a more balanced view of the pros and cons of all of the options.
Weekend Reading
FogOfWar and I have compiled a short list of weekend reading for you that you may enjoy:
- What’s the right way to think about China’s economy?
- Is Japan’s “lost decades” a media myth?
- Can I hear a FUCK YEAH for Elizabeth Warren? I feel a follow-up post coming on how much she rocks.
- Get ready to be depressed by how few natural resources there really are.
- This essay really pins Robert Rubin to the wall in a totally awesome way. I will add more in another post.
- The Republicans are holding the entire nation for ransom over the possibility of default. Is it all political posturing? Or is it for the sake of the insanely shitty idea of a tax repatriation holiday? Here’s another article about this crappy idea; when Bloomberg makes you out as a selfish bastard then you know you’re a truly selfish bastard. I’m convinced that the politicians (and union leaders) arguing for this are just counting on the average person not understanding the actual issues well enough to know how evil it is (and how much kickback they must be getting). Another example of asymmetric information that really gets my goat.
- I think it’s fair to say we all need a little more of this in our lives.
Asymmetrical Information
From my experience, there are only a few basic kinds of trading models encountered on Wall Street. These are:
- chasing dumb money, which I’ve described already,
- asymmetrical information, which I want to talk about today,
- market-making,
- providing “insurance”,
- seasonality, which I’ve touched on, and
- taking advantage of macroeconomic misalignment (think Soros’s pound trade)
The concept of asymmetrical information is incredibly simple: I know more than you so I can make a more informed assessment of the value of some underlying contract. This could mean I know inside information about a company and trade before the announcement (illegal but common), or that I know the likelihood of bankruptcy for a company is higher than the market seems to think, or that the underlying mortgages of a packaged security are likely to default.
I could go on, and probably will in another post, but I’d like to make a very basic point, which is this: a lot of money is made every day via asymmetrical information, and in particular there’s a major motivation to obfuscate data in order to create asymmetry. One of the missions of this blog is to uncover and expose major, unreasonable examples of obfuscated information that I know about.
At this point it’s critical to differentiate between two things which typically get confused by non-nerds. Namely, the difference between a technical but thorough explanation and true information obfuscation. A technical explanation, if thorough, can be worked through eventually by someone with enough expertise, or someone who is motivated enough to get that expertise, whereas true information obfuscation just doesn’t provide enough details to really know anything.
The worst is when you are given pretty specific technical information, but which only explains half of the story. This leads to an imprecise false sense of security, which I suspect underlies most of the very large mistakes we’ve seen in finance in the last few years.
For example, let’s talk about the bank stress tests in the United States in 2009. They were conducted in two distinct phases. In the first, a bunch of economists were asked to write down two scenarios. The first was kind of a prediction of how 2009 and 2010 would play out, and the second was a more negative scenario. Okay so far, even though economists aren’t all that pessimistic as people (more on this on another post). The scenarios were averaged in some way and then publicly posted. The good news is, if you thought the scenarios were unrealistic, you’d at least know how to complain about them. The bad news is that they are pretty vague, only really specifying the GDP growth and the unemployment rate.
In the second phase, the banks were allowed to predict the impact of those two scenarios on their portfolios using their own internal models, which were not made public. Here’s the white paper if you don’t believe me. So, in the name of asymmetrical information, why is this a problem? Here are a few reasons:
- Banks had bad internal risk models
- Banks had clear motivation to mark their portfolios to their advantage
- The fact that their methods weren’t made public gives them ample cover to do whatever they wanted
There are two reasons I say that banks had bad internal risk models. The first reason is the one you know about already- they evidently bought a whole bunch of toxic securities leading up to 2008 and seemed to have no idea about the risks. But moreover, my personal experience working in the risk field is that banks used external risk modeling companies as a rubber stamp, essentially to placate those worrywarts who insisted on obsessing about risks. To be more precise without getting anyone into trouble, it was commonplace for banks to not notice when a model at a risk software company had very basic problems and would spit out nonsensical numbers. It was almost as if you couldn’t trust the banks to look at their risk numbers at all. This isn’t true of every bank at all times, but as a general rule when models had major problems it was hedge funds, not banks, who would bring attention to those problems. Moreover, the banks did not seem to have internal risk modeling across their desks. In other words, a trading desk which trades a certain kind of instrument may have some risk monitoring in place (mostly to bound the amount of trading of that type), but when it comes to understanding systemic risk across instrument types, the external risk companies were the source.
It is obvious that banks were motivated to mark their portfolios to their advantage. The ultimate result of bank stress tests were possible additional capital requirements, which they clearly wanted to avoid. This temptation meant it would benefit them to make every assumption of their risk model liberal to their cause.
Finally, they didn’t expose their methods- not even to explain in general terms how they dealt with, say, interest rate risks across instrument types. This meant that only the Fed people involved got to decide how honest the banks were. This is the opposite of what is needed in this situation. There is no reasonable need to keep these methodologies secret from the general public, since it is we who are on the hook if their methods are flawed, as we have seen.
Here’s where I admit that it’s actually really hard to come up with good methodologies to measure impact of vague GDP growth and unemployment estimates. But that admission is only going to add to my rant, because my overall point is that the instruments themselves have been created to make that hard. They are examples, especially tranched mortgage-backed securities but others as well, of intentional obfuscation for the sake of creating asymmetrical information. Instead of living in a world where banks who own things like this are allowed to measure them at their whim, and benefit from that obfuscation, we need to create a system where they are penalized for having illiquid or complex instruments.
And here’s where I admit that I’m not an expert on all of these instruments – some would say I don’t have the right to talk about how they should be assessed. Yet again, I choose to use that fact to add to my rant: if, after working for four years in finance as a quant at a hedge fund and then a researcher and account manager at a risk company, I can’t have an opinion about how to assess risk, then the system is too freaking complicated.
Better risk modeling: motivating transparency
In a previous post, I wrote about what I see as the cowardice and small-mindedness of the U.S. government and in particular the regulators for not demanding daily portfolios of all large investors. Of course this goes for the governments in Europe as well, and especially right now. The Economist had a good article this past Friday which attempted to quantify the results of a Greek default, but there were major holes, especially in the realm of “who owns the CDS contracts on Greek bonds, and how many are there?”. This fear of the unknown is a root cause of the current political wrangling which will probably end in a postponement of resolving the Greek situation; the question is whether the borrowed time will be used properly or squandered.
It’s ridiculous that nobody knows where the risk lies, but as a friend of mine pointed out to me last week at lunch, it probably won’t be enough to demand the portfolios daily, even if you had the perfect quantitative risk model available to you to plug them into. Why? Because if “transparency” is what the regulators demand, then “transparency” is what they would get – in the form of obfuscated lawyered-up holding lists.
In other words, let’s say a bank has a huge pile of mortgage-backed securities of dubious value on their books, but doesn’t want to accept losses on them. If they knew they’d have to start giving their portfolio to the SEC daily instead of quarterly, it would change the rules of the game. They’d have to hide these holdings by pure obfuscation rather than short-term month- or quarter-end legal finagling. So for example, they could invest in company A, which invests in company B, which happens to have a bunch of mortgage-backed securities of dubious value, but which is too small to fall under the “daily reporting” rules. This is just an example but is probably an accurate portrayal of the kind of thing that would happen with enough lead time and enough lawyers.
What we actually want is to set up a system whereby banks and hedge funds are motivated to be transparent. Read this as: will lose money if they aren’t transparent, because that’s the only motivation that they respond to.
In some sense, as my friend reminded me, we don’t need to worry about hedge funds as much as about banks. This is because hedge funds do their trades through brokerages, which force margin calls on trades that they deem risky. In other words, they pay for their risk through margins on a trade-by-trade, daily basis. If you are thinking, “wait, what about LCTM? Isn’t that a hedge fund that got away with murder and almost blew up the system and didn’t seem to have large margins in place?” then the answer is, “yeah but brokers don’t get fooled (as much) by hedge funds anymore”. In other words, brokers, who are major players in the financial game, are the policemen of hedge funds.
There are two major limits to the above argument. Firstly, hedge funds purposefully use multiple brokers simultaneously so that nobody knows their entire book, so to the extent that risk of portfolio isn’t additive (it isn’t), this policing method isn’t complete. Secondly, it is only a local kind of risk issue- it doesn’t clarify risk given a catastrophic event (like a Greek default), but rather a more work-a-day “normal circumstances” market risk.
Even so, what about the banks? Are there any brokers measuring the risk of their activities and investments? Since the banks are the brokers, we have to look elsewhere… I guess that would have to be at the government, and the regulators themselves, maybe the FDIC… in any case, people decidedly not players in the financial game, not motivated by pay-off, and therefore not prone to delving into the asperger-inspiring details of complicated structured products to search out lies or liberal estimates.
The goal then is to create a new kind of market which allows insiders to bet on the validity of banks’ portfolios. You may be saying, “hey isn’t that just the stock price of the bank itself?”, and to answer that I’d refer you to this article which does a good job explaining how little information and power is actually being exercised by stockholders.
I will follow up this post with another more technical one where I will attempt to describe the new market and how it could (possibly, hopefully) function to motivate transparency of banks. But in the meantime, feel free to make suggestions!
Women on S&P500 boards of directors
This is a co-post with FogOfWar.
Here’s an interesting article about how many board of directors for S&P500 companies consist entirely of men. Turns out it’s 47. Well, but we’d expect there to be some number of boards (out of 500) which consist entirely of men even if half of the overall set of board members are women. So the natural question arises, what is the most likely actual proportion of women given this number 47 out of 500?
In fact we know that many people are on multiple boards but for the sake of this discussion let’s assume that there’s a line of board seekers standing outside waiting to get in, and that we will randomly distribute them to boards as they walk inside, and we are wondering how many of them are women given that we end up with 47 all-men boards out of 500. Also, let’s assume there are 8 slots per board, which is of course a guess but we can see how robust that guess is by changing it at the end.
By the way, I can think of two arguments as to why the simplification that nobody is on multiple boards argument might skew the results. On the one hand, we all know it’s an old boys network so there are a bunch of connections that a few select men enjoy which puts them on a bunch of boards, which probably means the average number of boards that a man is on, who is on at least one board, is pretty large. On the other hand, it’s also well-known that, in order to seem like you’re diverse and modern, companies are trying to get at least a token woman on their board, and for some reason consider the task of finding a qualified woman really difficult. Thus I imagine it’s quite likely that once a woman has been invited to be on a board, and she’s magically dubbed “qualified,” then approximately 200 other boards will immediately invite that same woman to be on their board (“Oh my god, they’ve actually found a qualified woman!”). In other words I imagine that the average number of boards a given woman is on, assuming she’s on one, is probably even higher than for men, so our simplifying assumptions will in the end be overestimating the number of women on boards. But this is just a guess.
Now that I’ve written that argument down, I realize another reason our calculation below will be overestimating women is this concept of tokenism- once a board has one woman they may think their job is done, so to speak, in the diversity department. I’m wishing I could really get my hands on the sizes and composition of each board and see how many of them have exactly one woman (and compare that to what you’d expect with random placement). This could potentially prove (in the sense of providing statistically significant evidence for) a culture of tokenism. If anyone reading this knows how to get their hands on that data, please write!
Now to the calculation. Assuming, once more, that each board member is on exactly one board and that there are 8 people (randomly distributed) per board, what is the most likely percentage of overall women given that we are seeing 47 all-male boards out of 500? This boils down to a biased coin problem (with the two sides labeld “F” and “M” for female and male) where we are looking for the bias. For each board we flip the coin 8 times and see how many “F”s we get and how many “M”s we get and that gives us our board.
First, what would the expected number of all-male boards be if the coin is unbiased? Since expectation is additive and we are modeling the boards as independent, we just need to figure out the probability that one board is all-male and multiply by 500. But for an unbiased coin that boils down to (1/2)^8 = 0.39%, so after multiplying by 500 we get 1.95, in other words we’d expect 2 all-male boards. So the numbers are definitely telling us that we should not be expecting 50% women. What is the most likely number of women then? In this case we work backwards: we know the answer is 47, so divide that by 500 to get 0.094, and now find the probability p of the biased coin landing on F so that all-maleness has probability 0.094. This is another way of saying that (1-p)^8 = 0.094, or that 1-p is 0.744, the eighth root of 0.094. So our best guess is p = 25.6%. Here’s a table with other numbers depending on the assumed size of the boards:
If anyone reading this has a good sense of the distribution of the size of boards for the S&P500, please write or comment, so I can improve our estimates.
Working with Larry Summers (part 2)
This is the second part of a description of my experiences working at D.E. Shaw, which was started here and continues here.
I want to describe the culture of working at D.E. Shaw during the credit crisis, so from June 2007 to June 2009, because I think it’s emblematic of something that most news articles and books written about hedge funds really miss out on when they fixate on the average I.Q. of the people working there, which is in the end a distraction and nothing more, or the bizarre or quirky personalities that exist there, which is only idiosyncratic and doesn’t explain anything deeply.
I promised myself I’d put focus on the following phrase, which struck me down when I first heard it used and still makes me shake my head, namely the concept of “dumb money.” The phrase was tossed around constantly and cleverly, and really, to understand what it means inside the context of the hedge fund culture, is to understand the culture. So I’ll try to explain it. First a bit of context.
Most of the quants at D.E. Shaw were immigrant men. In fact I was the only woman quant when I joined, and there were quite a few quants, maybe 50, and I was also one of the only Americans. What nearly all these men had in common was a kind of constant, nervous hunger, almost like a daily fear that they wouldn’t have enough to eat. At first I thought of them as having a serious chip on their shoulder, like they were the kind of guy that didn’t make the football team in high school and were still trying to get over that. And I still think there’s an element of something as simple as that, but it goes deeper. One of my colleagues from Eastern Europe said to me once, “Cathy, my grandparents were coal miners. I don’t want my kids to be coal miners. I don’t want my grandchildren to be coal miners. I don’t want anybody in my family to ever be a coal miner again.” So, what, you’re going to amass enough money so that no descendent of yours ever needs to get a job? Something like that.
But here’s the thing, that fear was real to him. It was that earnest, heartfelt anxiety that convinced me that I was really different from these guys. The difference was that, firstly, they were acting as if a famine was imminent, and they’d need to scrounge up food or starve to death, and secondly, that only their nuclear family was worth saving. This is where I really lost them. I mean, I get the idea of acts of desperation to survive, but I don’t get how you choose who to save and who to let die. However, it was this kind of us-against-them mentality that prevailed and informed the approach to making money.
Once you understand the mentality, it’s easier to understand the “dumb money” phrase. It simply means, we are smarter than those idiots, let’s use our intelligence to anticipate dumb peoples’ trades and take their money. It is our right as intelligent, imminently starving people to do this. Chasing dumb money can take various forms, but is generally aimed at anticipating lazy fund managers: if you know that they always wait until Friday afternoon to balance their books, or that they wait until the end of the month, or that they are required to buy certain kinds of things, you can anticipate their trades, make them yourself a bit before they do, thereby forcing them to pay more, and getting a nice little profit for yourself. In short this works in general, since statistically speaking the anticipated trade wasn’t driving up the intrinsic value of the underlying, but rather was being affected by trade impact for a short amount of time. If we can anticipate big trades by lots of dumb money, then the short-term market impact will be large enough and last long enough to buy in beforehand and sell at the top, while it still lasts, assuming there’s sufficient liquidity. The subtext of taking dumb money, going back to the football team issue, is: if we don’t somebody else will, and then we will feel like fools for not doing it ourselves.
To tell you the truth, I was completely naive when I went to work there. I had kind of accepted the job because I wanted to be a business woman, wanted a brisk pace after the agonizing slowness of academics, and I had really no moral judgment on the concept of a hedge fund; I thought it was morally neutral, at worst a scavenger on the financial system, like a market maker or someone who provides insurance for something. Well I’ve decided it’s more like a leech.
Getting to the part about actually working with Larry Summers. I did work on a couple of his ideas, although in order not to get sued I can’t be detailed about what his ideas were. And I had various meetings with him and a bunch of managing directors. One thing I remember about these meetings was the eery way the managing directors seemed intimidated by him, even though behind his back they kind of scoffed at the possibility that he could actually offer good modeling ideas. It was basically a publicity stunt, or at least rumored to be, to have him work there. It was after he had gotten pushed out of the Presidency at Harvard for talking out of his ass about women in math, and yes it was a bit surreal to be the only woman quant in the place, and to be working on his project considering that. Since I am pretty much never intimidated for some reason, I had no problem. He kept on grilling me about various things to try and I kept explaining what I’d done and how I’d already thought of that. It was fine, pretty combative and pushy, but actually kind of fun. I really have nothing to say about him treating me differently because I was a woman.
But when I think about that last project I was working on, I still get kind of sick to my stomach. It was essentially, and I need to be vague here, a way of collecting dumb money from pension funds. There’s no real way to make that moral, or even morally neutral. There’s no way to see that as scavenging on the marketplace. Nope, that’s just plain chasing after dumb money, and I needed to quit. I still don’t know if that model went into production.
Did someone say regulation?
FogOfWar has kindly offered the background below on the OTC market and an analogy with the bond market, inspired by this recent article describing the latest round of watering-down of derivatives regulations. The bottomline for me is that whenever you see people using the phrases “needlessly tying up capital that would otherwise be used to create jobs and grow the economy,” “would damage America,” or especially an emphasis on “U.S. firms,” it probably means they are trying to engender a local nationalistic fervor to camouflage a very basic greedy instinct. Here’s the background:
OTC derivatives, by definition, are not traded on an open exchange, but are entered into between two parties in a private transaction. We can use JPMorgan and United Airlines as a running example. United has some risk it has that it wants to hedge. Or maybe some banker has convinced United that they should be hedging a risk that they didn’t know they had until the banker showed up to tell them about it.
For some simple things, United could just go to an exchange (a stock market, but not limited to stocks). So, for example, United could buy a future on oil prices to lock in its cost of oil over the next year. The problem is that there actually aren’t that many different contracts traded on exchanges, and the risks don’t usually fit neatly into the contracts that are there to buy. There’s a whole chapter on how to get a best-possible hedge in this situation in Derivatives 101 (and probably a whole class after that and people who make a living doing it in real life). So you could ‘dirty hedge’ (do an imperfect hedge), or you could go to JPMorgan and ask for an exact hedge.
JPMorgan is happy to give you the hedge and either delta hedge out the risk and/or match against offsetting risk they have on their books (or even use the opportunity to take a speculative position they were thinking about anyway). The key point is that JPMorgan quotes you a price, but it isn’t a price on a transparent market–it’s just whatever price they think you’ll pay. If United is smart, they’ll farm out the hedging for a bunch of bids from different banks and try to get the best price, but they’ll never actually know if they got ripped off or not, because they don’t see how much it actually costs JPMorgan to cover that risk internally.
There are some areas where the hedges are common enough and enough people offer them OTC that the profit margins are pretty low (simple interest rate swaps are a good example). However, there is also a lot of money to be made from ripping off dumb customers like United when they wander outside of these areas into other areas where they get crap pricing. This is how derivatives trading desks make their bonus.
And that’s why JPMorgan cares about this legislation. They want to keep ripping off the United Airlines of the world, and if the government makes United go to an actual exchange with open prices, there’ll be competition and the profit margin will shrink. Adding a margin requirement is a bit more wonky, but at the end JPMorgan doesn’t like it because it might drive United to an exchange and away from an OTC derivatives trade with JPMorgan.
It may go without saying, but Jamie Dimon, JPMorgan, GS, BofA, etc. do not give a shit whatsoever about United, Shell, Alcoa, or any other corporate. They just want the profits from their OTC derivatives trading desk to keep rolling in–profits that come off the backs of their customers–and they’ll say whatever garbage they think Congress and the Agencies will swallow to keep the trades rolling.
Felix Salmon wrote all this up a ways back when Barney Frank was caving to the investment banks and putting the end-user exception into Dodd-Frank to begin with. That was around the time my opinion of Barney Frank went from “rock star” to “big fat pussy”. The history (Salmon honed in on this) tells the story in the world of bond trading–what follows is a very general overview from memory:
Once upon a time, if a corporate wanted to buy bonds, they went to their investment bank. They didn’t see exchange-listed prices, and maybe they got a few quotes to try to get good pricing, but at the end of the day, much like the OTC derivatives market described above, they either had to take a price offered by a bank or not.
Then the government came in and said bonds should be traded on open exchanges (with bid and ask prices available for participants to see). The banks said it would destroy the market, they said the corporates would suffer, they said the markets would move overseas, they probably said it would “hurt America” to do this. All of exactly the same horse shit Jamie Dimon and the banks are saying now about moving derivatives to exchanges.
Well, bond trading got moved to exchanges and exactly none of the things the banks warned about actually happened. Instead, the thing all of the banks were secretly fearing did happen: customers got good execution at lower prices and bank profit margins in the bond business slowly collapsed over time to a fraction of what they were back in the OTC-bond days. Go figure.
Guest post: Tax Repatriation Day
I’m delighted to have my first guest blogger!
“FogOfWar” (named after the documentary) is someone I’ve known for some time who comes from a mathy background, with a detour through accounting, tax & law winding up in banking (not as a quant). FOW & I have jammed finance policy many times and we tend to agree on a lot of things–I hope it will bring a “what really happens on the ground” perspective to thoughts about modeling as well as some useful insight into some of the technical rules (like accounting) that can matter a lot. Here’s his post:
The NYT ran an article on tax repatriation yesterday. Often, as someone in the industry, these articles can be infuriating for their lack of accuracy, misdirection or imprecision. In this case, however, my hat is off to the NYT for some damn fine traditional journalism. They’ve taken a fairly complicated issue (one I happen to know more than a little about), understood the core points in play and laid them out in an interesting, informative and readable article. Yes, it really is as bad as they make it out to be.
The “repatriation holiday” makes my vague-and-unofficial list of “10 worst tax ideas out there”. Unfortunately, every bad idea ultimately finds its way to Congress & this one is back for seconds. The NYT article lays out the case well, but here’s are two additional reasons on why this idea seems to have lasting appeal, which come in the form of catchy phrases:
“The money is trapped overseas”
We all know what “money”, “trapped” and “overseas” mean, and we can form an immediate idea of how this would be a bad thing, and how freeing that trapped money and bringing it back to the US would be good for the economy. Thus we get the inference: “The money is trapped overseas, and if we could bring it back it would create jobs.” Unfortunately, the second half of the second sentence is completely false. A more accurate sentence would be “The money is trapped overseas, and if we could bring it back corporations would pay slightly larger dividends this year, but not create any jobs or invest in any US plants that weren’t already in their strategic planning.” Doesn’t have quite the same ring to it…
“Structural subordination”
Not nearly as catchy as the first phrase, and uses two words for which most people don’t have a quick definition (at least not when paired together). Relevant, however, and a quick wonkish example with illustrate the thrust:
Let’s take a hypothetical US company, called (just to pick a name at random) “Lehman Holdings”. Lehman Holdings has assets claimed at $900 on its books and debt of $800. Lehman Holdings also owns 100% of another company, who we’ll call “Lehman UK”, which has assets claimed at $100 on its books and no debt. So, at first blush, one might think that Lehman has a 20% equity buffer: $1,000 of assets and $800 of debt (or a 4:1 debt ratio). This is nice easy math, which happens to be wrong in practice. The hidden assumption is that the people who loaned Lehman Holdings $800 can get access to all $1,000 of assets. They certainly can access the $900 of assets (or whatever they’re worth by the time bankruptcy hits), but the UK subsidiary is subject to UK bankruptcy rules, not US bankruptcy rules. Thus, when US creditors try to pull the $100 of assets out of the UK, they may find it’s more difficult than they anticipated (international bankruptcy gets sticky fast). Perhaps they could sell the stock of the subsidiary, but in real life that would involve untangling a whole host of interconnected contractual arrangements between Lehman Holdings and Lehman UK, which could take years. Not to mention the fact that to pull the $100 back, they’d have to pay ($35) in US taxes, so really there may be only $65 net to work with (other facts could zero out the tax bill). Probably in the end they can get the $100 of assets ($65 post taxes), but it can mean a significant time delay, and when you’re dealing with an imminent default, delay in action can translate to financial loss.
So, for all of these reasons, having $100 in a subsidiary isn’t worth quite the same thing as having $100 in the parent company. The fancy name for this is “structural subordination”, a term used by the credit rating agencies. So, if you’re a tech or pharma company with many billions of USD in your tax-shelter Irish/Dutch/Singapore subsidiaries, this can become a problem for your credit rating (which can impact your cost of borrowing). It’s probably not the primary reason for the lobbying efforts on tax repatriation, but it is definitely a factor, as the ($35) in tax is what’s preventing Holdings in the above example from pulling the $100 out of UK.
-FOW
Quantitative risk management
After the credit crisis hit we all realized that there’s a lot more risk out there than can be described by trailing volatility measures. Once I decided to leave the hedge fund world, I was thinking about working for the “other side,” namely to help quantify risk and/or work on the side of the regulators. I applied to the SEC, the New York Fed, and Riskmetrics, a software company which had a good reputation. I never heard from the Fed, and the SEC didn’t seem to have something for me, but I landed a job at Riskmetrics.
I figured it this way: if you work on a risk in a good way, if you make a better risk model, then you can at least argue you are improving the world. If you are instead making a bad risk model, and you know it, then you’re making the world a worse, riskier place. For example if you are working for a rating agency and get paid to ignore signs of riskiness, then that would be the not improving the world kind.
I really enjoyed my job, and after some months I was put in charge of “risk methodology,” which meant I got to think about how to quantify risk and why. I worked on our credit default model, which was super interesting, and I got to talk to the head trader of one of the biggest CDS trading desks regularly to understand the details of the market. In fact many of the biggest hedge funds and banks and pension funds send their portfolios daily to companies such as Riskmetrics to get overnight assessments of the riskiness of their portfolios. Bottomline is that my job kind of rocked, but it didn’t last forever; we were acquired soon after that by a company which didn’t offer me the same kind of position and I left pretty soon.
Here’s an article that very clearly articulates some of the problems in the field of quantitative risk. In my opinion it doesn’t go far enough with respect to their last point, or maybe it misses something, where they talk about “forecasting extreme risks.” This refers to the kind of thing that happens in a crisis, when all sorts of people are pulling out of the market at the same time and there are cascading, catastrophic losses.
What gets to me about this is that everyone talks about moments like these as if they can’t be modeled, but of course they can be, to a limited extent. Namely, although we don’t know what the next huge crisis will be, there are a few obvious candidates (like the Greek, Portuguese, Irish, or U.S. defaulting on their debt) which we should be keeping an eye on to the best of our quantitative abilities. Many of the “panic” situations (like the mortgage-backed securities debacle) were pretty obvious risks weeks or months in advance of their occurring, but people just didn’t know how to anticipate the consequences. That’s fine for a given individual trader but shouldn’t be true for the government.
I think the first step should be to compile a longish list of possible disaster scenarios (include the ones we’ve already seen happen) and decide what the probability of each scenario is- these probabilities can be updated each week by a crew of economists or what have you. Secondly and separately, set up a quantitative model which tries to capture the resulting cascade of consequences that each scenario would create; this would be complicated and involve things like guessing the losses at which hedge funds start liquidating their books, but should be aided by amassing huge amounts of information of the underlying portfolios of the largest institutions.
In my opinion the regulators have made a huge mistake in the past three years by _not_ insisting on getting the entire portfolio from every major hedge fund and bank every night (which from above we know is possible for them since they already send them to Riskmetrics-like software companies, although I’ve read articles where they claim this would be way too onerous a task) and, with that deep information, model the effect of a crisis scenario from our above list; how would it affect the bond market? The CDS market? The model which already exists at quantitative hedge funds now, which measures the impact and decay on trades, is a great start. Moreover, this model is not impossible to train (i.e. the actual coefficients inside the model’s formulas aren’t that hard to estimate), in fact it wouldn’t be that big a deal if we had as much data as I’m talking about. To me it’s unbelievable that we aren’t getting this portfolio information every day (or even intraday) and creating a “systemic impact model,” because it would clearly make us better prepared for future events (although not of course perfectly prepared) and no hedge fund or bank could argue that we shouldn’t be worried – it should be one of the costs of doing business on Wall Street.
The Greek situation
If you’re anything like me, you eat up the news on the Greek situation whenever and wherever you can. It’s like watching a slow-motion train wreck that takes years to hit. No, even better, it’s like this:
Imagine there’s a family that you know as self-absorbed, undisciplined, and indulgent, especially with their kids- they let their kids watch too much TV, they give their kids every gadget they can’t really afford, flat-screen TVs on credit, they stay up too late, eat crap food, they bribe their kids to like them, bringing them presents after every trip. It borders on neglect, for God’s sake, and it will come back to haunt them, you think to yourself. Then imagine seeing them in a crowded restaurant with their kids, older now, and utterly obnoxious and lazy and entitled, screaming at the top of their lungs that whatever the complaint is, it’s definitely not their fault, it’s their stinking parents’ fault, and why should they get a job. It’s an obnoxiously satisfying scene to watch as an exhausted parent who has been sure to feed their kids broccoli and have their kids tucked in by 9 with their homework done and their backpacks ready for school the next day.
But here’s the thing, I kind of have to side with the spoiled kids. I mean, it is the parents’ fault if they’ve completely spoiled their kids. As bratty as the kids are, you really can’t blame them on this until they are rational adults.
In summation, Greece is the European version of the Kardashians.
Here’s an article which kinds of proves my point. The politicians have spoiled the Greeks for so long, by buying votes with do-nothing government jobs, and simply ignoring the state of the deficit and anything involving money or taxes (mostly because the politicians themselves are the worst of the tax-evaders and don’t want to rock the boat), that the people living there are looking anywhere but at themselves for where the problem lies. In other words, a completely backwards-looking approach with no forwards-looking solution in mind. They are that kid at that restaurant, somewhere in late adolescence but not quite adults.
Another aspect of this crisis is the enormous disconnect between the economists and bankers on the one hand, who have absolute certainty that the banking system must be kept functional at any cost, and the actual people living in a country on the other hand, who don’t want to pay for the mistakes of the rich bankers. What makes this gulf so wide? It’s wide in any country actually, but in Greece you have the extra layer of spoiled entitlement. I’ll talk about this disconnect in my second post about working at D.E. Shaw, where I experienced it first-hand.
Update
After quite a bit of feedback (love feedback!) I’ve decided to add to this post because I think I was too glib and didn’t make my point well. First, let me be clear that I don’t think that the Greek workers are spoiled. I have a lot of compassion for the working people of Greece- especially the youth. The young people of Greece have a broken system, filled with closed guilds, high unemployment, and corrupt politicians. I am extremely empathetic to their plight and if I were them I’d be protesting in the streets too. What I mean to get across with the spoiled kid thing is that spoiling kids really is neglect and really is the fault of the authorities, and it sets up someone to fail and it gives them no tools to correct systemic mistakes. In this analogy I’m trying to point out that the political class has neglected its people and its duty to create a working system. They have done nothing for those young people, and now they are trying to make inside deals with the European bankers and don’t seem to understand why the actual working (or unemployed) people of Greece don’t see why this is a great opportunity.
Working with Larry Summers (part 1)
This post is continued here and then here.
After I had been working at D.E. Shaw for a few months, I was asked by the American Mathematic Society to write an expository article on leaving academics for finance. Here’s what I wrote. It was infinitely vetted by the legal department, and they removed a bunch of stuff- by the time they approved it I couldn’t remember why I had wanted to write it in the first place. Oh yeah, something about answering a bunch of questions that math grad students kept asking me. The one edit I refused to budge on, I remember, was that they objected to the word “rich” in the sentence “However, it is clear that if you stay in finance for long enough, and are successful, you do become rich”. They wanted change the word to “wealthy”. As if that was going to soften the blow to the poor suckers who weren’t privileged enough to work at this holy place.
Ever since it was published, I’ve wanted to write a second edition. It would go something like this (taken from a letter I wrote to a friend recently who is applying to another hedge fund):
I actually never really intended to stay in finance, it was just the only “real job” I could get with my number theory skills. In the end I decided I wanted to work at a startup and there are more internet startups than finance ones. The truth is, there are a bunch of jerks
in finance, very likely due to the amount of money floating around, and I noticed a correlation with the size/age of the company and the douchebagginess of the “leaders” of the firms. I don’t know alot about ****** but word on the street is that they are huge douchebags. On the other hand, I myself don’t regret working with douchebags for four years, because it thickened my skin quite a bit (and in particular made me realize how impotent and feeble the academic douchebags are in comparison) and made me strive for something better. Although to be honest it sometimes really sucked.
I could sum it up pretty well thus: people who are successful for a while think they know everything. People who are rich think they are always right. People who are both successful and rich are absolutely incredible douchebags. It seems like a law of nature (i.e. I can only assume that if I ever become rich and successful I will also become a douchebag. One more reason not to be wishing too hard for things like that.).
So instead I work for *pretty good* money (better than I’d have gotten in academics but not as good as at DE Shaw) and I enjoy things like oatmeal in the morning, biking to work on the bike path, my incredible adorable macho developer colleagues, a really cool hands-off boss, and a bunch of awesome karaoke-loving beer-drinking coworkers who think I have special powers since I can do math. Oh, and the possibility that someday my numerous stock options in this startup may make me a douchebag someday.
I just want to add that, of course, not everyone I worked with at D.E. Shaw is a douchebag, not even all the leaders. In fact I still have many friends from there. But it’s definitely not a random cut of the population, and I would have to believe that people in it would agree with that (and would say it’s worth it).
In part 2 of this post I will talk about what specifically made me decide to leave the hedge fund industry.
What is seasonal adjustment?
One thing that kind of drives me crazy in economic or business news (which I’m frankly addicted to (which makes me incredibly old and boring)) is the lack of precision exactly when there seems to be some actual data- so at the very moment when you think you’re going to be told what the hard cold facts are, so you can make up your own mind about whether the economy is still sucking or is finally recovering, you get a pseudo-statistic with a side of caveat. I make it a point to try to formally separate the true bullshit from the stuff that actually is pretty informative if you know what they are talking about. I consider “seasonal adjustment” pretty much in the latter category, although there are exceptions (more on that later).
So what does “seasonal adjustment” mean? Let’s take an example: a common one is home sales. It’s a well known fact that people don’t buy as many homes in January and February as they do in May and June– due to some combination of people sitting in their houses eating ice cream straight from the Ben & Jerry’s container when it’s cold outside and the dirty snow tracks on their immaculate rugs during open houses making people trying to sell their houses enraged. So people delay house-hunting til Spring and they delay house-selling til house-hunting starts (side note: because of this, desperate people getting divorced or being forced to move often have to sell their houses at major discounts, so always do your house-hunting right after a huge blizzard).
Considering the cyclical and predictable nature of home sales, people want to “seasonally adjust” the data so that they can discern a move that is *not* due to the time of the year, in other words they want to detect whether a more macroeconomic issue is affecting home sales, such as a recession or housing glut (or both). It’s a reasonable approach- how does it work exactly?
Say you have a bunch of housing data, maybe 20 years of monthly home sales. You see that every single year the same pattern emerges, more or less. Then you could, for a given year, compute the average sale per month for that year. It’s important to compute this average, as we will see, because one golden rule of adjusting data is that the sum of the adjusted data must equal the original data, otherwise you introduce a problem that’s bigger than the one you’re solving.
Once you have the average sale per month, you figure out (using all 20 years) the typical divergence from the average that you see per month, as a percentage of the average per month that year. So for example, January is the worst month for home sales, and in the 20 years of data you see that on average there are 20% fewer home sales in January than there are on the average month of that year, whereas in June there are typically (in your sample) 15% more sales than in the average month that year. Using this historical data, you come up with numbers for each month (-20% for January, 15% for June, etc.). I can finally say what “seasonally adjusted” means: it is the rate of sales for the average month or for the year given these numbers. So if we saw 80,000 home sales in January, and our number for January is -20%, then we will say we have a seasonally adjusted rate of 100,000 sales per month or 1.2 million sales per year.
Note that this system of adjustment follows the golden rule at least for the historical data; by the end of each calendar year, we have attributed the correct overall number of sales, spread out over the months. However, if we start predicting July sales from what we’ve seen from home sales from January to March, taking into account these adjustments, we will also be tacitly assuming an overall number of sales for the year, and the golden rule will probably not hold. This is just another way to say that we won’t really know how many home sales have occurred in a given year until the year is over, so duh. But it’s not hard to believe that knowing these numbers is pretty useful if you want to make a ballpark estimate of the yearly rate of home sales and it’s only March.
A slightly more sophisticated way of doing this, which doesn’t depend as much on the calendar year, is to use the 20 years of data and a rolling 12 month window (i.e. where we add a month in the front and drop off a month in the back and thus always consider 12 consecutive months at a time) to compute the monthly adjustment for each month relative not to the average for the upcoming year, but rather relative to the average of the 12 past months. This has the advantage of be a causal model, (i.e. a model which only uses data in the past to predict the future- I’ll write a post soon about causal modeling) but has the disadvantage of not following the golden rule, at least in a short amount of time. For example, if housing sales are on a slow slide over months and months, this model will consistently fail to predict how low home sale figures should be.
The biggest problems with seasonally adjusted numbers are, in my opinion, that the model itself is never described- do we use 20 years of historical data? 3 years? Do we use a rolling window or calendar years? Without this kind of information, I’m frankly left wondering if you could frigging show me the raw data and let me decide whether it’s good news or bad news.
A few comments have trickled in from friends (over email) who are quants, and I wanted to add them here.
- First, any predicting is hard and assumes a model, i.e., each year is the same, or each month is the same. In other words, as soon as you are talking about something being surprisingly anything, you are modeling, even when you don’t think you are. Most assumptions go unnoticed in fact. Part of being a good quant is simply being able to list your modeling assumptions.
- As we will see when we discuss quant techniques further, a very important metric of a model is how many independent data points you have going into the model- this informs the calculation of statistical significance, for example. The comment then is that modeling seasonal adjustment as I’ve described above lowers your “number of independent data points” count by a factor of 12, because you are basically using all 12 months of a year to predict the _next year_, so what looked like 12 data points is really becoming only one. However, you could try to fit a smaller (than 12) parameter curve to the seasonal data differences, but then there’s overfit from having chosen the family of curves to be one that looks right. More on questions like this when we explore the concept of fitting model to the data, and in particular on how many different models you try for a given data set.
- The final comment is this: all predictions likely violate the golden rule, but the point is you at least want one that isn’t biased, so in expectation it matches the rule.
Hello world! [stet]
Welcome to my new “mathbabe” blog! I’d like to outline my aspirations for this blog, at least as I see it now.
First, I want to share my experiences as a female mathematician, for the sake of young women wanting to know what things are like as a professional woman mathematician. Second, I want to share my experiences as an academic mathematician and as a quant in finance, and finally as a data scientist in internet advertising. (Wait, did I say finally?)
I also want to share explicit mathematical and statistical techniques that I’ve learned by doing these jobs. For some reason being a quant is treated like a closed guild, and I object to that, because these are powerful techniques that are not that difficult to learn and use.
Next I want to share thoughts and news on subjects such as mathematics and science education, open-source software packages, and anything else I want, since after all this is a blog.
Finally, I want to use this venue to explore new subjects using the techniques I have under my belt, and hopefully develop new ones. I have a few in mind already and I’m really excited by them, and hopefully with time and feedback from readers some progress can be made. I want to primarily focus on things that will actually help people, or at least have the potential to help people, and which lend themselves to quantitative analysis.
Woohoo!
mathbabe 