Archive
Is too big to fail a good thing?
I read this blog post a couple of days and it really got me thinking. This guy John Hempton from Australia is advocating the too big to fail model- in fact he things we should merge more big banks together (Citigroup and Wells Fargo) because we haven’t gone far enough!
His overall thesis is that competition in finance increases as a function of how many banks there are out there and is a bad thing for stockholders and for society, because it makes people desperate for profit, and in particular people increase their risk profiles in pursuit of profit and they blow up:
What I am advocating is – that as a matter of policy – you should deliberately give up competition in financial services – and that you should do this by hide-bound regulation and by deliberately inducing financial service firms to merge to create stronger, larger and (most importantly) more anti-competitive entities.
He acknowledges that the remaining banks will be hugely profitable, and perhaps also extremely lazy, but claims this is a good thing: we would, as a culture, essentially be paying a fee for stability. It’s something we do all the time in some sense, when we buy insurance. Insurance is a fee we pay so that disruptions and small disasters in our lives don’t completely wipe us out. So perhaps, as a culture, this would be a price worth paying?
The biggest evidence he has that this setup works well is that it works in Australia- they have four huge incompetent yet profitable banks there, and they don’t blow up. People who work there are sitting pretty, I guess, because they really are just living in a money press. There is no financial innovation because there’s no competition.
I guess I have a few different reactions to this scenario. First, it’s kind of an interesting twist on the too-big-to-fail debate, in that it’s combined with the idea I already talked about here of having a system of banks that are utilities. John is saying that, really, we don’t need to make that official, that as soon as banks are this huge, we are already done, they are essentially going to act like utilities. This is super interesting to me, but I’m not convinced it’s a necessary or even natural result of huge banks.
Second, I don’t buy that what happened in Australia will happen here- perhaps Australia squelched financial innovation through regulations and the existing boring system, but maybe the people who would have been financial innovators all just moved to the U.S. and became innovators here (there are plenty of examples of that!). In other words Australia may have made it just a bit too difficult to be competitive relative to what else is out there- if everyone tried to be that repressive to financial innovation, we may see people moving back into Australia’s financial waters (like sharks).
Third, I think what John is talking about is an example of a general phenomenon, namely that, in the limit as regulations go to infinity, there is only one bank left standing. This is because every additional regulation requires a lawyer to go over the requirements and a compliance person to make sure the rules are being followed continuously. So the more regulation, the more it behooves banks to merge so that they can share those lawyers and compliance officers to save costs. In the end the regulations have defined the environment to such an extent that there’s only one bank that can possibly follow all the rules, and knows how to because of historical reasons. And that one, last bank may as well be a government institution, albeit with better pay, especially for its managers.
But we don’t have that kind of regulatory environment, and hedge funds are alive and well. They have to follow some rules, it’s absolutely true, but it’s still possible to start a smallish hedge fund without a million lawyers.
I guess what I’m concluding is that if we had formed our very few, very huge banks because of a stifling regulatory environment, then maybe we would have an environment that is sufficiently anti-competitive to think that our banks would serve us as slightly overpaid utilities. However, that’s not why we have them – it was because of the credit crisis, and the rules and regulations haven’t changed that much since then.
At the same time, I don’t totally disagree that huge banks do become anti-competitive, just by dint of how long it takes them to make decisions and do things. But I’m not sure anti-competitive is the same thing as low-risk.
High frequency trading: Update
I’d like to make an update to my earlier rant about high frequency trading. I got an awesome comment from someone in finance that explains that my main point is invalid, namely:
…the statement that high frequency traders tend to back away when the market gets volatile may be true, but it is demonstrably true that other, non-electronic, non-high-frequency, market makers do and have done exactly the same thing historically (numerous examples included 1987, 1998, various times in the mortgage crisis, and just the other morning in Italian government bonds when they traded 3 points wide for I believe over an hour). While there is an obligation to make markets, in general one is not obliged to make markets at any particular width; and if there were such an obligation, the economics of being a marketmaker would be really terrible, because you would be saying that at certain junctures you are obliged to be picked off (typically exactly when that has the greatest chance of bankrupting your enterprise).
My conclusion is that it’s not a clear but case that high-frequency traders actually increase the risk.
By the way, just in case it’s not clear: one of the main reasons I am blogging in the first place is so that people will set me straight if I’m wrong about the facts. So please do comment if you think I’m getting things wrong.
What is an earnings surprise?
One of my goals for this blog is to provide a minimally watered-down resource for technical but common financial terms. It annoys me when I see technical jargon thrown around in articles without any references.
My audience for a post like this is someone who is somewhat mathematically trained, but not necessarily mathematically sophisticated, and certainly not knowledgeable about finance. I already wrote a similar post about what it means for a statistic to be seasonally adjusted here.
By way of very basic background, publicly traded companies (i.e. companies you can buy stock on) announce their earnings once a quarter. They each have a different schedule for this, and their stock price often has drastic movements after the announcement, depending on if it’s good news or bad news. They usually make their announcement before or after trading hours so that it’s more difficult for news to leak and affect the price in weird ways minutes before and after the announcement, but even so most insider trading is centered around knowing and trading on earnings announcements before the official announcement. (Don’t do this. It’s really easy to trace. There are plenty of other ways to illegally make money on Wall Street that are harder to trace.)
In fact, there’s so much money at stake that there’s a whole squad of “analysts” whose job it is to anticipate earnings announcements. They are supposed to learn lots of qualitative information about the industry and the company and how it’s managed etc. Even so most analysts are pretty bad at forecasting earnings. For that reason, instead of listening to a specific analyst, people sometimes take an average of a bunch of analysts’ opinions in an effort to harness the wisdom of crowds. Unfortunately the opinions of analysts are probably not independent, so it’s not clear how much averaging is really going on.
The bottomline of the above discussion is that the concept of an earnings surprise is really only borderline technical, because it’s possible to define it in a super naive, model-free way, namely as the difference between the “consensus among experts” and the actual earnings announcement. However, there’s also a way to quantitatively model it, and the model will probably be as good or better than most analysts’ predictions. I will discuss this model now.
[As an aside, if this model works as well or better as most analysts’ opinions, why don’t analysts just use this model? One possible answer is that, as an analyst, you only get big payoffs if you make a big, unexpected prediction which turns out to be true; you don’t get much credit for being pretty close to right most of the time. In other words you have an incentive to make brash forecasts. One example of this is Meredith Whitney, who got famous for saying in October 2007 that Citigroup would get hosed. Of course it could also be that she’s really pretty good at learning about companies.]
An earnings surprise is the difference between the actual earnings, known on day t, and a forecast of the earnings, known on day t-1. So how do we forecast earnings? A simple and reasonable way to start is to use an autoregressive model, which is a fancy way of saying do a regression to tell you how past earnings announcements can be used as signals to predict future earnings announcements. For example, at first blush we may use last earning’s announcement as a best guess of this coming one. But then we may realize that companies tend to drift in the same direction for some number of quarters (we would find this kind of thing out by pooling data over lots of companies over lots of time), so we would actually care not just about what the last earnings announcement was but also the previous one or two or three. [By the way, this is essentially the same first step I want to use in the diabetes glucose level model, when I use past log levels to predict future log levels.]
The difference between two quarters ago and last quarter gives you a sense of the derivative of the earnings curve, and if you take an alternating sum over the past three you get a sense of the curvature or acceleration of the earnings curve.
It’s even possible you’d want to use more than three past data points, but in that case, since the number of coefficients you are regressing is getting big, you’d probably want to place a strong prior on those coefficients in order to reduce the degrees of freedom; otherwise we would be be fitting the coefficients to the data too much and we’d expect it to lose predictive power. I will devote another post to describing how to put a prior on this kind of thing.
Once we have as good a forecast of the earnings knowing past earnings as we can get, we can try adding macroeconomic or industry-specific signals to the model and see if we get better forecasts – such signals would bring up or bring down the earnings for the whole industry. For example, there may be some manufacturing index we could use as a proxy to the economic environment, or we could use the NASDAQ index for the tech environment.
Since there is never enough data for this kind of model, we would pool all the data we had, for all the quarters and all the companies, and run a causal regression to estimate our coefficients. Then we would calculate a earnings forecast for a specific company by plugging in the past few quarterly results of earnings for that company.
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.
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!
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.
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.
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.
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 