This is a guest post by Marc Joffe, a former Senior Director at Moody’s Analytics, who founded Public Sector Credit Solutions in 2011 to educate the public about the risk – or lack of risk – in government securities. Marc published an open source government bond rating tool in 2012 and launched a transparent credit scoring platform for California cities in 2013. Currently, Marc blogs for Bitvore, a company which sifts the internet to provide market intelligence to municipal bond investors.
Obama administration officials frequently talk about the need to improve the nation’s infrastructure. Yet new regulations published by the Federal Reserve, FDIC and OCC run counter to this policy by limiting the market for municipal bonds.
On Wednesday, bank regulators published a new rule requiring large banks to hold a minimum level of high quality liquid assets (HQLAs). This requirement is intended to protect banks during a financial crisis, and thus reduce the risk of a bank failure or government bailout. Just about everyone would agree that that’s a good thing.
The problem is that regulators allow banks to use foreign government securities, corporate bonds and even stocks as HQLAs, but not US municipal bonds. Unless this changes, banks will have to unload their municipal holdings and won’t be able to purchase new state and local government bonds when they’re issued. The new regulation will thereby reduce the demand for bonds needed to finance roads, bridges, airports, schools and other infrastructure projects. Less demand for these bonds will mean higher interest rates.
Municipal bond issuance is already depressed. According to data from SIFMA, total municipal bonds outstanding are lower now than in 2009 – and this is in nominal dollar terms. Scary headlines about Detroit and Puerto Rico, rating agency downgrades and negative pronouncements from market analysts have scared off many investors. Now with banks exiting the market, the premium that local governments have to pay relative to Treasury bonds will likely increase.
If the new rule had limited HQLA’s to just Treasuries, I could have understood it. But since the regulators are letting banks hold assets that are as risky as or even riskier than municipal bonds, I am missing the logic. Consider the following:
- No state has defaulted on a general obligation bond since 1933. Defaults on bonds issued by cities are also extremely rare – affecting about one in one thousand bonds per year. Other classes of municipal bonds have higher default rates, but not radically different from those of corporate bonds.
- Bonds issued by foreign governments can and do default. For example, private investors took a 70% haircut when Greek debt was restructured in 2012.
- Regulators explained their decision to exclude municipal bonds because of thin trading volumes, but this is also the case with corporate bonds. On Tuesday, FINRA reported a total of only 6446 daily corporate bond trades across a universe of perhaps 300,000 issues. So, in other words, the average corporate bond trades less than once per day. Not very liquid.
- Stocks are more liquid, but can lose value very rapidly during a crisis as we saw in 1929, 1987 and again in 2008-2009. Trading in individual stocks can also be halted.
Perhaps the most ironic result of the regulation involves municipal bond insurance. Under the new rules, a bank can purchase bonds or stock issued by Assured Guaranty or MBIA – two major municipal bond insurers – but they can’t buy state and local government bonds insured by those companies. Since these insurance companies would have to pay interest and principal on defaulted municipal securities before they pay interest and dividends to their own investors, their securities are clearly more risky than the insured municipal bonds.
Regulators have expressed a willingness to tweak the new HQLA regulations now that they are in place. I hope this is one area they will reconsider. Mandating that banks hold safe securities is a good thing; now we need a more data-driven definition of just what safe means. By including municipal securities in HQLA, bank regulators can also get on the same page as the rest of the Obama administration.
When I was prepping for my Slate Money podcast last week I read this column by Matt Levine at Bloomberg on the Citigroup settlement. In it he raises the important question of how the fine amount of $7 billion was determined. Here’s the key part:
Citi’s and the Justice Department’s approaches both leave something to be desired. Citi’s approach seems to be premised on the idea that the misconduct was securitizing mortgages: The more mortgages you did, the more you gotta pay, regardless of how they performed. The DOJ’s approach, on the other hand, seems to be premised on the idea that the misconduct was sending bad e-mails about mortgages: The more “culpable” you look, the more it should cost you, regardless of how much damage you did.
I would have thought that the misconduct was knowingly securitizing bad mortgages, and that the penalties ought to scale with the aggregate badness of Citi’s mortgages. So, for instance, you’d want to measure how often Citi’s mortgages didn’t match up to its stated quality-control standards, and then compare the actual financial performance of the loans that didn’t meet the standards to the performance of the loans that did. Then you could say, well, if Citi had lived up to its promises, investors would have lost $X billion less than they actually did. And then you could fine Citi that amount, or some percentage of that amount. And you could do a similar exercise for the other big banks — JPMorgan, say, which already settled, or Bank of America, which is negotiating its settlement — and get comparable amounts that appropriately balance market share (how many bad mortgages did you sell?) and culpability (how bad were they?).
I think he nailed something here, which has eluded me in the past, namely the concept of what comprises evidence of wrongdoing and how that translates into punishment. It’s similar to what I talked about in this recent post, where I questioned what it means to provide evidence of something, especially when the data you are looking for to gather evidence has been deliberately suppressed by either the people committing wrongdoing or by other people who are somehow gaining from that wrongdoing but are not directly involved.
Basically the way I see Levine’s argument is that the Department of Justice used a lawyerly definition of evidence of wrongdoing – namely, through the existence of emails saying things like “it’s time to pray.” After determining that they were in fact culpable, they basically did some straight-up negotiation to determine the fee. That negotiation was either purely political or was based on information that has been suppressed, because as far as anyone knows the number was kind of arbitrary.
Levine was suggesting a more quantitative definition for evidence of wrongdoing, which involves estimating both “how much you know” and “how much damage you actually did” to determine the damage, and then some fixed transformation of that damage becomes the final fee. I will ignore Citi’s lawyers’ approach since their definition was entirely self-serving.
Here’s the thing, there are problems with both approaches. For example, with the lawyerly approach, you are basically just sending the message that you should never ever write some things on email, and most or at least many people know that by now. In other words, you are training people to game the system, and if they game it well enough, they won’t get in trouble. Of course, given that this was yet another fine and nobody went to jail, you could make the argument – and I did on the podcast – that nobody got in trouble anyway.
The problem with the quantitative approach, is that first of all you still need to estimate “how much you knew” which again often goes back to emails, although in this case could be estimated by how often the stated standards were breached, and second of all, when taken as a model, can be embedded into the overall trading model of securities.
In other words, if I’m a quant at a nasty place that wants to trade in toxic securities, and I know that there’s a chance I’d be caught but I know the formula for how much I’d have to pay if I got caught, then I could include this cost, in addition to an estimate of the likelihood for getting caught, in an optimization engine to determine exactly how many toxic securities I should sell.
To avoid this scenario, it makes sense to have an element of randomness in the punishments for getting caught. Every now and then the punishment should be much larger than the quantitative model might suggest, so that there is less of a chance that people can incorporate the whole shebang into their optimization procedure. So maybe what I’m saying is that arriving at a random number, like the DOJ did, is probably better even though it is less satisfying.
Another possibility to actually deter crimes would be to arbitrarily increasing the likelihood of catching people up to no good, but that has been bounded from above by the way the SEC and the DOJ actually work.
This is a great book. It’s well written, clear, and it focuses on important issues. I did not check all of the claims made by the data but, assuming they hold up, the book makes two hugely important points which hopefully everyone can understand and debate, even if we don’t all agree on what to do about them.
First, the authors explain the insufficiency of monetary policy to get the country out of recession. Second, they suggest a new way to structure debt.
To explain these points, the authors do something familiar to statisticians: they think about distributions rather than averages. So rather than talking about how much debt there was, or how much the average price of houses fell, they talked about who was in debt, and where they lived, and which houses lost value. And they make each point carefully, with the natural experiments inherent in our cities due to things like available land and income, to try to tease out causation.
Their first main point is this: the financial system works against poor people (“borrowers”) much more than rich people (“lenders”) in times of crisis, and the response to the financial crisis exacerbated this discrepancy.
The crisis fell on poor people much more heavily: they were wiped out by the plummeting housing prices, whereas rich people just lost a bit of their wealth. Then the government stepped in and protected creditors and shareholders but didn’t renegotiate debt, which protected lenders but not borrowers. This is a large reason we are seeing so much increasing inequality and why our economy is stagnant. They make the case that we should have bailed out homeowners not only because it would have been fair but because it would have been helpful economically.
The authors looked into what actually caused the Great Recession, and they come to a startling conclusion: that the banking crisis was an effect, rather than a cause, of enormous household debt and consumer pull-back. Their narrative goes like this: people ran up debt, then started to pull back, and and as a result the banking system collapsed, as it was utterly dependent on ever-increasing debt. Moreover, the financial system did a very poor job of figuring out how to allocate capital and the people who made those loans were not adequately punished, whereas the people who got those loans were more than reasonably punished.
About half of the run-up of household debt was explained by home equity extraction, where people took out money from their home to spend on stuff. This is partly due to the fact that, in the meantime, wages were stagnant and home equity was a big thing and was hugely available.
But the authors also made the case that, even so, the bubble wasn’t directly caused by rising home valuations but rather to securitization and the creation of “financial innovation” which made investors believe they were buying safe products which were in fact toxic. In their words, securities are invented to exploit “neglected risks” (my experience working in a financial risk firm absolutely agrees to this; whenever you hear the phrase “financial innovation,” please interpret it to mean “an instrument whose risk hides somewhere in the creases that investors are not yet aware of”).
They make the case that debt access by itself elevates prices and build bubbles. In other words, it was the sausage factory itself, producing AAA-rated ABS CDO’s that grew the bubble.
Next, they talked about what works and what doesn’t, given this distributional way of looking at the household debt crisis. Specifically, monetary policy is insufficient, since it works through the banks, who are unwilling to lend to the poor who are already underwater, and only rich people benefit from cheap money and inflated markets. Even at its most extreme, the Fed can at most avoid deflation but it not really help create inflation, which is what debtors need.
Fiscal policy, which is to say things like helicopter money drops or added government jobs, paid by taxpayers, is better but it makes the wrong people pay – high income earners vs. high wealth owners – and isn’t as directly useful as debt restructuring, where poor people get a break and it comes directly from rich people who own the debt.
There are obstacles to debt restructuring, which are mostly political. Politicians are impotent in times of crisis, as we’ve seen, so instead of waiting forever for that to happen, we need a new kind of debt contract that automatically gets restructured in times of crisis. Such a new-fangled contract would make the financial system actually spread out risk better. What would that look like?
The authors give two examples, for mortgages and student debt. The student debt example is pretty simple: how quickly you need to pay back your loans depends in part on how many jobs there are when you graduate. The idea is to cushion the borrower somewhat from macro-economic factors beyond their control.
Next, for mortgages, they propose something the called the shared-responsibility mortgage. The idea here is to have, say, a 30-year mortgage as usual, but if houses in your area lost value, your principal and monthly payments would go down in a commensurate way. So if there’s a 30% drop, your payments go down 30%. To compensate the lenders for this loss-share, the borrowers also share the upside: 5% of capital gains are given to the lenders in the case of a refinancing.
In the case of a recession, the creditors take losses but the overall losses are smaller because we avoid the foreclosure feedback loops. It also acts as a form of stimulus to the borrowers, who are more likely to spend money anyway.
If we had had such mortgage contracts in the Great Recession, the authors estimate that it would have been worth a stimulus of $200 billion, which would have in turn meant fewer jobs lost and many fewer foreclosures and a smaller decline of housing prices. They also claim that shared-responsibility mortgages would prevent bubbles from forming in the first place, because of the fear of creditors that they would be sharing in the losses.
A few comments. First, as a modeler, I am absolutely sure that once my monthly mortgage payment is directly dependent on a price index, that index is going to be manipulated. Similarly as a college graduate trying to figure out how quickly I need to pay back my loans. And depending on how well that manipulation works, it could be a disaster.
Second, it is interesting to me that the authors make no mention of the fact that, for many forms of debt, restructuring is already a typical response. Certainly for commercial mortgages, people renegotiate their principal all the time. We can address the issue of how easy it is to negotiate principal directly by talking about standards in contracts.
Having said that I like the idea of having a contract that makes restructuring automatic and doesn’t rely on bypassing the very real organizational and political frictions that we see today.
Let me put it this way. If we saw debt contracts being written like this, where borrowers really did have down-side protection, then the people of our country might start actually feeling like the financial system was working for them rather than against them. I’m not holding my breath for this to actually happen.
When you hate on certain people and things as long as I’ve hated on the banking system and Tim Geithner, you start to notice certain things. Patterns.
I read Tim Geithner’s book Stress Test last week, and instead of going through and sharing all the pains of reading it, which were many, I’m going to make one single point.
Namely, Tim was unqualified for his jobs and head of the NY Fed, during the crisis, and then as Obama’s Treasury Secretary. He says so a bunch of times and I believe him. You should too.
He even is forced at some point to admit he had no idea what banks really did, and since he needed someone or something to blame for his deep ignorance, he somehow manages to say that Brooksley Born was right, that derivatives should have been regulated, but that since she was at the CFTC everybody (read: Geithner’s heroes Larry Summers and Robert Rubin) dismissed her out of hand, and that as a result he had no ability to look into the proliferating shadow banking or stuff going on at all the investment banks and hedge funds. So it was kind of her fault that he wasn’t forced to understand stuff, even though she warned people, and when shit got real, all he could do was preserve the system because the alternative would be chaos. And people should fucking thank him. That’s his 600 page book in a nutshell.
Let’s put aside Tim Geithner’s mistakes and his narrow outlook on what could have been done better, and even what Dodd-Frank should accomplish, for a moment. It’s hard to resist complaining about those things, but I’ll do my best.
The truth is, Tim Geithner was a perfect product of the system. He was an effect, not a cause.
When I dwell on the fact that he got the NY Fed job with no in-the-weeds knowledge or experience on how banks operate, there’s no reason, not one single reason, to think it’s not going to happen again.
What’s going to prevent the next NY Fed bank head from being as unqualified as Tim Geithner?
Put it another way: how could we possibly expect the people running the regulators and the Treasury and the Fed to actually understand the system, when they are appointed the way they are? In case you missed it, the process currently is their ability to get along with Larry Summers and Robert Rubin and to look like a banker.
Before you go telling me I’m asking for a Goldman Sachs crony to take over all these positions, I’m not. It’s actually not impossible to understand this system for a curious, smart, skeptical, and patient person who asks good questions and has the power to make meetings with heads of trading floors. And you don’t have to become captured when you do that. You can remember that it’s your job to understand and regulate the system, that it’s actually a perfectly reasonable way to protect the country. From bankers.
Here’s a scary thought, which would be going in the exact wrong direction: we have Hillary Clinton as president and she brings in all the usual suspects to be in charge of this stuff, just like Obama did. Ugh.
I feel like a questionnaire is in order for anyone being considered for one of these jobs. Things like, how does overnight lending work, and what is being used for collateral, and what have other countries done in moments of financial crisis, and how did that work out for them, and what is a collateralized debt obligation and how does one assess the associated risks and who does that and why. Please suggest more.
I don’t want to say too much because I’m not even halfway through but here’s one thing: Geithner is surprisingly honest about certain things and predictably dishonest, or at least misleading, about other things.
And although at first I thought it would be purely painful to read this book, since I don’t have any respect for the guy, now I’m glad I’m doing it, because it exposes so much about how the old boys network operates. It’s material.
It’s unusual that I find myself in the position of defending Wall Street activities, but here goes.
I just don’t think HFT is that big of a deal relative to other Wall Street evils. I have written a couple of times about HFT and I’m not a huge fan, and I don’t buy the “liquidity is good and more liquidity is better” argument: at some point enough is enough. I do think that day-to-day investors have largely benefitted from it but that people whose money is in massive funds which are regularly traded have seen their money get skimmed every month. Overall it’s a smallish negative tax on the average person, I’d expect.
Here’s why HFT deserves some of our hatred: there’s way too much human resources going into this stuff and it’s embarrassing, what with the laying of cables and blasting through mountains and such. And it’s a great sociological look into the absolutely greed-led mindset of the Wall Street trader, but honestly I think we already had that. It’s really business as usual at a microscopic scale, and nobody should really be surprised to learn that people will do anything to make money that’s technically possible and technically legal, and that they will brag about how they’re making the world a better place while they do it. Same old same old.
So I’m not saying HFT is awesome and we should encourage more of it. I’m all for thinking about how to slow down trading to once a second and make it “more fair” for more players (although that’s hard to do even as a thought experiment), or taxing transaction to make things slow down by themselves, which would be easy.
But here’s the thing, it’s not some huge awful thing we should focus on, even though Michael Lewis is a really good and engaging writer.
You wanna focus on something? Let’s talk about money laundering in HSBC and now Citi that is not under control. Let’s talk about ongoing mortgage fraud and robo-signing and the ongoing bailout/ taxpayer subsidy and people still losing their homes, and the poor still being the targets of illegal and predatory loans, and Too-Big-To-Fail getting worse, and the direct line between the bailout and the broken pension promises for civil servants and the overall price list for fraud that has been built.
Let’s talk about the people who created the underlying fraud still at work in places like Bank of America, and how few masterminds have gone to jail and how the SEC and the Obama administration has made that happen through inaction and passivity and how Congress is sitting on its hands because of the money coming in from lobbyists. Let’s talk about the increasing distance between the justice system for the poor and the justice system for the rich in this country.
Tell me what I missed.
The HFT noise is misplaced and a distraction from the ongoing real story.
Before I begin this morning’s rant, I need to mention that, as I’ve taken on a new job recently and I’m still trying to write a book, I’m expecting to not be able to blog as regularly as I have been. It pains me to say it but my posts will become more intermittent until this book is finished. I’ll miss you more than you’ll miss me!
On to today’s bullshit modeling idea, which was sent to me by both Linda Brown and Michael Crimmins. It’s a new model built in part by the former chief economist for the Commodity Futures Trading Commission (CFTC) Andrei Kirilenko, who is now a finance professor at Sloan. In case you don’t know, the CFTC is the regulator in charge of futures and swaps.
I’ll excerpt this New York Times article which describes the model:
The algorithm, he says, uncovers key word clusters to measure “regulatory sentiment” as pro-regulation, anti-regulation or neutral, on a scale from -1 to +1, with zero being neutral.
If the number assigned to a final rule is different from the proposed one and closer to the number assigned to all the public comments, then it can be inferred that the agency has taken the public’s views into account, he says.
- I know really smart people that use similar sentiment algorithms on word clusters. I have no beef with the underlying NLP algorithm.
- What I do have a problem with is the apparent assumption that the “the number assigned to all the public comments” makes any sense, and in particular whether it takes into account “the public’s view”.
- It sounds like the algorithm dumps all the public comment letters into a pot and mixes it together to get an overall score. The problem with this is that the industry insiders and their lobbyists overwhelm public commenting systems.
- For example, go take a look at the list of public letters for the Volcker Rule. It’s not unlike this graphic on the meetings of the regulators on the Volcker Rule:
- Besides dominating the sheer number of letters, I’ll bet the length of each letter is also much longer on average for such parties with very fancy lawyers.
- Now think about how the NLP algorithm will deal with this in a big pot: it will be dominated by the language of the pro-industry insiders.
- Moreover, if such a model were to be directly used, say to check that public commenting letters were written in a given case, lobbyists would have even more reason to overwhelm public commenting systems.
The take-away is that this is an amazing example of a so-called objective mathematical model set up to legitimize the watering down of financial regulation by lobbyists.
Update: I’m willing to admit I might have spoken too soon. I look forward to reading the paper on this algorithm and taking a deeper look instead of relying on a newspaper.