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.
Here are two things you might have some trouble believing if you read the papers regularly and find yourself convinced we are in a housing recovery. First, there are still huge numbers of homeowners on the brink of, or just starting to enter, foreclosure. Second, many of the banks foreclosing on those properties do not have clear legal ownership over the mortgages in question.
Obama should have addressed the first problem through TARP way back in 2008. In fact mortgage modification was an intention of TARP that was promised Congress when it passed the second half of the money but it never happened. Instead Obama came up with the garbage called HAMP, which has been dreadfully implemented and possibly a net harmful program.
Even without Obama, we should have seen a willingness to renegotiate debt. After all, we can negotiate credit card debt, and businesses routinely renegotiate their mortgages. Why are private home mortgages kept airtight? I guess the banks see it as in their interest not to allow negotiations, and whatever the banks want, the banks seem to get.
The second problem, which is essentially one of botched paperwork (explained here), is probably technically the job of some regulator to deal with, but nobody wants to “blow up the system” so nobody is dealing with it. This is especially ironic considering how often we hear about the so-called sanctity of the contract.
The result of these huge looming problems is that banks got bailed out and the system never got cleared of its actual debt and paperwork problems,.
Enter the concept of using eminent domain to force these two issues. Strike Debt, an offshoot of Occupy Wall Street, is pushing this in a few nationwide court cases, for example in Richmond, California.
More recently, and what inspired this post this morning, is a plan cooked up by Strike Debt using eminent domain to force courts to clear up broken chains of title, written by Hannah Appel and JP Massar.
This idea is on its face unappealing, given the history of that crude tool eminent domain. Everyone I meet has their own stories, but start here for a short list of eminent domain abuses.
And it might not work, either. A district judge might not want to deal with the complexity of the issue and might just let the bad paperwork through.
For that matter, many concerns have been voiced about the practicality of this approach, and one that deeply resonates with me is the idea of using it against current mortgages – i.e. mortgages where the homeowner is up-to-date with payment. Using eminent domain in such a case could set a precedent whereby, even though someone has been taking care of their property, the city uses eminent domain to condemn it based on historical data which implies the owner is likely to neglect their property. That would not be good enough. As far as I know the current plan only uses mortgages where there have been missed payments, though.
The bottomline is this: we’re in a situation where all these homeowners are being crushed with unreasonable monthly payments, and hugely inflated principals, where the legal ownership of the mortgage itself is under question, and nobody seems to want to do squat about it. Maybe it’s time a crude tool is used against a cruel enemy.
A while back I was talking to some math people about how credit default swaps (CDSs), by their very nature, contain risk that is generally speaking undetectable with standard risk models like Value-at-Risk (VaR).
It occurred to me then that I could put it another way: that perhaps credit default swaps might have been deliberately created by someone who knew all about the standard risk models to game the system. VaR was commercialized in the mid 1990′s and CDSs existed around the same time, but didn’t take off for a decade or so until after VaR became super widespread, which makes it hard to prove without knowing the actors.
For that matter it is reasonable to assume something less deliberate occurred: that a bunch of weird instruments were created and those which hid risk the most thrived, kind of an evolutionary approach to the same theory.
I was reminded recently of this conspiracy theory when Joe Burns talked to my Occupy group last Sunday about his recent book, Reviving the Strike. He talked about the history of strikes as a tool of leverage, and how much less frequently we’ve seen large-scale strikes and industry-wide strikes. He made the point that the legality of strikes has historically been uncorrelated to the existence of strikes – that strikers cannot necessarily wait for the legal system to catch up with the needs of the worker. Sometimes strikers need to exert pressure on legislation.
Anyhoo, one question that came up in Q&A was how, in this world of subsidiaries and franchises, can workers strike against the upper management with control over the actual big money? After all, McDonalds workers work for franchisees who are often not well-off. The real money lives in the mother company but is legally isolated from the franchises.
Similarly, with Walmart, there are massive numbers of workers that don’t work directly for Walmart but do work in the massive supply chain network set up and run by Walmart. They would like to hold Walmart responsible for their working conditions. How does that work?
It seems like the same VaR/CDS story as above. Namely, the legal structure of McDonalds and Walmart almost seems deliberately set up to avoid legal responsibility from disgruntled workers. So maybe first you had the legal system, then lawyers set up the legal construction of the supply chain and workers such that striking workers could only strike against powerless figures, especially in the McDonalds case (since Walmart has plenty of workers working for the mother company as well).
Last couple of points. First, only long-term, powerful enterprises can go to the trouble of gaming such large systems. It’s an artifact of the age of the corporation.
And finally, I feel like it’s hard to combat. We could try to improve our risk or legal system but that makes them – probably – even more complicated, which in turn gives massive corporations more ways to game them. Not to be a cynic, but I don’t see a solution besides somehow separately sidestepping our personal risk exposure to these problems.
I am looking into the history of anti-discrimination laws like the Equal Credit Opportunity Act, (ECOA) and how it got passed, and hopefully find data to measure how well it’s worked since it got passed in 1974.
Putting aside the history of this legislation for now – although it is fascinating – I’d like to talk this morning about this paper from 1989 written by Gregory Elliehausen and Thomas Durkin from the Board of Governors of the Federal Reserve System, which discusses the abstract question of approaches to defining and regulation around discrimination.
This came up because when Congress passed ECOA, they left it to the regulators – in this case the Federal Reserve – to decide exactly how to write the rules, which pertain to credit decisions (think credit card offerings). From the article:
The term “discriminate against an applicant” was defined in Section 202. 2(n) as meaning “to treat an applicant less favorably than other applicants.” By itself, this rule does not offer an unquestionably unambiguous operational definition of socially unacceptable discrimination in a screening context where limited selections are constantly being made from a longer list of applicants.
The paper then goes on to list 3 separate regulatory approaches to anti-discrimination regulation. I have found these three definition really interesting and thought-provoking. I won’t even go into the rest of the paper on this post because I think just this list of three approaches is so interesting. Tell me if you agree.
1) The “effects-based” approach to regulation. This is the idea that, we don’t need to know how you actually make credit decisions, but if the effect is that no women or minorities ever get credit from you, then you’re doing something wrong. If you want to be really extreme in this category you get to things like quotas. if you want to be less extreme you think about studying applications that are similar except for one thing like race or gender, kind of like the the male vs. female science lab application test studied here. Needless to say, effects-based regulation is not in use, it’s considered too extreme.
2) The “intent-based” approach to regulation. This is where you have to prove intent to discriminate. It’s super rare that you can do that, because it’s super rare that people aiming to discriminate are dumb enough to make it obvious. Far easier to embed discrimination in a model where you can maintain plausible deniability. Although intent-based regulation is considered too extreme in the other direction, it seems to be what surfaces when there’s a legal case (although I’m not a legal expert).
3) The “practices-based” approach to regulation. This is where you make a list of acceptable or unacceptable practices in extending credit and hope you cover everything. So for example you aren’t allowed to explicitly use race or marital status or governmental assistance status in your credit models. This is what the Fed finally decided to use, and it makes sense in that it’s easy to implement, but of course the lists change over time, and that’s the key issue (for me anyway): we need to update those lists in the age of big data.
Tell me if you think there’s yet another approach not mentioned. And note these regulatory approaches correspond to different ways of thinking about or even defining discrimination, which is itself a great reason to list them comprehensively. I think my future discussions about what constitutes discrimination will be informed by which above approach will pick up on a given instance.
Every now and then you see a published result that has exactly the right kind of data, in sufficient amounts, to make the required claim. It’s rare but it happens, and as a data lover, when it happens it is tremendously satisfying.
Today I want to share an example of that happening, namely with this paper entitled Regulating Consumer Financial Products: Evidence from Credit Cards (hat tip Suresh Naidu). Here’s the abstract:
We analyze the effectiveness of consumer financial regulation by considering the 2009 Credit Card Accountability Responsibility and Disclosure (CARD) Act in the United States. Using a difference-in-difference research design and a unique panel data set covering over 150 million credit card accounts, we find that regulatory limits on credit card fees reduced overall borrowing costs to consumers by an annualized 1.7% of average daily balances, with a decline of more than 5.5% for consumers with the lowest FICO scores. Consistent with a model of low fee salience and limited market competition, we find no evidence of an offsetting increase in interest charges or reduction in volume of credit. Taken together, we estimate that the CARD Act fee reductions have saved U.S. consumers $12.6 billion per year. We also analyze the CARD Act requirement to disclose the interest savings from paying off balances in 36 months rather than only making minimum payments. We find that this “nudge” increased the number of account holders making the 36-month payment value by 0.5 percentage points.
That’s a big savings for the poorest people. Read the whole paper, it’s great, but first let me show you some awesome data broken down by FICO score bins:
This data, and the results in this paper, fly directly in the face of the myth that if you regulate away predatory fees in one way, they will pop up in another way. That myth is based on the assumption of a competitive market with informed participants. Unfortunately the consumer credit card industry, as well as the small business card industry, is not filled with informed participants. This is a great example of how asymmetric information causes predatory opportunities.
I’m just recovering from a killer flu that had me wheezing and miserable for 5 days. I have a whole backlog of rants and vents but no time this morning to even start, so instead let me suggest you read this article (hat tip Chris Wiggins) about a New York Times reporter who crashed the yearly party of Kappa Beta Phi, a Wall Street secret society. Pretty amazing, if true.
At first glance this seems totally weird, for two reasons. First, debit cards by construction have no ability to go below zero, so they are not directly relevant to the concept of credit, which is by definition when you borrow something and then hopefully pay it back. Second, my first, second, and third intuitive response to credit bureaus is to give them less information, not more. I already think they have way too much data about us. Their recent foray into using social media data is super creepy, for example, and threatens the “no outdated information” rule of the Fair Credit Act, for example.
I watched Orman explain her reasoning about her card, which I believe launched in 2012, and I kind of get her points about why she thinks this is a good idea (even though she clearly has a conflict of interest here): some people have trouble with credit cards, and for that reason they should use debit cards or cash, but cash has no data trail and thus people who are in only cash can never improve their credit scores enough to qualify for things like mortgages and car loans, which they may well be able to handle.
Here’s the thing, though. Her card actually has bad terms, and loads of fees, and it doesn’t look like FICO is actually going to use data from her cards to build peoples’ credit scores after all. Oh well.
Here’s an idea, which is not original at all but hasn’t gotten momentum because it doesn’t make bankers money: instead of shitty and expensive debit cards, let’s have the Post Office open a national bank and let people put money for free on their phones. Systems like this already exist in Kenya (Matt Stoller calls it a “M-Pesa style mobile cash system” in this fine post about the Post Office Bank idea) and in Ghana, and they work great, and let me once again mention there are no fees. It’s a free service as long as you have a cell phone, and it certainly doesn’t have to be a fancy smart phone.
In the short term, such a system will free poor people from getting ever increasingly ripped off by banks and companies with their crappy pre-paid debit cards. It might not give them stellar credit scores, but I’d argue that it’d still be an improvement.
In the asymptotic limit of that system, we’d have a pretty sharp division between people who live in the world of credit, with good FICO scores, and people who deal in cash and mobile cash, with bad or nonexistent FICO scores. It would be hard to get a good mortgage or car loan if you are in the latter group, but that’s already true (unless you count the kind of mortgages Wells Fargo gave to minorities to rip them off).
In the longer term, if we wanted to give credit scores to people who deal in cash, we could use their mobile cash records to deem their spending habits “credit worthy”.
In the much longer term, it would be great if we stopped pretending (I’m looking at you Suze Orman) that having a bad FICO score is a moral failing: it’s really mostly a sign of being broke. If we want to help people get out of debt spirals, then let’s talk about a Basic Guaranteed Income.
Crossposted on the Alt Banking blog, the below reflects a discussion at Alt Banking from last Sunday’s meeting.
People have been making a big fuss about JP Morgan Chase CEO Jamie Dimon’s recent raise. They seem to think that, what with all the lawsuits that JP Morgan Chase has been involved in this past year, exposing so much fraudulent behavior which directly contributed to so much human suffering, the guy should be somewhat humbled and punished. They even wanna question his right to stock options he shoulda had way back in 2008, when the world was on fire. The nerve!
I mean, maybe by some definition of “earned” he doesn’t deserve those 20 sticks. Maybe they think they have better plans for the bonus money. But from where I sit, the guy should have gotten way more, considering he set the price of fraud by big banks so low and in so many different ways.
I estimate that he should have gotten at least $100 million, using a very basic fact that the regulatory arbitrage which he displayed, and which now exists as a precedent for all bankers for the rest of eternity, benefitted not just him, not just JP Morgan Chase, but all the Too-Big-To-Fail banks. For that reason, every TBTF bank should give him at least $20 million as a reward for their future profitable fraudulent earnings. Since there are at least 5 TBTF banks, I’m just scaling up in a super reasonable way.
I know that might sound weird, for Bank of America and Goldman Sachs, which are generally speaking competitors to JP Morgan, to give Jamie Dimon cash money. And they might want to keep it on the DL for that matter, for the sake of appearances.
But after all, this is the guy who called Attorney General Eric Holder on the phone and negotiated a settlement, for christ’s sake! Who DOES that? That’s really above and beyond the chutzpah of even the most criminal of masterminds. Only the creamiest of the crop, only the most devoted of banker psychopaths can get away with that shit. That is to say, Jamie Dimon, and maybe Lloyd Blankfein (Dear Lloyd: I don’t doubt for a minute that you will have your day too, very soon, and then all the big boys will pitch in for your supersized bonus).
So what are you waiting for, Citigroup? Wells? When are you guys ponying up what we all know Dimon deserves from all of the elite institutions protected from prosecution? I say you guys perform the equivalent of a kowtow in Wall Street terms, which is of course monetized, in the form of a check. Send it on over.
Come to think of it we should also offer extra cash to HSBC’s legal team, and for that matter Eric Holder himself. If it hasn’t already been done.
A couple of days ago I was listening to a recorded webinar on K-12 student data privacy. I found out about it through an education blog I sometimes read called deutsch29, where the blog writer was complaining about “data chearleaders” on a panel and how important issues are sure to be ignored if everyone on a panel is on the same, pro-data and pro-privatization side.
Well as it turns out deutsch29 was almost correct. Most of the panelists were super bland and pro-data collection by private companies. But the first panelist named Joel Reidenberg, from Fordham Law School, reported on the state of data sharing in this country, the state of the law, and the gulf between the two.
I will come back to his report in another post, because it’s super fascinating, and in fact I’d love to interview that guy for my book.
One thing I wanted to mention was the high-level discussion that took place in the webinar on what regulation is for. Specifically, the following important question was asked:
Does every parent have to become a data expert in order to protect their children’s data?
The answer was different depending on who answered it, of course, but one answer that resonated with me was that that’s what regulation is for, it exists so that parents can rely on regulation to protect their children’s privacy, just as we expect HIPAA to protect the integrity of our medical data.
I started to like this definition – or attribute, if you will – of regulation, and I wondered how it relates to other kinds of regulation, like in finance, as well as how it would work if you’re arguing with people who hate all regulation.
First of all, I think that the financial industry has figured out how to make things so goddamn complicated that nobody can figure out how to regulate anything well. Moreover, they’ve somehow, at least so far, also been able to insist things need to be this complicated. So even if regulation were meant to allow people to interact with the financial system and at the same time “not be experts,” it’s clearly not wholly working. But what I like about it anyway is the emphasis on this issue of complexity and expertise. It took me a long time to figure out how big a problem that is in finance, but with this definition it goes right to the heart of the issue.
Second, as for the people who argue for de-regulation, I think it helps there too. Most of the time they act like everyone is a omniscient free agent who spends all their time becoming expert on everything. And if that were true, then it’s possible that regulation wouldn’t be needed (although transparency is key too). The point is that we live in a world where most people have no clue about the issues of data privacy, never mind when it’s being shielded by ridiculous and possibly illegal contracts behind their kids’ public school system.
Finally, in terms of the potential for protecting kids’ data: here the private companies like InBloom and others are way ahead of regulators, but it’s not because of complexity on the issues so much as the fact that regulators haven’t caught up with technology. At least that’s my optimistic feeling about it. I really think this stuff is solvable in the short term, and considering it involves kids, I think it will have bipartisan support. Plus the education benefits of collecting all this data have not been proven at all, nor do they really require such shitty privacy standards even if they do work.
This coming Sunday we’re having a special Alt Banking meeting where, instead of having our usual format, we’re all watching Four Horsemen, a recent documentary film put out by Renegade Economists (on twitter as @RenegadeEcon). We’ll first watch the film and then have a discussion about it.
The entire film is available on youtube here, although I paid 5 bucks to download it from this website in preparation for this coming Sunday’s viewing and discussion. Here’s the trailer, it looks amazing:
Feel free to come to Sunday’s meeting, it starts at 2pm and is uptown at Columbia University. Send an email to firstname.lastname@example.org and ask to be added to the email list for details, or go to the alt banking webpage for details.
United States District Judge Jed S. Rakoff is already kind of a hero to me, given that he’s the guy who rejected a “do not admit wrongdoing” settlement between Citigroup and the SEC over mortgage-backed securities fraud because, according to Rakoff, the proposed settlement was “neither fair, nor reasonable, nor adequate, nor in the public interest.”
More recently Rakoff has written a fine essay in the New York Review of Books entitled The Financial Crisis: Why Have No High-Level Executives Been Prosecuted? which I will summarize below but is well worth your time to read.
First Rakoff made the point that if there was no intentional fraud we should not scapegoat people and put them to jail. But on the other hand, if there was intentional fraud, then it’s a reflection on a dysfunctional justice system that nobody has gone to jail.
Then he examined that first possibility and found it unlikely, given that “… the Financial Crisis Inquiry Commission, in its final report, uses variants of the word “fraud” no fewer than 157 times in describing what led to the crisis…” In fact, fraud permeated at every level.
The Department of Justice (DOJ) has focused on explaining why nobody has gone to jail in spite of the existence of fraud. They have three reasons.
First, the DOJ claims it’s hard to prove intent for high-level management. But Rakoff demurs on this point, explaining that in cases of accounting fraud, “willful blindness” or “conscious disregard” is a well-established basis on which federal prosecutors have asked juries to infer intent.
Second, since many counterparties were “sophisticated,” it’s difficult to prove “reliance“. Again Rakoff demurs, pointing out that “In actuality, in a criminal fraud case the government is never required to prove—ever—that one party to a transaction relied on the word of another.”
Third, because of the “Too Big To Jail” problem, namely that prosecuting fraud would kill the economy. To this, Rakoff points out what that means in terms of class: that poor people can be prosecuted but the rich are protected.
Next, Rakoff says what he thinks is actually happening. First he discounts the revolving door: he thinks lawyers are thoroughly incentivized to make a name for themselves. Then what? He’s got three reasons.
Well, first, people were distracted. The FBI was distracted by terrorists, and the SEC was focused on Ponzi schemes and insider trading. The DOJ was inexperienced and the Southern District US Attorney’s Office was also focused on insider trading. And given the complexity and incentives, it’s hard for a given lawyer to decide to go with an MBS case instead of insider trading.
Second, the government had direct conflict in the fraud, given that the Fed and the regulators had deregulated everything in sight and then kept interest rates low to keep the mortgage machine going. They also meddled a lot during the crisis, deciding which failing bank should be taken over by whom. It made it hard for them to admit shit went wrong.
Finally, it’s because it’s now in vogue to prosecute corporations instead of people, but that really doesn’t work. Here’s Rakoff on this prosecutorial method:
Although it is supposedly justified because it prevents future crimes, I suggest that the future deterrent value of successfully prosecuting individuals far outweighs the prophylactic benefits of imposing internal compliance measures that are often little more than window-dressing. Just going after the company is also both technically and morally suspect. It is technically suspect because, under the law, you should not indict or threaten to indict a company unless you can prove beyond a reasonable doubt that some managerial agent of the company committed the alleged crime; and if you can prove that, why not indict the manager? And from a moral standpoint, punishing a company and its many innocent employees and shareholders for the crimes committed by some unprosecuted individuals seems contrary to elementary notions of moral responsibility.
And then his final conclusion:
So you don’t go after the companies, at least not criminally, because they are too big to jail; and you don’t go after the individuals, because that would involve the kind of years-long investigations that you no longer have the experience or the resources to pursue.
First, I am super grateful for Judge Rakoff’s essay, because as an experienced lawyer he has way more ammunition than I do to explain this stuff from the perspective of what is actually done in law. The “willful blindness” issue is particularly ridiculous. I’m glad to hear that courts have a way to deal with that problem, even if they aren’t using their tools against Jamie Dimon.
I am also grateful to hear him make the point that widespread fraud, unprosecuted, is not simply a theoretical issue. It exposes the dysfunctionality of our justice system and it exposes basic unfairness in society, where depending on how rich you are and how complicated your crime is, you can avoid going to jail. Personally, in the past few months I’ve gone from being angry at the bankers to being angry at the prosecutors.
Finally, I disagree with Rakoff on one point. Namely, his argument against the negative effect of the revolving door. His argument, I stipulate, only works for lawyers in a US Attorney’s office. I don’t think the average SEC lawyer or economist, or for that matter an employee at any captured regulator, has that much incentive to take on a big MBS case and be hard-assed. I think we would have seen more cases if that were true.
Yesterday Russ Roberts had Dallas Federal Reserve President Richard Fisher as his guest on his podcast EconTalk to talk about Too-Big-To-Fail (TBTF) banks and the Fed’s monetary policy. It was a fantastic discussion and I’m grateful to Roberts for continuing to discuss this important and nonpartisan issue.
We in Alt Banking have been impressed by Fisher’s stance on TBTF and have thought about trying to get him to come visit us to talk, so this was a great opportunity to get a preview of what he’d likely say if he ever made it over. Given that he’s an active central banker, he’s refreshingly open and honest about stuff, even if every now and then he deliberately makes it seem like everything that happened was a mistake rather than a criminal act.
Fisher and Roberts on TBTF
You should listen to the entire podcast, it’s about an hour long but well worth the time. I will submit a short summary of their conversation here:
- First they discuss the problem of TBTF banks, that instead of failing, large banks were merged into even larger banks during the crisis, and now we have institutions that are too big to manage and are being backed by an implicit government guarantee that the Dodd-Frank regulation isn’t removing.
- Next, the discuss a takeaway in terms of community banks. Is it a problem that it’s not a level playing field for community banks? Or is it primarily a problem that any bank that should fail is being propped up?
- Roberts makes the point that he doesn’t care so much that the system isn’t fair, he cares only that this banking system isn’t effective. If the threat is that France might overtake us in an ineffective field such as finance, then so be it.
- Then Fisher started talking solutions. He has a two-part plan. The first part is to make very explicit which parts of a bank have deposit insurance and access to the Fed window, namely only the commercial bank part that accepts deposits, not any special investment vehicles or insurance subsidiaries etc.
- The second part is to hold a ritual signing of a contract among every bank customer, creditor, investor, and counter-party (but not depositor!) which states that they know their investment is not protected by a government guaranteed. Roberts suggested this might be done in a public ceremony and include a statement that people will refuse government support, so that there will be an added element of public shaming if people go back on this promise. Here’s an example of such a covenant available at the Dallas Fed webpage:
- Next Roberts pushes back: the institutions that got bailed out in 2008 didn’t have insurance, so why will this work?
- To this Fisher acknowledges that it’s all about beliefs and expectations of market participants, but he thinks that the simplicity of this, as well as the signing of the covenant, might be convincing enough. By contrast he mentions that the current system, where we have a Financial Stability Oversight Council that decides which institutions need closing, has a very low probability of working. In particular, Fisher points out that it will take 24 million man hours to simply interpret current law to break down a bank.
- Roberts then asks, what about larger capitalization requirements of banks, or capping the size of the banks? Turns out that Fisher wants to minimize government intervention and maximize “market driven approaches.” He’s a real free market lover. He also says that, in the case of a liquidity run, capital cushions, even big ones, will be insufficient if you’re dependent on short-term funding.
- One last thing. They both mention that TBTF will only be solved when a president wants it to be solved. But they also both agree that no politician wants to lose the money they get from Wall Street lobbyists.
- Next, on to monetary policy. Fisher points out that the dual mandate of the Fed is supposed to focus on long term issues, and that QE’s policy of cornering the market on treasuries and mortgage-backed solutions (MBS) is both unsustainable and doesn’t work well in a long term way.
- Fisher mentions that the Fed has made money on its enormous, $4.2 trillion portfolio, to the tune of $300 billion in the past few years, but on the other hand that’s partly because interest rates are so low. What will happen when that changes?
- He’s worried long-term about inflation, since the Fed is basically printing money, which is being stored by the banks (and hedge funds and private equity) and not lent out, at least for now.
- Finally, and this is where I care the most, this is a policy that benefits rich people and doesn’t do much at all for the rest of Americans. The balance sheets of big US companies look great now because of QE, but they largely don’t hire people because they’ve realized they don’t really need to – they’ve harnessed IT. So actually there’s less need for credit in the system altogether. Conclusion: we should reduce QE sooner rather than later.
A couple of comments
I’m not as much of a fan of “free market solutions” as Fisher and Roberts. In particular I don’t think the influence of the Fed is going to be immediately forgotten, even if we scale it down, and the bailouts will take a while to forget as well. In other words I don’t think it will be realistic to think of our system as a free market any time soon. Plus I believe in good strong rules for the market to work well.
Having said that, I love the idea of implementing these two steps to end TBTF and then seeing how well they work. By all means protect only deposits and let everyone else risk their money. See if we can at least make a dent in the implicit government subsidy.
In terms of QE, there’s something to be said for everyone suffering together, and this policy is the opposite of that. Right now we see the GDP decoupled from the fate of the working man, and QE is a primary cause of that decoupling. Even so, we still use GDP as proxy for growth and success for our economy, even though the benefits are almost exclusively going to rich people. Here’s a chart showing what I mean, which I got here:
Women are underrepresented in businesses like Goldman Sachs and JP Morgan Chase, especially in the upper management. Why is that?
Many women never go into finance in the first place, and of course some of them do go in but leave. Why are they leaving, though? Is it because they don’t like success? Or they don’t like money? Are they forgetting to lean in sufficiently?
Here’s another possibility, which I dig. They’re less willing to sacrifice their ethics than their male colleagues for the sake of money and business success.
Last Friday I read this paper entitled Who Is Willing to Sacrifice Ethical Values for Money and Social Status? Gender Differences in Reactions to Ethical Compromises and written by Jessica A. Kennedy and Laura J. Kray. It offers ethical distaste problems as at least one contributing reason we don’t see as many women as we might otherwise.
Please read the paper for details, I’m only giving a very brief overview without figures of statistical significance. They have three experiments.
First they saw who were interested in jobs that had major ethical compromises. Turns out that women were way less interested than men.
Second, to check whether that was because of the ethical compromises or because of the “job” part, they had different kinds of job descriptions and found that, in the presence of a culture of good ethics, women were just as interested in a job as men.
Third, they checked on the existing assumptions about the connection between ethics and various kinds of jobs, like the law, medicine, and “business”. Turns out woman associate compromised ethics with business but less so with law and medicine.
Conclusion: we can attribute some of the lack of women in business to a combination of assumed and real ethical compromises.
First, I love that this paper was written by two women. Maybe that’s what it took for such an common sense idea to be tested.
Secondly, I think this paper should be kept in mind when we read things about how companies that are diverse are more successful. It’s probably because they are nice places to be that women and others are there, which in turn makes them more successful. It also explains why, when companies set out to be diverse, they often have so much trouble. They want to achieve diversity without changing their underlying culture.
Thirdly, I’m going to have to admit that men are under enormous pressure to succeed at all costs, which could explain why they’re more willing to become ethically compromised to be successful. That says something about our crazy expectations of men in this culture which I think we need to address. I say that as a mother of three sons.
Finally, whenever I hear someone talking about “leaning in” from now on, I will ask them, “lean in to what?”.
The raison d’être of hedge funds is to make the markets efficient. Or at least that’s one of the raisons d’être, the others being 1) to get rich and 2) to leave early on Fridays in the summer (resp. winter) to get a jump on traffic to the Hamptons (resp. ski area, possibly in Kashmir).
And although having efficient markets sounds like a great thing, it makes sense to ask what that would look like from the perspective of a non-insider.
This recent Wall Street Journal article on high-tech snooping does a pretty good job setting the tone here. First, the kind of thing they’re doing:
Genscape is at the vanguard of a growing industry that employs sophisticated surveillance and data-crunching technology to supply traders with nonpublic information about topics including oil supplies, electric-power production, retail traffic and crop yields.
Next, who they’re doing it for:
The techniques, which are perfectly legal, represent the latest advance in the longtime Wall Street practice of searching for every possible trading advantage. But the high cost of much of the new information—Genscape’s oil-supply report costs $90,000 a year—means that some forms of trading are becoming even more the province of firms with substantial resources.
Let’s put these two things together from the perspective of the public. The market is getting information from hidden cameras and sensors, and all that information is being fed to “the market” via proprietary hedge funds via channels we will never tap into. The end result is that the prices of commodities are being adjusted to real-world events more and more quickly, but these are events that are not truly known to the real world.
[Aside: I'm going to try to avoid talking about the "true price" of things like gas, because I think that's pretty much a fool's errand. In any case, let me just say that, in addition to the potentially realtime sensor information that goes into a commodity's price, we also have people trading on it because they are adjusting their exposure to some other historically correlated or anti-correlated instrument, or because they've decided to liquidate their books, or because they've decided the Fed has changed its macroeconomic policy, or because Spain needs to deal with its bank problems, or because someone wants to take money out of the market to rent their summer house in the Hamptons. In other words, I'm not ready to argue that we're getting close to the "true price" of gas here. It's just tradable information like any other.]
I am now prepared, as you hopefully are as well, to question what good this all does for people like us, who are not privy to the kind of expensive information required to make these trades. From our perspective, nothing happens, the price fluctuates, and the market is deemed efficient. Is this actually an improvement over the alternative version where something happens, and then the price adjusts? It’s an expensive arms race, taking up vast resources, where things have only become more opaque.
How vast are those resources? Having worked in finance, I know the answer is a shit-ton, if it is profitable in a short-term edgy kind of way. Just as those guys dug a hole through mountains to make the connection between New York to Chicago a few nanoseconds faster, they will go to any length to get the newest info on the market, as long as it is deemed to have a profitable edge in some time frame – i.e. the amount of time it will take a flood of competitors to do the same thing.
Just as there’s a kind of false myth that most of the web is porn, I’d like to perpetuate a new somewhat false myth that most data gathering and mining happens for the benefit of trading. And if that’s false now, let’s talk about it again in 100 years, when the market for celebrities is mature, and you can make money shorting a bad marriage.
This is a guest post by Tom Adams, who spent over 20 years in the securitization business and now works as an attorney and consultant and expert witness on MBS, CDO and securitization related issues. Jointly posted with Naked Capitalism.
Last week, the rules for the Volcker Rule – that provision of the Dodd-Frank Legislation that was intended to prevent (reduce?) proprietary trading by banks – were finalized. As a consequence, there has been a lot of chatter among financial types around the internet about what the rule does and doesn’t do and how it is good or bad, etc. Much of the conversation falls into the category of noise and distraction about unintended consequences, impacts on liquidity and broad views of regulatory effectiveness.
I call it noise, because in my view the real purpose of the Volcker Rule is to prevent another Citigroup bailout and therefore the measure of its effectiveness is whether the rule would accomplish this.
As you may recall, Citigroup required the largest bailout in government history in 2008, going back to the government well for more bailout funds several times. The source of Citigroup’s pain was almost entirely due to its massive investment in the ABS CDO machine. Of course, at the time of Citi’s bailout, there was a lot of noise about the potential financial system collapse and the risk posed by numerous other banks and institutions, so Citi as the main target of the TARP bailout, and ABS CDOs as the main cause of Citi’s pain, often gets lost in the folds of history.
The CDO market
In the years leading up to the financial crisis, Citi was an active underwriter for CDO’s backed by mortgage backed securities. Selling these securities was a lucrative business for Citi and other banks – far more lucrative than the selling of the underlying MBS. The hard part of selling was finding someone to take the highest risk piece (called the equity) of the CDO, but that problem got solved when Magnetar and other hedge funds came along with their ingenious shorting scheme.
The next hardest part was finding someone to take the risk of the very large senior class of the CDO, often known as the super-senior class (it was so named because it was enhanced at levels above that needed for a AAA rating).
For a time, Citi relied on a few overseas buyers and some insurance companies – like AIG and monoline bond insurers – to take on that risk. In addition, the MBS market became heavily reliant upon CDOs to buy up the lower rated bonds from MBS securitizations.
As the frenzy of MBS selling escalated, though, the number of parties willing to take on the super-seniors was unable to match the volume of CDOs being created (AIG, for instance, pulled back from insuring the bonds in 2006). Undeterred, Citi began to take down the super-senior bonds from the deals they were selling and holding them as “investments” which required very little capital because they were AAA.
This approach enabled Citi to continue the vey lucrative business of selling CDOs (to themselves!), which also enhanced their ability to create and sell MBS (to their CDOs), which enabled Citi to keep the music playing and the dance going, to paraphrase their then CEO Chuck Prince.
The CDO music stopped in July, 2007 with the rating agency downgrades of hundreds of the MBS bonds underlying the CDOs that had been created over the prior 24 months. MBS and CDO issuance effectively shut down the following month and remained shut throughout the crisis. The value of CDOs almost immediately began to plummet, leading to large mark-to-market losses for the parties that insured CDOs, such as Ambac and MBIA.
Citi managed to ignore the full extent of the declines in the value of the CDOs for nearly a year, until AIG ran into its troubles (itself a result of the mark-to-market declines in the values of its CDOs). When, in the fall of 2008, Citi finally fessed up to the problems it was facing, it turned out it was holding super-senior CDOs with a face value of about $150 billion which were now worth substantially less.
How much less? The market opinion at the time was probably around 10-20 cents on the dollar. Some of that value recovered in the last two years, but the bonds were considered fairly worthless for several years. Citi’s difficulty in determining exactly how little the CDOs were worth and how many they held was the primary reason for the repeated requests for additional bailout money.
Citi’s bailout is everyone’s bailout
The Citi bailout was a huge embarrassment for the company and the regulators that oversaw the company (including the Federal Reserve) for failing to prevent such a massive aid package. Some effort was made, at the time TARP money was distributed, to obscure Citi’s central role in the need for TARP and the panic the potential for a Citi failure was causing in the market and at the Treasury Department (see for example this story and the SIGTARP report). By any decent measure, Citi should have been broken up after this fiasco, but at least some effort should be made from a large bank ever needing such a bailout again, right?
Volcker’s Rule is Citi’s rule
So the test for whether the Volcker Rule is effective is fairly simple: will it prevent Citi, or some other large institution, from getting in this situation again? The rule is relatively complex and armies of lawyers are dissecting it for ways to arbitrage its words as we speak.
However, some evidence has emerged that the Volcker Rule would be effective in preventing another Citi fiasco. While the bulk of the rules don’t become effective until 2015, banks are required to move all “covered assets” from held to maturity to held for sale, which requires them to move the assets to a fair market valuation from… whatever they were using before.
Just this week, for example, Zions Bank announced that they were taking a substantial impairment because of that rule and moving a big chunk of CDOs (trust preferred securities, or TRUPS, were the underlying asset, although the determination would apparently apply to all CDOs) to fair market accounting from… whatever valuation they were using previously (not fair market?).
Here’s the key point. Had Citi been forced to do this as they acquired their CDOs, there is a decent chance they would have run into CDO capacity problems much sooner – they may not have been able to rely on the AAA ratings, they might have had to sell off some of the bonds before the market imploded, and they might have had to justify their valuations with actual data rather than self-serving models.
As a secondary consequence, they probably would have had to stop buying and originating mortgage loans and buying and selling MBS, because they wouldn’t have been able to help create CDOs to dump them into.
Given the size of Citi’s CDO portfolio, and the leverage that those CDOs had as it relates to underlying mortgage loans (one $1 billion CDO was backed by MBS from about $10 billion mortgages, $150 billion of CDOs would have been backed by MBS from about $1.5 trillion of mortgage loans, theoretically), if Citi had slowed their buying of CDOs, it might have had a substantial cooling effect on the mortgage market before the crisis hit.
This is a guest post by Marc Joffe, the principal consultant at Public Sector Credit Solutions, an organization that provides data and analysis related to sovereign and municipal securities. Previously, Joffe was a Senior Director at Moody’s Analytics.
As Cathy has argued, open source models can bring much needed transparency to scientific research, finance, education and other fields plagued by biased, self-serving analytics. Models often need large volumes of data, and if the model is to be run on an ongoing basis, regular data updates are required.
Unfortunately, many data sets are not ready to be loaded into your analytical tool of choice; they arrive in an unstructured form and must be organized into a consistent set of rows and columns. This cleaning process can be quite costly. Since open source modeling efforts are usually low dollar operations, the costs of data cleaning may prove to be prohibitive. Hence no open model – distortion and bias continue their reign.
Much data comes to us in the form of PDFs. Say, for example, you want to model student loan securitizations. You will be confronted with a large number of PDF servicing reports that look like this. A corporation or well funded research institution can purchase an expensive, enterprise-level ETL (Extract-Transform-Load) tool to migrate data from the PDFs into a database. But this is not much help to insurgent modelers who want to produce open source work.
Data journalists face a similar challenge. They often need to extract bulk data from PDFs to support their reporting. Examples include IRS Form 990s filed by non-profits and budgets issued by governments at all levels.
The data journalism community has responded to this challenge by developing software to harvest usable information from PDFs. Examples include Tabula, a tool written by Knight-Mozilla OpenNews Fellow Manuel Aristarán, extracts data from PDF tables in a form that can be readily imported to a spreadsheet – if the PDF was “printed” from a computer application. Introduced earlier this year, Tabula continues to evolve thanks to the volunteer efforts of Manuel, with help from OpenNews Fellow Mike Tigas and New York Times interactive developer Jeremy Merrill. Meanwhile, DocHive, a tool whose continuing development is being funded by a Knight Foundation grant, addresses PDFs that were created by scanning paper documents. DocHive is a project of Raleigh Public Record and is led by Charles and Edward Duncan.
These open source tools join a number of commercial offerings such as Able2Extract and ABBYY Fine Reader that extract data from PDFs. A more comprehensive list of open source and commercial resources is available here.
Unfortunately, the free and low cost tools available to modelers, data journalists and transparency advocates have limitations that hinder their ability to handle large scale tasks. If, like me, you want to submit hundreds of PDFs to a software tool, press “Go” and see large volumes of cleanly formatted data, you are out of luck.
It is for this reason that I am working with The Sunlight Foundation and other sponsors to stage the PDF Liberation Hackathon from January 17-19, 2014. We’ll have hack sites at Sunlight’s Washington DC office and at RallyPad in San Francisco. Developers can also join remotely because we will publish a number of clearly specified PDF extraction challenges before the hackathon.
Participants can work on one of the pre-specified challenges or choose their own PDF extraction projects. Ideally, hackathon teams will use (and hopefully improve upon) open source tools to meet the hacking challenges, but they will also be allowed to embed commercial tools into their projects as long as their licensing cost is less than $1000 and an unlimited trial is available.
Prizes of up to $500 will be awarded to winning entries. To receive a prize, a team must publish their source code on a GitHub public repository. To join the hackathon in DC or remotely, please sign up at Eventbrite; to hack with us in SF, please sign up via this Meetup. Please also complete our Google Form survey. Also, if anyone reading this is associated with an organization in New York or Chicago that would like to organize an additional hack space, please contact me.
The PDF Liberation Hackathon is going to be a great opportunity to advance the state of the art when it comes to harvesting data from public documents. I hope you can join us.
I’m looking forward to protesting in front of JP Morgan with my #OWS Alt Banking group this Wednesday at noon. The exact location is 270 Park Avenue, near 48th Street.
It’s part of a “Week of Action” being put together by a broad coalition of activist and labor groups here in New York. The overall theme of the week is to try to communicate to New Yorkers, in this time of transition from Bloomberg to de Blasio, that we can effect positive change in our city. The theme of the day on Wednesday, at least for us, is to “be in the know,” which makes it a bit more positive than other protests we’ve been part of.
I think this makes sense. There’s so much widespread distrust and hatred of the big banks at this point that I feel like Occupy’s role has gone from provoking people to be outraged to provoking people to be hopeful. Hopeful about the fact that things could be a whole lot better than this, if we work together.
Anyhoo, we spent yesterday planning the action, and made some signs. Here’s one based on an idea we borrowed from Alexis Goldstein from her recent twitter war with JPMorgan:
and here’s a sign we’ll hold up while playing a “rigged game” with props:
I also made a sign that referenced the London Whale and the risk model, but someone said we might need to give people a copy of our recent book, Occupy Finance, just to understand that sign. Sigh.
The facebook page is here, please share it with people who may be able to join us Wednesday!
I’m lucky to be working with a super fantastic python guy on this, and the details are under wraps, but let’s just say it’s exciting.
So I’m looking to showcase a few good models to start with, preferably in python, but the critical ingredient is that they’re open source. They don’t have to be great, because the point is to see their flaws and possible to improve them.
- For example, I put in a FOIA request a couple of days ago to get the current teacher value-added model from New York City.
- A friends of mine, Marc Joffe, has an open source municipal credit rating model. It’s not in python but I’m hopeful we can work with it anyway.
- I’m in search of an open source credit scoring model for individuals. Does anyone know of something like that?
- They don’t have to be creepy! How about a Nate Silver – style weather model?
- Or something that relies on open government data?
- Can we get the Reinhart-Rogoff model?
The idea here is to get the model, not necessarily the data (although even better if it can be attached to data and updated regularly). And once we get a model, we’d build interactives with the model (like this one), or at least the tools to do so, so other people could build them.
At its core, the point of open models is this: you don’t really know what a model does until you can interact with it. You don’t know if a model is robust unless you can fiddle with its parameters and check. And finally, you don’t know if a model is best possible unless you’ve let people try to improve it.
The first myth, and the one we spent the most time on, is the idea that people “deserve” the money they earn because it is an accurate measure of their “added value” to society.
There are two parts of this, or actually at least two parts.
First, there’s the idea that you can even dissect the meaning of one person’s value. And if you can, it’s likely a question of a marginal value: what does our society look like without Steve Jobs, and then with him, and what’s the difference between the two worlds? As soon as you say it, you realize that such a thought experiment is complicated, considering the extent to which Steve Jobs’ journey intersected with other people’s like Steve Wozniak and a huge crowd of Chinese workers.
If you think about it some more, you might conclude that the marginal value of a single person is impossible to actually measure, at least with any precision, and not just because of the counterfactual problem, i.e. the problem that we only have one universe and can’t run two parallel universes at the same time. It’s really because any one person succeeds or fails, or more generally contributes, within a context of an entire culture. Even Mozart wrote his symphonies within a cultural context. In another context he would have been a kid who hums to himself a lot.
Second, there’s the assumption that people who earn a lot of money are actually adding value at all. This isn’t clear, and you don’t need to refer to formally criminal acts to make that case (although of course there are plenty of rich people who have committed criminal acts).
In many examples of super rich people, they got that way through not paying for negative externalities like polluting the environment, or because they had control of the legal mechanisms to reap profits off of other peoples’ work. Not technically illegal, then, but also not exactly a fair measure of their added value.
Or, of course, if they worked in finance, they might have made money by keeping stuff incredibly complicated and opaque while providing liquidity to the credit markets. It’s not clear that such work has added any value to society, or if it has, whether it’s balanced the good with the bad.
Some observations about this myth that were brought up include:
- There’s a deep belief in “the markets” at work here which is rather cyclical. The market values you more than other people which is why you’re paid so well for whatever it is you do. Other people who have less to offer the market are get paid less. Anyone who doesn’t have a job doesn’t deserve a job since the market isn’t offering them a job, which must mean they are adding no value.
- There are exceptions where people add obvious value – caretakers of our children for example – but aren’t paid well. This is because of a different mechanism called supply and demand. For whatever reason supply and demand isn’t at work at high ends of the market.
- Or maybe it is and there’s really only one possible person who could do what Steve Jobs did. Personally I don’t buy it. And I chose Steve Jobs because so many people love that guy, but really he’s one of the best examples of someone who might have had a unique talent. Most rich people are generically good at their job and not all that unique.
- It’s mostly the people that benefit from the market system that believe in it. That kind of reminds me of the marshmallow study, or rather one of the many re-interpretations of the marshmallow study. See the latest one here.
- It’s patently difficult to believe in the market system if you consider a lack of equality of opportunity in this country due to extreme differences in school systems and the like. I’m about to start reading this book which explains this issue in depth.
- For other evidence, look at Pimco’s Bill Gross’s recent confessions about being born at the right time with easy access to credit.
- The unequal access of opportunities in this country is becoming increasingly entrenched, and as it does so the myth of the market giving us what we deserve is becoming increasingly difficult to swallow.