Tomorrow evening I’m meeting with the Columbia Data Science Society and talking to them – who as I understand it are mostly engineers - about “how to think like a data scientist”.
On October 11th I’ll be in D.C. sitting on a panel discussion organized by the Americans for Financial Reform. It’s part of a day-long event on the topic of transparency in financial regulation. The official announcement isn’t out yet but I’ll post it here as soon as I can. I’ll be giving my two cents on what mathematical tools can do and cannot do with respect to this stuff.
Finally, I’m going to Harvard on October 30th to give a talk in their Applied Statistics Workshop series. I haven’t figured out exactly what I’m talking about but it will be something nerdy and skeptical.
The idea is for the author of each chapter to come and lead a discussion about the subject in that chapter. We expect the feedback to improve the topic and be incorporated into the 2nd edition of the book.
Where: The book club will meet from 2-3pm on the Sundays mentioned below, before the regular Alternative Banking meeting, in Room 402 or 409 of the International Affairs Building of Columbia University at Amsterdam and 118th.
Here’s the schedule:
Occupy Finance Book Club Schedule
Yesterday was pretty amazing, in spite of the fact that we realized a page was missing from the book. The missing page, which was supposed to be between pages 38 and 39, is available here.
UPDATE: the online version of Occupy Finance is now complete.
Hey, but it’s still a great book, and we got lots of fantastic press. Here’s the list so far:
- FT Alphaville with Lisa Pollack
- NPR with Margot Adler
- Bloomberg with Matt Levine
- New York Times with William Alden
- Daily Kos with medicalquack
Thank you guys!
And readers, if you really want a copy of the book, send me email with your address. Our group meeting this Sunday will consist of an envelope-stuffing party. My email address is on my “About” page.
Hey, what are you doing for the 2nd anniversary of the occupation of Zuccotti Park?
I know what I’m doing, namely going down to the park and handing out hundreds of copies of my occupy group’s new book – now on scribd!!. Here’s a ridiculous gif of the pile of books that came from the printer yesterday with my kindergartner posing by it (you might need to click on it to see the animation!!):
I’m also planning a small speech at 10:15am,
which I’m still writing. I’ll post it here later. here it is:
Thank you for coming
Thank you for occupying
I am here today to announce a birth
The birth of a book
It’s called “Occupy Finance”
We wrote it
we are Alternative Banking
Who are we?
We are a working group of Occupy
we first met almost two years ago
we have been meeting ever since
we meet every Sunday afternoon
at Columbia University
our meetings are totally open
we want you to come
We discuss the financial system
we discuss financial regulation
we discuss how lobbyists destroy regulation
we discuss how Obama destroys regulation
we discuss what we can do to help
how we can make our opinions known
how we can make the system work for us
Last year we had a project
The 52 Shades of Greed
we came here to Zuccotti Park
we gave out hundreds of packs of cards
they explained the financial system
they called out the criminals
they called out the toxic ideas
and the toxic instruments
and the toxic institutions
that started this mess
This year we’ve come back
with another present to share
it’s a book we wrote
it’s a book for all of us
it explains how the financial system works
and how it doesn’t work
it explains how the system uses us
how the bankers scam us all
how the regulators fail to do their job
how the politicians have been bought
Why did we write this book?
we wrote it for you
and we wrote it for us
we wrote it for anyone
who wants to know
how to argue against
the side of greed
the side of corruption
the side of entitlement
let me tell you something
some people call us radicals
but listen up
when the top 1%
capture 95% of the income gains
since the so-called end
of the recession,
when more than half the country thinks
that we didn’t do enough
to put bankers in jail,
when the median household income
has gone down 7.3% since 2007,
when the actual employment rate
is 5% below 2007,
when the jobs that do exist are crappy
when we get paid with prepaid debit cards
that nickel and dime us all
then what we demand is not radical
it is only a system that works
we are asking for a just system
we are asking for a fair system
we are asking for an end to too-big-to-fail
we demand banks take less risk
with our money
and we are asking lawmakers
to stop banks
once and for all
from scamming people because they are poor
Please join us
we want you to come
you don’t need to be an expert
we started out as strangers
who wanted to know how things work
we have become friends
we have become allies
we have made something
out of our curiousity
and out of our hard work
and we are here today
to share that with you
and to ask you to join us
please join us
happy birthday to us!
So yesterday there I was with my Occupy group, we’d just gone over our plans for the second anniversary of Occupy Wall Street this coming Tuesday. We talked over releasing our book and the press conference in Zuccotti Park at 10:15am. We talked over the group’s web presence and how we had to improve it a bit before then, what flyers to hand out with the book, and how to get everyone copies of the book before then. In other words, logistics.
Then we turned to the content of the meeting, namely working on our op-ed focused on Larry Summers and why he shouldn’t be named the Fed Chair.
We’d brainstormed about it last week, and I put together a crappy first draft, and then another in our group had blown away my milquetoast logical argument with a couple of paragraphs of pure Occupy outrage. We were thinking about how to combine them, and we were also trying to decide whether to come up with a list of better candidates or just say “anyone would be better than Larry Summers.”
All of a sudden, someone in our group, who had been browsing the web in search for better candidates, suddenly interjects that “Larry Summers has withdrawn from consideration for the Fed job!”
That’s some good fucking karma.
And yes, it’s a pretty awesome moment when exactly what you’re working towards comes true like that, even if it’s only one thing on a very long list.
Here’s the next thing on the list: Too Big To Fail needs to end, people. Someone named Scott Cahoon sent me a video regarding that very topic last night which advertises his book called “Too Big Has Failed,” available on Amazon.
p.s. also, it’s been 5 years since the crisis, and not enough has changed.
Holy shit I’m just about bursting with pride to announce that my Occupy group’s book, Occupy Finance, is coming out Tuesday and is at the printer right now.
This is thanks in large part to all of you awesome people who sent contributions for the printing. You guys totally rock.
Our plan is to meet in Zuccotti Park Tuesday morning, September 17th, which is the 2nd anniversary of the occupation, give a wee press conference at 10am or so, and then hand out hundreds of copies of the book to whomever shows up.
Here’s the wrap-around cover to give you just a taste:
See you guys on Tuesday! The event is also on Facebook.
Yet another aspect of Gary Shteyngart’s dystopian fiction novel Super Sad True Love Story is coming true for reals this week.
Besides anticipating Occupy Wall Street, as well as Bloomberg’s sweep of Zuccotti Park (although getting it wrong on how utterly successful such sweeping would be), Shteyngart proposed the idea of instant, real-time and broadcast credit ratings.
Anyone walking around the streets of New York, as they’d pass a certain type of telephone pole – the kind that identifies you via your cell phone and communicates with data warehousing services and databases – would have their credit rating flashed onto a screen. If you went to a party, depending on how you impressed the other party go-ers, your score could plummet or rise in real time, and everyone would be able to keep track and treat you accordingly.
I mean, there were other things about the novel too, but as a data person these details certainly stuck with me since they are both extremely gross and utterly plausible.
And why do I say they are coming true now? I base my claim on two news stories I’ve been sent by my various blog readers recently.
[Aside: if you read my blog and find an awesome article that you want to send me, by all means do! My email address is available on my "About" page.]
First, coming via Suresh and Marcos, we learn that data broker Acxiom is letting people see their warehoused data. A few caveats, bien sûr:
- You get to see your own profile, here, starting in 2 days, but only your own.
- And actually, you only get to see some of your data. So they won’t tell you if you’re a suspected gambling addict, for example. It’s a curated view, and they want your help curating it more. You know, for your own good.
- And they’re doing it so that people have clarity on their business.
- Haha! Just kidding. They’re doing it because they’re trying to avoid regulations and they feel like this gesture of transparency might make people less suspicious of them.
- And they’re counting on people’s laziness. They’re allowing people to opt out, but of course the people who should opt out would likely never even know about that possibility.
- Just keep in mind that, as an individual, you won’t know what they really think they know about you, but as a corporation you can buy complete information about anyone who hasn’t opted out.
In any case those credit scores that Shteyngart talks about are already happening. The only issue is who gets flashed those numbers and when. Instead of the answers being “anyone walking down the street” and “when you walk by a pole” it’s “any corporation on the interweb” and “whenever you browse”.
After all, why would they give something away for free? Where’s the profit in showing the credit scores of anyone to everyone? Hmmmm….
That brings me to my second news story of the morning coming to me via Constantine, namely this TechCrunch story which explains how a startup called Fantex is planning to allow individuals to invest in celebrity athletes’ stocks. Yes, you too can own a tiny little piece of someone famous, for a price. From the article:
People can then buy shares of that player’s brand, like a stock, in the Fantex-consumer market. Presumably, if San Francisco 49ers tight end Vernon Davis has a monster year and looks like he’s going to get a bigger endorsement deal or a larger contract in a few years, his stock would rise and a fan could sell their Davis stock and cash out with a real, monetary profit. People would own tracking or targeted stocks in Fantex that would depend on the specific brand that they choose; these stocks would then rise and fall based on their own performance, not on the overall performance of Fantex.
Let’s put these two things together. I think it’s not too much of a stretch to acknowledge a reason for everyone to know everyone else’s credit score! Namely, we can can bet on each other’s futures!
I can’t think of any set-up more exhilarating to the community of hedge fund assholes than a huge, new open market – containing profit potentials for every single citizen of earth – where you get to make money when someone goes to the wrong college, or when someone enters into an unfortunate marriage and needs a divorce, or when someone gets predictably sick. An orgy in the exact center of tech and finance.
Are you with me peoples?!
I don’t know what your Labor Day plans are, but I’m getting ready my list of people to short in this spanking new market.
Don’t know about you, but for some reason I have a sinking feeling when it comes to the idea of Larry Summers. Word on the CNBC street is that he’s about to be named new Fed Chair, and I am living in a state of cognitive dissonance.
To distract myself, I’m going to try better to explain what I started to explain here, when I talked about the online peer-to-peer lending company Lending Club. Summers sits on the board of Lending Club, and from my perspective it’s a logical continuation of his career of deregulation and/or bypassing of vital regulation to enrich himself.
In this case, it’s a vehicle for bypassing the FTC’s Equal Credit Opportunities Rights. It’s not perfect, but it “prohibits credit discrimination on the basis of race, color, religion, national origin, sex, marital status, age, or because you get public assistance.” It forces credit scores to be relatively behavior based, like you see here. Let me contrast that to Lending Club.
Lending Club also uses mathematical models to score people who want to borrow money. These act as credit scores. But in this case, they use data like browsing history or anything they can grab about you on the web or from data warehousing companies like Acxiom (which I’ve written about here). From this Bloomberg article on Lending Club:
“What we’ve done is radically transform the way consumer lending operates,” Laplanche says in his speech. He says that LendingClub keeps staffing low by using algorithms to screen prospective borrowers for risk — rejecting 90 percent of them – - and has no physical branches like banks. “The savings can be passed on to more borrowers in terms of lower interest rates and investors in terms of attractive returns.”
I’d focus on the benefit for investors. Big money is now involved in this stuff. Turns out that bypassing credit score regulation is great for business, so of course.
For example, such models might look at your circle of friends on Facebook to see if you “run with the right crowd” before loaning you money. You can now blame your friends if you don’t get that loan! From this CNN article on the subject (hat tip David):
“It turns out humans are really good at knowing who is trustworthy and reliable in their community,” said Jeff Stewart, a co-founder and CEO of Lenddo. “What’s new is that we’re now able to measure through massive computing power.”
Moving along from taking out loans to getting jobs, there’s this description of how recruiters work online to perform digital background checks for potential employees. It’s a different set of laws this time that is subject to arbitrage but it’s exactly the same idea:
Non-discrimination laws prohibit employers from asking job applicants certain questions. They’re not supposed to ask about things like age, race, gender, disability, marital, and veteran status. (As you can imagine, sometimes a picture alone can reveal this privileged information. These safeguards against discrimination urge employers to simply not use this knowledge to make hiring decisions.) In addition to protecting people from systemic prejudice, these employment laws intend to shield us from capricious bias and whimsy. While casually snooping, however, a recruiter can’t unsee your Facebook rant on immigration amnesty, the same for your baby bump on Instagram. From profile pics and bios, blog posts and tweets, simple HR reconnaissance can glean tons of off-limits information.
Along with forcing recruiters to gaze with eyes wide shut, straddling legal liability and ignorance, invisible employment screens deny American workers the robust protections afforded by the FTC and the Fair Credit Reporting Act. The FCRA ensures that prospective employees are notified before their backgrounds and credit scores are verified. Employees are free to decline the checks, but employers are also free to deny further consideration unless a screening is allowed to take place. What’s important here is that employees must first give consent.
When a report reveals unsavory information about a candidate, and the employer chooses to take what’s called “adverse action,”—like deny a job offer—the employer is required to share the content of the background reports with the candidate. The applicant then has the right to explain or dispute inaccurate and incomplete aspects of the background check. Consent, disclosure, and recourse constitute a straightforward approach to employment screening.
Contrast this citizen-empowering logic with the casual Google search or to the informal, invisible social-media exam. As applicants, we don’t know if employers are looking, we’re not privy to what they see, and we have no way to appeal.
As legal scholars Daniel Solove and Chris Hoofnagle discuss, the amateur Google screens that are now a regular feature of work-life go largely unnoticed. Applicants are simply not called back. And they’ll never know the real reason.
I think the silent failure is the scariest part for me – people who don’t get jobs won’t know why.
Similarly, people denied loans from Lending Club by a secret algorithm don’t know why either. Maybe it’s because I made friends with the wrong person on Facebook? Maybe I should just go ahead and stop being friends with anyone who might put my electronic credit score at risk?
Of course this rant is predicated on the assumption that we think anti-discrimination laws are a good thing. In an ideal world, of course, we wouldn’t need them. But that’s not where we live.
I’m helping organize a protest against HSBC with my Alternative Banking group. We’re going to be joined by Everett Stern, an HSBC whistleblower. You can learn more about that guy by reading Matt Taibbi’s Rolling Stones article on him.
Here’s the press release, I hope I see you there!
Members of the Alt Banking Occupy group have been hard at work recently writing a book which we call Occupy Finance. Our blog for the book is here. It’s a work in progress but we’re planning to give away 1,000 copies of the book on September 17th, the 2nd anniversary of the Occupation of Zuccotti Park.
I want to tell you more about our book, which we’re writing by committee, but I did want to mention that in order to get the first 1,000 copies printed by September 17th, we’ll need altogether $2,500, and so far we’ve collected $2,150 from the various contributors, editors, and their friends. So we need to collect $350 at this point. If we get more then we’ll print more.
If you’d like to help us towards the last $350, we’d appreciate it – and I’ll even send you a copy of the book afterwards. But please don’t send anything you don’t want to give away, I can’t promise you some kind of formal proof of your contribution for tax purposes. This is Occupy after all, we suck at money. Consider this a crappy version of Kickstarter.
Anyway if you want to help out, send me a personal email to arrange it: cathy.oneil at gmail. I’ll basically just tell you to send me a personal check, since I’m the one fronting the money.
Audience and Mission
The mission of the book, like the mission of the Alt Banking group, is to explain the financial system and its dysfunction in plain English and to offer suggestions for how to think about it and what we can do to improve it.
The audience for this book is the 99% who are Occupy-friendly or at least Occupy-inquisitive. Specifically, we want people who know there’s something wrong, but don’t have the background to articulate what it is, to have a reference to help them define their issues. We want to give them ammunition at the water cooler.
What’s in the book?
After a stirring introduction, the book is divided into three basic parts: The Real Life Impact of Financialization, How We Got Here, and Things to do. I’ve got links below.
Keep in mind things are still in flux and will be changed, sometimes radically, before the final printing. In particular we’re actually using DropBox for most of our edits so the links below aren’t final versions (but will be eventually). Even so, the content below will give you a good idea of what we have in mind, and if you have comments or suggestions, please do tell us, thanks!
Our table of contents is as follows, and the available chapters have associated links:
The Real Life Impact of Financialization
- Heads They Win, Tails We Lose: Real Life Impact of Financialization on the 99%
- The bailout: it didn’t work, it’s still going on, and it’s making things worse
How We Got Here
- What Banks Do
- Impact of Deregulation
- The top ten financial outrages
- The muni bond industry and the 99%
Things to Do
Update: I’ve got $
225 $301 pledged so far! You people rock!!
So here’s something potential Fed Chair Larry Summers is involved with, a company called Lending Club, which creates a money lending system that cuts out the middle man banks.
Specifically, people looking for money come to the site and tell their stories, and try to get loans. The investors invest in whichever loans look good to them, for however much money they want. For a perspective on the risks and rewards of this kind of peer-to-peer lending operation, look at this Wall Street Journal article which explains things strictly from the investor’s point of view.
A few red flags go up for me as I learn more about Lending Club.
First, from this NYTimes article, “The company [Lending Club] itself is not regulated as a bank. But it has teamed up with a bank in Utah, one of the states that allows banks to charge high interest rates, and that bank is overseen by state regulators and the Federal Deposit Insurance Corporation.”
I’m not sure how the FDIC is involved exactly, but the Utah connection is good for something, namely allowing high interest rates. According to the same article, 37% of loans are for APR’s of between 19% and 29%.
Next, Summers is referred to in that article as being super concerned about the ability for the consumers to pay back the loans. But I wonder how someone is supposed to be both desperate enough to go for a 25% APR loan and also able to pay back the money. This sounds like loan sharking to me.
Probably what bothers me most though is that Lending Club, in addition to offering credit scores and income when they have that information, also scores people asking for loans with a proprietary model which is, as you guessed it, unregulated. Specifically, if it’s anything like ZestFinance, could use signals more correlated to being uneducated and/or poor than to the willingness or ability to pay back loans.
By the way, I’m not saying this concept is bad for everyone- there are probably winners on the side of the loanees, and it might be possible that they get a loan they otherwise couldn’t get or they get better terms than otherwise or a more bespoke contract than otherwise. I’m more worried about the idea of this becoming the new normal of how money changes hands and how that would affect people already squeezed out of the system.
I’d love your thoughts.
I want to bring up two quick topics this morning I’ve been mulling over lately which are both related to this recent post by Economist Rajiv Sethi from Barnard (h/t Suresh Naidu), who happened to be my assigned faculty mentor when I was an assistant prof there. I have mostly questions and few answers right now.
In his post, Sethi talks about former computer nerd for Goldman Sachs Sergey Aleynikov and his trial, which was chronicled by Michael Lewis recently. See also this related interview with Lewis, h/t Chris Wiggins.
I haven’t read Lewis’s piece yet, only his interview and Sethi’s reaction. But I can tell it’ll be juicy and fun, as Lewis usually is. He’s got a way with words and he’s bloodthirsty, always an entertaining combination.
So, the two topics.
First off, let’s talk a bit about high frequency trading, or HFT. My first two questions are, who does HFT benefit and what does HFT cost? For both of these, there’s the easy answer and then there’s the hard answer.
Easy answer for HFT benefitting someone: primarily the people who make loads of money off of it, including the hardware industry and the people who get paid to drill through mountains with cables to make connections between Chicago and New York faster.
Secondarily, market participants whose fees have been lowered because of the tight market-making brought about by HFT, although that savings may be partially undone by the way HFT’ers operate to pick off “dumb money” participants. After all, you say market making, I say arbing. Sorting out the winners, especially when you consider times of “extreme market conditions”, is where it gets hard.
Easy answer for the costs of HFT is for the companies that invest in IT and infrastructure and people to do the work, although to be sure they wouldn’t be willing to make that investment if they didn’t expect it to pay off.
A harder and more complete answer would involve how much risk we take on as a society when we build black boxes that we don’t understand and let them collide with each other with our money, as well as possibly a guess at what those people and resources now doing HFT might be doing otherwise.
And that brings me to my second topic, namely the interaction between the open source community and the finance community, but mostly the HFTers.
Sethi said it
well (Cathy: see bottom of this for an update) this way in his post:
Aleynikov relied routinely on open-source code, which he modified and improved to meet the needs of the company. It is customary, if
not mandatory(Cathy: see bottom of this for an update) for these improvements to be released back into the public domain for use by others. But his attempts to do so were blocked:
Serge quickly discovered, to his surprise, that Goldman had a one-way relationship with open source. They took huge amounts of free software off the Web, but they did not return it after he had modified it, even when his modifications were very slight and of general rather than financial use. “Once I took some open-source components, repackaged them to come up with a component that was not even used at Goldman Sachs,” he says. “It was basically a way to make two computers look like one, so if one went down the other could jump in and perform the task.” He described the pleasure of his innovation this way: “It created something out of chaos. When you create something out of chaos, essentially, you reduce the entropy in the world.” He went to his boss, a fellow named Adam Schlesinger, and asked if he could release it back into open source, as was his inclination. “He said it was now Goldman’s property,” recalls Serge. “He was quite tense. When I mentioned it, it was very close to bonus time. And he didn’t want any disturbances.”
This resonates with my experience at D.E. Shaw. We used lots of python stuff, and as a community were working at the edges of its capabilities (not me, I didn’t do fancy HFT stuff, my models worked at a much longer time frame of at least a few hours between trades).
The urge to give back to the OS community was largely thwarted, when it came up at all, because there was a fear, or at least an argument, that somehow our competition would use it against us, to eliminate our edge, even if it was an invention or tool completely sanitized from the actual financial algorithm at hand.
A few caveats: First, I do think that stuff, i.e. python technology and the like eventually gets out to the open source domain even if people are consistently thwarting it. But it’s incredibly slow compared to what you might expect.
Second, It might be the case that python developers working outside of finance are actually much better at developing good tools for python, especially if they have some interaction with finance but don’t work inside. I’m guessing this because, as a modeler, you have a very selfish outlook and only want to develop tools for your particular situation. In other words, you might have some really weird looking tools if you did see a bunch coming from finance.
Finally, I think I should mention that quite a few people I knew at D.E. Shaw have now left and are actively contributing to the open source community now. So it’s a lagged contribution but a contribution nonetheless, which is nice to see.
Update: from my Facebook page, a discussion of the “mandatoriness” of giving back to the OS community from my brother Eugene O’Neil, super nerd, and friend William Stein, other super nerd:
Eugene O’Neil: the GPL says that if you give someone a binary executable compiled with GPL source code, you also have to provide them free access to all the source code used to generate that binary, under the terms of the GPL. This makes the commercial sale of GPL binaries without source code illegal. However, if you DON’T give anyone outside your organization a binary, you are not legally required to give them the modified source code for the binary you didn’t give them. That being said, any company policy that tries to explicitly PROHIBIT employees from redistributing modified GPL code is in a legal gray area: the loophole works best if you completely trust everyone who has the modified code to simply not want to distribute it.
William Stein: Eugene — You are absolutely right. The “mandatory” part of the quote: “It is customary, if not mandatory, for these improvements to be released back into the public domain for use by others.” from Cathy’s article is misleading. I frequently get asked about this sort of thing (because of people using Sage (http://sagemath.org) for web backends, trading, etc.). I’m not aware of any popular open source license that make it mandatory to give back changes if you use a project internally in an organization (let alone the GPL, which definitely doesn’t). The closest is AGPL, which involves external use for a website. Cathy — you might consider changing “Sethi said it well…”, since I think his quote is misleading at best. I’m personally aware of quite a few people that do use Sage right now who wouldn’t otherwise if Sethi’s statement were correct.
Here is an idea I’ve been hearing floating around the big data/ tech community: the idea of having algorithms embedded into law.
The argument for is pretty convincing on its face: Google has gotten its algorithms to work better and better over time by optimizing correctly and using tons of data. To some extent we can think of their business strategies and rules as a kind of “internal regulation”. So why don’t we take a page out of that book and improve our laws and specifically our regulations with constant feedback loops and big data?
No algos in law
There are some concerns I have right off the bat about this concept, putting aside the hugely self-serving dimension of it.
First of all, we would be adding opacity – of the mathematical modeling kind – to an already opaque system of law. It’s hard enough to read the legalese in a credit card contract without there also being a black box algorithm to make it impossible.
Second of all, whereas the incentives in Google are often aligned with the algorithm “working better”, whatever that means in any given case, the incentives of the people who write laws often aren’t.
So, for example, financial regulation is largely written by lobbyists. If you gave them a new tool, that of adding black box algorithms, then you could be sure they would use it to further obfuscate what is already a hopelessly complicated set of rules, and on top of it they’d be sure to measure the wrong thing and optimize to something random that would not interfere with their main goal of making big bets.
Right now lobbyists are used so heavily in part because they understand the complexity of their industries more than the lawmakers themselves. In other words, they actually add value in a certain way (besides in the monetary way). Adding black boxes would emphasize this asymmetric information problem, which is a terrible idea.
Third, I’m worried about the “black box” part of algorithms. There’s a strange assumption among modelers that you have to make algorithms secret or else people will game them. But as I’ve said before, if people can game your model, that just means your model sucks, and specifically that your proxies are not truly behavior-based.
So if it pertains to a law against shoplifting, say, you can’t have an embedded model which uses the proxy of “looking furtive and having bulges in your clothes.” You actually need to have proof that someone stole something.
If you think about that example for a moment, it’s absolutely not appropriate to use poor proxies in law, nor is it appropriate to have black boxes at all – we should all know what our laws are. This is true for regulation as well, since it’s after all still law which affects how people are expected to behave.
And by the way, what counts as a black box is to some extent in the eye of the beholder. It wouldn’t be enough to have the source code available, since that’s only accessible to a very small subset of the population.
Instead, anyone who is under the expectation of following a law should also be able to read and understand the law. That’s why the CFPB is trying to make credit card contracts be written in Plain English. Similarly, regulation law should be written in a way so that the employees of the regulator in question can understand it, and that means you shouldn’t have to have a Ph.D. in a quantitative field and know python.
Algos as tools
Here’s where algorithms may help, although it is still tricky: not in the law itself but in the implementation of the law. So it makes sense that the SEC has algorithms trying to catch insider trading – in fact it’s probably the only way for them to attempt to catch the bad guys. For that matter they should have many more algorithms to catch other kinds of bad guys, for example to catch people with suspicious accounting or consistently optimistic ratings.
In this case proxies are reasonable, but on the other hand it doesn’t translate into law but rather into a ranking of workflow for the people at the regulatory agency. In other words the SEC should use algorithms to decide which cases to pursue and on what timeframe.
Even so, there are plenty of reasons to worry. One could view the “Stop & Frisk” strategy in New York as following an algorithm as well, namely to stop young men in high-crime areas that have “furtive motions”. This algorithm happens to single out many innocent black and latino men.
Similarly, some of the highly touted New York City open data projects amount to figuring out that if you focus on looking for building code violations in high-crime areas, then you get a better hit rate. Again, the consequence of using the algorithm is that poor people are targeted at a higher rate for all sorts of crimes (key quote from the article: “causation is for other people”).
Think about this asymptotically: if you live in a nice neighborhood, the limited police force and inspection agencies never check you out since their algorithms have decided the probability of bad stuff happening is too low to bother. If, on the other hand, you are poor and live in a high-crime area, you get checked out daily by various inspectors, who bust you for whatever.
Said this way, it kind of makes sense that white kids smoke pot at the same rate as black kids but are almost never busted for it.
There are ways to partly combat this problem, as I’ve described before, by using randomization.
It seems to me that we can’t have algorithms directly embedded in laws, because of the highly opaque nature of them together with commonly misaligned incentives. They might be useful as tools for regulators, but the regulators who choose to use internal algorithms need to carefully check that their algorithms don’t have unreasonable and biased consequences, which is really hard.
I have time for one thought: a bunch of people have chatted me up recently with the theory that Larry Summers is being put in the running for the Fed Chair alongside Janet Yellen just so that, when Yellen gets the call, we can all breathe a sigh of relief it didn’t go to Summers.
In other words, it’s a wholly political ploy so the Obama can look like a hero for women everywhere when he chooses Yellen, and so that we can all conclude that at least Obama’s learned this one lesson with regards to dealing with the ongoing financial crisis: Summers isn’t the solution.
Depending on my mood I sometimes buy into this theory, but obviously I’m still worried.
I’ve blogged before about how I find it outrageous that the credit scoring models are proprietary, considering the impact they have on so many lives.
The argument given for keeping them secret is that otherwise people would game the models, but that really doesn’t make sense.
After all, the models that the big banks have to deal with through regulation aren’t secret, and they game those models all the time. It’s one of the main functions of the banks, in fact, to figure out how to game the models. So either we don’t mind gaming or we don’t hold up our banks to the same standards as our citizens.
Plus, let’s say the models were open and people started gaming the credit score models – what would that look like? A bunch of people paying their electricity bill on time?
Let’s face it: the real reason the models are secret is that the companies who set them up make more money that way, pretending to have some kind of secret sauce. What they really have, of course, is a pretty simple model and access to an amazing network of up-to-date personal financial data, as well as lots of clients.
Their fear is that, if their model gets out, anyone could start a credit scoring agency, but actually it wouldn’t be so easy – if I wanted to do it, I’d have to get all that personal data on everyone. In fact, if I could get all that personal data on everyone, including the historical data, I could easily build a credit scoring model.
So anyhoo, it’s all about money, that and the fact that we’re living under the assumption that it’s appropriate for credit scoring companies to wield all this power over people’s lives, including their love lives.
It’s like we have a secondary system of secret laws where we don’t actually get to see the rules, nor do we get to point out mistakes or reasonably refute them. And if you’re thinking “free credit report,” let’s be clear that that only tells you what data goes in to the model, it doesn’t tell you how it’s used.
As it turns out, though, it’s now more than like a secondary system of laws – it’s become embedded in our actual laws. Somehow the proprietary credit scoring company Equifax is now explicitly part of our healthcare laws. From this New York Times article (hat tip Matt Stoller):
Federal officials said they would rely on Equifax — a company widely used by mortgage lenders, social service agencies and others — to verify income and employment and could extend the initial 12-month contract, bringing its potential value to $329.4 million over five years.
Contract documents show that Equifax must provide income information “in real time,” usually within a second of receiving a query from the federal government. Equifax says much of its information comes from data that is provided by employers and updated each payroll period.
Under the contract, Equifax can use sources like credit card applications but must develop a plan to indicate the accuracy of data and to reduce the risk of fraud.
Thanks Equifax, I guess we’ll just trust you on all of this.
I wrote a post yesterday to discuss the fact that, as we’ve seen in Detroit and as we’ll soon see across the country, the math isn’t working out on pensions. One of my commenters responded, saying I was falling for a “very right wing attack on defined benefit pensions.”
I think it’s a mistake to think like that. If people on the left refuse to discuss reality, then who owns reality? And moreover, who will act and towards what end?
Here’s what I anticipate: just as “bankruptcy” in the realm of airlines has come to mean “a short period wherein we toss our promises to retired workers and then come back to life as a company”, I’m afraid that Detroit may signal the emergence of a new legal device for cities to do the same thing, especially the tossing out of promises to retired workers part. A kind of coordinated bankruptcy if you will.
It comes down to the following questions. For whom do laws work? Who can trust that, when they enter a legal obligation, it will be honored?
From Trayvon Martin to the people who have been illegally foreclosed on, we’ve seen the answer to that.
And then we might ask, for whom are laws written or exceptions made? And the answer to that might well be for banks, in times of crisis of their own doing, and so they can get their bonuses.
I’m not a huge fan of the original bailouts, because it ignored the social and legal contracts in the opposite way, that failures should fail and people who are criminals should go to jail. It didn’t seem fair then, and it still doesn’t now, as JP Morgan posts record $6.4 billion profits in the same quarter that it’s trying to settle a $500 million market manipulation charge.
It’s all very well to rest our arguments on the sanctity of the contract, but if you look around the edges you’ll see whose contracts get ripped up because of fraudulent accounting, and whose bonuses get bigger.
And it brings up the following question: if we bailed out the banks, why not the people of Detroit?
I wrote a post three months ago talking about how we don’t need better models but we need to stop lying with our models. My first example was municipal debt and how various towns and cities are in deep debt partly because their accounting for future pension obligations allows them to be overly optimistic about their investments and underfund their pension pots.
This has never been more true than it is right now, and as this New York Times Dealbook article explains, was a major factor in Detroit’s bankruptcy filing this past week. But don’t make any mistake: even in places where they don’t end up declaring bankruptcy, something is going to shake out because of these broken models, and it isn’t going to be extra money for retired civil servants.
It all comes down to wanting to avoid putting required money away and hiring quants (in this case actuaries) to make that seem like it’s mathematically acceptable. It’s a form of mathematical control fraud. From the article:
When a lender calculates the value of a mortgage, or a trader sets the price of a bond, each looks at the payments scheduled in the future and translates them into today’s dollars, using a commonplace calculation called discounting. By extension, it might seem that an actuary calculating a city’s pension obligations would look at the scheduled future payments to retirees and discount them to today’s dollars.
But that is not what happens. To calculate a city’s pension liabilities, an actuary instead projects all the contributions the city will probably have to make to the pension fund over time. Many assumptions go into this projection, including an assumption that returns on the investments made by the pension fund will cover most of the plan’s costs. The greater the average annual investment returns, the less the city will presumably have to contribute. Pension plan trustees set the rate of return, usually between 7 percent and 8 percent.
In addition, actuaries “smooth” the numbers, to keep big swings in the financial markets from making the pension contributions gyrate year to year. These methods, actuarial watchdogs say, build a strong bias into the numbers. Not only can they make unsustainable pension plans look fine, they say, but they distort the all-important instructions actuaries give their clients every year on how much money to set aside to pay all benefits in the future.
One caveat: if the pensions have actually been making between 7 percent and 8 percent on their investments every year then all is perhaps well. But considering that they typically invest in bonds, not stocks – which is a good thing – we’re likely seeing much smaller returns than that, which means their yearly contributions to the local pension plans are in dire straits.
What’s super interesting about this article is that it goes into the action on the ground inside the Actuary community, since their reputations are at stake in this battle:
A few years ago, with the debate still raging and cities staggering through the recession, one top professional body, the Society of Actuaries, gathered expert opinion and realized that public pension plans had come to pose the single largest reputational risk to the profession. A Public Plans Reputational Risk Task Force was convened. It held some meetings, but last year, the matter was shifted to a new body, something called the Blue Ribbon Panel, which was composed not of actuaries but public policy figures from a number of disciplines. Panelists include Richard Ravitch, a former lieutenant governor of New York; Bradley Belt, a former executive director of the Pension Benefit Guaranty Corporation; and Robert North, the actuary who shepherds New York City’s five big public pension plans.
I’m not sure what happened here, but it seems like a bunch of people in a profession, the actuaries, got worried that they were being used by politicians, and decided to investigate, but then that initiative got somehow replaced by a bunch of politicians. I’d love to talk to someone on the inside about this.
A few months ago, at the end of January, I wrote a post about Bill Gates naive views on the objectivity of data. One of the commenters, “CitizensArrest,” asked me to take a look at a related essay written by Susan Webber entitled “Management’s Great Addiction: It’s time we recognized that we just can’t measure everything.”
Webber’s essay is really excellent, not to mention impressively prescient considering it was published in 2006, before the credit crisis. The format of the essay is simple: it brings up and explains various dangers in the context of measurement and modeling of business data, and calls for finding a space in business for skepticism. What an idea! Imagine if that had actually happened in finance when it should have back in 2006.
Please go read her essay, it’s short.
Recently, when O’Reilly asked me to write an essay, I thought back to this short piece and decided to use it as a template for explaining why I think there’s a just-as-desperate need for skepticism in 2013 here in the big data world as there was back then in finance.
Whereas most of Webber’s essay talks about people blindly accepting numbers as true, objective, precise, and important, and the related tragic consequences, I’ve added a small wrinkle to this discussion. Namely, I also devote concern over the people who underestimate the power of data.
Most of this disregard for unintended consequences is blithe and unintentional (and some of it isn’t), but even so it can be hugely damaging, especially to the individuals being modeled: think foreclosed homes due to crappy housing-related models in the past, and think creepy models and the death spiral of modeling for the present and future.
Anyhoo, I’m actively writing it now, and it’ll be coming out soon. Stay tuned!
I’ve enjoyed reading Anat Admati and Martin Hellwig’s recent book, The Bankers’ New Clothes, which explains a ton of things extremely well, including:
- Differentiating between what’s “good for banks” (i.e. bankers) versus what’s good for the public, and how, through unnecessary complexity and shittons of lobbying money, the “good for bankers” case is made much more often and much more vehemently,
- that, when there’s a guaranteed backstop for a loan, the person taking out the loan has incentive to take on more risk, and
- that there are two different definitions of “big returns” depending on the context: one means big in absolute value (where -30% is bigger than -10%), the other mean big as in more positive (where -10% is bigger than -30%). Believe it or not, this ambiguity could be (at least metaphorically) taken as a cause of confusion when bankers talk to the public, in the following sense. Namely, when the expected return on an investment is, say, 3%, it makes sense for bankers to lever up their bets so they get “bigger returns” in the first sense, especially since there’s essentially no down side for them (a -30% return doesn’t affect them personally, a 30% return means a huge bonus). From the perspective of the public, they’d like to see the banks go for the “bigger return” in the second sense, so avoid the -30% scenario altogether, via restrained risk-taking.
Admati and Hellwig’s suggestion is to raise capital requirements to much higher levels than we currently have.
Here’s the thing though, and it’s really a question for you readers. How do derivatives show up on the balance sheet exactly, and what prevents me from building a derivative that avoids adding to my capital requirement but which adds risk to my portfolio?
I’ve been getting a lot of different information from people about whether this is possible, or will be possible once Basel III is implemented, but I haven’t reached anyone yet who is actually expert enough to make a definitive claim one way or the other.
It’s one thing if you’re talking about government interest rate swaps, but how do CDS’s, for example, get treated in terms of capital requirements? Is there an implicit probability of default used for accounting purposes? In that case, since such instruments are famously incredibly fat-tailed (i.e. the probability of default looks miniscule until it doesn’t), wouldn’t that encourage everyone to invest extremely heavily in instruments that don’t move their capital ratios much but take on outrageous risks? The devil’s in the detail here.
If this article from yesterday’s New York Times doesn’t make you want to join Occupy, then nothing will.
It’s about how, if you work at a truly crappy job like Walmart or McDonalds, they’ll pay you with a pre-paid card that charges you for absolutely everything, including checking your balance or taking your money, and will even charge you for not using the card. Because we aren’t nickeling and diming these people enough.
The companies doing this stuff say they’re “making things convenient for the workers,” but of course they’re really paying off the employers, sometimes explicitly:
In the case of the New York City Housing Authority, it stands to receive a dollar for every employee it signs up to Citibank’s payroll cards, according to a contract reviewed by The New York Times.
Thanks for the convenience, payroll card banks!
One thing that makes me extra crazy about this article is how McDonalds uses its franchise system to keep its hands clean:
For Natalie Gunshannon, 27, another McDonald’s worker, the owners of the franchise that she worked for in Dallas, Pa., she says, refused to deposit her pay directly into her checking account at a local credit union, which lets its customers use its A.T.M.’s free. Instead, Ms. Gunshannon said, she was forced to use a payroll card issued by JPMorgan Chase. She has since quit her job at the drive-through window and is suing the franchise owners.
“I know I deserve to get fairly paid for my work,” she said.
The franchise owners, Albert and Carol Mueller, said in a statement that they comply with all employment, pay and work laws, and try to provide a positive experience for employees. McDonald’s itself, noting that it is not named in the suit, says it lets franchisees determine employment and pay policies.
I actually heard about this newish scheme against the poor when I attended the CFPB Town Hall more than a year ago and wrote about it here. Actually that’s where I heard people complain about Walmart doing this but also court-appointed child support as well.
Just to be clear, these fees are illegal in the context of credit cards, but financial regulation has not touched payroll cards yet. Yet another way that the poor are financialized, which is to say they’re physically and psychologically separated from their money. Get on this, CFPB!
Update: an excellent article about this issue was written by Sarah Jaffe a couple of weeks ago (hat tip Suresh Naidu). It ends with an awesome quote by Stephen Lerner: “No scam is too small or too big for the wizards of finance.”