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Archive for October, 2011

#OWS Alternative Banking update

I’m excited about the meeting from yesterday and I’m trying to help coordinate next steps. As before, the caliber of the people and the conversation was inspiring – people from all over the place, with so many different background and perspectives. Really exciting! At the same time, we were left with a bunch of open questions and issues; we could really use your help!

The group was quite large, on the order of 55 or 60 people, and after some deliberation we split into two groups: Carne’s group went to the other side of the room to discuss a true alternative banking system, and quite a few of us stayed on the first side of the room to discuss problems with our current system and incremental (but not minor) changes to improve it.

We discussed, (not in this order) how the financial system can be divided into four parts, according to FogOfWar:

  1. Traditional Banking: taking deposits, checking accounts, CDs, making loans/mortgages, credit cards, debit cards, banks, Thrifts, Credit Unions, Payday Lenders
  2. Investment Funds: collecting money from investors and making investment in the capital markets: 401(k), 403(b), IRAs, pension funds, mutual funds, index funds, hedge funds (also money market funds with a big asterisk)
  3. Investment Banking: traditionally two categories: I-banking (giving advice to companies on raising money in the capital markets, M&A, etc), and broker/dealer activities (making trades on behalf of clients and market making) including derivatives
  4. Insurance: pooling risks amongst large statistical pools to spread large losses into smaller, manageable premiums. Home insurance, life insurance, car insurance, etc.

We also talked about the power grid, how the capital markets and the players in the capital markets control the small businesses which leads to what we see today, with people feeling disempowered from their own money and their own business. We talked about the shadow banking system, politics and the power of lobbyists, and about how we might be able to effect change on the state level by trying to influence where pensions are being invested. We also heard from a fantastic woman who helped form the Dodd-Frank bill and is an expert on the FDIC and various other regulators and understands where their vested interests lie (this line of thought makes me want to write a post on an idea my friend has of paying SEC lawyers on commission, in reaction to the Citigroup – SEC debacle).

[We will write the minutes of the meeting soon, hopefully; the above is just my recollection. Please comment if I've missed something.]

It was all very stimulating, and made me want to draw a bunch of visuals to help with the (very large) educational background required to really tackle these problems. Visuals like this or this would also help me prepare for my upcoming Open Forum this Friday.

At the end, Carne invited us to form a separate group from Alternative Banking, which makes sense as we are on the one hand quite large for his office and on the other hand interested in improving the current system more than a completely alternative one. That leaves us with a bunch of things to do though:

  1. Formally create a new working group through #OWS
  2. Choose a name
  3. Choose a representative to go to the #OWS meetings and explain our activities
  4. Find a place to meet
  5. Find a way to communicate
There may be more! Please comment if you have suggestions for solving these problems, or if you want to join the group; I will definitely be creating an email list at the very least so I can announce the next meeting once we figure this stuff out.
Categories: #OWS, finance, FogOfWar

#OWS Alternative Banking meeting today

I’m excited to go to the second meeting today of the Alternative Banking working group, whose web page is still a work in progress. We’re meeting at 3pm at Carne Ross’s office.

Dial-in instructions: 

At 3 PM Eastern Standard Time (in the US), please dial: (530) 881-1000
When you are asked to enter the conference code, enter this: 451166
Then hit the pound key (#).

Last week we set up an ambitious agenda for ourselves. After passing the “Move your Money” campaign to Alternative Economy (because they are already on it), we decided to focus on two things:

1) What are the legislative steps we think need to be made to fix the current system? To this end the discussion has been centered on commenting on the Volcker Rule, which the public can do until January 13th.

2) Reimagining our current financial system – what would it look like where our deposits, our retirements, and small businesses weren’t being held hostage by a few powerful banks and corporations? Much of this discussion will probably be centered on how things used to work in this country and how things work or worked in other countries.

In the meantime, I’ll share some great links with you:

  1. Credit unions are seeing a surge in new members.
  2. When you hear “recapitalization of banks,” what does that mean?
  3. How do CDS’s work, and what is going to happen in Greece if their 50% haircut counts as a default?
  4. My hero judge Jed Rakoff tells the SEC to do their job.
  5. More background on Jed Rakoff.
  6. A former derivatives guys explains where Wall St. went wrong and comments on #OWS.
  7. Trick-or-treating tips for #OWS sympathizers
Categories: #OWS, finance, news

Data Science and Engineering at Columbia?

Yesterday Columbia announced a proposal to build an Institutes for Data Sciences and Engineering a few blocks north of where I live. It’s part of the Bloomberg Administration’s call for proposals to add more engineering and entrepreneurship in New York City, and he’s said the city is willing to chip in up to 100 million dollars for a good plan. Columbia’s plan calls for having five centers within the institute:

  1. New Media Center (journalism, advertising, social media stuff)
  2. Smart Cities Center (urban green infrastructure including traffic pattern stuff)
  3. Health Analytics Center (mining electronic health records)
  4. Cybersecurity Center (keeping data secure and private)
  5. Financial Analytics Center (mining financial data)

A few comments. Currently the data involved in media 1) and finance 5) costs real money, although I guess Bloomerg can help Columbia get a good deal on Bloomberg data. On the other hand, urban traffic data 2) and health data 3) should be pretty accessible to academic researchers in New York.

There’s a reason that 1) and 5) cost money: they make money. The security center is kind of in the middle, since you can try to make any data secure, you don’t need to particularly pay for it, but on the other hand if you can find a good security system then people will pay for it.

On the other hand, even though it’s a great idea to understand urban infrastructure and health data, it’s not particularly profitable (not to say it doesn’t save alot of money potentially, but it’s hard to monetize the concept of saving money, especially if it’s the government’s or the city’s money).

So the overall cost structure of the proposed Institute would probably work like this: incubator companies from 1) and 5) and maybe 4) fund the research going on in (themselves and) 2) and 3). This is actually a pretty good system, because we really do need some serious health analytics research on an enormous scale, and it needs to be done ethically.

Speaking of ethics, I hope they formalize and follow The Modeler’s Hippocratic Oath. In fact, if they end up building this institute, I hope they have a required ethics course for all incoming students (and maybe professors).

Hmmm… I’d better get my “data science curriculum” plan together fast.

Open Forum next Friday

I went back to Occupy Wall Street two nights ago after work. I hadn’t been there since last Friday, and all of the tents made the place awfully depressing. I was getting kind of skeeved out when I found myself next to the “red structure” and in the middle of the beginning of an “Open Forum” about Media Justice.

It was the first time I was an actual human microphone for a meeting, and the speeches were really good (they explained net neutrality and the cell phone industry). I was super impressed, and afterwards I introduced myself to the organizer. She explained it’s part of the Education and Empowerment working group.

Bottomline is, I’m giving an Open Forum about the financial system next Friday, November 4th. Very exciting! This format is exactly what I was hoping for when I tried to do the “teach-in” a couple of weeks ago. It’s also a chance to hand out my flyer.

I have to go write a speech consisting of 4-word phrases now. Kind of like poetry.

Categories: #OWS, finance, rant

Is Big Data Evil?

Back when I was growing up, your S.A.T. score was a big deal, but I feel like I lived in a relatively unfettered world of anonymity compared to what we are creating now. Imagine if your SAT score decided your entire future.

Two days ago I wrote about Emanuel Derman’s excellent new book “Models. Behaving. Badly.” and mentioned his Modeler’s Hippocratic Oath, which I may have to restate on every post from now on:

  • I will remember that I didn’t make the world, and it doesn’t satisfy my equations.
  • Though I will use models boldly to estimate value, I will not be overly impressed by mathematics.
  • I will never sacrifice reality for elegance without explaining why I have done so.
  • Nor will I give the people who use my model false comfort about its accuracy. Instead, I will make explicit its assumptions and oversights.
  • I understand that my work may have enormous effects on society and the economy, many of them beyond my comprehension.

I mentioned that every data scientist should sign at the bottom of this page. Since then I’ve read three disturbing articles about big data. First, this article in the New York Times, which basically says that big data is a bubble:

This is a common characteristic of technology that its champions do not like to talk about, but it is why we have so many bubbles in this industry. Technologists build or discover something great, like railroads or radio or the Internet. The change is so important, often world-changing, that it is hard to value, so people overshoot toward the infinite. When it turns out to be merely huge, there is a crash, in railroad bonds, or RCA stock, or Pets.com. Perhaps Big Data is next, on its way to changing the world.

In a way I agree, but let’s emphasize the “changing the world” part, and ignore the hype. The truth is that, beyond the hype, the depth of big data’s reach is not really understood yet by most people, especially people inside big data. I’m not talking about the technological reach, but rather the moral and philosophical reach.

Let me illustrate my point by explaining the gist of the other two articles, both from the Wall Street Journal. The second article describes a model which uses the information on peoples’ credit card purchases to direct online advertising at them:

MasterCard earlier this year proposed an idea to ad executives to link Internet users to information about actual purchase behaviors for ad targeting, according to a MasterCard document and executives at some of the world’s largest ad companies who were involved in the talks. “You are what you buy,” the MasterCard document says.

MasterCard doesn’t collect people’s names or addresses when processing credit-card transactions. That makes it tricky to directly link people’s card activity to their online profiles, ad executives said. The company’s document describes its “extensive experience” linking “anonymized purchased attributes to consumer names and addresses” with the help of third-party companies.

MasterCard has since backtracked on this plan:

The MasterCard spokeswoman also said the idea described in MasterCard’s April document has “evolved significantly” and has “changed considerably” since August. After the company’s conversations with ad agencies, MasterCard said, it found there was “no feasible way” to connect Internet users with its analysis of their purchase history. “We cannot link individual transaction data,” MasterCard said.

How loudly can you hear me say “bullshit”? Even if they decide not to do this because of bad public relations, there are always smaller third-party companies who don’t even have a PR department:

Credit-card issuers including Discover Financial Services’ Discover Card, Bank of America Corp., Capital One Financial Corp. and J.P. Morgan Chase & Co. disclose in their privacy policies that they can share personal information about people with outside companies for marketing. They said they don’t make transaction data or purchase-history information available to outside companies for digital ad targeting.

The third article talks about using credit scores, among other “scoring” systems, to track and forecast peoples’ behavior. They model all sorts of things, like the likelihood you will take your pills:

Experian PLC, the credit-report giant, recently introduced an Income Insight score, designed to estimate the income of a credit-card applicant based on the applicant’s credit history. Another Experian score attempts to gauge the odds that a consumer will file for bankruptcy.

Rival credit reporter Equifax Inc. offers an Ability to Pay Index and a Discretionary Spending Index that purports to indicate whether people have extra money burning a hole in their pocket.

Understood, this is all about money. This is, in fact, all about companies ranking you in terms of your potential profitability to them. Just to make sure we’re all clear on the goal then:

The system “has been incredibly powerful for consumers,” said Mr. Wagner.

Ummm… well, at least it’s nice to see that it’s understood there is some error in the modeling:

Eric Rosenberg, director of state-government relations for credit bureau TransUnion LLC, told Oregon state lawmakers last year that his company can’t show “any statistical correlation” between the contents of a credit report and job performance.

But wait, let’s see what the CEO of Fair Isaac Co, one of the companies creating the scores, says about his new system:

“We know what you’re going to do tomorrow”

This is not well aligned with the fourth part of the Modeler’s Hippocratic Oath (MHO). The article goes on to expose some of the questionable morality that stems from such models:

Use of credit histories also raises concerns about racial discrimination, because studies show blacks and Hispanics, on average, have lower credit scores than non-Hispanic whites. The U.S. Equal Employment Opportunity Commission filed suit last December against the Kaplan Higher Education unit of Washington Post Co., claiming it discriminated against black employees and applicants by using credit-based screens that were “not job-related.”

Let me make the argument for these models before I explain why I think they’re flawed.

First, in terms of the credit card information, you should all be glad that the ads coming to us online are so beautifully tailored to your needs and desires- it’s so convenient, almost like someone read your mind and anticipated you’d be needing more vacuum cleaner bags at just the right time! And in terms of the scoring, it’s also very convenient that people and businesses somehow know to trust you, know that you’ve been raised with good (firm) middle-class values and ethics. You don’t have to argue my way into a new credit card or a car purchase, because the model knows you’re good for it. Okay, I’m done.

The flip side of this is that, if you don’t happen to look good to the models, you are funneled into a shitty situation, where you will continue to look bad. It’s a game of chutes and ladders, played on an enormous scale.

[If there's one thing about big data that we all need to understand, it's the enormous scale of these models.]

Moreover, this kind of cyclical effect will actually decrease the apparent error of the models: this is because if we forecast you as being uncredit-worthy, and your life sucks from now on and you have trouble getting a job or a credit card and when you do you have to pay high fees, then you are way more likely to be a credit risk in the future.

One last word about errors: it’s always scary to see someone on the one hand admit that the forecasting abilities of a model may be weak, but on the other hand say things like “we know what you’re going to do tomorrow”. It’s a human nature thing to want something to work better than it does, and that’s why we need the IMO (especially the fifth part).

This all makes me think of the movie Blade Runner, with its oppressive sense of corporate control, where the seedy underground economy of artificial eyeballs was the last place on earth you didn’t need to show ID. There aren’t any robots to kill (yet) but I’m getting the feeling more and more that we are sorting people at birth, or soon after, to be winners or losers in this culture.

Of course, collecting information about people isn’t new. Why am I all upset about it? Here are a few reasons, which I will expand on in another post:

  1. There’s way more information about people nowadays than their Social Security Number; the field of consumer information gathering is huge and growing exponentially
  2. All of those quants who left Wall Street are now working in data science and have real skills (myself included)
  3. They also typically don’t have any qualms; they justify models like this by saying, hey we’re just using correlations, we’re not forcing people to behave well or badly, and anyway if I don’t make this model someone else will
  4. The real bubble is this: thinking these things work, and advocating their bulletproof convenience and profitability (in the name of mathematics)
  5. Who suffers when these models fail? Answer: not the corporations that use them, but rather the invisible people who are designated as failures.

What’s your short list of actionable complaints?

After reading this article from the New York Times about what Volcker says still needs to be done about the financial system (the title of his speech was “Three Years Later: Unfinished Business in Financial Reform”), I’m wondering if he wants to join the #OWS Alternative Banking working group. He’s got his own “short list of actionable complaints” list, not that different from mine:

  • make capital requirements for banks tough and enforceable,
  • make derivatives more standardized and transparent,
  • ensure auditors are truly independent by rotating them periodically,
  • end too big to fail,
  • create and enforce reserve requirements and capital requirements for money market funds, and
  • get rid of Fannie and Freddie, or at least make a plan to.

He also pointed to the weakness of the ratings agencies as one of the big reasons for the credit crisis, so I assume that “making the ratings agencies accountable” may be on the list too, at least in the top 10.

I was interviewed last night about being on the Alternative Banking working group for #OWS (I will link to the article if and when it comes out), and I mentioned this speech as well as the general fact that many of these problems named above are really non-partisan, especially “Too Big to Fail”. This column from the New York Times, written by former IMF chief economist Simon Johnson points this out as well.

That makes me encouraged and depressed at the same time. Encouraged because there really does seem to be a consensus about what’s terribly wrong with at least some of the most obvious issues, but depressed because in spite of this we haven’t solved any of them. To make this vague sense of depression precise, just take a look at what has happened to the original “Volcker Rule”: it has expanded by a factor of 100, from 3 to 300 pages, making it impossibly difficult to understand or probably to follow (unless you have fancy lawyers who do nothing else besides find loopholes). It’s reminiscent of our tax code. Speaking of which, here’s yet another “short list of actionable complaints” to fix that.

I’m enjoying how many people are now coming up with personal short lists of actionable complaints (even if it’s in response to complete stalemate of the political process). It’s a way of claiming and maintaining our freedom and agency. It isn’t as easy at is seems, because you have to sort out the important from the annoying, and the actionable from the existential. If you haven’t already, I encourage you to write your own short list, and feel free to post it here.

Categories: #OWS, finance, news, rant

Emanuel Derman’s Models.Behaving.Badly.

This morning I want to talk about Emanuel Derman’s beautifully written and wise new book “Models. Behaving. Badly.”, available in some book stores now and on Amazon starting tomorrow.

It is in some sense an expanded version of this essay he wrote in January 2009 with Paul Wilmott. I particularly like the end of the essay where they present the “Modeler’s Hippocratic Oath”:

  • I will remember that I didn’t make the world, and it doesn’t satisfy my equations.
  • Though I will use models boldly to estimate value, I will not be overly impressed by mathematics.
  • I will never sacrifice reality for elegance without explaining why I have done so.
  • Nor will I give the people who use my model false comfort about its accuracy. Instead, I will make explicit its assumptions and oversights.
  • I understand that my work may have enormous effects on society and the economy, many of them beyond my comprehension.

This was written in direct reaction to the financial crisis of 2008, clearly, but I think data scientists should all be asked to sign at the bottom of the page. As I’ve said before, financial modelers are just data scientists working in the most sophisticated (under some metrics) subfield of data science; just as they have ridiculous powers of profit using their methods, data scientists in other fields have ridiculous powers as well, sometimes in ways that affect people even more directly than money. Modelers absolutely need to be aware of and wary of these powers. This oath is an excellent step towards that.

In the book, Derman sets up a dichotomy between models and theories. For him, theories are stand-alone descriptions of how things are, whereas models are relative descriptions of how things work, by analogy. He also differentiates between the models (and theories) and the way that humans ascribe truth to them, which is to me the most profound and important message of his book. I’ll discuss below.

His examples of theories come mostly from physics: he has a really beautiful explanation of the evolution of the theory of electro-magnetism, for example, which actually explains how people can sometimes develop theories using temporary models. One idea that emerges is that, sometimes, models work out so well that they eventually become part of the theory. The obvious love that Derman has for physics (which he trained in as a young man) shines through this entire part of the book, and it’s beautiful and intimate reading.

Another example Derman gives of a theory was Spinoza’s theory of human emotion, wherein the basic objects were pleasure and pain, and everything was a derivative of those. For example, love is defined as “pleasure associated with an external object,” pity as “pain arising from another’s hurt.” My favorite: to Spinoza, cruelty is “the desire to inflict pain on someone you love.”

To Derman, Spinoza’s theory is a theory, even though it’s not mathematical, and even though it may not be even “true” in the sense that you could just as well have an alternative theory (although it may not be as beautiful). It is a theory, then, because it describes a mini universe of existence without depending on an assumed external frame of reference. It describes emotions themselves rather than comparing them to something else.

What then is a model? It is something that tried to explain (and predict) the behavior of something through analogy or proxy; its accuracy depends on external conditions. He talks about the Black-Scholes option pricing model as a good model in that, in its purest form, it is actually a model for the price of something depending on the abstract concept of “risk”. Then the fact that it can be misused is due more to the fact that people incorrectly proxy risk itself as described by some brownian motion somewhere. With a better model of risk, then, we’d be happy to use Black-Scholes.

Of course Black-Scholes, or rather its use, is not what caused the financial crisis. It was rather the Efficient Market Model and its corollary the Capital Asset Pricing Model that he considers much more dangerous (I agree). He describes these models very clearly, for the uninitiated, and talk about their ubiquitous use in the vocabulary of finance (and how money mangers describe their Sharpe ratios, which is a ratio of their (past) return and their perceived risk, as if they are meaningful statistics).

The basic mistake people make in ascribing power to their financial models, he says, is that they depend on human beings and their actions to be as predictable as physics. Electrons, it turns out, are much more predictable than people with money on the line.

He also goes into a beautiful riff on how there is, but shouldn’t be, a “Fundamental Theorem of Finance.” Not only is it not fundamental (and not even understandable), but there shouldn’t even be such a thing, because it depends on a model which isn’t true, and cannot deserve the name “theorem”, and moreover can only serve as a false sense of security. This kind of mathematical idolatry, he believes, is at the very root of the problem which led us into the financial crisis. Agreed.

Another aspect of the book I want to bring up, because I find it fascinating, is the way we model things in our lives. As Derman correctly points out, we each model our futures; he describes growing up in South Africa during Apartheid and his involvement with a youth Zionist movement, and how he felt pressure to model his life in a very precise way from the youth leaders there, to move to Israel and work as a laborer (examples among many of how sometimes people blithely model other peoples’ lives as well as their own). He then went on to talk about studying physics, moving to finance and his early desire to find a “theory of everything” in finance.

It may be reasonable to say that, until we die, we are on an endless quest for the perfect model for ourselves. In other words, it’s not only that we use models for our internal lives, but we intensely desire models as well – they give us pleasure, they alleviate our worries and stress. We have trouble letting go of our internal models, even when they don’t work (or we even ignore their failure completely; this was described in a recent New York Times article on confidence). When we model the person we love loving us back, it gives us enormous strength and hope. We model our future success: getting tenure, a promotion, or a child. We model our gods.

But our models are not always realistic, and they don’t always account for bad conditions; just look at the Eurozone for one huge example of this. Sometimes our beloved doesn’t care, and sometimes we don’t get tenure.

The most essential question for me then is: how do we react in that moment when we realize our model is bad? Otherwise put, how do we disagree with ourselves? It’s an excruciating moment that we can learn from – do we take away from a moment like that only the pain? Or do we grab hold of it as an opportunity for growth? Can I train myself to be the kind of person who learns from her broken models?

A related question, which I will take up in another post, is whether the people in finance (or mathematics, or data science) are people who react well to their models failing.

Categories: finance
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