Theory versus Statistics, Financial Economics edition

Uh-oh. Here comes an economist, to tell finance what’s up. The very smart and talented Barry Eichengreen:

We thought that financial institutions and markets had come to be self-regulating—that investors could be left largely if not wholly to their own devices. Above all we thought that we had learned how to prevent the kind of financial calamity that struck the world in 1929…

Soon after, business schools jumped to supply graduates to write those reports. Value at Risk, as that number and the process for calculating it came to be known, quickly gained a place in the business-school curriculum. The desire for up-to-date information on the risks of doing business was admirable. Less admirable was the belief that those risks could be reduced to a single number which could then be estimated on the basis of a set of mathematical equations fitted to a few data points…

Because it was not that economic theory had nothing to say about the kinds of structural weaknesses and conflicts of interest that paved the way to our current catastrophe. In fact, large swaths of modern economic theory focus squarely on the kind of generic problems that created our current mess…What got us into this mess, in other words, were not the limits of scholarly imagination. It was not the failure or inability of economists to model conflicts of interest, incentives to take excessive risk and information problems that can give rise to bubbles, panics and crises…

Maybe so. But amid the pervading sense of gloom and doom, there is at least one reason for hope. The last ten years have seen a quiet revolution in the practice of economics. For years theorists held the intellectual high ground…Now every graduate student has a laptop computer with more memory than that decades-old university computing center. And she knows what to do with it…Their next step, of course, is to download securities prices from Bloomberg and see how blue skies and rain affect the behavior of financial markets. Finding that stock markets are more likely to rise on sunny days is not exactly reassuring for believers in the efficient-markets hypothesis….

The top young economists are, increasingly, empirically oriented. They are concerned not with theoretical flights of fancy but with the facts on the ground. To the extent that their work is rooted concretely in observation of the real world, it is less likely to sway with the latest fad and fashion. Or so one hopes.

It’s a good piece, worth your time. He draws an line between statistical and theoretical economics and finance. He also believes the future of economics and finance belongs to the empiricists, doing statistics, not theory, and that this might mitigate future calamities. I think this is wrong for a number of reasons.

1) Value at Risk is a statistical concept, not theoretical. It’s a confidence interval. You take the historical data, project it into the future like any other time series regression, and report back the interval. The 95% (or 99%, or 99.9%) interval is your value-at-risk. There is no theory behind it, in the sense that he means it. It is one of the longer running empirical projects in current quant finance.

2) Finance has been leading the way in empirics and statistics since the late 1980s. Finance in particular with massive data sets that one needs to worry about data quality issues, things like survivorship bias, and over analysis, things like data mining, has been leading the economic sub-field pack with its intensity since the 1990s, with labor economics running side-by-side with the bells and whistles. The options pricing, and crazy math stuff in the academy was mostly done by the late 1970s.

In fact, if I grab one of the more mathy, but quant standard, financial economics textbooks off my shelf, Duffie’s Dynamic Asset Pricing Theory, I get this from the preface:

To someone who came out of graduate school in the mid-eighties, the decade spanning roughly 1969-1979 seems like a golden age of dynamic asset pricing theory…[history of the 1970s]…Theoretical developments in the period since 1979, with relatively few exceptions, have been a mopping-up operations. (p.2-3)

hip-hop, like quant finance, better in the 70s

quant finance, like hip-hop, better in the 70s

You can almost here the lament of this quant that the real math theory has been dead since 1980, and that it has all been applied and statistics ever since. It’s like Fischer Black was Kool Herc and Myron Scholes was Afrika Bambaataa, and they’d all go plug in their computers into lamp posts and do martingale representations in the streets and at house parties. And, of course, it was all ruined in 1979 when it went commercial.

2.a) And looking at finance in the 1990s, all the major projects are one where statistics leads the theory. The Fama French Three Factor Model, from 1992, was done with very little theory in the sense mentioned here. There were abnormal returns in the cross sectional data of stock returns, and those returns must be attributable to risk factors unidentified. Or is it that people are impatient, or have poor behavioral quirks? We don’t know – we’ll need to do some theory arguments later to figure it out. And that’s from Eugene Fama, the father of Efficient Markets Theory. This is a case where the statistics are leading the theory, and it continues through the whole wave of abnormal pricing research that has been going on (momentum, earnings-diffusion, etc.).

Also the paper he mentioned about weather returns, Good Day Sunshine, was published in the leading Journal of Finance in 2003. It didn’t stop the financial crisis, nor did it impact people’s thoughts about financial markets working. So a call for “more statistics, more empirical work” doesn’t move us an inch further in the stopping financial crisis, or re-thinking markets, game as far as I see the field.

3) Coming to finance and economics with no background except math and modeling (sadly not the male kind), when I first heard the justification for Efficient Markets Hypothesis I was so surprised I laughed and was confused. The theory is duck-taped together because sophisticated arbitrageurs will simply do the exact opposite of noise traders. Since they are the exact opposite, what is left over is identically, independently distributed, hence efficient markets.

It’s pretty obvious to me that this won’t happen in theory, won’t happen in practice, and hasn’t happened in practice. That leaves a gaping hole in the theory, and it isn’t clear what to fill it with. His arguments about information already exists as the bid-ask spread in the micro-market finance literature. Where can we go for new theory?

4) The best line I have read on the problems currently facing finance and economics comes from an english graduate student, writing about The Wire: “Thus, both [Marlo’s] quasi-aesceticism and his amoral approach to corporate entities: more than any other character, he succeeds because he comes closest to the capitalist ideal of existing within institutional frameworks whose limitations only affect others, ruling structures whose rules only he can ignore.” (his two parts on ‘suction’ are must reads if you are Wire fans.) Replace limitations with the economic idea of ‘externality’ and you got it. Scaling businesses past their market limitations can exist if one can make sure the externalities all don’t come back to your bonus until it is too late.

Somewhere, 5 years ago, in a pig field in Mexico, there’s a risk guy who said “we run the risk of generating a nasty strain of influenza. If that happens our value-at-risk is losing the whole farm.” Management goes “Given how much we are making, that is acceptable.” The risk guy follows “but if it spreads, and even 1,000 people across the country get infected, the costs are…” And Management says “Stop. Not our problem.”

Somewhere, 5 years ago, on Wall Street, there’s a risk guy who said “if we have a nation-wide housing downturn, we are probably going to lose most of the book.” Management goes “given what we are making, that is acceptable.” The risk guy follows “but the costs to our shareholders, our counterparties, the taxpayers if we are judged too big to fail…” And Management says “Stop. Not our problem.”

A general economic model of “money goes uphill, (pig) shit goes downhill” would be great to see, especially in the context of increasing/decreasing costs, scales, and externalities. This is where government has to step in, not in order to handle quirky behavioral cues but to make sure the costs get assigned correctly. One can get very large indeed if someone else is holding the bag for you…

Update: Felix Salmon asks “who’s the Grandmaster Flash of the quant world?” Clearly, Robert Merton is the Grandmaster Flash. In the same way that DJs no longer went back to trying to simply guess on where to drop the needle but instead had to develop a system of cueing, quants no longer used the complicated CAPM derived proof and argument that Black and Scholes used for the Black-Scholes equation, but instead the dynamic replication proof of Merton. It’s easier intuitively, and it makes the MBAs and investors a lot more eager to party with your models.

I think, though this is controversial, that Rubinstein’s Binomial Model is the Bomb Squad of the quant world. One takes the box off the beats and gets deep in, recording specifically for sampling, sampling for just seconds, etc, in the same way one can math it up in a pinpoint matter with the discrete modeling of Black-Scholes.

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13 Responses to Theory versus Statistics, Financial Economics edition

  1. Larisa says:

    This. Is an awesome post. So well describing the problem with “externalities.”

    What is the relationship between the “elegance” of a model and its ability to exclude (not account for) troubling outcomes? I dunno, something amazing about how many people love to be able to exclude certain effects, costs, or classes of people (which is often how those outcomes sort out) from their analysis.

    My first experience with it, which I never forgot, was in micro 101 class in college, when I suggested some external constraints on people’s choices (i think i used the phrase “mouths to feed”) and he said “stop talking like a journalist.” By which it became clear he didn’t mean my phrasing, but the content – i.e. including recognizable humans and their connections to each other into the model..

  2. Larisa says:

    I should add. it’s not just statistics, but evidence… also, where’s the love for the Agricultural economists (and the historical economists of the Austrian school?) who were on this ages ago..

  3. Mike says:

    Part of it is placement in the academic sphere. You consistently read Psychology:Economics::Chemistry:Physics. And by psychology, I mean the bias type stuff – overestimating, underestimating, etc.

    Given how much economics and finance fumbled “how do people invest in their housing” question – which should be one of the PRIMARY questions of micro economics – I don’t have much hope in psychology providing the basis. Sociology – why do we want housing, how do our neighborhoods effect us, who do we want to live by and away from, and where do we get these expectations – is a better cue than the science of trying to figure out the bias in guessing the number of gumballs in a gumjar.

    Part of it is structural. I remember an English professor of mine complaining about going to a interdisclipinary workshop, and instead of being about, say, methodology, to all the scientists it was all about cash. I think Economics runs the same way – instead of being “how can we discuss with sociologists how to approach a neighborhood’s housing” interdisclipinary it is “how can we get cash to pay for an MRI machine, so we can make people trade tokens under an MRI machine” – and sharing the credit/cost.

    Have you heard of Julie Nelson, an accomplish micro person who was probably denied tenure for having an interest in Women’s Studies?

    That’s how interdisclipinary works in Econ, I fear. Here’s a great quote from her: “I have found many heterodox as well as mainstream economists more than willing to take off on flights of reaction without actually reading any works by feminist economists. Does it seem that there may be a teensy bit of gender bias in the assumption that economic actors are rational, autonomous individual mental choice-makers (while somebody else takes care of emotions, dependencies, and bodies?).”

  4. Steve Sailer says:

    Fundamentally, Wall Street’s risk management models were all based on economists’ Rational Person assumptions, which assume that all people are quite similar. You can argue over whether the finance industry bought into the equality assumption because of government discrimination lawsuits or because of neo-libertarian ideology, but nobody on Wall Street in 2006 would imagine publicly say something like, “Hey, why are lending so much mortgage money to Mexicans? Aren’t Mexicans kind of poor and not real good at saving money? And who is going to want to pay more money to move into a Mexican neighborhood?”

    The Gaussian Copula that did so much damage was dreamed up by an actuary. Wall Street then used it to assess mortgages like life insurance policies — what are the odds of somebody dying?

    What they paid no attention to was the radical change in the population of homebuyers. Consider the Riverside-San Bernardino MSA, home to the most foreclosed houses in the country. Between 1999 and 2006, home purchase dollars lent to Hispanics in Riverside-San Bernardino increased 782% according to the federal government’s Home Mortgage Disclosure Act, versus 134% by whites. By 2006, minorities were getting about two-thirds of all mortgage dollars, prime and subprime, in the Inland Empire.

    There was only one little problem. This demographically new population of borrowers couldn’t earn enough on average to pay back the mortgages once the home prices stopped rising and they couldn’t find greater fools wanting to pay more money to move into neighborhoods turning into barrios.

    In MSA’s across the country, the correlation between % of mortgage dollars loaned to NonAsian Minorities in 2006 and default rates in 2009 is tremendously high.

  5. Andrew says:


    I have a graduate degree, and I’m English, and I have no idea wtf the English graduate student is saying in point 4 – is it something like, Marlo succeeds because he doesn’t give a shit? I mean, you write “Scaling businesses past their market limitations can exist if one can make sure the externalities all don’t come back to your bonus until it is too late,” but I guess you could also say “once you’ve cashed the bonus check, why should you care if the firm blows up?”

  6. zunguzungu says:

    Thanks for the shout, Mike!

    Andrew, as the grad student in question, my point about Marlo was, crudely, not only that he doesn’t give a shit but that he doesn’t have to give one. The thing that’s different about Marlo’s crew is that he doesn’t make any pretense to taking care of his people, nor does he need to; his soldiers need him more than he needs them — he seems to have an aptitude for picking out people who have been damaged in just the right way to be useful to him — so his relationship to his labor can be the neo-liberalism of an employer happy to outsource (rather than the pretensions to familial Fordism that characterized the Barksdale crew). But the point is that precisely because he makes them more dependent on him than he is on them, he is never vulnerable the ways the Barksdales were to having their people flipped: within Marlo’s crew, there are the people who are unflippable (Chris, Snoop) and the ones that are too disposable to matter if they flip. That’s why it was important to establish Marlo’s lack of desire for anythign but constant growth and power: like the Weberian capitalist spirit, he has no desire to consume anything, only to capitalize.

  7. Terry Ivanauskas says:

    Unless for philosophical interest (which is good), I think this discussion is pointless for being a chicken-and-egg question. What should come first? Modeling or statistics? Can you look data without some sort of theory? Can you have a theory without see the world? Personally, I can’t separate easily these things. It is true that we have much more information available nowadays, but it doesn’t guarantee quality: it just guarantees that there will be a lot of noise out there (and who the hell can think properly without some silence!). Models will always be imperfect but necessary maps to understand reality, and would be useless to have a map as perfect as the own reality we don’t understand.

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  9. khappucino says:

    Nice. Your Efficient Market Hypothesis comment made me ROFL… That’s how I felt too the first time I heard that.

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  12. Winslow says:

    Great post, but no self-respecting rap head would claim that the 70s were better than the 80s. It is called the Golden-Age for a reason. Hell, (and I know this is blasphemy) Kool Herc is NOT that great of a DJ (this is taken from conversations with bboys that have heard him spin, rather than my own experience, so make of it what you will), though we gotta respect his contributions. Bam and Flash are amazing though. Still, 79 to 96 (three hip hop generations) produced stuff a lot more heads like. 79 did not make rapping necessarily more commercial (mcs were always trying to get paid) but it allowed them to be commodified in ways they never could be. If you want to talk about elemental hip-hop, yeah, but two great mcs and one mediocre dj does not a great decade make.

  13. chuki says:

    The merits of modeling both mathmatical and behavioral applied to markets are interesting however so far unable to produce consistant returns and most importantly cannot account for and take advantage of black swan events therefor one should use the practicality of a system that recognizes simple supply and demand trends to enhance and/or preserve the size of ones assets

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