Posts tagged with quant

Jim Simons, a hedge fund manager, to the US Congress: “Most culpable [for the crisis of 2008], in my opinion, were the ratings agencies.”

  • "Our strategies are usually contrarian."
  • "Medallion Fund is almost entirely employee-owned."
  • "We charge ourselves fees."
  • 'In my view hedge funds were not a major contributor to the [crisis of 2008]. Generally [they] have increased liquidity and reduced volatility in the markets.”
  • "Each hedge fund’s leverage is controlled by its lenders."
  • He’s in favour of more financial regulation.


Did (do)

  • financial derivatives,
  • synthetic vehicles,
  • structured products fine-tuned for clients’ risk appetites (in a CAPM sense of risk),
  • securitisation,
  • public corporations,

and “modern finance" generally add real value to the economy?

Aaron Brown addresses the question by imagining a finance quant sent to the fantasy-history of Medieval Europe.

Do we [quants] knowanything … that is useful or interesting on its own? Or do… we … represent a small and arbitrary cog in a large machine? Have we addressed basic human questions of interest to the ages? Or are our skills specific to our era, about as useful in the sixth century as knowing the keystrokes to use Windows without a mouse or the names of all the Academy Award winners for costume design?

Basically, AB’s answer is that:

  • securitisation spreads risk around to various parties, making the financing of large projects possible. For example small merchants could band together, with the aid of contracts, to jointly finance ventures they would be unable to finance alone. (Also less economic inequality is required to finance ventures.) More trade voyages means more trade means more wealth.
  • because of reasons given in the Central Limit Theorem and Modern Portfolio Theory, pooled risks can make an overall safer portfolio. So a large investment portfolio composed of fractions of twenty ships bound for different ports has lower variance than a portfolio composed of one ship.

It’s a clever restatement of the standard reasons that finance Is supposed to be good.

The Nielsen PRIZM groups people into 66 “demographic and geographic market segments” for the purpose of advertising to them.

Each of the segments has a nice description to go along with it. It’s the kind of story you want to hear as a marketer: it uses relatively in-depth knowledge of Americans, plus stereotypes or shallow summaries, to draw a character with enough roundness that you could pitch to him/her. That is, you could write copy or film a creative spot that you believe could speak to members of this cohesive segment.

As I read more deeply into the Nielsen-Claritas PRIZM, however, the 66 segments started to sound like perhaps they were generated by a simple formula. From their slideshow I learned that they divide the US population by:

  • affluence
  • population density
  • kids/no kids + age

Rather than use continuous on the implied cube (3 dimensions above), they lump various ranges together. They also lump the interaction terms unevenly—for example, (suburban & income) is lumped more finely and (urban & income) is lumped more coarsely. Specifically,

  • 4 totally -ordered levels of urbanity (measured by population density per zip code) urban  suburban  second city  town & rural
  • 14 levels of Affluence Groups (so they consider finer gradations of wealth & income within suburban and low-density zip codes and coarser income gradations in cities and second-cities)
  • Three life-stage categories, accommodating both those who do and don’t raise children at some point. {youngish && no kids, kids, oldish && no kids at home}.

    Younger folks (this is under-35’s or under-45 DINKs) are less graduated by affluence than families or older folks (over-55’s or over-45 DINKs).

    By the way, over-65’s are outside PRIZM’s marketing groups. I guess it’s assumed that they won’t buy big-ticket items or change their ways much unless the Monday lima-bean special becomes 25cents cheaper at Lida’s Diner than Bill’s Diner. Then you’ll see the entire community switch to Lida’s.

Like the MBTI, it assumes that: People fit in rectangles.

Unlike the MBTI, rather than using four sliding scales [0,1]⁴, the PRIZM uses discrete, totally ordered sets—something you could build with the letters and combn functions in R.

I started to wonder: is it really true that members of segment 26 are “urbane” and “love the nightlife” — even the empty-nesters and older homeowners of the segment? Is there really a “laid-back atmosphere” to segment 25? Or are these merely colourful papier-mâché rudely draped over a box?

Mostly, of course, I’m concerned with segment 31, the well-known Urban Achievers:

And proud we are of all of them.


When I look at a painting, I’m tempted to glance quickly and pass on. In order to appreciate a piece, I imagine the strokes and colour choices that make up the painting. I imagine myself painting the same thing. What would it have felt like to be inside Cy Twombly's hand while he painted Apollo 17? That gives me a better feeling of the art.

When I look at the Nielsen Prizm the same way — try to get inside the heads of its creators — I sense that they adopted the [0,1]⁸ rectangular structure simply because they’re not aware of alternatives. MBA’s do plenty of mathematics, but I’ve never seen any business mathematics cross over into CW-complexes, 3-tori, arborescences, or Lobachefskyan geometries. It could be that the people who designed the Prizm simply didn’t have anyone on their team who had heard of this stuff. All the quants were working on Wall Street rather than Madison Avenue. (Wacker Drive rather than Michigan Ave.)

The ribbon-farm guy (Venkatesh Rao) is a rocket scientist who crossed over into marketing, but so far I haven’t read enough of his stuff to say if he dove into algebraic geometry—it seems he did more functional analysis, optimisation / control theory, and differential geometry. Which is what I would expect rocket science consists of.

I will admit that the PRIZM’s use of two “matrix” presentations with colour-coding, pictures, defined ranges, and toss-away combinations is quite clear. Probably works better than when I tell clients “Just picture a 5-dimensional manifold, I won’t say the norm because I think it’s spaced differently in the center than the edges—and let’s not get into the interaction terms yet”. But—the bones of their model are really just [0,1]³. They’ve dressed it up and they’ve done more than that (segmenting and dropping). But a cube is the underlying architecture.

Is the Prizm simple or oversimplified? I feel it’s the latter. Not that I object to mathematical models of behaviour, emotions, or any human thing—but the hypercube metaphor just doesn’t fit my presumption of the shape of the space.

  • Does consumer space have 8 corners to it?
  • What’s the best interpretation of “distance” in the consumer space?
  • Do all of the lines really cross at right angles, in a hyper-grid? Was that supposed to be implied?


I don’t want to carp about somebody else’s work without at least offering constructive criticism. What are some potentially better ways to think about the space of all consumers—potential buyers of houses, cars, vacations, DVD’s, washers, ‘n’all that?

Mathworld’s picture of a few topological objects gives one starting point:

One thing I noticed pretty quickly: you remember playing Star Fox battle mode? Or any video game where there is a lower-right thumbnail of you on a limited square map—such that when you go leftwards off the map you appear on the right, and when you go upwards off the map you appear on the bottom? As a kid I thought I was flying on the surface of a planet, but in fact it was the surface of a torus. (Why? If you go up to the top of the North Pole you don’t come out again at the South Pole. See the picture of the sphere with B ≠ C, i.e. N ≠ S.)

In other words, a torus (donut) is the product of a_loop × a_loop. Whereas a sphere (ball) is the product of a_loop (east/west) × a_line_segment (north/south).


Following from this short lesson in topology, one alternative to multiplying only “linear” dimensions of characteristic attributes would be to multiply lines with loops. For example a_loop × a_loop × a_line_segment. I’m not sure what the name for that shape is, but you can imagine it — like a cylindrical torus. And it’s logically possible that there are two circle-like dimensions in marketing. Something like, as politics goes further and further left, it starts to resemble the far right more than the middle. But relevant to marketing.

A second alternative then might be to consider, like in the image above, the endpoints of some line segments from the 3 dimensions of Nielsen. What if some of them were identified rather than left distinct? What kind of shapes could you create with that and would that resemble the consumer space more than a rectangle?

Some other ideas of things to question:

  • How do angles meet up? (inner product)
  • How do distances work? (norms)
  • Look through an algebraic geometry book, or Solid Shape. Are there any shapes—umbilics, furrows, biflecnodes, dimples, trumpets—that have an analogue in the space of all consumers?
  • Is backwards just the opposite of forwards? Or does that wrongly assume commutativity?

I don’t know if that would result in a better model. I don’t know if thinking about things this way would reduce wasteful ad spending. I don’t have data to test these ideas on. I just wanted to share this thought.

Say you find a sucker and you make a bet with him. A bet that is so good for you and so bad for him that you can barely believe he signed on to it. You are smirking inside and when you’re not facing him anymore you break into gleeful, villainous laughter.

A couple months later the contract comes due. You find the sucker and he has made dumb bets all over town. He’s cleaned out and you can’t draw blood from a stone.

That’s counterparty risk.

CAPM assumes a positive correlation between risk and reward. The exact opposite is true with value investing. If you buy a dollar bill for 60 cents, it’s riskier than if you buy a dollar bill for 40 cents, but the expectation of reward is greater in the latter case.

One quick example: The Washington Post Company in 1973 was selling for $80 million in the market. At the time, that day, you could have sold the assets to any one of ten buyers for not less than $400 million, probably appreciably more. The company owned the Post, Newsweek, plus several television stations in major markets. Those same properties [were] worth $2 billion [in 1984], so the person who would have paid $400 million would not have been crazy.

What’s the social function of Warren Buffett's style of value investing? It saves worthy, established companies from being unfairly shorted below a base sanity valuation.

Consider that large companies have more employees, more customers, and more suppliers. Saving them is like stopping a large swath of forest from burning.

But for my personality type, I’m not as interested in big, publicly held companies. Maybe it’s unreasonable of me. But I am more for shorting the big dogs (the over-valued ones) and for longing the small guys (the ones that just need to be given a chance).

I think that’s more exciting and more interesting. Maybe it’s also more speculative; maybe you have less control over the risk.


In 1949, the total value of listings on the New York Stock Exchange was $150 billion. In 1984, the total value of all US public equities was $2 trillion. In 2005, the total value of Big Board listings was $20 trillion, plus $3 trillion listed on NASDAQ.

Then, from Oct 2007 - Oct 2008, US stock market capitalization lost $7 trillion.

various sources including several versions of The Intelligent Investor



Nassim Taleb wants us all to go long vol — not just be able to withstand volatility, but to actively seek it out.

He can certainly bet that way (and does — though it’s not paying off), but it’s a bad idea to make society anti-fragile.

Let me define a few words describing potential responses to volatility:

  • fragile — Taleb means systems that break when catastrophic volatility is applied; he’s thinking of people who deep short volatility or at least indirectly bet on stability
  • robust — like a bridge, or an earthquake-resistant building: built to withstand shocks
  • agile — able to adapt to shocks
  • anti-fragile — shock-loving; shock-seeking; volatility-loving; risk-avid

Taleb points out that there is no word for "the opposite of fragile”; only for “not fragile”. True.

But we really shouldn’t try to make the system break in the case of no catastrophes. Imagine a bridge that shattered only-and-always, when no cars drove on it. Or a building that toppled only-and-always, when no earthquakes were shaking it. (Those would be anti-fragile things.)

It would be stupid to build things that way. Same with the financial system — we want to be prepared for bad times but also, ready to capitalise on good times. A mouse who’s so afraid of cats that it never goes to look for food, will die.

What makes anti-fragility an especially bad idea in finance, is that people might try to sabotage, tweak, or influence the system to make their bet pay off. Let’s say some powerful crook is long volatility — that is, s/he will only get paid if some huge catastrophe happens within the next year. Maybe s/he will engineer a catastrophe. That could be truly terrible.

UPDATE: @nntaleb has clarified on twitter that he does intend “antifragility” to mean “long gamma”.

UPDATE 2: Jared (@condoroptions) suggests at minute 30 of this Volatility View podcast that @nntaleb must mean long convexity, not long gamma. I interpret that to mean buying stability, selling normal levels of volatility, and buying extreme levels of volatility. In other words things will usually stay the same, but when they change they’ll change more than people expect.
That answers the finance part. I still don’t see how to practically design real things to be antifragile without giving up normal functionality under typical circumstances.

Derivatives contracts allow you to make bets on an asset without owning it. Like a side bet. I don’t have to own, watch, or like the Dallas Cowboys to bet on their success / failure.

You can bet on a company by buying its stock, or you can bet someone that it will go up. The latter is a derivative contract. Today the $$$ value of side bets exceeds the trading in original securities by a factor of 40.

For example there is $800 million worth of wheat (3 million metric tonnes) in the world — or at least that’s this year’s production of hard red winter wheat #1, ordinary protein.

But on the Chicago Board of Trade alone there were 22 million bets this year, betting about those 3 million MT.

Supposedly there are $1 quadrillion worth of derivatives contracts being traded today.

People talk about it like it’s a bad thing — but it’s neither good nor bad, by itself. It’s scary for regulators because they can barely measure, much less control, what people are doing with their money. They worry about systemic collapse on the one hand, if people make correlated mistakes — but on the other hand, if they over-regulate then they will choke off freedom of property.


Volatility, in finance, refers to the wiggliness of the time series. You observe the price of a security go up and down over time. If it changes a lot, that’s high vol: unstable, unpredictable. If it changes only a little, that’s low vol: stable, consistent.
There are many ways to define volatility, just as there are different ways to measure distance. Portfolio variation should be measured with a quasimetric (unidirectional metric).
But for all those definitions, it should mean roughly: the magnitude of change in the price, during some time interval.


Volatility, in finance, refers to the wiggliness of the time series. You observe the price of a security go up and down over time. If it changes a lot, that’s high vol: unstable, unpredictable. If it changes only a little, that’s low vol: stable, consistent.

There are many ways to define volatility, just as there are different ways to measure distance. Portfolio variation should be measured with a quasimetric (unidirectional metric).

But for all those definitions, it should mean roughly: the magnitude of change in the price, during some time interval.


The point of high-frequency trading is to smooth markets over time.

Imagine that you know a big block order is coming in. Some huge pension fund needs to send 70,000 checks out next week so they’re selling some asset.

The asset’s price doesn’t really deserve to go down. The fund just wants to cash out. So you buy the huge block order and dole it out in smaller pieces as regular buy orders ebb in over the next few weeks. Or, you know, minutes.

You just smoothed the asset’s price, as well you should.

That’s not ultra-high-frequency trading — which is more about having the fastest technology — making it possible to liquidate a position RIGHT, RIGHT, RIGHT now.