So from here on out I’m going to be writing a little something about every book I read, in an effort to make more stick. These won’t be formal, just a brief synopsis and a collection of takeaways and other random thoughts. The only rule is that the post has to go up before the next book is finished.
First up: An Engine, Not a Camera: How Financial Models Shape Markets by Donald MacKenzie, which delves into the history of financial models and examines the impact of their adoption in trading. The central thesis is that some financial models have, through their use by traders, made real economic processes converge more closely to or diverge further from the theoretical economics those models assume.
One of the book’s main illustrations of this idea is the empirical history of option pricing. MacKenzie divides this history into three periods: pre-1976, when prices did not adhere well to Black-Scholes values; from 1976 to 1987, when they did; and post-1987, which has seen a persistent volatility skew that pulls prices out of line with theory. The mechanism MacKenzie cites for both shifts was the application of the theory by traders.
Following its publication in 1973, Black-Scholes allowed analysts to identify options that were mispriced according to the model. As traders rushed in to take advantage of those opportunities, their activity whittled away discrepancies until real prices more or less matched theory, beginning around 1976. One might be tempted to believe that this simply implies that the theory was correct, and the shift to match theoretical prices was the result of the market waking up to reality and fixing itself.
In 1987, however, things changed again. Option pricing theory was crucial to the development of portfolio insurance, which allowed investors to limit their exposure to losses from declining stock portfolios by selling corresponding index futures as prices went down. Two key assumptions underlying portfolio insurance (inherited from Black-Scholes) were that there would be no large gaps between successive prices (allowing time to make adjustments to the portfolio), and that trades (made to realize those adjustments) did not affect broader market prices. While it was accepted that the first assumption could be violated as a result of the market reacting to a dramatic world event, it was less appreciated that it could also come under pressure if the second assumption were violated, which grew more likely as the popularity of portfolio insurance increased:
By June of 1987, the portfolios “insured” by LOR and its licensees were sufficiently large that Leland was pointing out that “if the market goes down 3 percent, which, in those days, would have been a very large one-day move, we could double the volume [of trading] in the New York Stock Exchange”
On October 19th, it fell 22.6 percent. MacKenzie shares from an interview with Nassim Taleb:
… the crowd detected a pattern of a guy who had to sell [as] the market went lower. So what do you do? You push lower … and you see him getting even more nervous. … It’s chemistry between participants. And here’s what happened. You understand, these guys are looking at each other for ten years…. They go to each other’s houses and they’re each other’s best friends and everything. Now one of them is a broker. He has an order to sell. They can read on his face if he’s nervous or not. They can read it. They’re animals. They detect things. So this is how it happened in the stock-market crash. They kept selling. They see the guys sell more….
This is a great example of one of my favorite aspects of the book: its use of a large number of personal interviews to bring focus to the human aspect of all this, the individuals and groups behind the development and application of these theories. The pages are filled with little behind-the-scenes anecdotes: the pay-to-play deal behind Milton Friedman’s 1971 paper “The Need for Futures Markets in Currencies”, which provided a ‘stamp of authenticity’ ahead of the opening of the Chicago Mercantile Exchange’s International Money Market; the personal cajoling required to get traders to actually participate in the early days of the new exchanges; the exploitation of temporary put-call parity discrepancies on the Amex by literally standing between the two different specialists responsible for puts and calls; the phone calls throughout the night and early morning that, with 3 minutes to spare, allowed the Merc to re-open on October 20th, 1987. The list goes on.
A particularly interesting section is the account of how index futures were brought to market. Futures trading was legally distinguished from gambling by the fact that a future implies the possibility of delivery of the underlying commodity. If a future could only be settled in cash (as is the case for an index future), it was legally a gamble, and therefore illegal according to state-level gambling laws. To get around this, Chicago Mercantile Exchange chairman Leo Melamed and other futures exchanges lobbied for the creation of a new federal agency, the Commodity Futures Trading Commission, whose 1974 charter allowed it to preempt state laws. The legislation creating the CFTC was carefully drafted to expand the definition of ‘commodity’ to cover securities without actually using the word ‘securities’, which would have drawn the attention of the SEC (who would have tried to block the legislation to protect their authority). Once established, though, the CFTC was in a position to negotiate with the SEC, and, in 1982, reached an agreement with them that explicitly allowed index futures to be traded under the CFTC’s jurisdiction, thus circumventing those state gambling restrictions. The key to the SEC acquiescing? The fact that what was being traded was “a figment of Melamed’s imagination” – that is, not a security or a derivative, but a future that could only be settled in cash, the very trait that had made them illegal in the first place. If you don’t like the regulatory climate, just convince Leo Melamed to get you a new one.
The one disappointing aspect of the book is something MacKenzie couldn’t have possibly avoided: it was published in 2006, predating the 2008 financial meltdown. The other two arguably most significant market events of the last 30 years, the 1987 crash and the collapse of Long Term Capital Management, are covered in great detail, but the fact that such a large event related directly to the subject matter of the book is missing makes it feel incomplete. Luckily enough, MacKenzie has almost a dozen papers written since, available on his website.
One last minor note: in setting up discussion of Mandelbrot’s work to use the Lévy distribution in place of the normal distribution in modeling the random walks of stock prices, MacKenzie discusses how mathemeticians “redefin[ed] ‘polyhedron’ so that an anomalous polyhedron isn’t a polyhedron at all” to work around apparent counterexamples to a prized theorem. This struck me because I recognized that theorem as Euler’s Polyhedron Formula, V – E + F = 2, which is the subject of a book I read last year called Euler’s Gem by David Richeson. Chapter 15 of that book actually goes into detail about the history of the issue of what makes a polyhedron. In Richeson’s telling, the refinement of the definition of ‘polyhedron’ was not an effort to sweep something unpleasant under the rug; to the contrary, it was one of the key efforts underlying the development of topology.
Matt Gattis tweeted a quiz earlier tonight: 10 girls and 10 guys in a group. Sally dated 5 of the guys, Bob dated 2 of the girls. What’s the probability that Bob dated Sally? Think about it for a bit, then read on.
Kui Tang has a nice write up of the solution over on his blog, but I thought I’d bang out a quick alternate explanation for those of us who like to visualize our probabilities: imagine a 10×10 grid of cells, the x axis corresponding to the men and the y axis to the women, with each cell either on or off depending on whether the x,y pair had been on a date. Take and count up all unique grid configurations that have Sally going on 5 dates and Bob going on 2. That’s your denominator. Your numerator is then the number of these unique grids that have Sally matched with Bob. These are huge numbers, but then recognize that all possible non-Bob/non-Sally cell state configurations repeat for every unique Bob/Sally configuration, and so neatly cancel out.
The math given in Kui’s post is the same thing expressed with counting formulas, but I think picturing the problem as a set of unique grid layouts helps give a better intuitive understanding of what’s going on. It’s hard to accidentally overcount, for instance, because its clear that the visual equivalent to (10 2) * (10 5) counts the Bob+Sally cell too many times, and it erases the questions about the cases where only one other woman has dated at all or 2 other women have dated 10 guys, because it’s clear they’ve been taken into account as part of the massive number of states that cancel out when you do the tally.
Color is a new photo sharing app that builds social networks based on proximity. You take a picture with the app, and it turns around and starts grouping you with and sharing photos from other people nearby who have done the same. Sounds kind of dumb, right? Why would I want to see photos from nearby strangers?
Well, Sequoia thinks there’s something there, and has put $41 million into the company before it’s really even launched (thanks to a killer pitch deck). “Not since Google” have they seen this. Given that “this” currently refers to an app that I can’t even get to work on my phone, I’m left hoping that there’s a lot more going on here.
So what could that be? I’m going to put on my magic hat of credulity now, and describe what I (yes, I, random internet wantrepreneur) would be willing to bet $41 million on in this space.
Color is being run by Bill Nguyen, who sold Onebox for $850M in 2000, Lala to Apple for over $80M in 2009, and (at least until 11:41am today) spent time at AdGent. I’m not going to say that his presence means Color will be successful, but I do take it as a pretty good sign that there’s no possible way their actual business story is “Color shows you photos taken by people in the same room and then money pours out”.
From the TechCrunch writeup:
Color is also making use of every phone sensor it can access. The application was demoed to me in the basement of Color’s office — where there was no cell signal or GPS reception. But the app still managed to work normally, automatically placing the people who were sitting around me in the same group. It does this using a variety of tricks: it uses the camera to check for lighting conditions, and even uses the phone’s microphone to ‘listen’ to the ambient surroundings. If two phones are capturing similar audio, then they’re probably close to each other.
Remember The Dark Knight, when Batman hacked into everyone’s cellphone and streamed back sonar data to build a cohesive picture of what was happening everywhere in the city? That sounds awfully similar to what’s going on here – photo, GPS, and audio streams feeding back to Color in such a way that they can build a real-time model of where all their users are, who they’re with, and what’s happening around them.
With that kind of technology, who cares what their frontend does? Based on the quality of the first release of the phone apps, they’re clearly not sweating it too much. Whatever hook they try to snag users with is just a way to get that datastream, so they should ride whatever wave is currently popular. This week it’s Instagram and Path, so, sure, do that. Next week it’s going to be something else, so next week they’ll shift their apps towards that, or if they really can’t figure out how to get traction, they’ll release an api and let others do it for them. It doesn’t matter how that data comes in, as long as it comes in.
The web is training advertisers how to most effectively work with real time data (tracking cookies, ad auctions, sentiment analysis, twitter monitoring, all of that). How many companies work on this? How much money is being spent on these efforts, and how much is being made? There’s already one $190B company in this space on the web; the startup that can bring the same sort of tools into the real world might actually have a shot at becoming another.
Facebook, Foursquare, Yelp, Gowalla, Brightkite, Loopt, and everyone else with check-in functionality are already going for this. The biggest differences with Color seem to be that they want check-ins to be implicit byproducts of actions users have other motivations for (you’re not trying to get a free soda, you’re taking a picture to, uh, show to strangers in the same room), and that they’re handling far more inputs than just location.
These differences are both potentially huge. Other services risk crossing a mental line where explicitly checking in feels like work done for compensation (which is bad, which is why Foursquare is set up as a game), whereas this is an attempt to keep motivation purely social. And using multimedia opens up the door for all kinds of data points – facial recognition to keep track of people who aren’t actively using their product, brand recognition to note logos on clothes or labels on bottles, song recognition to track what music people are actually listening to – that advertisers would pay through the nose for.
Taking the credulity hat back off, even though I really do think the potential business models could make a ton of money, I’m equally convinced that this initial attempt at getting users isn’t going to make it very far. With $41M in the bank, though, they’ve got plenty of room to fail.
Update: Bill Nguyen confirms all of this almost point-by-point in an interview with Business Insider:
Photo sharing is not our mission. We think it’s cool and we think it’s fun, but we’re a data mining company.
It’s amazing how long you can go without basic life skills. Pretty much all the food that went into my body from college until last year came from a can, a box, or a restaurant, and either tasted bad, actively tried to kill me, or drained my bank account (or all three). The first step is acknowledging that you have a problem. The second step is learning to cook. The third step, apparently, is writing a post about what you learn over the course of a year of step two.
From learning to cook, I’ve saved hundreds of dollars, eaten much better, and picked up a new skill that I might actually be able to use in a post-apocalyptic setting (my first!). This is all basic stuff, but if you’re starting from zero like I was, it may be helpful.
– Get a decent chef’s knife. I picked up an OXO for under $20, and I love it. I’ve used other peoples’ nicer knives since getting this one, and there is a difference, but starting with a decent knife for cheap means you get to practice knife skills and maintenance without caring too much when you drop it on the floor a half-inch from your toe. Related: picking up hot things without putting sharp objects down first is not advised.
– Epicurious is awesome. Probably 80% of the recipes I tried this year came from this site. The recipes are usually well written and you can find ones from all over the difficulty spectrum (from quick-and-easy to spend-your-Saturday-hating-yourself-for-sucking-before-finally-just-ordering-a-pizza).
– When I first started, I thought salt was something that primarily goes on at the table so everyone gets as much or as little as they want. This is dead wrong, especially for meat. If you’re cooking steak, chicken, or pork, get some kosher salt (big flakes), sprinkle it on generously, and let it sit for a bit at room temperature before throwing it into the pan or oven. This locks in the moisture, and if you do it right, you shouldn’t really notice a salty flavor, it should just taste better. In college, I had a tradition of cooking myself a steak whenever I finished a big project, and I always wondered why it never tasted as good as what you get at a restaurant. It was the salt thing.
– When you’re chopping things up, make the results the same size so they cook at the same rate.
– If you’re frying, sauteing, or grilling chicken or pork, make sure you use cuts that are thin enough or that you can finish cooking in the oven. I’ve started butterflying chicken breasts before throwing them on the pan, and the difference is stark. A full breast takes too long to cook through and will either burn on the outside or dry out before it’s done, where the half breast stays moist and picks up a nice brown color while cooking in a much shorter time.
– I’ve been amazed at how many recipes want an onion. Learn how to chop one and save time and tears.
– Keep the pan hot. Every time you add something cool to the pan, you cool the pan off (stupid physics), so right when doing so, ramp up the burner and then taper it back to where it was as the food heats up to where you want it. I’ve only internalized this one in the last couple of months, after repeatedly banging my head on needing twice as much time as a recipe suggests. Keep things hot, and they cook faster. Similarly, it takes a lot less time to boil water if you put a lid on the pot. Weird how that works, right?
– Get to know by heart how long different basics take to cook, and how long things can sit when they’re done, so you only panic appropriately when everything else isn’t finished yet. Rice takes 20 minutes and can sit for a while, while steamed veggies take 10 and can’t. Chicken dries out quickly, while steak can rest for a spell. If you turn the heat down a notch, you can keep onions sauteing for a good while, but not so much for garlic, and not at all for peppers. That sort of thing.
A couple of days ago, TechCrunch featured a favorable story about a new startup called ZestCash, which provides an online lending alternative to existing payday loans (I’m not going to link to them directly, you can get to them on your own easily enough). The story regurgitates ZestCash’s copy about the evils of the existing payday loan industry, including numbers highlighting just how usurious the sector is. What it fails to mention is ZestCash’s own rates, which run between 242% and 462% APR at the time that I’m writing this.
To put that into perspective, consumer advocates regularly warn against the abusive nature of the ~30% APRs charged by many credit cards. The Center for Responsible Lending, which is frequently mentioned on ZestCash’s website at the time of this writing, supports a 36% annual interest rate cap. To make that point absolutely clear: ZestCash *repeatedly* cites a consumer advocacy group in making the case that they’re a responsible lender, and then turns around and charges rates up to more than 12x those advocated for by that same group.
Beyond the ridiculously high rates, the entire site is filled with disingenuous copy that seems designed to make unsavvy consumers feel smart and responsible for using ZestCash. They claim in big letters on the front page that ZestCash is “up to 50% cheaper than a payday loan”, but you have to click two links deep to find the explanation for where that number comes from, at which point you learn that 50% is over a payday loan that has been “rolled over” 7 times. They have an entire page dedicated to trying to convince you that APR doesn’t really matter. They make a big deal about the fact that they don’t have a lot of extra fees, but the fees they do have are massive: a 30% ‘origination fee’ on every loan, and a $35 late fee per missed payment on top of whatever overdraft fees your bank charges. They make a big deal of the fact that they clearly disclose their terms, but they’re required to do so by federal law. Almost every sentence on their website makes me tremble with rage for one reason or another.
The worst part about all of this is that their marketing message seems to have worked, at least this early on. I learned about them from a tweet by an entrepreneur I admire, which said he liked how ZestCash was trying to do payday loans in a “don’t be evil” way (he seemed to back off this when their rates were pointed out). A twitter search right now shows an overwhelmingly positive reaction, and the coverage of the service from major tech and business sites has been mostly positive as well. What gives? Do people really trust the press release they get from a company that much? Do they not go to the front page of the service and click around at least a little bit? Are Douglas Merrill and Shawn Budde big enough players that nobody’s willing to criticize them? Are their investors? I was similarly confused by the positive reaction to Betterment, a service that launched a little while ago that appears to try to convince consumers that ETFs are just savings accounts with higher returns, but this really takes it to a whole new level.
(ed. note: Betterment is no longer pushing the marketing approach mentioned above as aggressively as when they launched. Thanks, KW)
Busy day yesterday, so I only managed to tweet it, but Rank-o-Matic Week 6 rankings are up! New this week: I’ve always found myself clicking around the rankings to check the records of a given team’s opponents. In an effort to reduce that a bit, I’ve added some at-a-glance info. Now wins against teams with winning records are denoted with a W+, and wins against teams for which that game was the only loss are shown with a W1. With that information emphasized, it’s easier to understand why LSU, who has a tendency to win in a dirt-ugly fashion, is #1 in the current rankings: they’ve beaten 4 winning teams, including handing West Virginia their only loss of the season so far. Ugly or not, on the field, they’re walking away with Ws against some of the highest-quality competition in college football. Compare this to the AP poll: a #2 ranking for an Oregon team with only one quality win against Stanford and a resume otherwise made up of teams in the bottom half of the league or not in the league at all, and an undefeated LSU behind an Alabama team that just got spanked by South Carolina. AP polls like this week’s are exactly why I put the Rank-o-Matic together. I’m sick of seeing petty regional and personal biases matter more than what happens on the field.
Your week 5 is now complete, as the latest Rank-o-Matic rankings are up. This week adds a zeitgeist summary and tweaks the formula to give teams a bit less credit for close wins. Enjoy!
Rank-o-Matic week 4 rankings are up. The big change this week is that I’ve decided that the experiment to stop special-casing non-division schools is a failure. Instead, I’m arbitrarily deciding on 0.05 for a win and -0.95 for a loss, on the idea that a 12 win team with a non-division game should have a razor-thin edge over an otherwise identical 11 win team, and that any team that loses a non-division game should have no real shot at a high ranking. I’ve applied the change retroactively, so if you browse back to previous weeks, you’ll see the reports as they would have been had I been using this scoring system all season.
In the works for the coming weeks are a Zeitgeist view showing biggest movers and conference overviews, and a more principled way of rewarding road wins and penalizing home losses. And as always, big thanks to James Howell for collecting and hosting the score data I use to build the ranking. Enjoy!
After a year-long hiatus, I’m happy to announce that the Rank-o-Matic is back! Deep down in your heart, you’ve always yearned to know what my laptop thinks of the current college football season, and now, once again, you can. New features this time around include full-precision summation (thanks to Raymond Hettinger) and inter-weekly comparisons showing each game’s change in value and each team’s change in rank order. I’ve also temporarily stopped special-casing games against non-Division IA schools, a tweak I’ll be monitoring as the season progresses.
Big thanks again to James Howell, who keeps an awesome historical index of college football seasons, and whose current season listing I use as the source for the Rank-o-Matic. He’s also got a ranking of his own.
Questions or comments can be sent to me at jfager -at- gmail. Enjoy!
Steven Skiena rapping on combinatorial search in the Algorithm Design Manual:
[Chess] has inspired many combinatorial problems of independent interest. The combinatorial explosion was first recognized with the legend that the inventor of chess demanded as payment one grain of rice for the first square of the board, and twice as much for the (i + 1)st square than the ith square. The king was astonished to learn he had to cough up 265 – 1 = 36,893,488,147,419,103,231 grains of rice. In beheading the inventor, the wise king first established pruning as a technique for dealing with combinatorial explosion.