Halloween—two images, one film

I guess yesterday was “mischief night” for the markets. So happy Halloween. For those who can’t do risk by the numbers (or who can, but find images powerful) here are two.



(the second image is compliments of Hack the Market)

And I can’t let Halloween go by without a very belated movie recommendation. If you haven’t seen Pi—Faith in Chaos (1998) and if you have some time away from normal social relations (trust me, unless you have the weirdest friends in the world, they wouldn’t sit through more than ten minutes of this film and would worry about your sanity for ever subjecting them to it), rent the film. I watched it not long before I saw A Beautiful Mind, and I considered the Hollywood blockbuster a faint echo of the independent film. It’s a horrific, haunting tale of Wall Street’s search for the holy grail and of our addiction to kabbalistic (or Fibonacci) mathematics.

Stock Trader’s Almanac, 2010

Every year about this time I have to make a major executive decision—what kind of calendars and appointment book(s) I want for next year. You may guffaw, but this decision is not inconsequential. It defines how I plan to frame my time and what I consider to be noteworthy events in my life. I of course have an online “appointment book,” but its main function is to record upcoming economic reports so I don’t get caught unaware. I also get innumerable wall calendars; I hang up a couple and never look at them except when I flip the month and see a new nature picture. But I always need some kind of calendar I can touch and write on.

The Stock Trader’s Almanac by Jeffrey A. Hirsch and Yale Hirsch (Wiley, 2009) fills the bill perfectly. It’s a spiral-bound hardcover, which means that once opened it lies flat. The calendar section of the almanac (about 2/3 of the book) has on facing pages historical data on market performance (verso) and a week’s worth of calendar entries (recto). Each trading day’s entry also includes the probability, based on a 21-year lookback period, that the Dow, S&P, and Nasdaq will rise. Particularly favorable trading days (based on the performance of the S&P) are flagged with a bull icon; particularly unfavorable trading days get the bear icon. A witch icon appears on options expiration days. At the bottom of each entry is a quotation. There’s about a five-square-inch space in which to write.

Published every year for over forty years, the Stock Trader’s Almanac is a handy source for a wealth of statistical information. It records intraday performance, day of the week performance, and monthly performance. It highlights the ten best and worst days, weeks, months, quarters, and years for the major averages. It continues to update the January barometer, devised by Yale Hirsch in 1972, and looks at the correlations between politics and markets. And on and on.

Seasonal trading strategies have taken a hit recently, but they’re in good company. Moreover, the track record for most of these strategies is detailed, so we can see if and when they broke and perhaps, in light of this information, devise alternatives.

Whatever the rationale for owning this almanac, I consider it indulgence on the cheap. And unlike the chocolates I buy for the Halloweeners who never come (and admittedly often devour before Halloween, trusting that the outlier won’t happen) this almanac not only gets you through a whole year but remains a valuable reference work long into the future.

Northington, Volatility-Based Technical Analysis

Kirk Northington’s Volatility-Based Technical Analysis: Strategies for Trading the Invisible (Wiley, 2009) is a 450-page infomercial for his MetaStock add-on. Although he purports to offer both MetaStock and Trade Station formulas unique to his system, all but the most basic MetaStock fomulas have proprietary components and hence are useless. He seems to have been a bit more generous with his Trade Station coding. Looking at the MetaStock charts that are chosen to demonstrate the potency of his indicators, I’m not wowed. And since, despite his system building, he claims to be a discretionary trader, there are virtually no backtests, no statistics, to lend credibility to all those entry and exit symbols on his charts.

Now that I’ve vented, let me look at this book more constructively. The basic premise is sound: volatility is a critical descriptor of market movement and hence should not be ignored in constructing a trading system. Of course, volatility is not a univocal concept. Historical volatility is easily described by traditional technical analysis software; implied volatility is captured only by options software (which usually includes historical volatility indicators as well). Writing a trading system for equities that successfully incorporates both elements is not for the faint of heart or the mathematically challenged. Northington does not undertake this task, though he offers an “equalizer,” what he calls projected implied volatility. For the most part he focuses on the usual suspects in the indicator world—standard deviation, average true range, linear regression, and R squared.

One theme that runs throughout the book is the power of combining and compounding. This, I think, may have some merit. At least, it’s something I might play with a bit. Here are two examples. First, create a composite security from two related securities. The chart of the composite security is sometimes clearer than that of either of the individual securities. When your system signals an entry on the chart of the composite security, split your order equally between the two securities. Second, compound indicators to highlight extremes. This is important for Northington because, he argues, “money is to be made at the extremes.” (p. 16) For example, multiply a normalized value of the distance of price from its x-period moving average by its average true range. “When both values are at extremes, the calculated value will be truly magnified.” (p. 72) In its simplest form, using MetaStock coding, the function would be (((C – Mov(C, 40, E)) / C) * 100) * ((ATR(14) / C) * 100). Northington tweaks this function, looking at highs and lows instead of closes, overlaying standard deviation bands, and optimizing values, but the basic idea remains intact.

Another point, not original but nonetheless important enough to keep repeating, is that a critical step in system building is breaking the system. Every strategy has its blind spot, and when a developer breaks his system he discovers its vulnerability. He can then decide how to address this vulnerability—whether to hedge, whether to diversify, whether to cut back on position size, whether to keep a tighter stop, or whether simply to throw the damned thing out!

A final idea that deserves a brief paragraph occurs in the subtitle of the book: trading the invisible. In some ways, of course, it’s merely a catchy phrase, a bit too cute. But Northington’s point is that the eye tricks us into seeing patterns that aren’t there and missing patterns that are. To study a chart and then trade based solely on what we see is to trade the visible but perhaps the illusory.

Howe, Crowdsourcing

I was driving back from the grocery store listening to
CT public radio when I heard a pitch (pun intended) for a proposed crowd-managed baseball team. One of the guests on the show was Jeff Howe, author of Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business (Crown Business, 2008). So, naturally, I had to add this book to my series on crowds. For those of you who are sick to death of the theme, this will be my last post on crowds, at least for the foreseeable future. Of course, my foresight isn’t any better than anyone else’s.

Perhaps the best known instance of crowdsourcing was the Netflix Prize, awarded last month. For $1 million Netflix in effect hired groups of Ph.D.s for a dollar an hour to improve its movie recommendation system. The winning team of seven was made up of statisticians, machine-learning experts, and computer engineers from four countries.

Howe, a contributing editor at Wired magazine, takes the reader on a quick crowdsourcing journey from T-shirt company Threadless, iStockphoto, and open source software to P&G’s Connect and Develop initiative (responsible for Swiffer). He highlights the national bird counts coordinated by the Cornell Lab of Ornithology, the world of the citizen journalist, and of course Wikipedia. And just as Page cited Surowiecki, Howe cites Page. (You see, I could have sequenced my reading simply by date of publication instead of my more complicated heuristic that reached the same end.)

Today I’m going to focus on the trading crowdsourcing model at Marketocracy, where investors can open a virtual portfolio of $1 million and join the more than 85,000 virtual fund managers on the site. Marketocracy then monitors these portfolios in search of ideas for its own Masters 100 mutual fund, launched in 2001. Initially, the fund followed a simple model, weighting its positions to match those of the top hundred portfolios. The model was effective. By the end of its first year of operation it beat the S&P 500 by 14%. In both bear and bull markets the model outperformed. But then came 2004 when the market became choppy. The Masters 100 began to underperform its benchmark dramatically; as investors fled, its assets under management withered from $100 million to $50 million in just over a year.

The problem was not only a changing market but that old demon--herding. “As the top investors got to know one another, they started conferring on their positions.” (p. 175) In response the Marketocracy team introduced changes, among which was one that made it impossible for members to see each other’s trades. The team also decided that its pool of 100 was too small (and probably insufficiently diverse) and its algorithm too simplistic. For instance, it was overlooking the specialists—those who, though they didn’t perform brilliantly overall, had unique expertise.

This hybrid model flagged an oil-shipping company called Knightsbridge Tanker whose stock a sub-set of the Marketocracy traders (none in the top 100) was buying aggressively. Intrigued, the management at Marketocracy sent e-mails to these traders to find out why they were loading up on the stock. It turned out that the company had a lot of tankers about to be scrapped. How did the traders know this? Someone checked in Singapore where the tankers were registered. “The conventional wisdom is that when a tanker reaches the end of its life, it’s worth zero. But the price of steel started to go through the roof in the interim, and all that was about to be returned to investors as dividends. Marketocracy made a killing.” (p. 176)

Marketocracy took a good idea, found flaws in it, improved on it, and I’m sure continues to improve. But it’s not a poster child for crowdsourcing. It has only a two-star Morningstar rating and assets under management of less than $20 million (last year it lost a whopping 46%). So far this year it is beating the S&P 500 handily (28% vs. 20% as of October 23) but trailing the S&P MidCap 400 against which Morningstar benchmarks it; MID is up 30%.

Problem-solving traps

I first saw a reference to Michael Shermer’s book Why People Believe Weird Things in David Aronson’s Evidence-Based Technical Analysis. The Shermer book isn’t particularly revelatory; in fact, today’s takeaway comes not from Shermer himself but from a 1981 study by Singer and Abell that he cites. Again, not groundbreaking, but worth repeating.

When people are asked to select the right answer to a problem after a series of guesses and positive or negative feedback from the investigator, they regularly fall into dangerous traps. First, they form a hypothesis and look only for confirmation of the hypothesis, not for evidence to disprove it. Second, they are very slow to change the hypothesis even when it’s obviously wrong. Third, in the face of complex data, they adopt overly-simple hypotheses or strategies to solve the problem. Finally, they always find causality, even if the investigator’s “right or wrong” feedback was given randomly.

Traders and investors fall into the same problem-solving traps as the rest of the population but they often pay a higher price. Perhaps it would be time to have a checklist to accompany each investing or trading hypothesis. (1) What would qualify as evidence against this hypothesis? (2) When should I throw in the towel? (3) Is my strategy adequate to the problem? With certain kinds of investments and trades you might add: (4) Am I mistaking correlation for causality?

The checklist won’t guarantee profits, but it might dampen losses.

Sornette, Why Stock Markets Crash, part 2--Herding and Minority Games

Game theory, an analytical tool in support of Adam Smith’s “invisible hand,” maintains that all the decisions we make are optimization problems. Didier Sornette disputes this claim. People, he argues, “have natural intuitive mechanisms—mind modules that serve them well in daily interchanges—enabling them to ‘read’ situations and the intentions and likely reactions of others without deep, tutored, cognitive analysis.” (Why Stock Markets Crash, p. 84) Even the ill informed and error prone may converge over time to Smith’s general equilibrium, where everything works out for the best. Then again, equilibrium may be upset.

One way to upset equilibrium is through herding. Sornette distinguishes multiple types of herding, but the most interesting is the informational cascade. An informational cascade occurs when “the existing aggregate information becomes so overwhelming that an individual’s single piece of private information is not strong enough to reverse the decision of the crowd. Therefore, the individual chooses to mimic the action of the crowd.” Sornette continues: “The two crucial ingredients for an informational cascade to develop are: (i) sequential decisions with subsequent actors observing decisions (not information) of previous actors; and (ii) a limited action space.” (p. 95) In the Internet bubble it was irrelevant to know that a particular business model was doomed (I still use my pets.com mousepad); the crowd was buying the stock, so the only sensible thing to do was to go along for the ride.

Since herding is such a prevalent phenomenon in markets (from analysts’ recommendations to momentum trading), if a trader lacks information it is optimal for her to imitate. She should look to the crowd, in essence “polling the members” to analyze how likely they are to behave in the future. Mathematically, she is taking part in an infinitely iterative loop. “The opinion s_i at time t of an agent i is a function of all the opinions of the other ‘neighboring’ agents at the previous time t – 1, which themselves depend on the opinion of the agent i at time t – 2, and so on.” (p. 103)

The problem with following the herd is that investors cannot all win at the same time, so here and there they have to take a minority view. In the simplest terms the investor would want to be in the minority when buying but in the majority during the holding period of the investment. Therefore, the relative impact of contrarian behavior on majority behavior is the ratio of the entry time to the holding time. Sornette speculates about the impact of this ratio on the intraday trader. “The large amount of works on minority games . . . suggests that changing one’s strategy often may be profitable in that situation. It also suggests that only when the information complexifies or when the number of traders decreases will the traders be able to make consistent profits. In contrast, the buy-and-hold strategies profit as long as the information remains simple, such as when a trend remains strong. The problem then boils down to exit/reverse before or at the reversal of the trend.” (p. 119) Sornette oversimplifies the profile of the intraday trader and overlooks the existence of intraday trends, but you get the idea.

Okay, intraday traders, you now have a new project—to learn about minority games, abstractions of the famous El-Farol Bar problem. Although I don’t understand why the total number of traders is relevant to the profitability of individual traders, my projects right now are out of control!

Behavioral finance links

The other day I linked to a paper by Richard Thaler. There's a lot more where that came from on his Booth School of Business, University of Chicago page.

And if you really want a feast, here's the Behavioural Finance website.

Quotation of the day and Groundhog Day

We are all creatures of habit and have routines with which to start the day. Here are a couple of sites that offer quotations, some inspirational, some amusing: bookreporter.com and brainquote.com. (At least I’m not sending you to a daily horoscope site!)

Examples: "No pressure, no diamonds." (Thomas Carlyle) "You can't build a reputation on what you are going to do." (Henry Ford) "I don't think necessity is the mother of invention--invention, in my opinion, arises directly from idleness, possibly also from laziness. To save oneself trouble." (Agatha Christie)

In a totally different vein, here's a link to Bespoke's chart overlay comparing September and October intraday price movement in the S&P 500: Groundhog Day.

Sornette, Why Stock Markets Crash, part 1—Drawdowns as Outliers

In 2001 Didier Sornette, a scientist who taught geophysics at UCLA and today wears many hats (among them, professor of entrepreneurial risks at ETH Zurich, professor of physics at ETH Zurich, professor of geophysics at ETH Zurich, and visiting professor of geophysics at UCLA), finished his book Why Stock Markets Crash: Critical Events in Complex Financial Systems. It was published by Princeton University Press in 2003. Some of the themes of his book have been expressed by other thinkers, but Sornette’s analysis is both extensive (the text alone is just shy of 400 pages) and accessible to the layman.

Sornette contends that financial markets are complex systems that cannot be explained reductionistically by decomposing them into their elements and then studying these elements. Complex systems often exhibit “coherent large-scale collective behaviors with a very rich structure, resulting from the repeated nonlinear interactions among its constituents: the whole turns out to be much more than the sum of its parts.” (p. 16) Moreover, says Sornette, “it is widely believed that most complex systems are not amenable to mathematical, analytic descriptions” and that “many complex systems are said to be computationally irreducible; that is, the only way to decide about their evolution is to actually let them evolve in time. Accordingly, the ‘dynamical’ future time evolution of complex systems would be inherently unpredictable.” (p. 17)

Sornette is dissatisfied with the hypothesis that the prediction of complex systems is impossible and sets out to find patterns that can be compared “simultaneously and iteratively, at multiple scales in hierarchical systems.” (p. 20) These patterns can then be used to compare the dynamical state of systems before and after a financial crash. Sornette’s hypothesis is that “stock market crashes are caused by the slow build-up of long-range correlations leading to a global cooperative behavior of the market and eventually ending in a collapse in a short, critical time interval.” (p. 23) With very few exceptions, he argues, “log-periodic power-laws adequately describe speculative bubbles on the Western markets as well as on the emerging markets.” (p. 25)

How does Sornette reach this conclusion? After rejecting the frequency distribution model as inadequate for characterizing anomalous events, Sornette turns to the familiar concept of drawdowns. Drawdowns, he argues, “embody a rather subtle dependence since they are constructed from runs of the same sign variations.” (p. 52) For instance, consider a drawdown of 10% a day over three days. There is a persistence here that is not captured in the distribution of returns that counts only (uncorrelated) frequency and minimizes the chance of an outlier occurring. The sequence of four drops marking the largest drawdown in the DJIA (Black Monday, October 1987) would occur, according to distribution theory, once in about 4 thousands of billions of billions of years. Oops!

The very largest drawdowns, Sornette shows, are outliers even though, with the exception of Black Monday, the very largest daily drops are not outliers. What accounts for the persistence that creates outliers? What mechanisms in the stock market and in the behavior of investors can lead to positive feedback? That’s the subject for part 2 of this post.

Myopic loss aversion

In reading Aswath Damodaran’s Strategic Risk Taking: A Framework for Risk Management (Wharton School Publishing, 2007), a very gentle introduction to a tough subject, I lingered over his discussion of how we seem to be hard-wired to pursue irrational risk management strategies.

One important finding from behavioral finance is that loss aversion becomes more pronounced as the frequency of a person’s monitoring increases, a phenomenon known as myopic loss aversion. In one experiment researchers ran nine lotteries. They provided feedback after each round to one group and provided composite feedback only after three rounds to the other group. People in the first group were willing to bet far less than those in the second group.
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A 1997 paper co-authored by probably the best-known names in behavioral finance--Richard H. Thaler, Amos Tversky, Daniel Kahneman, and Alan Schwartz—explores this phenomenon in more detail. “The Effect of Myopia and Loss Aversion on Risk Taking: An Experimental Test” (available for download from Thaler’s faculty page) grounds myopic loss aversion in two behavioral principles. First, people are more sensitive to decreases in their wealth—in fact, twice as sensitive—than they are to increases in their wealth. Second, people engage in mental accounting, both cross-sectionally (for instance, “are securities evaluated one at a time or as portfolios”) and intertemporally (how often portfolios are evaluated). The upshot: “An investor who frames decisions narrowly will tend to make short-term choices rather than adopt long-term policies. An investor who frames past outcomes narrowly will evaluate his gains and losses frequently. In general, narrow framing of decisions and narrow framing of outcomes tend to go together, and the combination of both tendencies defines a myopic investor.” (p. 648)

In their experiment the researchers asked 80 undergraduates to imagine that they were the investment manager for a small college’s endowment and that they had to decide how to allocate its investments. They had only a binary choice—Fund A or Fund B. The students were not told about the composition or the risks and returns of the two funds, though in fact Fund A was a bond fund with a mean return per period of 0.25% and a standard deviation of 0.177% and Fund B was a stock fund with a mean return of 1% and a standard deviation of 3.54%. Subjects were randomly assigned to one of four conditions—a so-called monthly condition (actually 6.5 weeks) in which they had to make 200 decisions, a “yearly” condition (comprised of eight periods) in which they made 25 decisions, a “five-yearly” condition where they made five decisions (each of which was binding for 40 periods), and finally an “inflated monthly” condition. In this last condition “the returns were translated upward by 10 percent so that subjects always experienced nominally positive returns from both funds. Subjects in this condition were told that there was a high rate of inflation which was responsible for returns always being positive.” (p. 652) After each decision, the subject saw the results on a bar graph. At the end of the trials, each subject had to make a final allocation that would be binding for 400 periods.

The worst performer by far was the group that had to make the most decisions and that received the most feedback. The “inflated monthly” group had mean final results that did not differ significantly from the “yearly” and “five-yearly” group, indicating that if the risk premium is high enough even myopic investors will be more daring.
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Perhaps not surprisingly, Damodaran reports that professional traders have been found to exhibit more myopic loss aversion than inexperienced subjects. As if they didn’t have enough problems already! I know this is a glib retort. Successful professional traders who take frequent small losses and monitor their positions constantly must have trained themselves to defy evolution and adopt rational risk management strategies. Otherwise, they would be middle managers or trading strategy pitchmen. The wannabe trader must learn to be less “human” and recognize that myopic loss aversion is the first, and perhaps last, step into fatal quicksand.

Asness on Momentum

In a recent paper “Value and Momentum Everywhere” available for download from the Social Science Research Network Clifford Asness, Tobias Moskowitz, and Lasse Pedersen argue that value and momentum are negatively correlated both within and across asset classes and therefore make perfect portfolio partners. Asness, co-founder of AQR Capital Management which launched three three momentum mutual funds in July, has been beating the drum for momentum for some time. He argues that momentum has far outperformed growth over the last 80 years, although 2009 wasn’t kind to the strategy.

In an address at the Schwab Impact Conference in mid-September summarized in the Advisor Perspectives newsletter, Asness offered some hypotheses about why momentum works. First, investors are slow to adjust to news because of anchoring, so the price of a stock moves only part of the way on good news. Second, investors may overreact to good news, sending the price of a stock higher than is warranted. Third, investors are likely to sell their winners too early so there will be inventory available for those who want to ride the wave longer.

The momentum strategy does not itself exhibit the characteristics of momentum. It wins about two out of three years, and its odds of winning in a following year are independent of how it performed the previous year. The AQR momentum strategy, by the way, is based on the standard twelve-month time frame with a one-month lag (since stocks tend to mean revert in the short term). This time frame is critical, as the following graph indicates. (Click on images to enlarge.)


How has this strategy performed? According to AQR research:

Sorkin, Too Big to Fail

Saturday morning I did a favor for a friend, skimming and critiquing a mediocre fictional political thriller. And then, with a sigh of relief and a sense of anticipation, I entered the complex real world where personal relationships, firm rivalries, government policies, and Armageddon scenarios both took the world’s financial system to the brink and (we trust) saved it from collapse. Andrew Ross Sorkin’s Too Big to Fail: The Inside Story of How Wall Street and Washington Fought to Save the Financial System from Crisis—and Themselves (Viking, 2009) tells a story we thought we already knew. It turns out we knew only its public face.

Sorkin, a well-respected reporter for The New York Times, provides background on the main characters in the drama, both people and firms, as they make their entrance and shares the assessments of others when appropriate (some blunter and more profane than others). For instance, Robert Steel, deputy secretary of the Treasury and a Goldman alum, always said to staffers seeking advice on how to deal with their new boss: “One: Hank’s really smart. Really smart. He’s got a photographic memory. Two: He’s an incredibly hard worker, incredibly hard. The hardest you’ll ever meet. And he’ll expect you to work just as hard. Three: Hank has no social EQ [Emotional Quotient], zero, none. Don’t take it personally. He has no clue. He’ll go to the restroom and he’ll only halfway close the door.” (p. 48)

The cast of characters in the crisis, by the way, was large and their relationships incestuous. Rivals lived in the same building and ate at the same restaurant. They both depended on and deeply distrusted one another. Strong banks tried to solidify their positions through cheap, strategic acquisitions; the weak were either in denial or ready to offer themselves up on the takeover altar. Everyone feared Goldman Sachs; many had worked there at some point in their lives. Lurking in the background, threatening to engulf the entire financial system, was AIG.

Early in the crisis, as traders began to hammer away at financial stocks, Dick Fuld, the dysfunctional CEO of Lehman, was quick to blame short sellers for all the woes of his firm. A ban on short selling, he argued, would give Lehman room to breathe, which was all it needed. Fuld was not alone in pointing the finger. John Mack ranted that a raid on Morgan Stanley’s stock was “immoral if not illegal.” (p. 420) His CFO was more Darwinian, describing short sellers as “cold-blooded reptiles [who] eat what’s in front of them.” (p. 421)

Short sellers, of course, didn’t bring down Lehman. The firm was in trouble long before they entered the picture. I didn’t realize, however, how close Lehman came to being rescued and how, despite the best efforts of bleary-eyed Wall Street bankers, the plan was scuttled.

Throughout the cascade of crises, the public and private sectors were in constant communication. Phone call after phone call, meeting after meeting. Although government teams were lean, bankers often moved in hordes. Greg Curl, the chief deal maker at Bank of America and a possible successor to Ken Lewis, brought more than a hundred bankers to New York from Charlotte to work on the Lehman deal. Once that deal was dead, he sent them home, only to need them back some four hours later to look into a deal with Merrill.

Sorkin’s 600-page book is gripping. He fills in a story harrowing in its own right with behind-the-scene details and enough dialogue to make it read like a novel. There are even tidbits of comic relief, such as the disclosure that Hank Paulson’s pension from Goldman Sachs after he turns 65 will be a whopping $10,533 a year. (p. 424)

Too Big to Fail is not a morality tale with heroes and villains; in fact, after countless nights of sleep deprivation everybody becomes grey and haggard. Neither Wall Street nor Washington is glamorous. As I read this book I often thought of John Le Carré.

Santa Fe Institute

I’m sure I’ve mentioned complex adaptive systems on this blog more than once (and, trust me, I’ll do it again); perhaps it’s time to acknowledge the source of most of the research, the Santa Fe Institute. I first learned about its work via one of my overly complicated brain paths. A Yale biology professor who managed to start my power washer and subsequently had some affiliation or other with the Santa Fe Institute, his publisher wife, probably a gap here, Mauboussin of Legg Mason, and then reference after reference. That’s the best I can do at reconstruction.

The Santa Fe Institute was founded in 1984 with the goal of “fostering a multidisciplinary scientific research community pursuing frontier science.” Its focus, the early visionaries soon decided, would be on complexity. Though still a key area of study, by now SFI has expanded its horizons. Its combination of resident faculty, “external” faculty, postdoctoral fellows, and a constant stream of visitors puts it at the forefront of research on many critical issues of the day. The “news and announcements” page on its website highlights some of its work: econophysics to model markets, institutions and their complex interactions; agent-based modeling to take on the complex system of financial economics and to track pandemics; a theory of parochial altruism (a predisposition to be cooperative towards group members and hostile towards outsiders); and a map of knowledge. (Click on image to enlarge.)


Since the researchers of SFI are doing so much work on the financial markets, there’s no doubt I’ll encounter even more of their books and papers as well as popularizations of their ideas. So stay tuned.

A tease

Saturday morning FedEx pulled up to the door, delivering a book I had been eagerly awaiting, Andrew Ross Sorkin’s Too Big to Fail. The release date is tomorrow, October 20, so even though I spent the lion’s share of Sunday engrossed in the 600-page book (well, I didn’t read the endnotes or the index) I am going to respect the release date. I will not echo Tom Wolfe’s “advance praise” of the book as “a fascinating, scene-by-scene saga of the eyeless trying to march the clueless through Great Depression II.”

Ways to twiddle your thumbs

It’s the weekend and time to kick back. (Well, not for me. I’ve just received three fascinating books that add up to over 1,400 pages of reading.) Anyway, I’m not your weekend camp counselor. Instead, I’m offering analgesics for weekday boredom. The markets aren’t moving, you’ve answered all your e-mail, you’ve updated your trading stats. You could walk away from your computer and do something constructive but, like Dostoevsky’s gambler, you’re attached by invisible chains. You’ve run out of other more productive ways to spend your time.

Here are a few things you can do that aren’t entirely mindless.


First, Ken Ken—“the most addictive puzzle since Sudoku,” according to Will Shortz. While you’re on The New York Times puzzle page, you can try SET!, though I personally find it irritating because I’m not especially good at it.

Second, Lumosity’s Word Bubbles. This is one of those cognitive training exercises.

Third, Web Sudoku, available in different levels of difficulty. I do the “evil” ones.

Fourth, JigZone’s Jigsaw puzzle. You have the option of changing the puzzle cut, making it more or less difficult.

Fifth, if you pride yourself on your vocabulary or simply hope to improve it and want to do a little bit of good, try Free Rice where for each answer you get right the site donates ten grains of rice through the UN World Food Program to help end hunger.

Sixth, there are the endless online crossword puzzles.

And finally, of course, how about dipping into a good book?

Of course, don’t let these diversions overwhelm your primary passion, trading.

Agent-based models

Agent-based models have been used to simulate financial markets. The idea is to capture the complex dynamics of a large diversity of investors and traders with different strategies, different trading time frames, and different investment goals. If you want to see an agent-based model at work, you can download a free evaluation edition of Altreva’s Adaptive Modeler software. It’s lots of fun to play with. I am not promoting the software; I’m just trying to highlight yet another approach to the markets.

Dalton & Jones, Mind over Markets

Many traders rely, at least in part and often in a less rigorous way, on the Market Profile model first introduced in 1984 by J. Peter Steidlmayer. Mind over Markets: Power Trading with Market Generated Information (Traders Press, 2d ed., 1999) by James F. Dalton, Eric T. Jones, and Robert B. Dalton is a thorough explication of Market Profile charting concepts. It should be required reading for anyone using Steidlmayer’s model and its innumerable knock-offs.

I happen not to trade this way, though my style shares a few of the ingredients in the Market Profile auction model. I could therefore read the text looking for more general insights and skip the pictures! It made my task much less daunting.

In analyzing market structure, the authors are inclined to anchor intraday activity to the way the market opens. “With an understanding of market conviction, it is possible to estimate very early on where the market is trying to go, which extreme is most likely to hold (if any), and even what type of day will evolve. In other words, the market’s open often foreshadows the day’s outcome.” (p. 63) There are four basic types of opens—the open-drive, open-test-drive, open-rejection-reverse, and open-auction, all analyzed in some detail.

One of the theses of this book--familiar to many, very profitable to few, and treacherous to the unwary--is that “the best trades often fly in the face of the most recent market activity.” (p. 107) That is, when a market trades above or below an accepted reference point and fails to follow through, this often sets up a powerful reversal trade. And if the reference point of a higher time frame supports that of the lower time frame, the reversal move can be even greater.

It’s important to note that the reversal trade first requires a failure. For instance, the market trades just above a previous intraday high and then runs out of steam; the buying dries up. Or the market trades just below the previous day’s low and then stutters; the sellers start to mark up their merchandise. After a “confirmation,” the trader should place a trade in a direction opposite to the previous trend.

The reversal trade, improperly executed, will be no different from the doomed attempt to pick tops and bottoms. But the trader can put some filters in place to improve his odds. And he can keep a pretty tight initial stop since his hypothesis is straightforward—a powerful reversal trade should be strong right out of the gate. By the way, I would add a note here. On very small timeframe charts there’s almost always a little back and forth movement at obvious support and resistance points, so it’s imperative to move up at least one timeframe to get rid of the noise and determine whether there is continuation or failure.

Although Mind over Markets is most definitely not a book about the psychology of trading, in the final chapter on proficiency the authors offer practical advice about such topics as managing inventory and knowing your competition. They also venture into the realm of left-brained and right-brained activities and suggest that “after the market’s close . . . the analysis of the day’s activity and preparation for the following trading session are best accomplished by using predominantly left, analytical talents. On the other hand, when the market is in full gear, the fast-processing, free-associating right brain should have more control. Ideally, we should strive to operate in a more ‘central’ hemisphere, freely calling on both sides of the brain to contribute when needed.” (pp. 317-18) For those who are predominantly right-brained or left-brained and can’t achieve the ideal of whole-brained trading there’s a fallback position: “By knowing your left/right brain strengths and weaknesses, you can begin to develop a trading strategy that best uses your individual talents.” (p. 318) If you don’t know where you fall on the left brain/right brain spectrum, there are lots of free tests available on the Internet.

Rogue bidders in auctions, a bizarre behavioral finance anecdote

For years I have been involved in a basset hound pedigree project. The vital data for this project come from the American Kennel Club “stud books.” I had an assortment of these stud books, some via subscription, others from friends of the project. Once the appropriate pages were photocopied and the information entered in a genealogical database (free software compliments of the Mormons), I no longer needed the books. So I could sell them to raise money to buy books the project needed (alas, far more than I owned).

The obvious venue to sell these books was eBay. There were four or five collectors, and the books were going for about $20 a copy. Then came Agnes. The bids would get to the normal topping-out range, and she would place an aggressive bid. The other bidders couldn’t stand to see the book get away from them, and they kept bidding it up until Agnes finally silenced them at a little over $100.

The problem was that, after winning quite a few books (and many other dog-related items, as it turned out), Agnes didn’t pay. eBay eventually expelled her. As soon as I could, according to eBay rules, I contacted all the underbidders in my auctions, explained the situation, and said that I would offer each book that I had auctioned to the person who had placed the highest bid before Agnes entered the process. Other sellers apparently weren’t so generous and agreed to sell the books only at the bid just below Agnes’s highest bid—in other words, for something around $90.

No sooner had I breathed a sigh of relief and thought that things were back to normal than up popped a new persona with exactly the same bidding pattern. Once eBay expelled this second rogue, a third took her place. What was absolutely fascinating was that although the bidding process was about as transparent as possible—that is, virtually everybody knew about the rogue bidder in various personae and the way sellers were dealing with the problem, the legitimate bidders didn’t change their behavior. They kept bidding up the books—a strategy that cost them dearly with other sellers. Sometimes there were two real bidders plus the rogue, so one might surmise that each was trying to be the bid just under the rogue’s high. That would have made some (irrationally expensive) sense when it came to other vendors, but they did the same thing in my auctions even though they knew that my rules were different.

Once Agnes and clones left the scene, one might have expected the price of the books to fall back into the $20 range. But no, Agnes’s $100 bids became something of an anchor and for quite awhile I sold books for the bargain basement price of $30-$40.

Just an anecdote, but it serves to further undercut the model of homo economicus.

Millard, Channels & Cycles

There are always books that are to me, for lack of a better word, thorny. I’m convinced that there is something sweet in them, but I can’t get past the thorns. One of these books is J. M. Hurst’s The Profit Magic of Stock Transaction Timing, published in 1970. Innumerable traders say that this book changed their lives; so far I am not one of them. Hurst was clearly a visionary. Not only did he propose a theory of cycles, he also offered hints about what was later to be known as fractals. But I had no clue how to translate this into either an intellectual road map or a course of action.

Along came Brian J. Millard, who in Channels & Cycles: A Tribute to J. M. Hurst (Traders Press, 1999) set out to make Hurst’s work more accessible to traders without an engineering background. He starts with Hurst’s basic premise that higher frequency trading trounces (long only) buy-and-hold. Trade all the zig zags, long and short, and the contributions from the short side add significantly to the bottom line. The problem, of course, is that zig zag indicators work only in hindsight, so how can the trader identify a swing high or low more or less in real time?

Enter cycles and channels. The standard Hurst indicators use offset moving average channels of various lengths; these nested envelopes help to show how price cycles have evolved in the past. Unfortunately, since they are offset, they stop half a cycle short of the hard right edge. So programmers have had to develop algorithms to extend these channels to the present and into the near future. There are a host of commercial products that purport to do this. Although in this book Millard offers some homespun ways to extrapolate cycles into the future, he also sold software to do this--first Microvest 5.0 and Sigma-p, then Channalyze and CCS Visions. Since his death these are no longer available. But new programmers have stepped up to the plate, some using polynomial regression (for instance, the Arps Hurst Bands and perhaps Sigma Bands), others relying on Fourier transform spectral analysis (for instance, Clyde Lee’s Swing Machine). One problem with all this code is that you can’t backtest it because you have shifted the unknown back in time and made it appear to have been known. That is, it’s like trading based on tomorrow’s or next week’s Wall Street Journal.

One concept I finally understood from Millard’s book is that trends are additive. Since he considers it one of the most important ideas in his book I think it’s fair to say that I may be wiggling my way between the thorns. Here’s his example. A stock rises $2 in four weeks, then falls by $2 over the next four-week period, then does nothing over the following four weeks. A longer-term trend coexists during which the stock has risen by $8 over 40 weeks. It’s simple arithmetic to combine the short-term trends with the long-term trend. Over the long term the stock has risen by an average of 20 cents a week. So we can add this average long-term weekly rise (times four, of course) to the four-week rise of $2, for a total of $2.80. “The total effect of the two trends is to cause a rise in price of $2.80.” (p. 48) Over the next four weeks the long-term trend again adds 80 cents, but the short-term trend takes away $2.00, for a net fall of $1.20. For the final four weeks the short-term trend adds nothing, so we have a gain of $0.80 from the long-term trend. It’s important to note here that the initial leg of the combined trends rises faster than the rise in the component trends.

In an idealized chart of short-term cycles contained within a long-term channel we can see that, because of the additive nature of cycles, “the short term rises are enhanced relative to short term falls while the outer channel is rising. The opposite happens while the outer channel is falling.” (p. 123)



This is to my mind a very effective way of understanding the interplay between higher time-frame and lower time-frame price movements.

Page, The Difference, part 2

Though referring frequently to Suriowecki’s book The Wisdom of Crowds, Page maintains that “crowds are not wise, but crowds of models are.” (p. 341) This is an important distinction. “The best predictions should come from collections of diverse models. These models should parse reality differently. They should rely on interpretations based on diverse perspectives that look at different attributes in the same perspective, or interpretations that slice up the same perspective into different clumps. If so, each model will be accurate, and the collection of models will be diverse. This combination of accuracy and diversity makes for a wise crowd.” (p. 235)

Page offers a test recipe for creating a crowd of models in such small groups as juries and boards of directors; in larger groups diversity is almost guaranteed. He also acknowledges that “individuals can amass their own crowds of models” and points to Buffett’s sidekick Charlie Munger who “bases his investment decisions on what he calls a lattice of mental models: a collection of logically coherent diverse models that combine to help him make accurate forecasts. His crowd of models, we can only surmise, is an intelligent, diverse bunch.” (p. 235)

Page’s book is so lush, combining what one reviewer called a “dazzling eclecticism” with scientific rigor, that it’s impossible to do it justice in two blog posts. Nonetheless, I will limit myself to just one more market-oriented insight—how a wise crowd differs from a diversified portfolio containing “rainy day bonds, sunny day stocks, and cloudy day cash.” (p. 340) Diversification pairs holdings to “states of the world”; stocks pay off in sunny weather, bonds in rainy weather, and cash is handy to have during cloudy times.

By contrast, a group of people working together to solve a problem are not simply trying to realize a state. One person makes an improvement, and others may build on this improvement to introduce further improvements. “Diverse perspectives and diverse heuristics apply sequentially. . . . Diversity is superadditive.” (p. 340) An example that has brightened the lives of many kids: at the 1904 World’s Fair there was an array of food vendors. “Attendees could choose from a diverse portfolio of alternatives. Unfortunately, one day the ice cream vendor ran out of cups. Ernest Hami, a Syrian waffle vendor in the booth next door, rolled up some waffles to make cones. The rest, as they say, is history. The parts of the portfolio—the waffles and the ice cream—combine to create something new and better, the ice cream cone.” (pp. 340-41) Actually, the parts didn’t combine; a vendor combined them. Investment bankers can create new products out of combinations or slices of old products, but an individual diversified portfolio just rides out changes in the weather, more or less successfully.

And there are dog treats and DOG TREATS!

Page, The Difference, part 1

I originally intended to look at Scott E. Page’s book The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies (Princeton University Press, 2007) strictly within the context of my series of posts on crowds. But the book is such a bounteous harvest of insights applicable to the trading and investing world that I decided to take a little more time savoring it. Today I’m going to concentrate on frameworks for modeling diversity--what Page, in a rare accommodation to the trite, refers to as the diverse toolbox.

Page unpacks the notion of diversity, by which he means cognitive differences, into four formal frameworks: perspectives, heuristics, interpretations, and predictive models.

“A perspective is a map from reality to an internal language such that each distinct object, situation, problem, or event gets mapped to a unique word.” (p. 31) For example, we commonly use base ten as a perspective to represent numbers, though it’s only one of many possible perspectives. Or in trigonometry we can represent a point in space using Cartesian coordinates or polar coordinates. Adopting a new perspective can sometimes simplify problem solving. (By the way, one of the delights of Page’s book is his many examples. Since I have no room here to summarize any of them my recap makes the book sound stodgy. It’s actually a lot of fun!) Or, put more formally, “How hard a problem is to solve depends on the perspective used to encode it.” (p. 44)

To demonstrate this claim Page introduces the notion of a rugged landscape. Think of it (and this is my perspective) as akin to an equity curve with local peaks and valleys and a global peak, the point of highest value. “Instead of leading gracefully to a single peak (that looks like Mount Fuji), this perspective creates lots of ups and downs. Moving along it would be like hiking in the Adirondacks.” (p. 45) A rugged landscape perspective isn’t helpful. You get to a local peak and think it’s the global peak; you get stuck. But, Page contends, for any problem Mount Fuji exists; in fact, many Mount Fujis exist. We may not be able to find the perspectives that create Mount Fuji landscapes among the vast multitudes that don’t, but they’re there. (By the way, you don’t have to remind me that we never want equity curves to look like the real Mount Fuji; we want only the left half of the picture.)

Within a given perspective, a heuristic is a rule that tells a person how and where to search for solutions to a problem or what actions to take. For instance, “think like your opponent” or “do the opposite.” It is important to realize that no heuristic works better than any other across all possible problems—the No Free Lunch Theorem. Steven Covey’s popular heuristic “deal with bigger parts of a problem first” may work in some situations, but in other situations it may make sense to do exactly the opposite. Think back to my buttering bread post.

Unlike perspectives that map one-to-one, interpretations lump things together into categories. In a mind-boggling example, Page looks at some dimensions on which people can differ and the number of distinct categories on each dimension.


“The number of different types of people we can distinguish with these categories equals the product of the number of categories: 2*4*10*5*2*4*6*5*5*3*4*2*3. This number exceeds thirty million. Given that a mere three hundred million people live in the United States, this would create one category for every ten people.” (pp. 82-83) Who’d have thunk it? So the pundits rely on big categories, such as soccer moms.

The last framework in the diverse toolbox is predictive models. “A predictive model describes what we think will happen in some context in light of our interpretations.” (p. 90) Predictive models are ubiquitous in our thinking, though on this blog we are particularly interested in “important events such as stock market price changes.” (p. 93) In this context there are two takeaways I want to share from Page’s brief chapter.

First, an example from dog training. “Though lots of noise hinders our ability to predict, adding a little noise has an unexpected effect. It reinforces our beliefs in our models. This has practical consequences. If we want to teach a behavior, a bit of randomness can be helpful. For example, to teach a dog to sit, you should not always give her a treat as reward. Most of the time you should, but every once in a while you should withhold it. In doing so, you make the dog think, ‘I’m sitting, why am I not getting a treat? What is going on here? Didn’t he say sit?’ This helps her brain make even stronger connections between my command/plea to sit and her response.” (pp. 91-92)

Second, in case you didn’t already figure it out, there is no simple way, no Gladwell “blinking” to predict the movement of stock prices. In findings popularized in Moneyball, “more than two hundred studies conducted over the past seventy years demonstrate that simple linear regression models outperform experts in forecasting the future.” (p. 100) These regressions are, of course, based on variables chosen by people—what Page calls interpretations, so the human element is still present. But, he concludes, “Given the diversity of possible interpretations, we have lots of diverse experts. And, as we will see, that’s beneficial.” (p. 101)

Open university courses

Leading universities have been offering more and more free online courses. Here are two worth looking at.

Robert Shiller’s undergraduate Yale course on financial markets was recorded in the spring of 2008. Among its 26 sessions are guest lectures by David Swensen, Andrew Redleaf, Carl Icahn, Stephen Schwarzman, and Larry Summers.

MIT undergraduates have been developing and teaching courses for high school students for years. In the summer of 2007 they gave a six-part lecture series based on Douglas Hofstadter’s Pulitzer Price-winning Gödel, Escher, Bach: A Mental Space. The teaching is somewhat uneven, but the 30-year-old book is a classic.

Tobin’s Q-Ratio

As the market continues to reward the short-term bulls, let’s step into the mud-laden shoes of the buy-and-hold investor and, alas, into the world of negative returns.

As if this decade weren’t bad enough for investors, Tobin’s Q-Ratio is sending a long-term bearish signal. The Q-Ratio measures the market value of a company relative to the replacement cost of its assets. If the value is greater than one, the market is overvaluing the company; less then one, the market is undervaluing it. The investor, of course, wants the market to overvalue a company whose stock he holds.

According to John Mihaljevic, as of September 21 the market-wide Q-Ratio was 0.86, a level the ratio has reached 11 times since 1900. How much follow-through is there for the Q-Ratio after it reaches this level? On a ten-year time horizon the prospects are bleak. In every measurable instance it was lower 10 years later; the last time was 2002-03, so the jury is still out on that result.


Of course, as with any economic forecasting tool, there are always circumstances under which it won’t work. If, for instance, we have an inflationary environment where replacement costs increase at a faster than normal pace, stock prices could rise. But, then again, we know that high inflation is bad for equity prices.

The source for this information, by the way, is the Advisor Perspectives newsletter of October 6, 2009. In an earlier post I gave a link to sign up for a free subscription.

Surowiecki, The Wisdom of Crowds

When I started my series of posts on crowds I said that although I had the necessary material to write about the madness and the stupidity of crowds, I was dependent on the kindness of the inter-library loan system to look into the wisdom of crowds. In general I’m not prone to excess but, when it comes to books, my attitude is: why request just one when I can ask for three? Then, of course, came the decision which book to read first. Perhaps I implicitly relied on the wisdom of crowds in opting for James Surowiecki’s book of the same name (Doubleday, 2004) because I’m familiar with his New Yorker financial columns and knew that I would be treated to a combination of literary style and easy accessibility. I was not disappointed.

Surowiecki quickly glosses over Mackay and dismisses Le Bon out of hand, claiming that he had things “exactly backward.” In its place Surowiecki promotes the thesis, borne out by many behavioral experiments, that the decisions of a diverse crowd on balance trump the decisions of the individual, no matter how smart he is. Moreover, the decisions of a crowd made up of people spanning a reasonable range of the intellectual spectrum are better than those of a crowd made up exclusively of very bright people.

A crowd can make better decisions than its individual members only if the people in the group are relatively independent of each other. This means, first, that their mistakes are not correlated and, second, that more information is brought into the mix. By contrast, in a glaring example, when mutual fund managers, following Keynes’ piece of “worldly wisdom” that “it is better for reputation to fail conventionally than to succeed unconventionally,” imitate each other, they shrink the overall intelligence of the market. They don’t contribute any new information, and their mistakes become correlated. As a result, between 1983 and 1999 almost 90% of them underperformed the Wilshire 5000.

The problem is that we tend to be imitators. Indeed, “imitation is a kind of rational response to our own cognitive limits.” (p. 58) So how can we reconcile the wisdom of crowds with a bunch of “me-tooers”? Behavioral researchers quickly fine-tuned the class of imitators into two sub-classes: the rational imitators and the slavish or herding imitators. When there is rational imitation in crowds, its members initially have a wide array of options and information. Moreover, to prevent herding there have to be some people in the crowd who buck the trend. These mavericks are often the overly confident; “they overestimate their ability, their level of knowledge, and their decision-making prowess.” (p. 61) Left to their own devices they’re prone to bad decisions, but as members of crowds they serve as circuit breakers; they can sometimes, for instance, stop a negative information cascade.

Although Surowiecki draws examples from such diverse sources as football and traffic, let me zero in on his discussion of the stock market. He contrasts buying an apple with buying stock. Your decision whether to buy an apple depends on how much you like apples, whether this particular apple looks good, and what the grocer is charging for it. Your decision is effectively independent of what other shoppers for apples are deciding. “By contrast, the price of a stock often reflects a series of dependent decisions, because when many people calculate what a stock is worth, their evaluation depends, at least in part, on what everyone else believes the stock to be worth.” (p. 247) In brief, it echoes Keynes’s beauty contest model. “What Keynes recognized is that what makes the stock market especially strange is that often investors are concerned not just with what the average investor thinks but with what the average investor thinks the average investor thinks.” (pp. 247-48) Admittedly, not every investor follows the Keynesian model; some actually try to pick the prettiest girl, not what they think other investors will find the prettiest. Most of the time, Surowiecki contends, “the stock market is an ever-changing but relatively stable mix of independent and dependent decision making.” (p. 248) Problems arise when everybody starts piggybacking on the wisdom of the crowd and no one is adding to the wisdom of the crowd.

Surowiecki argues that CNBC exacerbated this problem; it “magnified the dependent nature of the stock market because it bombarded investors with news about what other investors were thinking.” (p. 253) Not only does this encourage herding, it is not conducive to good decision making. A series of experiments at MIT showed that “more news does not always translate into better information.” Two groups of students selected an initial portfolio of stocks, after which they could buy or sell at will. The first group saw only the price changes in the stocks in their portfolio; the second group had both price information and a constant stream of financial news that purported to explain what was happening. The first group’s performance far surpassed that of the “better-informed” group.

There’s an obvious moral here.

Boyko, We’re All Screwed!

I admit it was with some trepidation that I opened Stephen A. Boyko’s book We’re All Screwed! How Toxic Regulation Will Crush the Free Market System (W&A Publishing, 2009). I was sure that I was going to be met with simplistic right-wing rant. Instead, I was treated to an intellectually imaginative argument on behalf of what the author conceives to be a practical method of financial governance. I am no expert on regulation. Moreover, I have to confess that I’m not even particularly interested in the subject, so I can’t pass judgment on his proposal. But, as a person who relishes challenges to commonly accepted modes of thinking, I gleaned a lot from this book.

Boyko’s major thesis is that “segmenting governance into separate areas that apply to predictable, probabilistic, and uncertain regimes provides enhanced information correlation from which to issue best-practice commands.” (p. 56) This sentence is a mouthful, but the main point is that a one-size-fits-all approach to governance is doomed to failure. Rather, he suggests a troika—the predictable (for instance, money market instruments, U.S. Treasury), probabilistic (S&P 100 being the best example), and uncertain (small-cap, negative cash flow)—each with its own mode of governance. This segmentation is not incompatible with having a single financial regulator.

Central to Boyko’s segmentation is the accepted distinction between risk and uncertainty. Risk is quantifiable and has foreseeable consequences, uncertainty is indeterminate and has unforeseeable consequences. But Boyko goes further and, within the context of his argument, defines change as the movement in either direction between risk and uncertainty. “When uncertainty becomes risk, that’s learning or innovation; you have greater control over your underlying economic environment. On the other hand, when risk becomes uncertainty, there is either confusion (too much information), or ambiguity (too little information). Should the uncertainty become unstable . . . you have chaos.” (p. 61)

This relationship of change between risk and uncertainty is complicated by the fact that change has changed. “In the Industrial Age, change was binary—yes or no. A linear queuing theory drove change, and this theory had a first-move advantage. In the Information Age . . . change is now governed by wave and complexity theories. And unlike the Industrial Age, where usage depreciates an asset’s value, in the Information Age usage appreciates the value of intellectual property.” (p. 62)

In advance of his thesis Boyko draws on familiar literature, but he offers a unique perspective akin to a Rubik’s cube. Unfortunately, without the aid of the “how to solve a Rubik’s cube in under a minute” videos, we have to come up with our own solution to the matrix of risk, uncertainty, and changing change. And perhaps we can even re-craft the problem to our own liking. Boyko set out to address the issue of financial governance. I think that in the process he’s given hints about ways to rethink trading, investing, and portfolio management.

High frequency thinking

For this, my 100th post, I’m going to review the familiar notion of creative productivity and outline some steps and offer one exercise to get the “genius” juices flowing.

In Scientific Genius: A Psychology of Science Dean Keith Simonton contends that the distinguishing characteristic of genius is immense productivity. This may be an overstatement, but there is no doubt that productivity is a distinguishing characteristic of genius. Simonton studied 2,036 scientists throughout history and found that the most respected thinkers far outstripped the average in the sheer volume of their output. In terms of quality they produced both great works and many mediocre to bad ones.

Admittedly it’s a leap in logic to say that the more ideas you generate the closer you'll get to genius. But if you don’t try to generate a lot of ideas you’ll have a poor shot at coming up with some good ones.

In a 2005 web article “How to think like a Genius” Tina Konstant explores some of the qualities the world’s great thinkers had in common. Among them, “The idea generation was in pictures and images rather than words. . . . Ideas were explored using association. They looked at ideas from different perspectives. They were prolific and recorded everything. They fuelled their imaginations with knowledge. Their thinking was focused. . . . They saw mistakes and unexpected surprise results as valuable opportunities to learn from. They never gave up.”

The Study Guides and Strategies website outlines nine steps to thinking like a genius: 1. Look at problems in many different ways. 2. Visualize! 3. Produce! 4. Make novel combinations. 5. Form relationships. 6. Think in opposites. 7. Think metaphorically. 8. Prepare yourself for chance. 9. Have patience.

If these steps are too abstract, here’s one concrete exercise, compliments of Tony Buzan’s The Power of Creative Intelligence (2001/2002). In what he calls the fluency game the object is to pick at random any two words from the first group below and try to make associations between the pair of words, the wilder the better. He scores you as follows: 10 similarities = exceptionally well, 15 similarities = in the world’s top 1 percent, 20 similarities = creative genius (in this area). If you’re so inclined, go on to the second group of words; rinse and repeat.

The Decrepit Decade

According to Ron Surz in the latest Advisor Perspectives newsletter, we are approaching the end of what is likely to be the worst calendar decade on record for the S&P 500. We would need a fourth quarter return of 17% to bring the decade to breakeven.

Marks, Aqua Shock

This review is a change of pace for the blog, but I think the topic is important and not without relevance for investors. Susan J. Marks, in her recently released Aqua Shock: The Water Crisis in America (Bloomberg Press, 2009), provides a well-documented account of water as a finite resource. Despite the title, the book is measured in tone, sketching out an array of problems from shortages to contamination and offering a patchwork quilt of responsible solutions.

I was particularly interested in her discussion of legal approaches to water rights and, in that context, T. Boone Pickens’ Mesa Water venture. First of all, it should be noted that in the U.S. the East has a different approach to water rights from the West. Eastern states invoke the English common law notion of riparian rights. That is, if a person owns land that abuts a stream, he has the right to the reasonable use of that stream.

[A personal “midnote.” The picture accompanying this blog is of the stream that both abuts and is part of my property. This stream, euphemistically known on maps as the West River, contributes to the New Haven, CT water supply. The former owner of the property decided that he had a reasonable use of the stream and dug a mammoth pond, admittedly only marginally supplied by the stream. He was never subjected to the “sue me” test, and I’ve been reaping the benefits ever since. I mitigate my very occasional guilt by granting “catch and release” fishermen access to the pond.]

The riparian doctrine is vague (what constitutes reasonable use?), so most eastern states have moved to regulated riparianism where “the state manages the resource in trust for the public through time-limited permits.” (p. 112) In the West, where water is more often in short supply, the dominant legal approach is prior appropriation, which is based on the concept of first in time, first in right. That is, “the first person or group to take a quantity of water and put it to beneficial use has a higher priority of right than a subsequent user.” (p. 108) As you can imagine, there’s lots of adjudication over the claim to “beneficial use.” The prior appropriation doctrine also embraces a “use it or lose it” policy; “if you don’t use your full allocation under your water rights, those rights can be revoked, forfeited, or abandoned.” (p. 129) In brief, it encourages waste.

In some states in the West water is viewed as a commodity that can be freely sold by the property owner. And this brings us to Pickens and his group of Texas Panhandle landowners. Texas law recognizes that a landowner owns everything beneath his property, including oil, gas, water, and minerals. There are some legal limitations regarding the amount of water that can be pumped and to whom it can be sold. In 2002 and 2004 the Panhandle Groundwater Conservation District issued pumping permits to Mesa Water allowing it to transfer water to North Central Texas and San Antonio. As far as I know no one has signed up to buy water and no pipes have been laid. In fact, on the Mesa Water site the most recent press release is from 2005.

Experts predict, however, that plans like Mesa’s will only become more grandiose over time. Will water ever be a tradable commodity like oil or gold? Not likely. Why pay for something that you can get for free from the sky? And despite the water problems in the U.S., the ongoing global shortage of safe drinking water, and the prediction that by 2025 one half of the world’s population will live under conditions of severe water stress, ETFs that focus on water companies in the U.S. have underperformed the S&P 500 year to date and the global water ETFs have just about matched the Vanguard Total World ETF. Investors aren’t yet buying into the water crisis story.

The speculator as superhero

Many novice traders placing their one-lot e-mini orders dream of becoming the next financial superhero. So, presumably, did Nick Leeson with his increasingly desperate large orders. Perhaps the model is flawed.

In the course of his sociological study of financial markets (Framing Finance: The Boundaries of Markets and Modern Capitalism, University of Chicago Press, 2009), Alex Preda recalls two fictional portraits of speculators. The first is the speculator as gambler, set in stark contrast to the earn-and-save accumulator of wealth. For instance, in The Gambler Dostoevsky’s narrator counterposes the amassing of a family fortune that takes generations with the quick money that can be won at the roulette table. The gambler wants money for himself, not for his great grandchild. He sees himself as an individual “against the gods” trying to beat the unknown and be free. He does not place his bets randomly or idly; he works at his passion—observing, calculating, and memorizing. These acts of cognition, however, are trumped by irrational compulsions: “the narrator can neither leave the table (true gamblers, he explains, never leave) nor control his joy or fear. He is bound, attached to the table by invisible chains.” (p. 201)

In a variation on the theme, according to Preda, the speculator gives up the mantle of the gambler and, if he is truly fortunate, puts on the cape of the superhero. He enters a hierarchical world in which the top speculators, the superheroes, rely on a “vital force” that few traders possess. For instance, Edwin Lefevre described Sampson Rock, the hero of his novel Sampson Rock of Wall Street this way: “Rock’s friends often spoke of his habit of thinking in lightning flashes, of the marvelous quickness with which he abandoned old and settled on new policies, and, at the same time, of the systematic, von Moltke-like manner in which he planned some of his market campaigns. In their heart of hearts they sometimes doubted that any human mind could think so much and so quickly, or see so far and so clearly. Their minds did not.” (p. 207) Rock was viewed as a superhero because he was brilliant. But speculators can also be seen as superheroes because of the size and strength of their adversaries: consider the media portrait of George Soros as the man who single-handedly fought and defeated the Bank of England, as St. George who slew the dragon. And continuing the metaphor further, think of the title of the book I reviewed some weeks ago: When Supertraders Meet Kryptonite or Van K. Tharp’s new book Super Trader.

Superheroes need to have extraordinary powers and achieve outsized results. In the financial world the so-called superheroes engage in acts of derring-do that, unlike those of their fictional counterparts, rarely have social goals. They are not trying to save planet Earth from meteors or protect Fort Knox against the evil schemes of Auric Goldfinger. They are simply trying to make the kind of money that dwarfs the efforts of others and preserves a permanent space for them in the speculators’ hall of fame.

The problem is that the “true” superheroes are fictional, and in most instances it is ludicrous to hold them up as models. Who says, except metaphorically and perhaps ironically, “I’m on the path to becoming a financial Superman” or “I’m just one trade away from being Wonder Woman”? These superheroes have powers that we don’t possess, and they are the creations of a writer who keeps them away from fatal exploits. Nick Leeson wasn’t so fortunate.

Your Personality & Successful Trading--a link

Here's another way for you to spend part of a Sunday afternoon. Windsor Advisory Services published an e-book entitled Your Personality & Successful Trading. (By the way, this link takes you only to another link; click on it and you'll get the pdf file.) It doesn't break any new ground, and if Windsor is the same outfit profiled in the Guardian in 2003 the advice is coming from at best a questionable source. But it's only 17 pages, so a quick read.

Livevol

Bill Luby’s blog “Vix and More” is a wealth of information. On September 14 he wrote a post entitled “Livevol Pro: A World Class Suite of Volatility Tools” and described it much better than I could, so just click on the link and spare me some unnecessary work. Soon enough I signed up for the free end-of-day version to get a feel for the program. Both its analytics and its graphics are powerful. Even cheapskates can garner valuable information. (I don’t want to be seen as promoting anything you have to pay for—except books, of course.) Take a peek at the Livevol site and watch the demo.

Decision Point

I highly doubt that anyone is collecting quotations from technical analysts for a specialized Bartlett’s. But if they were, at the top of the list should be Carl Swenlin’s trademark “Technical analysis is a windsock, not a crystal ball.” Since a windsock is designed to measure two variables, wind direction and wind speed, I consider it a brilliant metaphor.

Swenlin contributes to the StockCharts bi-weekly newsletter and also has free analyses on his website. He’s one of the very few forecasters I pay any attention to. CXO Advisory's grading of his market calls is “pretty good” with not much confidence. But that’s way better than most.

My first trade

The story is a tad muddled because memory glosses over details. The upshot is that when I was a sophomore in college I wanted to go on a college-sponsored trip to Africa during the summer. The problem was one of economics. I could have funded the trip out of my earnings from high school jobs, but that would have depleted my savings and I needed the money to pay for college costs that weren’t covered by my scholarship and bursary job. My parents weren’t exactly sitting on excess cash, which was why I was a scholarship student in the first place. But then there was my great aunt. She, my grandfather, and my great uncle lived together, having long since buried their spouses. They were ancient, not just to a college student—all around the age of 90. Their income came from timber and mineral rights and a tiny fraction of the proceeds from a stone quarry on the property leased to Martin Marietta. Their income was modest, but their expenses were virtually nonexistent. They lived and ate sparingly--meat one night, potatoes the next, and vegetables the following. My Aunt Lizzie was a reader, not a cook. (Those who know me in real life would take this as anecdotal evidence for nature over nurture.)

Well, my mother convinced Aunt Lizzie to fund my trip in the form of a $1,000 loan. Aunt Lizzie agreed on the condition that I repay the full sum of the loan to my younger sister. My mother agreed. I changed the terms. Why should my sister get the full amount of the loan as opposed to half of the loan, a more reasonable distribution? But then how could I even repay half the loan? So I came up with a solution. At the age of eighteen I entered the offices of the local stock broker and probably asked some embarrassing question like how I could double my money in a short time. The stock broker, according to rumor also a bookie for local sports events, recommended Westinghouse. I did no research, I obviously never thought about risk, I simply accepted his recommendation. I bought $500 worth of Westinghouse stock. And then I travelled in Africa, every day buying a copy of the International Herald Tribune, absolutely fixated on the performance of my investment.

Most successful traders record their first blowup. Mine was a complete success. Westinghouse doubled in under two months. On my return I sold my Westinghouse stock, gave my sister half of what my great aunt had advanced me, and was forever hooked on trading. But I knew that my triumph was due either to dumb luck or, I suspect, to illegal inside information. It was an all too easy entry into a very difficult business.

Wyckoff on reading the mind of the market

As he recounts in Wall Street Ventures & Adventures through Forty Years, Wyckoff was besieged by people claiming to have a system to beat the market. “Few had any money. Always there was some reason why they had not made their fortune, even though they possessed the magic key.” (pp. 163-64) Wyckoff studied hundreds of plans and even seriously considered one trend following system that looked promising, but in the end he concluded that “methods of this kind, which substitute mechanical plays for judgment, must fail. For the calculations on which they are based omit one fundamental fact, i.e., that the only unchangeable thing about the stock market is its tendency to change. The rigid method sooner or later will break the operator who blindly follows it.” (pp. 164-65) This indeed happened to the method Wyckoff contemplated, especially after September 1909 when, with the death of E. H. Harriman, “the whole character of the market changed.” (p. 165)

In a series of articles entitled “Studies in Tape Reading” (still available in book form and essential reading for wannabe tape readers) Wyckoff outlined ways the stock market, “by its own action, continually indicates the probable direction of the immediate trend.” (p. 174) Wyckoff wanted to emulate the methods of floor traders and follow the big money. “If one were to become sufficiently expert to judge by the action of stocks what was in the mind of the insider or manipulator, one could scent the moves, go with them, and benefit by having these big operators working for one.” (p. 175)

Wyckoff suggested two models for understanding the market. First, the market is a collection of individuals, each with his own modus operandi. This is obviously a difficult model to apply because there are so many variables—traders, operators, and investors with unique styles and stocks with their own individual peculiarities. Second, and more useful, the market has only a single trader, the so-called composite operator. “The successful trader must endeavor to ascertain what is in the back of the head of that fellow and to anticipate his moves; for he is constantly expressing his intentions by what he does and the way he does it; by the urgent or leisurely character of his buying or selling; by the volume of the stocks he deals in, the width of their swings, especially in the leaders.” (p. 177) In the second model there’s only a single mind to read, though stock movements still need individual attention.

Wyckoff’s composite operator seems to come from the ranks of the manipulators, the smart money that starts campaigns. It is his mind that the trader must read. The only way to accomplish this task is through long study and continuous practice, practice “in actual trading.” Paper trading, Wyckoff argues, lacks the element of risk. Of those that study and practice, however, only a fraction will be able to develop the intuition necessary to be a truly successful trader. “Something in the very nature of most men seems to work against them.” (p. 179)