NAAIM papers

For those in the U.S. experiencing withdrawal symptoms as the result of a three-day weekend and for everybody else with a little bit of time on his hands, here's a set of papers submitted for the NAAIM 2010 Wagner Award, all downloadable. Thanks to Mebane Faber's World Beta blog for the heads up.

Backtesting data

A change of pace from my usual posts, but I recently saw a note from someone looking for the best tick by tick data available for backtesting. He was willing to lay out a lot of money for the data, yet he didn’t have the wherewithal to capture this level of granularity in his live trading. So what’s the point? He might come up with a great system, but if he’s trading from a home computer with a cable modem he’ll either never see those ticks (since some data providers don’t update with every tick) or experience so much latency that his trades will bear no resemblance to those in his backtest.

It’s better to keep things consistent: if the broker through whom you execute your trades also provides intraday data, trade and backtest with this price data. And be realistic about the kinds of fills you can expect. If, for instance, you're trading off a volume chart instead of a time chart and your signal fires on the first bar of, let's say, five trending bars that occur in the course of a minute, do you really think you'll be filled anywhere close to your signal?

Foster, Ticker Technique

Let me start by saying that I have no idea who authored Ticker Technique: The Art of Tape Reading (Traders Press, 2005). The book was originally published in 1965, but its focal point (part two of four parts) is an updated version of Orline D. Foster’s 1935 Ticker Technique. The other three parts expand on tape reading and include contributions by Herbert Liesner and Don Worden. Whoever the author was, he/she helped keep the tradition of tape reading alive and well.

Foster’s piece is only about 25 pages long, but it is both a first-rate introduction to tape reading for the novice and a quick refresher course for the technical trader. He writes about price, volume, breadth, and timing, all within the context of accumulation and distribution.

I’m not going to rehash the ideas presented in this book. If you’re familiar with them I would bore you; if you’re not I would lose you. Let me simply say that if you’re short on bookshelf space, you could most likely profit from replacing almost any of your more recent trading books with this one.

Can a trader’s life be meaningful?

[A prefatory meta-note: I personally have issues with what I’m writing here, but I know that many traders struggle to justify their chosen career path. So here, for those of you who suffer pangs of angst, is a philosopher’s (not my) take on your (and my) life.]

Susan Wolf, author of Meaning in Life and Why It Matters (Princeton University Press, 2010), would, I suspect, struggle in passing judgment on a trader’s life. She sets forth two criteria for meaningfulness. First, a person must love something and, second, it must be worthy of love. “Essentially, the idea is that a person’s life can be meaningful only if she cares fairly deeply about some thing or things, only if she is gripped, excited, interested, engaged, or . . . if she loves something—as opposed to being bored by or alienated from most or all that she does. Even a person who is so engaged, however, will not live a meaningful life if the objects or activities with which she is so occupied are worthless. A person who loves smoking pot all day long, or doing endless crossword puzzles, [or worse, as the author adds in a personal confession later in the text, doing Sudokus] and has the luxury of being able to indulge in this without restraint does not thereby make her life meaningful.” (p. 9) The things and activities we care about must link us to our world in a positive way.

As paradigms of a meaningful life, we might nominate Gandhi, Einstein, or Cézanne. They all actively engaged in projects of worth. Sisyphus is usually portrayed as the exemplar of a meaningless existence.

What kinds of things give meaning to life? The activities that engage us must have a value that “is in part independent of one’s own attitude to it.” (p. 37) But who’s to say which projects are independently valuable? Wolf answers, “No one in particular.” And yet “whether a life is meaningful has specifically to do with whether one’s life can be said to be worthwhile from an external point of view. A meaningful life is one that would not be considered pointless or gratuitous, even from an impartial perspective.” (p. 42)

I fear that the trader is quickly starting to look more and more like Sisyphus. But, wait, there’s hope! Wolf, struggling with the question of objective value and trying to distance herself from a narrow academic perspective, suggests that “almost anything to which a significant number of people have shown themselves to be deeply attached over a significant length of time, has or relates to some positive value.” (p. 128) Trading certainly has a long tradition and has attracted a sizable community.

Perhaps in that respect trading can be compared to basketball. Wolf writes: “Presumably, there is nothing especially valuable about a group of people running around, trying to throw a ball into a hoop, while another group runs around trying to stop them. Nor does the adoption of extra rules, constraining the moves that are permitted, lift their running around into the category of practices that in themselves the participants have reason to be proud of from a detached perspective. Even if basketball, removed or abstracted from its now established place in our culture, is not an objectively valuable activity in itself, it provides an opportunity for much that is of value. It provides an opportunity for the cultivation and exercise of skill and virtue, for the building of relationships, and for the communion that comes from enthusiasm for and immersion in a shared activity.” (p. 129)

In brief, according to Wolf we can freely admit that in and of itself scalping ticks in the e-mini S&P is a pretty worthless activity. (Of course, worthless does not mean profitless.) But, done with passion and ever-increasing skill, it can nevertheless be the lynchpin of a meaningful life.

Apropos of nothing

“Philosophy, even the philosophy of human values—and for that matter the search after knowledge and understanding in general—needs practical justification like a fish needs a bicycle.”

--Nomy Arpaly, commenting in Susan Wolf’s Meaning in Life and Why It Matters (about which much more tomorrow)

A question about CME order execution

After reading Chasing the Same Signals I started thinking about order execution, about which I know next to nothing. So I went to the CME site to educate myself. Alas, I’m still ignorant.

In a study dating from March 2009 the CME analyzed immediately executable orders--that is, orders that can be at least partially executed at the time they reach the central limit order book. One finding puzzled me: “order quantities between six and 49 contracts are being executed with lower market impact than orders of five or fewer contracts.” Market impact means “the difference between the middle of the market at the time of the order’s arrival and the order’s execution price, or the average execution price in the event of fills at different prices.”

The fact that the small trader often doesn’t get the best price is not a function of speed of execution. Orders to buy or sell between one and five contracts were filled within 30 milliseconds after their arrival at CME Globex 86% of the time and within 50 milliseconds 91% of the time. Orders for six to ten contracts were filled within 30 milliseconds 85% of the time and within 50 milliseconds 93% of the time. As order size increased so did average fill time.

Why do the smallest traders not get the best price? Perhaps they’re simply addicted to market orders and by definition always give up the spread. Perhaps CME’s matching algorithms tilt in favor of the larger trader, with FIFO being the default for small size. The upshot is that I don’t know the answer. I’m sure that many of my readers are more knowledgeable on this score than I am; if so, please share.

Brown, Chasing the Same Signals

If you believe that books should be written by people who know how to write, you’ll have a tough time with Brian R. Brown’s Chasing the Same Signals: How Black-Box Trading Influences Stock Markets from Wall Street to Shanghai (Wiley, 2010). The book suffers from a lack of structure, it is repetitive, and it has numerous factual and typographical errors.

Nonetheless, it is a useful book for those who are trying to peer into quant black boxes and, even more, for those who are interested in the interrelationships among market structure, algorithmic order execution, and liquidity. Brown introduces the reader to the world of high frequency trading and to some principles that inform hedge fund strategies. He does this from the vantage point of an insider who worked for Morgan Stanley as director of pan-Asia systematic trading and for Trout Trading Management, researching and managing statistical arbitrage strategies.

Brown claims that the market data metrics most commonly monitored in quantitative strategies are volatility, spread, and volume. In searching for price anomalies quants look for deviations from normal metric readings. An increase in volatility in an individual stock, usually viewed as the result of shifting supply and demand and/or price uncertainty, is taken to be a signal of increased risk. But on the level of market structure, Brown writes, there is another cause of greater volatility—higher transaction costs, which discourage short-term speculation and hence suck liquidity out of the markets.

The bid-ask spread also provides important information to quantitative traders. Traders are accustomed to shifts in the spread during the course of the day depending on the level of trading activity, but “an unusual movement in the spread can denote the beginning of a price rally or a reversal.” (p. 111) Spreads can also dramatically widen out en masse during market crises as market participants worry about underlying risks and the cost of providing liquidity. A review of the tape during the flash crash speaks volumes on this score. Brown writes (well before this particular event): “Algorithms are designed to minimize market impact but they largely depend on forecasting liquidity. When liquidity is weak, algorithms may respond unpredictably to the adversity. Price impacts can be rapid and severe.” (p. 103)

Consider, for instance, the practice known as pinging the book. High-frequency traders submit orders to an ECN; if they are not filled within 60-80 milliseconds the orders are cancelled. There’s a sleazy side to this practice, but let’s not go down that path. The point is that high-frequency traders are testing the waters, searching for liquidity. If they don’t find it, they don’t provide it. They are not magnanimous.

Yet Brown claims that “the essence of a black-box firm is to be a liquidity provider. . . . Before the financial crisis, the black-box influence on the world’s equity markets was observed with historical lows in volatility, dispersion, and spreads. The frictional conditions for long-term investors had never been better.” (p. 176) For instance, statarb and market-neutral strategies dampened down market volatility and dispersion (and hence over time became less profitable). But we can’t conflate plain vanilla hedge-fund strategies with high frequency strategies. They have very different risk profiles. And they will impact markets in very different ways.

Brown’s book is not a model of tight reasoning. Its strength is that it offers up hypotheses from various vantage points that might improve our perception, perhaps even regulation, of the brave new world of the financial markets.

I’m back

For those of you with a sense of humor here’s the first set of data points in a new statistical correlation series. When Reading the Markets is on vacation the market tanks. Well, the beginnings are at least as promising as the S&P-Bangladesh butter correlation. In fact, it just might be a savvy sentiment indicator. So if you lean long, perhaps you should keep encouraging me to stay the course when my energy flags (which it often does).

This week I’m going to share some thoughts on two books published 45 years apart—Brian R. Brown’s Chasing the Same Signals (2010) and Orline D. Foster’s Ticker Technique (originally published in 1965). I’ll fill in the gaps with some of my own blather.

On vacation

This blog will be on vacation for a week although I won’t. While I’m waiting for some review copies to arrive I’m going to shift my focus to things that can actually put food on the table. Better known as trading and gardening. Alas, the picture isn’t of my vegetable garden but of the world famous Inverewe Gardens in Scotland.

Use the blog’s “down” time to browse through the archive of more than 300 posts. Even I do that on occasion and rediscover a good idea or two.

“Fuzzy” option spreads

I was skimming through Kees Van Deemter’s book Not Exactly: In Praise of Vagueness (Oxford University Press, 2010) when I came across the following diagram:


It displays our intuitive answer to the question: When is a person tall? We know that all heights below a certain value definitely don’t qualify as tall and all those above another value unequivocally count as tall. That leaves a grey area. Van Deemter suggests that the following function might be suitable (where v stands for degree of truth):

v(Tall(x))=
0 if x < 150
1 if x > 190
(x-150)/(190-150) otherwise

We have three intervals—two horizontal lines--0 below 150 cm and 1 above 190 cm—with a diagonal line in between. Welcome to the world of fuzzy membership functions.

If we shift the binary functions 0 and 1 to defined negative and positive values and think in terms of financial instruments, what do we have? Well, yes, a bull call option spread.


(compliments of OIC)

There’s the maximum profit, the maximum loss, and all that fuzzy stuff in between.

And the point, you ask? Actually, I’m not sure there is any. But isn’t it interesting?

The virtue of inconsistency

We often read that we should be consistent traders. Have a plan and then take every signal it generates. Those signals and no others.

The advice may appear sound on the face of it, but Raymond Smullyan challenges it in his inimitable fashion in 5000 B.C. and Other Philosophical Fantasies (St. Martins Press, 1983). He starts with formal mathematical systems where “consistency is absolutely essential, for without it the whole system breaks down and everything can be proved.” But is it true, by extension, that “if a person is inconsistent, he will end up believing everything?” Of course not. Here’s an excerpt from Smullyan’s counter-argument.

“If we were consistent in our inconsistency, then we might end up believing everything, but it is more likely that an inconsistent person would be just as inconsistent in the way he carried out his inconsistency as he is about other things, and this would be the very thing that would save him from believing everything.

“The inconsistent people I have known have not seemed to have a higher ratio of false beliefs to true ones than those who make a superhuman effort to maintain consistency at all costs. True, people who are compulsively consistent will probably save themselves certain false beliefs, but I’m afraid they will also miss many true ones!” (pp. 39-40)

I couldn’t have said it better! But then I’ve never believed that consistency is always a desirable end—and not because, as we often hear, consistency is the hobgoblin of little minds. The accurate Emerson quotation, by the way, is: “A foolish consistency is the hobgoblin of little minds, adored by little statesmen and philosophers and divines.”

A trader’s job—and this holds for algo traders and discretionary traders alike—is to figure out when consistency is wise and when it is foolish, when inconsistency can offer greater rewards than consistency.

Speed kills

In his book How Math Can Save Your Life (Wiley, 2010) James D. Stein asks whether it makes sense to cut a few minutes off your trip by driving faster. The short answer is no. He looks first at kinetic energy, which increases as the square of the velocity. A car traveling at 70 mph has 36% more kinetic energy than one traveling at 60 mph (702/602) and a car traveling at 75 mph has 56% more kinetic energy than one traveling at 60 mph. You can do a lot more damage to yourself and your car in an accident at higher speeds.

Stein also cites the work of MIT physicist Max Tegmark who did some expected-value calculations based on data compiled in the early years of this century. Fasten your seatbelts! “Each hour of driving on an interstate freeway decreases life expectancy by 19 minutes. . . . Each hour of driving in local city traffic decreases life expectancy by 8 minutes. . . . Each hour spent riding a motorcycle decreases life expectancy by 5 hours.” (p. 82)

According to some hypotheses, at least, last Thursday’s tape indicates that high frequency trading may be even more dangerous than racing a motorcycle on a freeway when an “accident” happens. Speed kills—and traffic seizes up.

Redleaf and Vigilante, Panic

I first encountered Andrew Redleaf, founder and CEO of Whitebox Advisors, when he was a guest lecturer in Robert Shiller’s Yale economics course, available online. Even though he was somewhat ill at ease in the classroom, he came across as an intriguing thinker.

Panic: The Betrayal of Capitalism by Wall Street and Washington (Richard Vigilante Books, 2010) co-authored by Redleaf and Richard Vigilante, the communications director of Redleaf’s hedge fund, is a compelling work. It is for the most part an intellectual history of the financial meltdown, demonstrating how Wall Street became the victim of its own faulty paradigms. Unfortunately, the book could not be written in the past tense because most of these paradigms are still secure atop their pedestals. Until there is a paradigm shift we will continue to experience recurring economic havoc.

The overarching paradigm is that efficient markets are superior to free markets, that human judgment is inferior to structures and systems, and that, by extension, “public securities markets—computerized, blazingly fast, effusively liquid—are as close as mankind has ever come to realizing the perfectly efficient market of classical economic theory.” (p. 7) Even Thursday’s tape action has not called this model into question; rather, the solutions being bandied about focus simply on coordinating the existing structures and systems.

The authors proceed to dissect the notion of efficiency and some of its equally flawed ideological relatives. Among them: that investors are paid for taking risk, that if the so-called smart money (mutual fund managers are singled out because their performance is a matter of public record) can’t beat the market no one can, that technical analysis does not work because scientifically rigorous studies demonstrate that it provides at best only a minimal edge often erased by commissions, and that the primary skill of finance is diversification.

The book’s arguments are carefully developed. They are often nuanced, so summary will not do them justice. With that caveat I’m going to look briefly at the flawed idea that can most easily be separated out from the main argument of the book—that technical analysis doesn’t work.

The weak form of the efficient market hypothesis claims that technical analysis is bunk because “the very next price change in a publicly traded stock will be statistically indistinguishable from a random change.” Wrong, claim the authors. “In an efficient market, prices are fully ‘determined’ by the flow of information that the market is processing.” (p. 81) If a market is efficient we are, in the words of yesterday’s blog post, dealing with epistemic uncertainty, not stochastic uncertainty.

Critics of technical analysis would not be moved by this argument. Instead, they would press on, citing the numerous studies that have shown the limited value of technical analysis. The authors don’t fault the studies; they simply note that the academics were necessarily constrained by the rigors of scientific methodology. They couldn’t do what “adroit market practitioners do.” They couldn’t pick and choose, highlighting time periods when past prices predicted future prices and ignoring those blocks of time when they didn’t, or pointing to the handful of stocks where technical analysis worked and excluding evidence from the overwhelming majority. Savvy investors don’t have the scientific scruples of academicians. Here let me quote at length: “We assume that potentially profitable anomalies appear and disappear as market conditions change. We assume that such anomalies are almost certain to be more powerful and profitable for some sets of securities than for others. . . . When we build quantitative tools, our goal is not to find algorithms that work for all eternity across any arbitrarily defined class of securities. We look for tools that deliver very strong results over time periods biased to the near term. And in building the universe of securities to which to apply the algorithm, we do not choose some neutrally defined class that would please an academic such as every stock in the S&P or every large cap. We select a subset of securities with favorable characteristics that make them good candidates for the algorithm. . . . Once we go live, we monitor the universe, tossing securities whose behavior no longer seems to be well predicted by the algorithm and adding others that seem promising.” (p. 85)

In this review I’ve barely touched the surface of this book. For instance, I’ve not said a word about the role of Washington in the whole mess. Well, that’s the problem with good books—just too many ideas in them! This one also has the added benefit of being well written, with a healthy dose of humor. There’s even a theoretical rationale for the good style: “If economics were about entrepreneurship [which the authors advocate], it would not look like physics. It would look a little like philosophy. Mostly it would look like literature." (p. 49)

Panic is one of the best books I’ve read in a long time and one of the very few I can wholeheartedly recommend to everyone—liberal or conservative, investor or trader—who appreciates contrarian thinking.

Stochastic and epistemic uncertainty

Are there two kinds of uncertainty, one a property of systems and the other a property of our knowledge of systems? The first, known alternatively as stochastic uncertainty, aleatory uncertainty, and randomness, resides in the real world and is irreducible. The second, epistemic uncertainty, is a function of our lack of knowledge about the world and is reducible by acquiring more or better information.

Terje Aven in Misconceptions of Risk takes an unabashedly idealist position, contending that all uncertainties are epistemic. He quotes R. L. Winkler: “Consider the tossing of a coin. If we all agree that the coin is fair, then we would agree that the probability that it lands heads the next time it is tossed is one-half. At first glance, our uncertainty about how it lands might be thought of as aleatory, or irreducible. Suppose, however, that the set of available information changes. In principle, if we knew all of the conditions surrounding the toss (the initial side facing up; the height, initial velocity, and angle of the coin; the wind; the nature of the surface on which the coin will land; and so on), we could use the laws of physics to predict with certainty or near certainty whether the coin will land heads or tails. Thus, in principle, the uncertainty is not irreducible, but is a function of the state of knowledge (and hence is epistemic).” (p. 146)

How should we understand market uncertainty? And how does the model that we choose inform our trading strategies?

If we assume that markets move randomly or at least with enough randomness that we cannot predict a single sequence or do better than 50-50 calling a series of sequences, we should abandon all attempts to gain an edge with our entries. No amount of work on our part will improve our entries over those of the dart thrower. Either we buy an index fund and trust that over time markets will rise or we become really savvy risk managers, protecting our assets while steering our trades toward gains and counting on that inevitable outlier somewhere down the pike.

If we assume that market uncertainty is epistemic, we can gain an edge by increasing our knowledge of market movements. Analysts churn out reports on individual companies, economists provide data and every kind of opinion you’d ever want to hear, chartists study how the past can portend the future. There are enough pockets of success for us to believe that market uncertainty is at least in part epistemic and that knowledge can make a real difference.

Beginners are true believers in epistemic uncertainty; professionals tend to have more respect for the randomness of markets. As Don Kaufman said in a recent Think or Swim chat on the Greeks and expiration (one that I highly recommend for anyone with even the most marginal interest in options), “Most retail traders are good at finding trades, bad at managing trades. Most professional traders are good at managing risk, less good at finding trades.”

Even though I believe that market uncertainty is at least in part epistemic, I think we would all be well served to work under the hypothesis that market uncertainty is stochastic. That hypothesis forces us to manage risk in ways that can be both imaginative and effective. Whatever we earn over and above that from our stock picking skills or whatever other means we use to enter a position is pure gravy!

Coming attractions

I had a post all set to publish on Friday, but after Thursday’s terrifying tape I decided that no one would be in the mood for a reflection on stochastic vs. epistemic uncertainty, even though I personally think it’s important. So, barring some further calamity, it will appear on Monday.

Next week I’ll also review Andrew Redleaf and Richard Vigilante’s book Panic, which mercifully is ever so much richer than its title. It’s a very thought-provoking read.

What good are puzzles anyway?

Okay, all you geniuses who solved the puzzle and realized that the Norwegian drinks water and the Japanese owns the zebra, what have you accomplished? Do you just belong to the group of people who are, as someone once snidely described me, “good at taking tests”?

John Adair’s Decision Making and Problem Solving Strategies (first published in 1997, new edition reissued by Kogan Page in 2010) is addressed to business leaders. But it’s not a stretch to extend its audience to investors and traders.

As you might imagine, he doesn’t envisage a CEO spending his time figuring out who drinks water and who owns the zebra. But puzzle solving is not without its practical merits. For instance, it can distract the conscious mind so the “depth mind” that offers up educated intuitions can work. A case in point. In a psychological experiment volunteers were asked to decide which car out of a field of four to buy; one was clearly superior. They had a fixed amount of time to decide. Half of the group spent their time studying an abundance of information about the cars; the other half were given puzzles to solve to keep their minds busy. Guess who picked the best car? Well, of course, the puzzle solvers.

But solving puzzles has limited value. The reason is that puzzles, and many kinds of problems in general, are solutions in disguise. That is, to find a solution we only have to arrange or rearrange the elements we have been given in some clever way using skills we have acquired by solving many similar puzzles. Jigsaw puzzles are the most obvious example. But most math problems are also solutions in disguise; apply a rule or two, move a few letters or numbers around, and voilà!

Many system builders are puzzle solvers. They start with, let’s say, price, time, and volume and then try to find some way of expressing one or more of these elements with some indicator(s) that will expose market inefficiencies. They tweak here, optimize there, and more often than not start over again; they have, they admit, come up with an unsatisfactory solution. System design becomes addictive, most likely because for the most part it is a variety of puzzle solving, though one that never has a definitive answer.

To be truly creative we have to move beyond plain vanilla puzzle solving. We have to do more than perform some familiar operations to reach a conclusion or rearrange the parts to create a whole. Perhaps we could find real connections, however fleetingly applicable, that others have not found. (Our model here could be Renaissance Technologies, though most of us would be hard pressed to rise to their level of expertise.) Or we could devise a strategy (think of Ed Thorp’s convertible bond arbitrage, highly successful until everybody started copying it) that will profit from buying an undervalued asset and hedging risk. Of course, these tasks are ever so much more difficult than ordinary puzzle solving. But then whoever won a major math prize for solving an Algebra I problem?

In the meantime there’s still good money to be made by tapping into the “depth mind.” And here puzzles can distract the trader from making bad decisions stemming from information overload.

I enjoy puzzles far too much to proclaim them worthless wastes of time!

The paradox of diversification

Sometimes, writing this blog, I sense a woeful dearth of new ideas. I despair that I’ll come across as an oldster repeating the same story over and over, believing that I’m telling it for the first time. It’s true that I don’t remember every post I’ve written, but that’s not the problem. Rather, this blog occasionally becomes repetitive because there’s just as much herding in the world of financial literature as there is in the markets themselves.

In the wake of the financial collapse one of the “hot topics” is the failure of diversification to protect portfolios. I wrote about this briefly in my recent post on The Endowment Model of Investing. John Authers in The Fearful Rise of Markets: Global Bubbles, Synchronized Meltdowns, and How to Prevent Them in the Future (FT Press, 2010) joins in the conversation. He points to the new “paradox of diversification”—that “the more investors bought in to assets on the assumption that they were not correlated, the more they tended to become correlated.” (p. 166)

In fact, he writes, everyone was exposed to the same risks. Liquidity risk was the most serious; the second was that the run-up in commodity prices would end.

Echoing Mohamed El-Erian, he claims that asset allocation should be done according to type of risk. Instead of balancing asset classes, he suggests that it might be more sensible to balance, for instance, the risks of inflation and deflation. Admittedly, his prose is much clearer than El-Erian’s, but that may be because his suggestion is simpler. (I wrote about El-Erian’s idea early in the life of this blog and offered a few thoughts about how traders could flesh it out.) For those who don’t recall El-Erian’s words in When Markets Collide, here they are:

“The ideal situation is to come up with a small set (three to five) of distinct (and ideally orthogonal) risk factors that command a risk premium. The next step is to assess the stability of the factors and how they can be best captured through the use of tradable instruments. This provides for a portfolio optimization process whereby the factors are combined in a manner that speaks directly to the investors’ return objective and risk tolerance. The end product is a more robust and time-consistent combination of asset classes that map clearly to the underlying factors.” (p. 233)

In an unleveraged or modestly leveraged world this style of portfolio building makes eminent sense and is theoretically elegant to boot. But as leverage increases and, in a crisis, funds sell whatever they can to meet margin calls, it doesn’t matter how carefully constructed a long-only portfolio is and on what principles it is diversified; it will get whacked. (I specifically use the example of a long-only portfolio because it was for this kind of portfolio that the traditional asset allocation model was devised. A hedged portfolio is a different kettle of fish altogether.)

Personally I prefer the notion of a portfolio with many moving parts rather than one that is fixed for a certain period of time—let’s say rebalanced once a year. Indeed, why not make a very difficult task a herculean one?

A puzzle to fritter away some time

I’ve started reading John Adair’s Decision Making and Problem Solving Strategies, about which you will undoubtedly hear more later. But today let me share a puzzle that you can work on when the markets are quiet. Adair gives his readers 30 minutes to solve it.

1. There are five houses, each with a front door of a different color, and inhabited by people of different nationalities, with different pets and drinks. Each person eats a different kind of food.
2. The Australian lives in the house with the red door.
3. The Italian owns the dog.
4. Coffee is drunk in the house with the green door.
5. The Ukrainian drinks tea.
6. The house with the green door is immediately to the right (your right) of the house with the ivory door.
7. The mushroom-eater owns snails.
8. Apples are eaten in the house with the yellow door.
9. Milk is drunk in the middle house.
10. The Norwegian lives in the first house on the left.
11. The person who eats onions lives in the house next to the person with the fox.
12. Apples are eaten in the house next to the house where the horse is kept.
13. The cake-eater drinks orange juice.
14. The Japanese eats bananas.
15. The Norwegian lives next to the house with the blue door.

Who drinks water and who owns the zebra?

Please wait until tomorrow to post your answers.

Ward, The Devil's Casino

Over the weekend I read Vicky Ward’s The Devil’s Casino: Friendship, Betrayal, and the High-Stakes Games Played Inside Lehman Brothers (Wiley, 2010). Ward, who started off her career in England as a gossip columnist and is now a contributing editor for Vanity Fair, focuses on the people who ran Lehman and the culture they instilled. It’s harder to mourn the demise of the firm after reading this book.

Take, for instance, the chapter entitled “Lehman’s Desperate Housewives.” The wives of the top brass at Lehman were expected to attend countless corporate and social events, they were told what charities they were expected to donate to and how much they were expected to give, they were expected to dress appropriately for every occasion, and they were expected to attend the annual summer get-together at the Fulds’ ranch in Sun Valley, Idaho, where (among other things) they were expected to hike. One wife hated the rigorous hike up Bald Mountain so much that she arrived in Sun Valley with a fake cast on her leg. Unfortunately her scheme failed because another wife, higher up in the pecking order, arrived with a real broken leg and announced that, broken leg or not, she planned to climb.

The Lehman dress code was taken seriously. Dick Fuld’s motto was “Sloppy dress, sloppy thinking.” The dress code extended beyond the office. For instance, on the golf course men were expected to wear either a golf or a button-down shirt and khaki pants. And when the co-head of global equities appeared at an off-site meeting dressed business casual, Fuld announced “Off-site, yes. Out of mind, no.”

To meet these expectations and to indulge themselves some Lehman executives and their wives relied on outsized personal staffs. Niki Gregory had a staff of about 30 who seemed to do everything except shop for shoes; that she did herself, filling a closet “twice the size of the Jimmy Choo store in New York.” Joe Gregory had his own domestic staff of 30, presumably to take care of his fleet of boats, multiple houses, and private planes. And Erin Callan, named CFO in 2007 despite no finance background, saw nothing wrong with talking to the Wall Street Journal about her personal shopper at Bergdorf Goodman even as Lehman was fighting for its life.

To those who know Wall Street culture these stories may seem typical, not unique to Lehman. Worse, they may seem irrelevant to the financial crisis. I have no first-hand knowledge, but throughout Ward’s book one could see ways in which corporate expectations led to group-think. Just as no one in the upper echelons could challenge the dress code, so no one seemed prepared to challenge Lehman’s appetite for risk. The firm rewarded loyalty and discouraged dissent. In 2007 Hank Paulson warned securities firms to recapitalize, but Lehman kept growing its leveraged businesses and piling on billions of debt. The head of risk management was demoted and subsequently left; “she says she couldn’t believe the stupidity of what she was seeing—and she had seen a lot.”

Joe Gregory introduced a diversity program at Lehman, which received much praise. But diversity is useful to a firm only if it brings with it a diversity of opinions, not just a diversity of sex, skin color, or sexual orientation. And this diversity of opinions must have a voice. It’s hard to run a business effectively in an echo chamber. The demise of Lehman Brothers bears sad witness to this fact.