Sunday, October 4, 2015
Robert Carver is more modest—and more realistic. At the same time he has more to offer the investor or trader who has a spark of creativity and intellectual curiosity. Systematic Trading: A Unique New Method for Designing Trading and Investing Systems (Harriman House, 2015) is a thoughtful, and thought-provoking, journey through the process of creating modular rule-based portfolios.
Although the book addresses three classes of traders and investors—the staunch systems trader, the semi-automatic trader, and the asset allocating investor, Carver is at heart a systems guy. He himself runs a futures trading system with around 45 instruments, eight trading rules drawn from four different styles, and 30 trading rule variations. But this doesn’t mean that he is writing only for those with large portfolios who can code. It does mean, however, that his book will be of value only to those who either already think systematically or are open-minded about learning how to analyze and assess the ingredients of a model investing framework. I would wager to say that this group should include every investor and trader, though in practice of course it encompasses but a tiny fraction of people who have money in the financial markets.
Here I’m going to be decidedly unsystematic and pluck out two ideas that are illustrative of the topics covered in the book.
First, according to Carver, the most overlooked characteristic of a strategy is the expected skew of its returns. “Assuming they have the same Sharpe ratio, the returns from a positively skewed asset will contain more losing days than for those of a negatively skewed asset. But the losing days will be relatively small in magnitude. A negatively skewed asset will have fewer down days, but the losses on those days will be larger.” (pp. 44-45) Equities normally have a mildly negative skew, foreign exchange carry is a negative skew strategy (sometimes disastrously so—think the Swiss franc in January 2015), and gold tends to have a positive skew. Trend following strategies and long option strategies have a positive skew; fixed income relative value (remember LTCM?) and short option strategies have a negative skew. VIX futures have a highly positive skew, “around four times higher than their underlying index.” (p. 46) Negative skew trades often seem more attractive; after all, they are like selling an insurance policy. But managing risk in these trades is more difficult since losses are large and infrequent. Moreover, they often require leverage to achieve decent returns in normal times, so they get killed in bad times.
Second, systematic trading requires forecasting. “A forecast is an estimate of how much a particular instrument’s price will change, given a particular trading rule variation.” (p. 102) “A forecast shouldn’t be binary—buy or sell—but should be scaled. … There are three reasons why scaled forecasts make sense. Firstly, if you were to examine the returns made by a trading rule given the size of its forecasts, you’d normally find that forecasts closer to zero aren’t as profitable as those further away. Secondly, binary systems cost more to trade, since to go from long to short you’d need to sell twice a full size position immediately. Finally, the rest of the framework assumes that the forecasts you get are not binary or lumpy in other ways. It’s better to see forecasts changing continuously rather than jumping around.” (p. 113)
To set forecasts, Carver recommends using volatility standardization. Forecasts are “proportional to expected risk adjusted returns. For example, suppose that the Bund has expected returns of 2% a year and an expected annualized standard deviation of 8%. Schatz futures have an expected return of 1% a year, but you only expect volatility of 2% a year. After adjusting for risk the expected return on Schatz … is twice as much as on Bunds…. That implies the forecast for Schatz should be twice the forecast for Bunds. … If you continuously adjust your estimate of expected volatility then you also cope with risk changing over time.” (pp. 114-15)
Carver spent ten years in the City of London—initially trading exotic derivative products for Barclays and then serving as a portfolio manager for the hedge fund AHL, where he created its fundamental global macro strategy and managed its multi-billion dollar fixed income portfolio before retiring from the industry in 2013. So he isn’t just some ordinary Joe with a computer and a bunch of back-testing software. He has clearly thought about what makes a good systematic trader and a good systematically-driven portfolio. We can be grateful that he decided to share his insights with us.
Thursday, October 1, 2015
If this sounds familiar, I can assure you that Kay presents his case in a fresh way. In the process he offers what amounts to course on “clever” banking. For instance, he describes how banks have engaged in regulatory arbitrage, fiscal arbitrage, accounting arbitrage, and jurisdictional arbitrage.
He counters the claim that high-frequency traders contribute to market liquidity in the sense that, as a result of their activity, markets would be able to meet a sudden or exceptional demand without disruption. They problem is that they provide no capital to the market. “Speculators,” he suggests, “can help provide liquidity when they bring capital to the market and the scale of their activity is moderate relative to the activities of long-term investors. Matters are quite different when the dominant mode of market trading involves short-term speculators trading with each other. Ticket touts can serve a useful role at popular sporting events when demand may exceed supply: but when the majority of tickets are in the possession of ticket touts, the price will be volatile—determined mainly by the expectations of other ticket touts about future prices—and the needs of genuine fans ill served.”
Kay is at home with, and has opinions about, a wide range of issues that touch on finance. He claims, for example, that “probabilistic reasoning does not play a large part in our lives because the situations in which it can usefully be applied are limited. We deal with radical uncertainty [the unknown unknowns] through storytelling, by constructing narratives. … This, not the Panglossian world of ‘the Greenspan doctrine’, is the world in which business is conducted and securities are traded.”
Kay also believes that, although “transparency is a mantra in the modern world of finance, … the demand for transparency in intermediation is a sign that intermediation is working badly, not a means of making it work well. A happy motorist is one who need never look under the car bonnet. … The demand for transparency in finance is a symptom of the breakdown of trust.”
Other People’s Money is a book that many people, especially those satisfied with the status quo, will undoubtedly argue with. But it should sharpen their views, and perhaps even here and there change them.
Wednesday, September 30, 2015
Eliminating income inequality, Frankfurt argues, cannot be a fundamental goal because “inequality of incomes might be decisively eliminated … just by arranging that all incomes be equally below the poverty line. Needless to say, that way of achieving equality of incomes—by making everyone equally poor—has very little to be said for it.” (p. 3) Instead, we should focus on reducing both poverty and excessive affluence. “That may very well entail, of course, a reduction of inequality. But the reduction of inequality cannot itself be our most essential ambition.” (p. 5)
It is not morally important that everybody have the same. “What is morally important is that each should have enough. If everyone had enough money, it would be of no special or deliberate concern whether some people had more money than others.” (p. 7) That is, egalitarianism is not morally significant; sufficiency is.
When we are morally disturbed by the circumstances of the very poor, we are not upset that they have less money than others but that they have too little. “What directly moves us in cases of that kind … is not a relative quantitative discrepancy but an absolute qualitative deficiency.” (pp. 41-42)
“The fundamental error of economic egalitarianism lies in supposing that it is morally important whether one person has less than another, regardless of how much either of them has and regardless also of how much utility each derives from what he has. This error is due in part to the mistaken assumption that someone who has a smaller income has more important unsatisfied needs than someone who is better off. Whether one person has a larger income than another is, however, an entirely extrinsic matter. It has to do with a relationship between the incomes of the two people. It is independent both of the actual sizes of their respective incomes and, more importantly, of the amounts of satisfaction they are able to derive from them. The comparison implies nothing at all concerning whether either of the people being compared has any important unsatisfied needs.” (pp. 46-47)
Frankfurt replaces an easy to understand, though inherently flawed concept—equality—with a much thornier one—sufficiency. A person who has a sufficient amount of money is content (or it would be reasonable for him to be content) with what he has. He has no active interest in getting more.
Frankfurt deflects some obvious criticisms of this notion of sufficiency, but in the final analysis I don’t think sufficiency can be the centerpiece of either a theoretical or a practical model of income distribution. It rests on the classic economic model of the rational agent, which has been more or less debunked by behavioral economics. It assumes a state of mind (contentedness), impossible to quantify and even perhaps to know, as the touchstone of a moral economic society. And it flies in the face of reality. Does Warren Buffett, who certainly has an active interest in getting more money, have an insufficient amount of money? Does the retail clerk who is not actively searching for a way to make more money thereby have a sufficient amount of money? Frankfurt’s refocus on sufficiency, and thereby contentedness, reminds me somewhat of the attempt to use gross national happiness rather than gross domestic product as the measure of prosperity.
On Inequality may not solve the kinds of problems that liberal politicians in particular rail against, but it makes an important contribution by challenging the way these problems are formulated. It’s a worthwhile, stimulating read.
Sunday, September 27, 2015
Philip E. Tetlock, a professor at the University of Pennsylvania and co-leader of a multiyear online forecasting study, the Good Judgment Project, and Dan Gardner, a journalist, teamed up to produce one of the best books I’ve read this year. Superforecasting: The Art and Science of Prediction (Crown Publishers, 2015) argues that “it is possible to see into the future, at least in some situations and to some extent, and that any intelligent, open-minded, and hardworking person can cultivate the requisite skills. … Foresight isn’t a mysterious gift bestowed at birth. It is the product of particular ways of thinking, of gathering information, of updating beliefs.” (pp. 9, 20)
It’s pretty easy to get started learning to forecast more accurately. A tutorial for the Good Judgment Project covering some of the basic concepts in this book and summarized in its Ten (actually eleven) Commandments appendix “took only about sixty minutes to read and yet it improved accuracy by roughly 10% through the entire tournament year. …And never forget that even modest improvements in foresight maintained over time add up. I spoke about that with Aaron Brown, an author, a Wall Street veteran, and the chief risk manager at AQR Capital Management, a hedge fund with over $100 billion in assets. ‘It’s so hard to see because it’s not dramatic,’ he said, but if it is sustained, ‘it’s the difference between a consistent winner who’s making a living, or the guy who’s going broke all the time.’” (p. 20) Did that get your attention?
Admittedly, Superforecasting doesn’t focus on the financial markets because the authors recognize that they are rife with aleatory uncertainty (the unknowable), not just epistemic uncertainty (the unknown but potentially knowable). “Aleatory uncertainty ensures life will always have surprises, regardless of how carefully we plan. Superforecasters grasp this deep truth better than most. When they sense that a question is loaded with irreducible uncertainty—say, a currency-market question—they have learned to be cautious, keeping their initial estimates inside the shades-of-maybe zone between 35% and 65% and moving out tentatively.” (p. 116) Note that, even here, superforecasters don’t just throw up their hands and say 50-50.
In a second reference to the markets, the authors compare superforecasting investing to black swan investing. Playing the low probability, high reward card is not the only way to invest. “A very different way is to beat competitors by forecasting more accurately—for example, correctly deciding that there is a 68% chance of something happening when others foresee only a 60% chance. … It pays off more often, but the returns are more modest, and fortunes are amassed slowly.” (p. 195)
At its core, Superforecasting teaches its readers how to think probabilistically, something that doesn’t come naturally to most people. We tend to use a two- or three-setting mental dial. Something will happen, won’t happen, or may happen. But this way of thinking gets us into trouble. The “will” and “won’t” settings reflect a faulty view that reality is fixed. Even death and taxes may not be certain someday. And the “maybe” setting “has to be subdivided into degrees of probability. … The finer grained the better, as long as the granularity captures real distinctions—meaning that if outcomes you say have an 11% chance of happening really do occur 1% less often than 12% outcomes and 1% more than 10% outcomes.” (p. 117)
What does it take to be a superforecaster? Well, for starters, a lot of time and mental energy. Those who have a superabundance of both can join the thousands of people predicting global events at the Good Judgment Project. The rest of us can use this book to improve our own, most likely more modest predictions. If, that is, we have, or are willing to cultivate, certain qualities. Superforecasters are foxes, not hedgehogs. They look at problems from multiple perspectives. They tend to be, among other things, cautious, humble, nondeterministic, actively open-minded, intellectually curious, reflective, numerate, pragmatic, and analytical, with a growth mindset and grit. “The strongest predictor of rising into the ranks of superforecasters is perpetual beta, the degree to which one is committed to belief updating and self-improvement. It is roughly three times as powerful a predictor as its closest rival, intelligence.” (p. 155)
Superforecasting is a must-read book for everyone who is sick to death of “the guru model that makes so many policy debates so puerile: ‘I’ll counter your Paul Krugman polemic with my Niall Ferguson counterpolemic, and rebut your Tom Friedman op-ed with my Bret Stephens blog.’” (p. 24) As the authors write, “All too often, forecasting in the twenty-first century looks too much like nineteenth-century medicine. There are theories, assertions, and arguments. There are famous figures, as confident as they are well compensated. But there is little experimentation, or anything that could be called science, so we know much less than most people realize. And we pay the price. Although bad forecasting rarely leads as obviously to harm as does bad medicine, it steers us subtly toward bad decisions and all that flows from them—including monetary losses, missed opportunities, unnecessary suffering, even war and death.” (p. 42) It’s time for a change—for all of us to change.
Thursday, September 24, 2015
Weightman focuses on five familiar technologies: the airplane, television, bar code, personal computer, and cell phone.
Some of these stories are better known than others. Most people know a great deal, for instance, about the Wright brothers, and if you want to know even more, you now have David McCullough’s best-selling (though, to me, disappointing) biography.
But how many people know about the birth of the bar code? Joe Woodland isn’t exactly a household name, and his 1949 solution to the problem of a distraught supermarket manager didn’t exactly fly off the shelves. Without scanner technology and microcomputers the bar code was just a pipedream. Moreover, it had to be approved by a committee, the Symbol Selection Committee, made up of representatives of major supermarket chains and grocery manufacturers. Not until July 1974 was the first true UPC scanned in a supermarket—on a ten-pack of Wrigley’s gum.
Weightman’s book is a journey through the history of invention, some obvious precursors of the technologies on which we rely today, others more surprising steps along the way. To take but a single example: Alois Senefelder’s invention of lithography (one of those “mother of necessity” inventions because he needed a way to print his plays and didn’t have the money to buy presses and type). “And,” Weightman continues, “Senefelder’s discovery did more than revolutionise the art of printing: it inspired the creation of an entirely new way of copying images which in its early days went by the name of heliography” and, later, the Daguerrotype. Fast forward, we arrive at the technique of using photography to print circuits.
Eureka is of necessity a series of tangled stories. It isn’t guided by any overarching hypothesis about the history of science and technology (except that one thing leads to another), so the stories aren’t designed to illustrate a point. That makes them all the more enjoyable.
Wednesday, September 23, 2015
He argues that we can be great performers simply by being human, where being human means being social. “We are hardwired to connect social interaction with survival. No connection can be more powerful.” (p. 38) “Social interaction is what our brains are for.” (p. 39)
Computers may take over an increasing number of tasks that human beings used to perform, but, Colvin argues, there’s a limit to what we will accept computers doing. The question therefore is not what computers will never be able to do, a perilous line of inquiry, but what activities “we humans, driven by our deepest nature or by the realities of daily life, will simply insist be performed by other humans, regardless of what computers can do.” (p. 42)
He suggests that all important decisions will remain in the hands of human beings because “it’s a matter of social necessity that individuals be accountable for important decisions.” (p. 43) We’ll also perform the sorts of tasks that we haven’t clearly articulated and so aren’t amenable to computer analysis, goals and strategies that people must work out for themselves and that are best developed in groups. And then there are the tasks that “our most essential human nature demands” be performed by human beings—a doctor giving us a diagnosis, for instance, even if a computer supplied it.
The demand for cognitive skill in the workplace peaked in about the year 2000. The jobs that college graduates have been getting since that time require less brain work—“thus the widely noted upsurge in file clerks and receptionists with bachelor’s degrees.” (p. 47)
Cognitive skills are taking a back seat to social relationship skills. For instance, the work of lawyers is increasingly being taken over by infotech. Smart lawyers can still do well, “but not just because they’re smart. The key to differentiation lies entirely in the most deeply human realms of social interaction: understanding an irrational client, forming the emotional bonds needed to persuade that client to act rationally, rendering the sensing, feeling judgments that clients insist on getting from a human being.” (p. 48)
Beleaguered humanities majors—and women—may get a boost in the new economy. “Skills that employers badly want—critical thinking, clear communicating, complex problem solving—‘are skills taught at the highest levels in the humanities.’” (p. 178) And “the traits, tendencies, and abilities for which women have long shown greater strength than men will prove highly valuable for people of either sex who possess them.” (p. 164)
I would like to say that I was assuaged by Colvin’s book. But I keep thinking of instances of personal interaction that we once took for granted and that are now distant memories, retail clerks being a prime example. As technology advances, people adapt. In time we don’t miss having a human being on the other side of a transaction.
Moreover, Colvin’s world of social/economic relationships doesn’t create new jobs to replace the ones lost to technology. It simply, as far as I can ascertain, draws a line in the sand across which we dare (or don’t dare) technology to cross. I would hate to have to defend that line.
Sunday, September 20, 2015
In The Most Dangerous Trade: How Short Sellers Uncover Fraud, Keep Markets Honest, and Make and Lose Billions (Wiley, 2015), Richard Teitelbaum, a financial journalist, has written illuminating profiles of ten top short sellers, complete with their investing strategies. Combining interviews with well-researched back stories, he explores the highs and lows (and there are a lot of lows) of short selling.
Bill Ackman, Manuel Asensio, Jim Chanos, David Einhorn, Carson Block, Bill Fleckenstein, Doug Kass, David Tice, Paolo Pellegrini, and Marc Cohodes are the featured investors. We learn about their early years, how they ended up being short sellers, even the significance of their fund names. Why Muddy Waters, for instance? Block, trying to find a good name for his nascent firm, recalled a Chinese proverb: “Muddy waters make it easy to catch fish.”
We read about positions that worked and those that didn’t—and what these investors learned from the latter. We learn how they construct their portfolios (including long positions) and how they try to mitigate risk (sometimes with options).
Each short seller has his own style, but the investors profiled in this book share some common traits. They are passionate, they work exceedingly hard, and they are resilient—even those who ultimately didn’t make it. They scour the equity markets looking for stocks whose price significantly overstates their value. Some have macro theses, some are more akin to microbe hunters. But they are all looking for stocks that should, if they are correct and if other investors embrace their research, fall. Even in a rising market, though that is sometimes too much to hope for.
The Most Dangerous Trade is a book that’s hard to put down. Teitelbaum knows how to keep his reader involved. Whether you just like a good story or are thinking about starting a hedge fund, whether you are an individual investor who wants to learn how to pick stocks or an institutional investor debating portfolio construction, Teitelbaum’s book will speak to you. If you don’t come away with at least one or two good ideas, you didn’t read it carefully enough.