Frankfurt, On Inequality

Harry G. Frankfurt, professor emeritus of philosophy at Princeton University, is probably best known for his book On Bullshit. In his new book, a revised and expanded version of two journal articles published decades ago, he takes on those who find economic inequality morally objectionable. On Inequality (Princeton University Press, 2015) may not be the last word in the debate over income distribution, but it should sharpen the terms of the debate.

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.

Tetlock & Gardner, Superforecasting

We all crave knowledge of the future. Is it going to rain this weekend? Where will the equity markets be in a year? Next week? Five minutes from now? Predictive models, many using big data and statistical algorithms, have begun making inroads into this problem. But IBM Watson’s chief engineer, David Ferrucci, doesn’t think that machines will ever completely replace subjective human judgment. In forecasting, combinations of machines and experts may prove more robust than pure-machine or pure-human approaches. “So,” say the authors of Superforecasting, “it’s time we got serious about both.” (p. 24)

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.

Weightman, Eureka

“All modern inventions have an ancient history.” Thus begins Gavin Weightman’s Eureka: How Inventions Happen (Yale University Press, 2015). But, he adds, “what is striking” is that “the inventor who makes the breakthrough is invariably outside the mainstream of existing industry and technology.”

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.

Colvin, Humans Are Underrated

If you want to make it in our increasingly computerized world, you’d better learn to play well with others. This is the grossly simplified thesis of Geoff Colvin’s new book. (Colvin is a senior editor at large for Fortune, but you probably best know him for his Talent Is Overrated, which touted deliberate practice). In Humans Are Underrated: What High Achievers Know That Brilliant Machines Never Will (Portfolio / Penguin, 2015) Colvin asks how we human beings can carve out a meaningful work space for ourselves when computers do so many things—and will increasingly do even more things—better than we can.

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.

Teitelbaum, The Most Dangerous Trade

Of all the ways to make money in the financial markets, being a short seller is one of the toughest. The short seller is fighting the upward bias of the equity markets as well as the wrath of deep-pocketed, litigious individuals with vested interests in the stocks he is targeting. He has to be both a sleuth and a promoter; after all, what good is all his detective work if other investors don’t know what he uncovered and don’t join him in putting downward pressure on the stock?

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.

Fuld, Stock Market Trivia

Do you live and breathe the financial markets? Are you a trivia nut? Do you just want to have an hour or so of fun? If so, I can recommend Stock Market Trivia (2013) by Fred Fuld III. The author sent me a copy, and I had an enjoyable time flipping through it. “Flipping through” because, alas, my brain was already cluttered with a lot of market trivia. And with many of the weird words of Wall Street, defined in the second part of the book.

Here are a couple of examples of what you can find in this book. The smallest stock exchange in the world, measured by the number of stocks traded, is the Douala Stock Exchange in Cameroon, with only three listings at the moment.

One pound equals $490. No, of course, this is not an exchange rate. Rather, if you stack 490 dollar bills on a scale, they will weigh one pound.

From the two pages of “funny mergers”: “If the following companies were to merge: Caterpillar, Gottschalks, Uranium Energy, Tongjitang Chinese Medicines, you would end up with Cat Got Ur Tong.”

And can you believe that any company would have the ticker symbol SCAM? It is usually published as SCAM.L since it trades on the London Stock Exchange and is a highly regarded British mutual fund.

Javaheri, Inside Volatility Filtering, 2d ed.

Inside Volatility Filtering: Secrets of the Skew by Alireza Javaheri, the head of Equities Quantitative Research Americas at JP Morgan, is a book for quants. The first edition, which appeared ten years ago, was based on his Ph.D. dissertation and won the Wilmott Award. In this revised, updated second edition (Wiley, 2015), Javaheri draws on feedback he received at conferences and in the courses he taught at NYU’s Courant Institute of Mathematical Sciences and at Baruch College.

My mathematical skills, though ever improving, are not yet up to the task of writing a meaningful review of this book. So consider this a notice rather than a review.

Here’s a description of the book’s contents from the jacket copy: “Inside Volatility Filtering, Second Edition presents a new approach to volatility estimation identifying financial econometrics based on a more accurate estimation of the hidden state. Based on the idea of ‘filtering,’ this practical guide lays out a two-step framework involving a Chapman-Kolmogorov prior distribution followed by Bayesian posterior distribution to develop a robust estimation based on all available information. This new edition gives you an edge by showing you how to: base volatility estimations on more accurate data, integrate past observation with Bayesian probability, exploit posterior distribution of the hidden state for optimal estimation, and boost trade profitability by identifying ‘skewness’ opportunities.”