Bookstaber, The End of Theory

“As we embrace complexity we come to the end of theory.” So writes Richard Bookstaber, author of A Demon of Our Own Design, in his new book, subtitled Financial Crises, the Failure of Economics, and the Sweep of Human Interaction (Princeton University Press, 2017). Although he casts his analysis in the context of financial crises, it works perfectly well as an account of financial markets behaving “normally.”

Four phenomena are endemic to financial crises, Bookstaber believes: emergence, non-ergodicity, radical uncertainty, and computational irreducibility. Emergence occurs “when systemwide dynamics arise unexpectedly out of the activities of individuals in a way that is not simply an aggregation of that behavior.” Non-ergodicity is a feature of financial markets throughout. That is, markets vary over time; they do not follow the same probabilities today as they did in the past and will in the future. Uncertainty is radical when it cannot be expressed or anticipated, when we’re dealing with unknown unknowns. Finally, our economic behavior is so complex, our interactions so profound that “there is no mathematical shortcut for determining how they will evolve.”

How are we to survive in a complex, ever changing environment, where the future is not like the past, where projected probabilities are fictions? One short answer is: act like a cockroach. Use coarse, simple rules that ignore most information. “The coarse response, although suboptimal for any one environment, is more than satisfactory for a wide range of unforeseeable ones. … [P]recision and focus in addressing the known comes at the cost of reduced ability to address the unknown.” Alternatively put, don’t rely on optimization based on past data. Instead, use heuristics.

As Bookstaber boldly states, “if you can model it, you’re wrong.” It’s not just that all models are inherently wrong, it’s that models as normally conceived are useless under these circumstances. “If we want to understand a crisis, we have to construct a story, and we must be willing to do so in the ‘road in the headlights’ fashion: ready to change the narrative as the story line develops. A change in narrative means a change in model, and the model changes are not simply a matter of revising the values of various parameters, be it by the statistical tool of Bayesian updating or whatever. It might be a change in heuristics, in the types of agents in the system. … Models need to be like novels, molding to twists and turns and unexpected shifts.”

Bookstaber’s analysis is rooted in the work of the Santa Fe Institute, with a smattering of George Soros’s reflexivity theory added for good measure. It is pragmatic rather than axiomatic, inductive rather than deductive. It’s definitely a worthwhile read.

By the way, the Sante Fe Institute is re-offering its popular (and, I can attest, excellent) online course "Introduction to Complexity." The course started a couple of weeks ago.

Morduch & Schneider, The Financial Diaries

Traders and investors know how potentially devastating high volatility can be for all but the most nimble. In The Financial Diaries: How American Families Cope in a World of Uncertainty (Princeton University Press, 2017), Jonathan Morduch and Rachel Schneider show how income volatility wreaks havoc with a large number of American families.

A team of ten researchers followed 235 households for 12 months in communities in southwest Ohio and northern Kentucky, the San Jose (California) region, eastern Mississippi, and Queens and Brooklyn (New York City). None of these sites was thriving, “but all had opportunities.” To qualify for the study, a household had to have at least one working member. Otherwise, the participants were diverse. None was among the richest or the poorest in their communities.

The book alternates between family stories and economic analysis. In the cases that are highlighted, workers do not have a steady pay check. Instead, their income fluctuates week to week, month to month. For instance, a mechanic who worked on commission repairing long-haul trucks at a service center on an interstate highway did reasonably well in the winter and summer. More things went wrong with trucks during those seasons. During the spring and fall, however, his pay was about halved.

Most families whose income was volatile were able to smooth the ups and downs of their finances, “but only to a point. Then, illiquidity is felt sharply.” Thirty-one percent of the best-off, middle-class households were, in the course of the study year, threatened with (or actually experienced) eviction, the disconnection of utilities or cable, or repossession of an asset. Nearly half of the households overall had at least one bank overdraft.

Although most studies talk about income inequality, income volatility is perhaps even more important. In a 25-year study, beginning in 1984, “volatility increased for the poorest 10 percent of households, and it fell for the richest 10 percent. … [O]ver the past generation, the gap in income volatility between the poorest and the richest grew by more than 400 percent, reinforcing divides based on income and wealth.”

The people profiled in this book are hard workers who just can’t get ahead. They move in and out of poverty. (Nearly one-third of all Americans experienced poverty for two months or more between 2009 and 2011.) They save for short-term needs, deplete their savings to fulfill these needs, then start saving again. They never get to the point of benefiting from the miracle of compound interest.

The portraits the authors paint are depressing. They go a long way toward explaining why the U.S. is seeing “deaths of despair” and Donald Trump is in the White House. Moreover, with increasing automation and a freelance workforce, the problem is only going to get worse. The authors offer some suggestions for improving the situation, but most of them require government, employers, and financial institutions working together “in new and different ways.” In the present environment, that is unlikely.

2017 SBBI Yearbook

The 2017 SBBI Yearbook: Stocks, Bonds, Bills, and Inflation: U.S. Capital Markets Performance by Asset Class 1926-2016 by Roger G. Ibbotson and contributors from Duff & Phelps (Wiley, 2017) is the kind of book one rarely sees these days. Printed in two colors on high quality, heavy stock and measuring 8 1/2” x 11,” it offers 368 pages of beautifully presented returns data along with careful analysis. It is divided into 12 chapters and three appendixes: results of U.S. capital markets in 2016 and in the past decade, the long-run perspective, description of the basic series, description of the derived series, annual returns and indexes, statistical analysis of returns, company size and return, growth and value investing, liquidity investing, using historical data in wealth forecasting and portfolio optimization, stock market returns from 1815-2016, international equity investing, monthly and annual returns of basic series, cumulative wealth indexes of basic series, and rates of return for all yearly holding periods 1926-2016.

In addition to providing the reader with a trove of tables and graphs based on Morningstar data, the yearbook mines performance data for investing insights. Among them (and this is a very small sample):

• “The serial correlation of returns on large-cap stocks is near zero. For the smallest deciles of stocks, the serial correlation tends to be higher.”


• “Unlike the returns on large-cap stocks, the returns on small-cap stocks tend to be seasonal.”


• “Liquidity appears to be a much better predictor of returns than size.” Between 1972 and 2016 the geometric mean of annualized returns of the least liquid quartile of NYSE/MKT/NASDAQ stocks was about twice that of the most liquid quartile, with a significantly lower standard deviation.


• Market bubbles, over time and across geographical boundaries, appear to exhibit similar log-periodic power laws before the bubbles pop and markets crash.

The 2017 SBBI Yearbook is expensive, certainly not the kind of book you read in the bathtub or take to the beach. But for those who like to see their data on the page, not on the screen, and who appreciate meticulous analysis, it’s well worth the price.

Krishnan, The Second Leg Down

Investors, we know, are inclined to cut their profits short and hold on to their losers. In The Second Leg Down: Strategies for Profiting after a Market Sell-Off (Wiley, 2017) Hari P. Krishnan addresses investors who are seeing their portfolios shrink in value but are loath to sell. Anticipating further market declines, they want to hedge their portfolios. By then, however, traditional hedges such as index puts are expensive. Still, they need something to serve as a “hard backstop against portfolio disaster.”

Krishnan, who received a Ph.D. in applied math, was an options trading strategist at the Chicago Board of Trade and executive director and co-head of alternative asset allocation at Morgan Stanley. He is now a fund manager at Cross-Border Capital in London. Although he is writing primarily for institutional investors, many of his suggestions would work equally well for retail investors.

Options are the most affordable way to hedge a portfolio. When markets are going up, however, they are a waste of money. Month after month they expire worthless. So portfolio managers are disinclined to throw money away by hedging. When the flood waters are rising, however, they want to buy insurance—insurance that’s not prohibitively expensive. And they want to make money off that insurance.

Krishnan takes the reader through possible options hedging strategies, exposing the pitfalls of some of the more popular alternatives such as ratio and calendar spreads. Broken wing butterflies offer more advantages.

What about using VIX futures as a hedge? This doesn’t work; the hedge simply withers away over time. “Maintaining a long volatility position by mechanically rolling VIX futures is simply too expensive.” Simultaneously selling VIX futures and overbuying at-the-money VIX calls, however, is useful in risk-off regimes. “It benefits from large changes in volatility in either direction.”

Since options eventually expire, the question for a hedger is how far out in time to go. Let’s say you’re buying out-of-the-money puts. Short-dated options are cheap and insensitive to volatility. They are a gamma play, offering “tremendous potential when there are large realized moves.” They work well as an emergency hedge. Long-dated out-of-the-money options, those with more than a year to maturity, are best purchased when investors are overconfident. They occasionally offer exceptional value. It is wise to avoid those options that many institutions tend to favor—the 2% OTM 3 month to maturity puts. They are not a Goldilocks solution but rather “the worst of both worlds.”

Another portfolio protection strategy, an alternative to purchasing options, is to devote a portion of the portfolio to trend following. It is not capacity constrained (as weekly options in particular are) and, in a downtrending market, it will stay “piggishly” short.

I have outlined some of the main points of Krishnan’s book, but its real value comes from his sophisticated analysis of such critically important concepts as volatility and skew. This makes the book useful not only to hedgers but even to speculative options traders.

Waterhouse, The Land of Enterprise

A couple of years ago I audited a fascinating EdX course offered by Cornell on the history of American capitalism. It’s now archived.

Benjamin C. Waterhouse’s book The Land of Enterprise: A Business History of the United States (Simon & Schuster, 2017) surveys much of the same terrain, albeit in a more abbreviated form. The text of the book is less than 200 pages. And yet, even though the book ranges from the European “exploration, exploitation, and ultimate inhabitation of the New World” to the fallout from the financial crisis, it is not superficial. Waterhouse highlights “the most important historical developments, especially changes in business practices, the evolution of different industries and sectors, and the complex relationship between business and national politics.”

Take, for instance, the rise of general incorporation laws. Before 1800 corporate charters “had to be granted by the sovereign—the king or Parliament in colonial times; the state or federal legislature after independence.” Charters were issued to only 335 businesses during the entire eighteenth century. By the early nineteenth century states started granting corporate charters administratively rather than legislatively, making the process a lot less cumbersome. “In 1811, New York became the first state to enact such a law for manufacturing firms. In 1837, Connecticut became the first state to allow general incorporation for any kind of business. And by 1870, every state had some type of general incorporation law on the books.”

Or consider corporate opposition to environmentalism in the 1960s and 70s. Responding to new standards enacted in 1970 that limited automobile emissions, Chrysler claimed that “citizens have been needlessly frightened” about air pollution. In general, critics of the environmental movement, “conservatives as well as many labor unions,” worried about the social costs—“shuttered factories or higher-priced products”—that would result from stricter environmental regulations. Advocates of environmentalism, according to the president of the Heritage Foundation, were “zero-growth zanies. … Zero growth may help the elites, who can go out and till their organic gardens and watch the sun come up from the serenity of their redwood hot tubs, but it doesn’t do much for those among us who are still trying to make it up the economic ladder.”

Waterhouse is an academic, but The Land of Enterprise should appeal to a popular audience. It’s a most palatable introduction to American business, and by extension social and political, history. And it serves as an informative backdrop to what we’re seeing today.

Vaughan & Finch, The Fix

The Libor scandal, which broke in 2012, confirmed people’s worst suspicions about big banks and a system “in which manipulation was not just possible but inevitable.” In The Fix: How Bankers Lied, Cheated and Colluded to Rig the World’s Most Important Number (Bloomberg/Wiley, 2017) journalists Liam Vaughan and Gavin Finch profile the antihero Tom Hayes, “a brilliant, obsessive, reckless, irascible math prodigy who transformed rate-rigging from a blunt instrument into a thing of intricate, terrible beauty.” They also introduce us to his entourage of enablers and co-conspirators.

Hayes, who in 2015, when he was 35, was diagnosed with Asperger’s syndrome, had “a steely stomach for risk.” And a passion to win, whatever it took. In his case, it took getting his brokers to lie to the banks about what was happening in the cash markets.

The Fix is a riveting tale of illegal behavior, usually engaged in for profit, sometimes (or so the justification went) for the stability of the banking sector. It exposes a culture of corruption where even the guilty usually walk. “Of the more than 20 individuals identified by Hayes as taking part in the scheme, he is the only one to be convicted.”

Unlike the jurors in the brokers’ case, who kept falling asleep during the trial, readers of this book will be wide awake from beginning to end. The two authors provide only enough information about Libor to make their story understandable. Financial wonks will undoubtedly be disappointed, but most other readers will compulsively keep turning pages.

Chan, Machine Trading

Ernest P. Chan, a physics Ph.D. and a former researcher in machine learning at IBM’s T.J. Watson Research Center, is well known to the quant trading community. He is the author of Quantitative Trading: How to Build Your Own Algorithmic Trading Business and Algorithmic Trading: Winning Strategies and Their Rationale. His most recent effort is Machine Trading: Deploying Computer Algorithms to Conquer the Markets (Wiley, 2017).

In Machine Trading Chan discusses the basics of algorithmic trading, factor models, time-series analysis, artificial intelligence techniques, options strategies, intraday trading and market microstructure, bitcoins, and how algorithmic trading is good for body and soul. Where appropriate, he uses MATLAB code to develop his points.

Chan assumes a working knowledge of linear algebra, statistics, and basic computer science, as well as a familiarity with the financial markets, options in particular. Although he provides exercises at the end of each chapter, his work is not really suitable as a textbook. It is, I believe, best viewed as an overlay to a quant trader’s education.

Chan describes an array of trading strategies, most stemming from the academic literature. Many of these strategies were once profitable but have subsequently deteriorated in performance. (You didn’t really expect Chan, who manages money, to share his “winningest” strategies, did you?) But this isn’t the point. Individual strategies are either examples of the types of strategies that can work in particular markets (for instance, “statistical factors can be more useful for trading in markets where fundamental factors are less important for predictive purposes,” such as the forex market) or illustrative of the process of generating or testing a trading model.

Some of the material Chan presents is relevant only to professional traders with large research budgets. But even individual retail traders can extract nuggets of valuable information from this book—if, that is, they have the necessary background.

van Vliet & de Koning, High Returns from Low Risk

Pim van Vliet and Jan de Koning, both members of Robeco’s quantitative equities team (with van Vliet responsible primarily for the firm’s conservative equity strategies), have written a book challenging the claim that risk and return are positively correlated. High Returns from Low Risk: A Remarkable Stock Market Paradox (Wiley, 2017) is intended for a broad audience of investors. As a result, even though the authors obviously have quant skills, there’s no razzle-dazzle math on display here.

The book’s results are based on a dataset of monthly closing prices from January 1926 to December 2014 of the U.S. traded stocks of the largest 1,000 companies by market capitalization at any given moment in time. For each of these 1,000 stocks he (I assume van Vliet) measured the rolling three-year historical monthly return volatility and ranked them by risk (throughout risk is equated with volatility). Then he constructed two portfolios, one containing the 100 stocks with the lowest volatility, the other containing the 100 riskiest stocks (the high-volatility portfolio). He rebalanced the portfolios every quarter. Assuming that a person put $100 into each portfolio on New Year’s Day 1929 and reinvested any capital gains for 86 years until New Year’s Day 2015, the low-volatility portfolio was worth $395,000 at the beginning of 2015, the high-volatility, $21,000. Put another way, the low-volatility portfolio returned 10.2% annually on average whereas the high-volatility portfolio returned only 6.4%.

The disparity might be inflated somewhat by virtue of the fact that the calculations start in 1929 and “the low-volatility portfolio wins by losing less during times of stress.” The high-volatility portfolio would have been worth a little over $5 when the market bottomed out in the spring of 1932; the low-volatility portfolio would have been worth $30. Nonetheless, the authors contend, “if we were to start both portfolios in the spring of 1932, the low-volatility portfolio would still ‘win’ by a very significant margin.”

A low-volatility portfolio, it should be noted, doesn’t produce maximum returns. Given ten portfolios, each containing 100 stocks and ranked according to volatility (low to high), and using the same 86-year time frame, the portfolio in the fourth decile performed best (about 12% a year). Even the portfolio in the ninth decile performed better than the low-volatility portfolio. But the portfolio of the 100 stocks with the highest volatility performed far worse than any of the others.

The authors analyze why low-volatility stocks are overlooked in the market, thereby providing an opportunity for solid returns. Basically, “virtually everybody seems to be drawn to the dark and risky side of the stock market.” So, even though the paradox was first discovered over 40 years ago and even though it may become more well known, “there is every reason to believe the paradox will continue to exist and may even become stronger.”