Sunday, May 21, 2017
Along the way we learn curious facts about prison gangs, pirates, even Moldovans. We discover that, despite the many significant advantages of optimism—a longer life, for instance—depressed people (and “depression is pessimism writ large”) are better at making predictions. We learn how to turn something that’s boring or overwhelming into a game that’s winnable, has novel challenges and goals, and provides feedback. We are told to “use trying and quitting as a deliberate strategy to find out what is worth not quitting” and then to set aside a small percentage of time for “little experiments” to keep learning and growing. And, oh yes, eminent scientists have traditionally had a lot of hobbies. “Getting lots of different ideas crashing together turns out to be one of the keys to creativity.”
We all need role models. Barker offers us one: a Toronto raccoon. “Their ability to get into trash cans shows a level of grit and resourcefulness that is almost beyond compare.” In 2002 Toronto financed the development of “raccoon-proof” trash cans. “How well did they work? Well, let’s just say that in 2015 the city spent an additional $31 million dollars to create a new, redesigned ‘raccoon-proof’ trash can. Not a good sign, folks.”
Barker’s book is a first-rate read—illuminating, humorous, and compassionate. (“Self-compassion beats self-esteem.”) It’s by turns empowering and humbling. Sounds like life, doesn’t it?
Sunday, May 14, 2017
Steven Clifford, formerly CEO of King Broadcasting Company and National Mobile Television and a director of 13 companies, tackles these questions with wit and compelling logic in The CEO Pay Machine: How It Trashes America and How to Stop It (Blue Rider Press/Penguin Random House, 2017). He sets out to show “how the sausage is made—how the Pay Machine actually works, how its parts interact, and how every step in the process pushes CEO pay to higher and higher levels.”
He starts with a fairy tale about a loan officer at the Midwest Bank who asks his fairy godmother to help him get a promotion because his $75,000 salary isn’t enough. Ah, she replies, she can do better than that. She can get him more money for the same job. Applying CEO compensation practices to the loan officer’s pay package, the fairy godmother steers him, wide-eyed step by wide-eyed step, from his modest $75,000 salary to a whopping $5,845,000. In the process, the author sketches out the inner workings of the CEO Pay Machine.
Excessive CEO pay, the author argues, harms companies, shareholders, and the economy and undermines democracy. Not just because it is excessive but also because of the way in which it is typically structured. For instance, it usually misaligns CEO incentives with effective corporate practices and goals.
Clifford lays the blame for the emergence of the Pay Machine at the feet of “three totally unrelated actors: Michael Jensen, Milton Rock, and Bill Clinton. … They were not attempting to overpay CEOs and might be stunned and insulted to be grouped together as causal agents.” Jensen’s basically sound recommendations (that CEOs own a significant amount of company stock and be paid in part for performance) were largely misapplied.
Rock and his fellow compensation consultants introduced the idea of using peer groups to calibrate executive pay and benchmarking (usually above-average) to the salaries of the peer group CEOs. It’s easy to see that “the above-average benchmarking of pay within peer groups creates a relentless upward spiral in pay—a game of CEO leapfrog. Every time a CEO leaps, he establishes a higher compensation base for the next CEO in the group to leap over.” By the way, at this year's annual Berkshire Hathaway meeting, Charlie Munger said:"I have avoided all my life compensation consultants. I hardly can find the words to express my contempt." He did, however, find the words at the 2012 meeting when he said: for "compensation consultants, prostitution would be a step up." Warren Buffett added at this year's meeting, "If the board hires a compensation consultant after I go, I will come back mad."
The last culprit, Bill Clinton, executing on his campaign promise to clamp down on excessive executive compensation, set out to eliminate tax deductions for executive pay--at first above a certain level and then, in a compromise move, above a certain level that wasn’t performance based. Business was still unhappy, so he agreed to exempt stock options from this cap. “Boards could now pay unlimited amounts as long as they could pass it off as ‘performance based’ and could grant unlimited stock options with no performance requirements.”
Clifford examines the pay packages and performance of the highest-paid executives of 2011 thru 2014—the CEOs of UnitedHealth Group, McKesson, Cheniere Energy, and Discovery Communications. The disconnect should come as no surprise.
By way of a solution, Clifford proposes a simple, blunt instrument: “For every dollar above $6 million that the companies pay their CEO or any other executive [and this includes all forms of compensation], they would pay a dollar in luxury tax. It would not be tax deductible.” Punkt. No loopholes. And, he realizes, no way it would ever get through this Congress. But maybe someday….
Wednesday, May 10, 2017
This set is not for the casual reader who has only a passing interest in derivatives. It’s a textbook for those who want a solid foundation in derivatives, a foundation from which to engage in financial engineering, managing a trading book, or managing client portfolios. Or for those who simply have a keen interest in financial markets and want more in-depth insight into how derivatives can be used to hedge as well as to speculate.
Here’s but a single example of how derivatives, in this case equity swaps, can be useful: reducing insider exposure. Let’s say the personal wealth of the founder of a publicly traded company is almost entirely exposed to the fortunes of that company. The founder controls about 10% of the company and wants to retain this degree of control, so he doesn’t want to sell any of his shares. A swap dealer might offer him the following deal: the founder would pay the dealer the return on some of his shares in exchange for a diversified portfolio return. In this way the founder would keep his level of control but reduce his risk.
Sunday, May 7, 2017
Covel may be criticized for relying too much on the words of others. He is inclined to string quotations together with minimal commentary. He also uses the margins for more quotations—rightly so, I suppose, since some of them are only marginally related to trend following. This criticism is, however, primarily a stylistic one. The people Covel quotes were in the trenches and knew what they were talking about, so better to hear from them than from an outsider.
Trend trading is no longer as fashionable a concept as it once was—for instance, in the heyday of the “turtles.” It has been replaced, at least in part, by its cousin, momentum trading. What’s the difference between the two? Aside from the fact that relative (cross-sectional) momentum is a more popular factor than time-series momentum, one can say, in very rough outline, that time-series momentum is more forward-looking. In assessing momentum, analysts use a range of inputs, frequently including fundamentals and economic news. Trend following is, as its name indicates, backward-looking; it focuses on where price has been as an indication of where it will be in the future.
Trend trading, traditionally defined as a longer-term strategy, has always been most prevalent among commodity traders, the rationale being that commodity markets trend more than equity markets do. Still, many traders in all kinds of markets, short-term as well as longer-term, employ trend following strategies. Despite some premature obituaries, trend following outside of the managed futures world is far from dead.
And so a fifth edition of Covel’s classic was definitely in order. Covel was wise to add interviews and research articles to his book. They make it all the more valuable.
Wednesday, May 3, 2017
Tian, who runs the website GuruFocus.com, draws on the insights of Peter Lynch, Warren Buffett, Donald Yacktman, and Howard Marks to advocate for a style of investing that avoids the sometimes bottomless pits of deep-value investing. Buy only good companies, he recommends, following Warren Buffett’s famous advice: “It’s far better to buy a wonderful company at a fair price than a fair company at a wonderful price.” He offers a 20-point checklist for buying a good company at a reasonable price, covering the nature of the business, its performance, financial strength, management, and valuation. The final question is more personal: “How much confidence do I have in my research?” That question, he says, “determines your action once the stock suddenly drops 50 percent after you buy.”
In 2009 GuruFocus began tracking a portfolio of 25 companies that had been consistently profitable over the previous ten years and were undervalued as measured by the discounted cash flow model. Between January 2009 and September 2016 the portfolio returned an annualized 15.7%, whereas the S&P 500 returned 12.0%. In 2010 the website started two other portfolios of consistently profitable companies that sold at close to the 10-year price/sales low and price/book low. Both of them outperformed the S&P 500 by about 2.5% a year.
Tian outlines some of ways analysts evaluate companies. It goes without saying that “none of these valuation methods can justify the stock prices of Amazon and Netflix.” Maybe not, but Bill Miller certainly juiced his Legg Mason Value Trust fund’s returns by investing in Amazon.
Sunday, April 30, 2017
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.
Wednesday, April 26, 2017
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.