Wednesday, January 28, 2015
When it comes to choosing, we essentially have three options: impersonal default rules, active choosing, and personalized default rules. Sunstein lays down some guiding principles about which option is preferable in what circumstances. Among them, “impersonal default rules should generally be preferred to active choosing when (1) the context is confusing, technical, and unfamiliar, (2) people would prefer not to choose, (3) learning is not important, and (4) the population is not heterogeneous along any relevant dimension. … [A]ctive choosing should generally be preferred to impersonal default rules when (1) choice architects are biased or lack important information, (2) the context is familiar or nontechnical, (3) people would actually prefer to choose …, (4) learning matters, and (5) there is relevant heterogeneity. To favor active choosing, it is not necessary that all five conditions be met. … [P]ersonalized default rules should generally be preferred to impersonal ones in the face of relevant heterogeneity. “ (pp. 18-19)
Increasingly, as information accumulates about people’s actual choices, personalized default rules will be available, something Sunstein considers to be on balance a plus. I’m going to skip straight to his discussion of this option since it is an obvious follow-up to my piece on The Black Box Society.
Sunstein admits that the idea of personalized default rules raises serious concerns. “Some of these involve narrowing our horizons; others involve the exercise of autonomy; others involve identification and authenticity; still others involve personal privacy.” Even so, in many cases such default rules, he maintains, “can make life not only simpler and more fun but also longer and healthier.” (p. 159)
In an extreme case, we could have a political system with personalized voting defaults, so that people are automatically defaulted into voting for the candidate or party suggested by their previous votes (subject of course to opt-out). But, Sunstein notes, there is a devastating problem with such a voting system, “the internal morality of voting. The very act of voting is supposed to represent an active choice, in which voters are engaged, thinking, participating, and selecting among particular candidates. Of course this is an ideal, and far from a reality for everyone. … But the aspiration is important.” (p. 164)
What about shopping? So far retailers don’t offer default rules, simply annoying (at least to me) recommendations. If you bought a book by a certain author, they suggest, you’ll probably like books by another author who is somehow “similar.” But what if sellers knew, “with perfect or near-perfect certainty,” what people wanted to buy even before they themselves did? (This is creepy big data in full swing.)
Sunstein conducted some surveys to ascertain whether people would approve or disapprove of a scheme where a seller sends you books that it knows you will purchase, and bills you (though you can send the books back if you don’t want them). In a nationwide survey respondents didn’t buy into automatic enrollment—71% disapproved; even if you could voluntarily sign up for such a program, 59% said they would decline to do so. Why wouldn’t everybody opt in? Some may distrust the incentives of the seller, others might view searching for a book as a benefit instead of a cost, and of course people’s preferences change. How many James Patterson books do you really want to buy? “Even if the algorithms are extraordinarily good, they must extrapolate from the past, and the extrapolation might be hazardous if people do not like in the future what they liked in the past, or if they like in the future what they did not like in the past.” (p. 182)
Perhaps, Sunstein suggests, we should distinguish among types of purchases. He offers a two-by-two matrix: easy or automatic, difficult and time-consuming on the x-axis, not fun or pleasurable and fun or pleasurable on the y-axis. In the upper left quadrant we have impulse purchases, where there is little reason for predictive shopping. The upper right quadrant—books, vacations, cars—is again not the obvious place for predictive shopping because for a lot of people such shopping is fun. The lower left quadrant (not fun or pleasurable but easy or automatic) includes household staples. Here the costs of choice are low, so there is no urgent need for automaticity. The lower right quadrant is the one where there would be real value in automaticity. This quadrant includes retirement plans and health insurance. Since for most people the choice of a retirement plan or health insurance is difficult and time-consuming and not fun, “if predictive shopping could be made accurate and easy, there would be a good argument for automatic purchases.” (p. 186)
But beware the slippery slope.
Sunday, January 25, 2015
Frank Pasquale, a law professor, takes us deep into these dark, often creepy worlds in The Black Box Society: The Secret Algorithms That Control Money and Information (Harvard University Press, 2015). And offers some ways out, “toward an intelligible society.”
To understand the problem of dark money, data brokers, proprietary methods, supercookies, trade secrecy, and the like, Pasquale uses the metaphor of the black box. It is particularly apt, “given its own dual meaning. It can refer to a recording device, like the data-monitoring systems in planes, trains, and cars. Or it can mean a system whose workings are mysterious; we can observe its inputs and outputs, but we cannot tell how one becomes the other. We face these two meanings daily: tracked ever more closely by firms and government, we have no clear idea of just how far much of this information can travel, how it is used, or its consequences.” (p. 3)
We are constantly being profiled, and targeted. “It’s a cinch,” the author writes, “for companies to compile lists of chronic dieters, or people with hay fever.” The vice president of a company in the health sector went even further: “Based on your credit-card history, and whether you drive an American automobile and several other lifestyle factors, we can get a very, very close bead on whether or not you have the disease state we’re looking at.” (p. 30) Are you going to marriage counseling? Well, at least one credit card company pays attention to this fact. Counseling is a tip-off that “marital discord may be about to spill over into financial distress” and you thereby become less creditworthy. “Once one piece of software has inferred that a person is a bad credit risk, a shirking worker, or a marginal consumer, that attribute may appear with decision-making clout in other systems all over the economy.” (p. 32) On the other side of the financial spectrum, splurge on a pair of headphones and you can see higher prices on sneakers in a later online search.
Black box scoring misapplies natural science methods to the social realm. “A civil engineer might use data from a thousand bridges to estimate which one might next collapse; now financial engineers scrutinize millions of transactions to predict consumer defaults. But unlike the engineer, whose studies do nothing to the bridges she examines, a credit scoring system increases the chance of a consumer defaulting once it labels him a risk and prices a loan accordingly. Moreover, the ‘science’ of secret scoring does not adopt a key safeguard of the scientific method: publicly testable generalizations and observations. As long as the analytics are secret, they will remain an opaque and troubling form of social sorting.” (p. 41)
And then there are the big banks, which exploit “two levels of black box finance: obfuscation in the service of illegality, and opacity resulting from complexity.” (p. 103) Pasquale focuses on risk modeling in the subprime crisis, another misapplication of natural science methods, and high frequency trading. In the former case, to cite but a single problem, “the models had to assume the stability of certain kinds of human behavior, which could change in response to widespread adoption of the models themselves.” (p. 114)
The Black Box Society is a frightening portrait of the ever more powerful shadowy world that blocks light from reaching our everyday lives. It is also a call to action, with a range of suggestions that inevitably pale in comparison to the gargantuan task at hand. But small steps sometimes have outsize consequences. Just ask the folks who control what we see, influence what we buy, and determine whether we can pay for it.
Wednesday, January 14, 2015
Kelly starts with the assumption that the stock market is a zero-validity environment, what is commonly known as a random walk. Outcomes are unpredictable, and both expert and amateur stock pickers are wrong about half the time. Market timers face even tougher odds. In a 1975 study William Sharpe found that “timers need a 74 percent accuracy rate to beat a passive portfolio taking on the same amount of risk.” (p. 25)
Is there any way to beat the market? Yes, the author claims. His solution is the 3% signal. It has six components: “the growth vehicle where we keep most of our capital during our working years; the safety vehicle where we keep a smaller portion of our capital; the target allocation of capital between the growth and safety vehicles; the safety vehicle allocation at which a rebalance back to its target is triggered; the timing of our growth signal; and the growth target.” (p. 37) Although the investor can define his own permutations of these components, the default plan is “a small-company stock fund as the growth vehicle; a bond fund as the safety vehicle; an 80/20 target allocation between the stock and bond funds; a 30 percent bond allocation threshold that triggers rebalancing back to 80/20; a quarterly timing schedule; and a 3 percent growth target.” (p. 38)
As you may gather, what sets this system apart from and makes it superior to most rebalancing plans is the 3% signal. At the end of each quarter you rebalance based on how much your stock fund grew or didn’t—more than 3%, sell the extra profits and put them into your bond fund; less than 3%, use bond proceeds to bring your stock fund up to its target 3% quarterly growth rate.
The author’s research indicates that 3% per quarter is the outperformance sweet spot. This quarterly performance yields an annual return of 12.6%, 26% better than the market’s annual performance of 10% over the past ninety years. (p. 56) And we know how that extra performance compounds.
Kelly carefully describes how investors can put this outline of a plan into action—what kinds of funds they might use, how they might opt to adjust the default allocation as they age, how they can survive market crashes. He even follows three hypothetical investors as they try to navigate the stock market from December 2000 to June 2013. It should come as no surprise that the one who used the 3% signal fared best.
We often hear, and have come to believe, that models beat experts. Kelly offers the individual investor a simple, mechanical model that instills discipline, removes a lot of self-sabotaging emotion, and has a good track record. Will it continue to outperform? Actually, it just might.
Monday, January 12, 2015
I’m going to touch on three of these skills: drive for daylight, fly the OODA loop, and fail wisely.
The “drive for daylight” concept is borrowed from race-car drivers who say that “the trick to managing speed at 200 miles per hour is to drive for daylight. They go too fast to navigate by the lines on the pavement or the position of their fellow drivers. Instead, they focus on the horizon and, at high speeds, their hands follow their eyes.” Similarly, creators “navigate around immediate obstacles by keeping their long-term mission in mind. … creators don’t benchmark themselves against the competition or focus on industry norms. … they set their sights on the horizon, scan the edges, and avoid nostalgia.” (p. 49)
Integral to the “drive for daylight” mindset is a “to-go” way of thinking. That is, a person doesn’t focus on how far he’s already come but on what remains to be done. “To-go” thinking, researchers have found, accelerates momentum. “Motivating yourself by thinking about how much of the marathon remains before you cross the finish line can inspire you to run harder, faster, more competitively, and with greater enthusiasm.” (p. 55)
The notion of the OODA loop comes from John Boyd, an Air Force fighter pilot who “crafted a framework for making rapid decisions that would ensure success in fast-changing environments. Boyd’s ‘OODA loop’—observe, orient, decide, and act—is as pertinent to business [and to trading] as it is to aerial combat.” (p. 68) Since there’s a fairly extensive literature on the OODA loop, I’ll not say more about it here.
Finally, let’s look at the failure ratio. We’ve all read the advice to fail early and often, but how much failure is acceptable? The author found that “a surprising number of creators decide that ratio ahead of time. They aim not for perfection but to ensure that they take enough risk.” (p. 94) This is, I think, an important metric to consider. Entrepreneurs worry if they experience too few failures. As LinkedIn cofounder Reid Hoffman said, “Frankly, if you tune it so that you have zero chance of failure, you usually also have zero chance of success.” (p. 96) Just think of that beautifully over-optimized trading system that crashes in real time.
Traders focus on minimizing their risk by setting stops or keeping their position size small. But the other side of the equation is equally important. Their portfolio can grow only if the risk they assume is large enough. Institutions have metrics to look at both sides of the equation. Individual traders rarely do, and then they wonder why they come up short.
Wednesday, January 7, 2015
Overweighting bonds is not an intuitive asset allocation strategy, so Shahidi takes pains to explain its rationale. He sets the stage with a description of Ray Dalio’s economic machine, more vividly shown in Bridgewater’s 30-minute animated video. He then explains why a 60/40 portfolio is not well balanced. First, “the impact of an asset class on the total portfolio is only dependent on two factors: (1) how volatile the asset class is, and (2) how much of the total portfolio is weighted toward it. … The more volatile asset class should get a lesser weight to make up for the fact that it is more volatile. The less volatile segment should receive a higher allocation so that its impact on the portfolio matches that of the higher-volatility asset class.” Second, “the traditional 60/40 allocation is 99 percent correlated to the stock market!” (p. 24)
Instead of viewing an asset class as something that offers returns, the Bridgewater model looks at it as “something that offers different exposures to various economic climates.” (p. 26) Some sources of volatility, such as the future cash rate and risk appetite, are not diversifiable. But the most hazardous risk to investors--shifts in the economic environment--can in large measure be neutralized through proper asset allocation.
A simple two-factor model (growth and inflation) shows the economic bias of each major asset class. Equities want growth but not inflation, treasuries want neither, commodities want both, and TIPS want inflation but not growth.
With these basic pieces of the allocation puzzle in place, Shahidi explains each in more detail. TIPS, the most unlikely candidates for a major role in a balanced portfolio, are, according to the author, “possibly the most influential of the asset classes.” (p. 97) Their economic bias is opposite that of equities, so an outperformance in equities is normally matched with an underperformance in TIPS. In 2013, for instance, TIPS suffered big losses as equities soared. Bridgewater’s All Weather fund ended the year down 3.9%.
Shahidi admits that his basic portfolio is oversimplified, that other asset classes can be added to a portfolio and asset classes can be more narrowly defined—as long as you identify the environmental bias and overall volatility of each. For instance, your balanced portfolio can include global equities, emerging market bonds, commercial real estate, or small-cap stocks.
The bottom line is that a portfolio manager should weight asset classes to balance the economic exposure of the portfolio, not simply to equalize the risk of the asset classes (what has come to be known as risk-parity). If a portfolio is well constructed, it should be able to withstand future economic shocks.
Readers who want to emulate Bridgewater’s approach to asset allocation—professional portfolio managers as well as individual investors—will find a lot of useful information in Shahidi’s book. Just don’t expect miracles.
Monday, January 5, 2015
In Private Wealth Management: The Complete Reference for the Personal Financial Planner (McGraw-Hill, 2015) G. Victor Hallman and Jerry S. Rosenbloom, both affiliated with the Wharton School, lay out a daunting curriculum. The wealth manager should be skilled in investment planning and financial management, income tax planning, financing education expenses, retirement planning, charitable giving, insurance planning and risk management, estate planning, and planning for business interests.
In over 600 pages the authors cover a head-spinning number of topics. Unless you’re a masochist, this is not a book you read straight through. Even I, usually a conscientious reviewer, picked my way through it, choosing some areas in which I felt competent and others about which I knew next to nothing. The reason for this strategy was to see whether the authors did a good job with material with which I was familiar and whether they presented the unfamiliar in a way that was easily understood. They scored well on both fronts, which is probably one reason this book is now in its ninth edition.
Private Wealth Management is a reference/textbook that should be in the library of every financial planner.