Wednesday, January 17, 2018

Muller, The Tyranny of Metrics

We’re fixated on metrics, in part because we have come to embrace two dictums: “If you cannot measure it, you cannot improve it” (Lord Kelvin) and “What gets measured gets done” (Tom Peters).

Jerry Z. Muller, in The Tyranny of Metrics (Princeton University Press, 2018), sets out to show “the unintended negative consequences of trying to substitute standardized measures of performance for personal judgment based on experience. The problem is not measurement, but excessive measurement and inappropriate measurement—not metrics, but metric fixation.”

Metric dysfunction manifests itself in multiple ways. Problems that fall under the general heading of distortion of information include: (1) measuring the most easily measurable, (2) measuring the simple when the desired outcome is complex, (3) measuring inputs rather than outcomes, and (4) degrading information quality through standardization. Then there are the inevitable attempts to game the metrics. This gaming can manifest itself in (1) creaming, (2) improving numbers by lowering standards, (3) improving numbers through omission or distortion of data, and, when all else fails, (4) cheating.

We are deluged with quantitative data that are viewed as the answer; we just have to come up with the right question. But these data rarely give the full answer to a meaningful question. For instance, back when spreadsheets were becoming “a worldview—reality by the numbers,” Seth Klarman warned (in 1991) that “spreadsheets created the illusion of depth of analysis.” By now, at least in certain quarters, that illusion has been transformed into accepted dogma.

In a chapter titled “The Mismeasure of All Things?” Muller analyzes case studies from the fields of education, medicine, policing, the military, business and finance (especially pay-for-performance schemes and short-termism), and philanthropy and foreign aid.

The Tyranny of Metrics may not break a lot of new ground, but it shows how metric fixation permeates, and often creates hazards for, so many aspects of our society. And it does so in a thoroughly convincing way. As Muller concludes, “Ultimately, the issue is not one of metrics versus judgment, but metrics as informing judgment, which includes knowing how much weight to give to metrics, recognizing their characteristic distortions, and appreciating what can’t be measured.”

Wednesday, January 10, 2018

Marston, Type R

Mother and daughter Stephanie and Ama Marston have teamed up to write Type R: Transformative Resilience for Thriving in a Turbulent World (PublicAffairs/Hachette, 2018). The authors specifically reject the model of “bouncing back” from misfortune, arguing that “there’s no going back to who or where we were before challenging times.” Instead, they focus on how to grow and create opportunity from adversity, to “leverage change and hardship into opportunity as individuals and carry that progress into the world as a contribution to the collective.”

The authors share stories of people who have demonstrated transformative resilience. They also analyze the six common characteristics and skills that allow for transformative resilience: adaptability, healthy relationship to control, continual learning, purpose, leveraging support, and active engagement. Most of these characteristics are pretty straightforward. I’ll look at only one, which often trips people up: a healthy relationship to control.

The Marstons begin by saying that “believing that we control the outcomes of our lives and our successes isn’t only empowering but also a starting point for creating Transformative Resilience. Yet, focusing too intensely on an internal locus of control and our ability to control has significant downsides.” If we believe that we alone are responsible for what happens to us, this belief can be “a huge source of stress.” And so, Type Rs learn “to assess what’s within our sphere of influence and what’s not. We realize that strength isn’t always determined by triumph over the outside world but sometimes by changing our inner world. As a result, we can respond appropriately, investing energy in areas where we have influence, acknowledging and shifting focus away from areas where we don’t, and redirecting our energy into cultivating Transformative Resilience.”

The authors apply their model first to individuals, then to organizations and leaders, and finally to families.

Sunday, January 7, 2018

Lo, Adaptive Markets

Andrew W. Lo first proposed the adaptive markets hypothesis (AMH) in 2004 as an alternative to the efficient markets hypothesis (EMH). Four years later, in Hedge Funds: An Analytic Perspective, he reiterated his hypothesis. Few people did cartwheels over it. This past year he wrote a more popular, though nearly 500-page, book to advance his view, Adaptive Markets: Financial Evolution at the Speed of Thought (Princeton University Press).

The first third of the book—dare I say the best third of the book?—is a stroll through, and critique of, competing hypotheses and an introduction to evolution, with the mantra “It’s the environment, stupid!” emerging as a dominant motif and the notion of evolution at the speed of thought becoming an organizing principle. (“We can use our brains to test our ideas in mental models, and to reshape them if they’re found lacking. This is still a form of evolution, but it’s evolution at the speed of thought.”)

As Lo repeats more than once, it takes a theory to beat a theory. His hypothesis is, he suggests, “the new contender. But these are still early days for the challenger—the incumbent has had a five-decade head start—and a great deal more research is needed before these ideas become as immediately useful as the existing models of quantitative finance.” This is indeed the problem for the AMH. It’s just not immediately obvious how to use it in a way that is neither trivial (e.g., market regimes change) nor supportive of far too many alternatives.

According to the AMH, “market behavior adapts to a given financial environment.” The EMH, in Lo’s view, describes an abstraction, an idealized market. “An efficient market is simply the steady-state limit of a market in an unchanging financial environment.

Lo offers a new investment paradigm to replace or modify the five principles of the traditional investment paradigm.

1. The risk/reward trade-off. Although during normal market conditions there’s a positive association between risk and reward, “when the population of investors is dominated by individuals facing extreme financial threats, they can act in concert and irrationally, in which case risk will be punished.”

2. Alpha, beta, and the CAPM. “Knowing the environment and population dynamics of market participants may be more important than any single factor model.”

3. Portfolio optimization and passive investing. “Portfolio optimization tools are only useful if the assumptions of stationarity and rationality are good approximations to reality.” As for passive investing, “risk management should be a higher priority.”

4. Asset allocation. “The boundaries between asset classes are becoming blurred.”

5. Stocks for the long run. “Over more realistic investment horizons, … investors need to be more proactive about managing their risk.”

Lo is a good enough scientist to realize that “between theory, data, and experiment, the Adaptive Markets Hypothesis will survive, perhaps be replaced with an even more compelling theory in the future, or fall short and be forgotten.” I hope it’s not the last alternative because, even though I have my doubts about its efficacy, the hypothesis has some very attractive features.

Wednesday, January 3, 2018

Cochrane & Moskowitz, eds. The Fama Portfolio

I’m going to start 2018 on a high note, with The Fama Portfolio: Selected Papers of Eugene F. Fama (University of Chicago Press, 2017), edited by John H. Cochrane and Tobias J. Moskowitz. The subtitle is a tad misleading because, although this volume, over 800 pages in length, contains 20 papers that Fama either authored or co-authored, it also includes papers and commentaries by colleagues and former students.

Eugene Fama is best known, of course, for the efficient market hypothesis—that, as he succinctly described its strong version, “security prices fully reflect all available information.” But since a precondition for this version of the hypothesis is that there are no information and trading costs, it is, he readily admitted in his second paper on the EMH, “surely false. Its advantage, however, is that it is a clean benchmark that allows me to sidestep the messy problem of deciding what are reasonable information and trading costs. I can focus instead on the more interesting task of laying out the evidence on the adjustment of prices to various kinds of information. Each reader is then free to judge the scenarios where market efficiency is a good approximation (that is, deviations from the extreme version of the efficiency hypothesis are within information and trading costs) and those where some other model is a better simplifying view of the world.”

I quote this passage because I believe it illustrates Fama’s dedication to empiricism. He was no “so much the worse for the facts” theorist. As Kenneth French wrote in “Things I’ve Learned from Gene Fama,” “Gene is arguably the best empiricist in finance.”

In addition to his papers on efficient markets, which includes one he coauthored with French on “Luck versus Skill in the Cross-Section of Mutual Fund Returns,” this volume contains papers on risk and return, return forecasts and time-varying risk premiums, and corporate finance and banking. Especially notable are the Fama and French papers on “Common Risk Factors in the Returns on Stocks and Bonds” and “Multifactor Explanations of Asset Pricing Anomalies.”

As long as the reader has a basic grasp of statistical principles, Fama’s papers are eminently comprehensible. And, despite all the criticism of the EMH, they should still be studied with care, both as case studies in how to do first-rate financial research and for the insights they provide into financial markets. I laud the editors for gathering such important work into a single volume. It’s a book every student of finance and financial professional should have in his library.

Sunday, December 31, 2017

Happy New Year

May you all find peace, prosperity, health, and happiness in 2018.

Friday, December 22, 2017

Happy holidays

If you have a book you didn't like and time on your hands, you too can produce a holiday decoration. Just fold away.

Wednesday, December 20, 2017

Chambers, Trading Cryptocurrencies

If you’re itching to get in on the cryptocurrency mania and don’t have enough capital to trade in either of the two new futures markets, Clem Chambers’ book is a useful how-to manual. Trading Cryptocurrencies: A Beginner’s Guide (ADVFN Books, 2017) is brief, at only 70 pages of text, but it covers the basics of both trading and mining and explains at least part of the rationale behind these digital currencies. It also issues appropriate caveats all along the way.

If you want to mine, don’t expect big bucks using only your PC—and you can’t even play if you have a Mac. At the moment “you can make pennies [a day] mining with your PC, dollars with a gaming computer, $30 upwards with a mining rig and $50+ with the right ASIC miner. The costs are from nothing to a lot, say $10,000 for a single ASIC device.” And, of course, the cost of electricity to run these hot, noisy devices. The fancier the graphics card (think top-of-the-line NVDA and AMD) the more money you can bring in. But the potential for making money as a miner is ever in flux, as with practically everything cryptocurrency-related.

If, instead, you want to trade, Chambers points to the most reliable sites, warning that the cryptocurrency world is “awash with scammers.”

I personally don’t like trading what appears to be a bubble, so this book isn’t for me. But, if I’m wrong and cryptocurrencies are just in their infancy, traders who want to get on the bandwagon should do their homework. Chambers has made that task a lot easier.