Wednesday, March 7, 2018

Botelho and Powell, The CEO Next Door

What are the four behaviors that transform ordinary people into world-class leaders? This is the question that Elena L. Botelho and Kim R. Powell ask and answer in The CEO Next Door (Currency / Crown Publishing, 2018). From a database of more than 17,000 CEOs and C-suite executives, they analyzed over 2,600 leaders. They coupled this analysis with 13,000 hours of interviews and two decades of experience advising CEOs and executive boards to distill the common attributes of successful CEOs.

The CEO Next Door is divided into three sections. The first hundred pages deal with the book’s leading question. Then come 60 pages on how to get a CEO job and 80 pages on navigating the challenges of the role. Here I’ll touch on only the first section, titled “Get Strong: Master the CEO Genome Behaviors.”

Those who reach the top are decisive, opting for speed over precision. They engage for impact, understanding exactly who their stakeholders are and what they want. They exhibit relentless reliability, delivering consistently. And they adapt boldly, riding the discomfort of the unknown. These behaviors, it is important to stress, are not inborn traits but habits shaped by practice and experience.

Decisive CEOs, the authors not unexpectedly found in their study, were twelve times more likely to be high performers. So, what are the keys to being an effective decision maker? Three things stand out: making decisions faster, making fewer decisions, and putting in place practices to get better at decision making.

I’ll skip the next two behaviors to get to the fourth, adapting boldly. “The best leaders,” the authors tell us, “thrive in a condition of relentless discomfort, adapting their organizations and themselves. These CEOs chart new paths before they have to, not when there’s no other choice. … They take the attitude, If I am not uncomfortable, then I am probably not learning or changing fast enough.”

Adaptive CEOs are willing to let go of approaches that have worked before. Otherwise, their companies may end up like Kodak, where in 1975 a young engineer developed a digital camera but the company did nothing with it, or Blockbuster, which had three chances to buy Netflix.

The authors illustrate their points with stories about and interviews with CEOs.

Wednesday, February 28, 2018

Mayer-Schönberger and Ramge, Reinventing Capitalism in the Age of Big Data

Businesses are facing their most formidable challenge in decades: a shift from firms to data-rich markets which, in turn, will upend traditional, money-based ones. These transformations are the focus of Reinventing Capitalism in the Age of Big Data (Basic Books / Hachette, 2018) by Viktor Mayer-Schönberger and Thomas Ramge. The authors “connect the dots between the difficulties faced by traditional online markets; the error of the stock market’s trusted pricing mechanism; and the rise of markets rich with data.” They “argue that a reboot fueled by data will lead to a fundamental reconfiguration of our economy, one that will be arguably as momentous as the Industrial Revolution, reinventing capitalism as we know it.” That’s a bold claim, one not yet borne out but here and there showing some proverbial green shoots.

The most basic difference between markets and firms is “the way information flows and is translated into decisions, and by whom. This is reflected in their structures: the market mirrors the flow of information from everyone to anyone and the decentral decision-making by all market participants, [whereas] the hierarchical firm mirrors information streaming to its center, where leaders make the key decisions.”

Markets may offer the potential for greater information for everyone, but traditional markets tend to be reductionist. They translate the full gamut of preference information into an information trickle around price. But recent advances in data-handling, which themselves are founded on data, are improving our ability to choose based on data. For example, BlaBlaCar “allows riders and drivers to get matched along multiple dimensions, including their self-reported level of chattiness…. With less opportunity for negotiating on price, riders are more likely to take other information into account when selecting a ride.”

What will this reconfiguration of our economy mean for the financial markets? The authors argue that more money is now available for capital investments and fewer companies are looking it, which means that returns on investment will plummet. “This spells the end of finance capitalism as we know it…. The economy will thrive, but finance capital not with it; it epitomizes the shift from money-based markets to data-rich ones.” The authors continue: “data-rich markets devalue money, and investors will be paying the bill. … If there is reassurance to be had, it is that although data-rich markets will cause a drastic shock to the system, with thousands of billions of dollars in individual holdings evaporating as rates of return drop and investments lose their value, this shock will likely be one-time, rather than recurring. Once capital has been devalued and our expectations of the anticipated returns from it are reset, capital’s value will likely hold steady, rather than continue to slide.” And “in the long run, … data-rich markets will help investors to better identify opportunities that match their preferences and are less clouded by human bias. … We’ll still need financial advice, but it will likely come from a machine rather than a human being.”

Whether or not you follow the authors all the way to financial Armageddon (I personally find their hypothesis about finance capital unconvincing), their book is a stimulating read.

Wednesday, February 21, 2018

Shenq and Hong, Value Investing in Asia

I can’t begin to guess how many feet of library shelves it would take to house all the books that have been written on value investing. The best answer is probably “too many.” So do we need yet another one? Yes. Value Investing in Asia by Stanley Lim Peir Shenq and Cheong Mun Hong (Wiley, 2018) takes the value investor into uncharted waters, waters rife with dangers but with the potential for solid profit.

The authors offer general, somewhat eclectic, guidelines to screen for companies that may be worth investing in. More important, however, as they stress, is knowing what not to invest in. They highlight both financial and non-financial red flags. Among the financial red flags are abnormally high margins, trade receivables growing faster than revenue, inventory growing faster than revenue, consistent excessive fair value gains, companies in a dilutive mood, leverage, and seemingly unnecessary borrowings. Among the non-financial red flags are massive reshuffling of the company’s officers, infamous directors and shareholders, when things vanish into thin air (e.g., a fire destroys a company’s books and financial records or a truck carrying five years of financial documents is stolen—the truck is later recovered but not the documents), and “innovative” business deals.

Five case studies illustrate the way the authors invest, each with a unique “hook”: value through assets, current earning power, growth through cyclicality, special situation, and high growth (Tencent).

The book concludes with five interviews with Asian fund managers. There’s also some online bonus content.

Investors who are thinking about buying individual Asian stocks would do well to read this book, not so much as a value investing primer but as an Asian investing primer.

Wednesday, February 14, 2018

Schilling, Quirky

What makes some people spectacularly innovative? This is the question that Melissa A. Schilling addresses in Quirky: The Remarkable Story of the Traits, Foibles, and Genius of Breakthrough Innovators Who Changed the World (PublicAffairs/Hachette, 2018. Although the book’s title is catchy, the answer is not that they’re quirky (though most of them were/are—as are millions of people who are not at all innovative).

Schilling focuses on eight innovators—Benjamin Franklin, Thomas Edison, Nikola Tesla, Marie Curie, Albert Einstein, Steve Jobs, Dean Kamen, and Elon Musk—and looks for significant commonalities.

Although superior intelligence is insufficient to make someone a serial breakthrough innovator, exceptional creativity is likely to be more common in the presence of high intelligence. Working memory may be the link between the two. “In my work modeling cognitive insight as a network process, I showed that individuals who are more likely or more able to search longer paths through the network of associations in their mind can arrive at a connection between two ideas or facts that seems unexpected or strange to others.” Moreover, as a result of exceptional working memory and executive control, highly creative people can do this much more quickly than less creative people. Tesla and Musk are textbook examples. “Both men had such extraordinary cognitive power that they were able to process a long path of calculations almost instantly in their heads. Their conclusions appear to arrive almost by magic!”

Innovators tend to exhibit high levels of social detachment and extreme faith in their ability to overcome obstacles. They work tirelessly, often at great personal cost, and many are driven by idealism. They also benefit from situational advantages conferred by time and place—and luck.

Based on the characteristics of the eight innovators she studied, Schilling makes some recommendations for nurturing “the innovation potential that lies within us all.” No, she isn’t offering a formula for creating the next Einstein. As she notes, “The life of the serial breakthrough innovator is not for everyone.” But we can tap some of their traits, such as separateness, even if we ourselves don’t crave to be socially detached. And we can improve people’s situational advantages.

Thursday, February 8, 2018

Duke, Thinking in Bets

Annie Duke, a near Ph.D. in cognitive psychology and a renowned poker player, shares what she learned “in smoky poker rooms” (and from academic research) about decision-making in general. Thinking in Bets: Making Smarter Decisions When You Don’t Have All the Facts (Portfolio/Penguin, 2018) is itself a smart book for the reader who isn’t comfortable with thinking probabilistically in an uncertain, unpredictable world.

Duke’s basic premise is that “thinking in bets starts with recognizing that there are exactly two things that determine how our lives turn out: the quality of our decisions and luck. Learning to recognize the difference between the two is what thinking in bets is all about.” In practice, however, knowing whether something happened as a result of skill or as a result of luck is rarely clear-cut; ambiguity reigns. And yet, if we can get this right, we can “focus on experiences that have something to teach us (skill) and ignore those that don’t (luck).” And thus get closer to our goals.

We can never know precisely how anything will turn out. And so, good decision-makers, “instead of focusing on being sure, … try to figure out how unsure they are, making their best guess at the chances that different outcomes will occur. The accuracy of those guesses will depend on how much information they have and how experienced they are at making such guesses.”

All decisions, she postulates, are bets, and most bets are bets against ourselves. In my favorite two sentences of the book, she writes: “In most of our decisions, we are not betting against another person. Rather, we are betting against all the future versions of ourselves that we are not choosing.”

Duke tackles a range of subjects integral to decision-making: for instance, belief systems, habit formation, and perspective (taking the long view).

In the final analysis, she writes, “none of us is guaranteed a favorable outcome, and we’re all going to experience plenty of unfavorable ones. We can always, however, make a good bet. And even when we make a bad bet, we usually get a second chance because we can learn from the experience and make a better bet the next time.”

Wednesday, February 7, 2018

Bos, Deep Value Investing

Jeroen Bos, portfolio manager of the UK-regulated Deep Value Investments Fund, has updated his 2013 book Deep Value Investing: Finding Bargain Shares with Big Potential in this second edition (Harriman House, 2018).

His guiding principle in finding bargain stocks is to look for companies whose current assets minus both current liabilities and long-term liabilities are greater than the current market capitalization. He leaves fixed assets out of the equation altogether since they are relatively illiquid. As a result, his favorite value stocks are “those that are light on fixed assets and heavy on current assets. And these tend to be service companies—for example, recruitment firms, financial services, consultants, housebuilders (from time to time) and so on.”

Since the deep value investor focuses on assets rather than earnings, he looks to buy stock in companies just when the majority of investors are selling. That is, “cyclical stocks always look cheapest on an earnings basis (i.e. measured by their P/E level) at the top of their cycle and most expensive at the bottom of the cycle, when their P/E levels are sky-high as their earnings have collapsed. … The outlook in the short term may indeed be terrible, but the nature of such service companies is that their business models tend to be pretty flexible. They are able to contract their operations before they really hit trouble, unlike (for example) manufacturers, who have far less flexibility: vast workforces, factories, supply chains etc.”

Bos enters a trade based on deep value but exits “into an earnings-driven market.” He doesn’t sell when a stock hits its net asset value but waits for earnings to re-establish themselves. As Bos writes, “Great deep value stocks are hard enough to find in the first place, and I am certainly not in the mood to let them go just when it starts to get interesting.” It’s not at all uncommon for deep value stocks to return 100% or 200%.

After spelling out his investment philosophy, Bos devotes the rest of the book to analyzing individual investments—one stock per chapter. These are British stocks, but the principles are of course applicable to other markets as well.

In the epilogue Bos summarizes his approach to deep value investing. “It is often said that this kind of equity investing must be quite risky. Unsurprisingly, I disagree! The companies may look distressed and be down in the doldrums. But we are largely purchasing liquid assets at a discount. It’s like paying £20 for a £50 note. If these deep value stocks drop further after we’ve bought them, it usually means a chance to simply buy more for less--£50 for £10 or £5. The long term is what matters. And quality will out: either other investors will notice, or other companies will swoop in for a buyout.”

Sunday, February 4, 2018

Bradley, Hirt, & Smit, Strategy Beyond the Hockey Stick

Three McKinsey partners—Chris Bradley, Martin Hirt, and Sven Smit—teamed up to write Strategy Beyond the Hockey Stick: People, Probabilities, and Big Moves to Beat the Odds (Wiley, 2018). Although it’s a business book, it’s replete with insights for the investor.

The book’s organizing graphic is the power curve, which is divided into three sections: the bottom quintile (a steep curve down), the middle three quintiles (basically a straight line), and the top quintile (a steep curve up). The majority of businesses, those in the middle three quintiles, make almost no profit. In power law fashion, value accrues exponentially to the top quintile. The challenge for businesses on the flat line is how to make it to the top quintile. By definition, of course, few ever succeed. We don’t live in Lake Wobegon, and, even there, “above average” doesn’t mean extraordinary success.

Market forces are pretty efficient, with profits tending toward zero over time because they get competed away, but markets aren’t perfect, so profits are possible. As they are in the financial markets. Just ask companies like Apple or investors like Warren Buffett.

The role of industry in a company’s position on the power curve is so substantial that you’d rather be an average company in a great industry than a great company in an average industry. Once again, we see an obvious parallel to investing. It’s a lot easier to make money on investments in companies in leading sectors than in lagging sectors.

A metaphor that I especially liked was that of corporate peanut butter. The authors argue that spreading resources thinly, like peanut butter on bread, across all parts of the business almost guarantees that you won’t make a big enough move to get to the top of the power curve. Similarly, a broad diversification of investable assets may be the closest thing to a free lunch in the investment world, but a free lunch isn’t usually such a great perk. (Especially if it’s a peanut butter sandwich.)

The authors recommend thinking in terms of pot odds. They explain that if you have only a slim chance of winning but the bet costs you little and the potential payoff is huge, that might be an investment worth making. And, conversely, an expensive investment that generates a high probability of a small success may be a bad idea. It may be like picking up pennies in front of a steamroller.

Among the eight shifts that the authors suggest to a business that wants to move up the power curve are, echoing the points above, that it should stop spreading peanut butter and start picking its 1-in-10s. And it should shift from long-range planning to forcing the first step.

Strategy Beyond the Hockey Stick is a thoughtful, pragmatic guide to outsize business success, with models grounded in hard data. I found it surprisingly engrossing and read it in one sitting. As a bonus, it has some great cartoons.

Wednesday, January 31, 2018

Coyle, The Culture Code

Daniel Coyle, author of the bestselling The Talent Code, has, in my opinion, topped that book with The Culture Code: The Secrets of Highly Successful Groups (Bantam Books, 2017). He explores three skills that he believes are critical to group success. They might seem a bit vapid at first, but Coyle develops and illustrates them convincingly. First, build safety: “signals of connection generate bonds of belonging and identity.” Second, share vulnerability: “habits of mutual risk drive trusting cooperation.” And third, establish purpose: “narratives create shared goals and values.”

As one of many examples of building safety, Coyle describes an experiment consisting of two scenarios and a question. First, you’re standing in the rain at a train station. A stranger approaches and politely asks, “Can I borrow your cellphone?” Second, in the same setting, the stranger politely says, “I’m so sorry about the rain. Can I borrow your cellphone?” To which stranger are you more likely to hand over your cellphone? Staggeringly, the second scenario caused the response rate to jump 422 percent.

When it comes to the efficacy of sharing vulnerability, Coyle recalls Steve Jobs’s penchant for starting conversations with “Here’s a dopey idea.” (And some of his ideas really were dopey.) And then there was the MIT team that won the $40,000 DARPA red balloon challenge, which consisted of locating ten large red balloons deployed at secret locations throughout the United States. The team found out about the challenge only four days before launch, so they had no time to craft an organized approach. Instead, they built a website that invited people to join the team and have all their friends sign up as well. And they promised to give $2000 per balloon to the first person to send the correct coordinates, $1000 to the person who invited them, $500 to whoever invited the inviter, etc. As Coyle writes, “This wasn’t a well-equipped team; it was closer to a hastily scrawled plea shoved into a bottle and lobbed into the ocean of the Internet: ‘If you find this, please help!’” Thousands of teams competed in the DARPA challenge, which organizers figured would take up to a week to complete. But in less than eight hours, the MIT team had found all ten balloons, with the help of 4,665 people. They had created “a fast, deep wave of motivated teamwork and cooperation.”

As for the third skill, establishing purpose, Coyle argues that “creating engagement around a clear, simple set of priorities can function as a lighthouse, orienting behavior and providing a path toward a goal.” Coyle recounts the Tylenol disaster, which Johnson & Johnson handled brilliantly by hewing to the company credo. Or, for me a more local example is the men’s hockey team at Quinnipiac University in Hamden, CT. The coach built a culture around a behavior he calls “Forty for Forty,” which refers to back-checking (“rushing back to the defensive end in response to the other team’s attack”). Back-checking happens around forty times a game, and the coach’s goal is that his players go all-out on each one. “Back-checking is exhausting, requires keen attentiveness, and—here’s the key—rarely makes a difference in the game.” But the perhaps one in forty times it makes a difference, it can change a game.

If you’re on a team or lead a team, this book may just change your game too.

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.