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.

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.

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.

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.”

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.”

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.