Tulchinsky, The UnRules

In some ways Igor Tulchinsky has written an “un-book,” and it’s undoubtedly the better for it. The UnRules: Man, Machines and the Quest to Master Markets (Wiley, 2018) is a short book, coming in at about 150 pages. As Tulchinsky explains in the preface, he is a man of few words. As it is, even to get to 150 pages, he blends memoir, the history of finance and mathematics, sometimes arcane examples, and quantitative analysis. But the result is a captivating personal account of what it takes for an immigrant to succeed in the world of quantitative finance and how data analytics are changing our future, and not only our financial future.

Tulchinsky is the founder, chairman, and CEO of WorldQuant, a global quantitative investment management firm. He is also the founder of WorldQuant Foundation and WorldQuant University, which offers a tuition-free two-year online master’s degree program in financial engineering and a free eight-week module on data science.

The UnRule, which Tulchinsky admits is loosely related to the liar’s paradox (loosely because it is an empirical rule), is that “All theories and all methods have flaws. Nothing can be proved with absolute certainty, but anything may be disproved, and nothing that can be articulated can be perfect.”

This UnRule informs his investing philosophy: create many competing points of view or, more precisely formulated in the case of his investment firm, alphas. In ten years WorldQuant went from 19 alphas to 10 million! “Today a typical portfolio may contain tens of thousands of alphas; the largest may contain 100,000. To our portfolio strategists, individual alphas, which may have vectors of hundreds or thousands of securities, remain black boxes. The algorithms, logic, and intellectual property remain with the researchers; the strategists know individual alphas only as mathematical expressions of a market signal. … [A] portfolio is all math.”

Readers who are looking for the mathematical secret sauce will be disappointed. But those in search of the qualities necessary for a person to thrive in today’s financial markets will be richly rewarded.

Belsky, The Messy Middle

We’ve all been there. We start a project with great enthusiasm. Then, as we proceed, the goal seems farther and farther away. We have self-doubts, we are mired in the mundane, we are on a roller coaster of successes and failures. The journey to make something great is definitely not linear.

In The Messy Middle: Finding your way through the hardest and most crucial part of any bold venture (Portfolio/Penguin, 2018) Scott Belsky, an entrepreneur and venture investor who is now chief product officer at Adobe, explores the ups and downs between a project launch and its (sometimes) successful conclusion.

“The middle,” he writes, “makes and breaks you, and ending up on the right side of this line depends on how you manage everything in between. It requires immense perseverance, self-awareness, craftsmanship, and strategy. It also requires luck, harvested whenever you encounter it.”



Belsky draws on his experience with Behance, his first company, which he founded in 2006 and sold to Adobe in 2012. He gives advice in two- or three-page bites. Among his words of wisdom: attempt a new perspective of it before you quit it; just stay alive long enough to become an expert; moving fast is great, so long as you slow down at every turn; too much scrutiny creates flaws; data is only as good as its source, and doesn’t replace intuition; the science of business is scaling, the art of business is the things that don’t.

There’s a lot of sound advice in this book. And since it’s laid out like a huge smorgasbord, you can read it that way as well, picking and choosing your way through it. And you can go back for seconds when you’re at a different stage of your endeavor.

Marks, Mastering the Market Cycle

Howard Marks, cochairman and cofounder of Oaktree Capital Management and author of The Most Important Thing, expands on one of his twenty “most important things” in Mastering the Market Cycle: Getting the Odds on Your Side (Houghton Mifflin Harcourt, 2018). Marks paints in broad strokes, so the reader will not come away from this book with any concrete trade ideas. But, after reading Marks’s analysis, he should better understand how to sync his portfolio with the ebb and flow of the market, so as not to buy at the top and sell at the bottom, even though “the tendency of people to go to excess will never end.”

Marks discusses multiple cycles that feed into the market cycle: the economic cycle, the cycle in profits, the pendulum of investor psychology, the cycle in attitudes toward risk, the credit cycle, the distressed debt cycle, and the real estate cycle. He also looks at the cycle in success. Of these, the one he considers most important is the risk cycle. So let me summarize some of his points on this front.

It is often set forth as a truism that, since there seems to be a positive relationship between risk and return (the ubiquitous upper-sloping line), “riskier assets produce higher returns” and hence “if you want to make more money, the answer is to take more risk.” This formulation, Marks explains, “cannot be true, since if riskier assets could be counted on to produce higher returns, they by definition wouldn’t be riskier.”

In general, of course, the capital market line, or risk/return continuum, makes sense if viewed in terms of rational expectations. We expect to make a higher return on investments in small cap stocks than on investments in money market funds, and the former is perceived to be proportionally riskier than the latter. On this continuum “there won’t be particular points … where risk-bearing is rewarded much more or much less than at others (that is, investments whose promised risk-adjusted return is obviously superior to the rest).”

But fluctuations in attitudes toward risk can upset this continuum. As investors become increasingly optimistic, even euphoric, they are willing to settle for skimpy risk premiums on risky investments. “This reduced insistence on adequate risk premiums causes the slope of the capital market line to flatten.” And so, Marks writes, “risk is high when investors feel risk is low. And risk compensation is at a minimum just when risk is at a maximum.” At the other end of the spectrum, when markets sell off, investors become excessively risk averse, and the slope of the capital market line increases, offering “an exaggerated payoff for risk-bearing. Thus the reward for bearing incremental risk is greatest at just the moment when—no, rather, just because—people absolutely refuse to bear it.”

When should investors begin to buy as the market is cascading downward? Marks strongly rejects the idea of waiting for the bottom. First, there’s no way to know, except in hindsight, when the bottom has been reached. “And second, it’s usually during market slides that you can buy the largest quantities of the thing you want, from sellers who are throwing in the towel and while the non-knife-catchers are hugging the sidelines. But once the slide has culminated in a bottom, by definition there are few sellers left to sell, and during the ensuing rally it’s buyers who predominate.” So, to repeat, when should investors start to buy? For Marks, the answer’s simple: buy when price is below intrinsic value. And if price continues downward, buy more. “All you need for ultimate success in this regard is (a) an estimate of intrinsic value, (b) the emotional fortitude to persevere, and (c) eventually to have your estimate of value proved correct.”

Knapp & Zeratsky, Make Time

Another day, another self-help book that takes time away from things I should really be doing. Call it a form of distraction. But, oops, this is precisely what Jake Knapp and John Zeratsky in Make Time: How to Focus on What Matters Every Day (Currency/Crown, 2018) try to steer us away from.

They offer a four-step process for “making time.” First, highlight—that is, decide what you want to make time for. “Each day, you’ll choose a single activity to prioritize and protect in your calendar.” It can be anything—finishing a presentation, cooking dinner, playing with your kids, or (yes) reading a book. It can be something that’s urgent and/or something that gives you satisfaction and/or joy. It should take between 60 and 90 minutes. Second, beat distraction. Third, energize. And fourth, reflect. Steps two through four should be pretty self-evident, but the authors explain them in sometimes seemingly controversial detail. For instance, “if you live a little more like a prehistoric human, we predict you’ll enhance your mental and physical energy.” This does not mean to go on a paleo diet but merely to move, eat real food, go off the grid, socialize, and get enough good sleep.

Many of the principles behind this book were inspired by the Google design sprints, created by one of the co-authors (Jake). A design sprint was “a workweek redesigned from the ground up. For five days, a team would cancel all meetings and focus on solving a single problem, following a specific checklist of activities.” In 2012 the authors started working together to run these sprints with startups in the Google Ventures portfolio. Over the next few years they ran more than 150.

From the design sprint laboratory the authors learned that something magical happens when you start the day with one high-priority goal. The five-day Google Ventures schedule was: Monday—the team creates a map of the problem, Tuesday—each person sketches one solution, Wednesday—the group decides which solutions are best, Thursday—they build a prototype, and Friday—they test it.

I think one can combine features of the design sprints with the much less imposing “make time” highlights to come up with reasonable ways to innovate and accomplish projects. If I succeed at my own design sprint, Make Time will not have been a distraction but an unwitting highlight.

Agrawal et al., Prediction Machines

Prediction Machines: The Simple Economics of Artificial Intelligence (Harvard Business Review Press, 2018) by Ajay Agrawal, Joshua Gans, and Avi Goldfarb, all chaired professors at the University of Toronto’s Rotman School of Management, is an excellent introduction to the opportunities and limitations of AI. Their thesis is that, as things now stand, AI is becoming an ever cheaper community that lowers the cost of prediction and will change how businesses operate. But it cannot replace judgment, so human beings will not be sitting around as unproductive idlers.

One of the goals of enhanced prediction is to replace satisficing with more optimal solutions. For instance, airport lounges are “imperfect solutions to uncertainty” and “will be undermined by better prediction.” With traffic apps and apps tracking flight delays, the traveler has new options such as “unless there is a traffic problem, leave later and go directly to the gate” or “if there is flight delay, leave later.”

The authors explore various ways in which artificial intelligence enhances decision-making, especially in the business context, but in the end (at least so far) it cannot replace human beings. We have data that machines don’t, from our senses to data that we opt to keep private. Moreover, prediction machines are often stymied by rare events that are difficult to predict because of a lack of data, “including presidential elections and earthquakes.” (Not that human beings and their statistical models did such a great job with the last presidential election.)

The authors speculate about which countries might have an advantage in developing AI. So far the United States is the world leader in terms of both research and commercial application. But “the trend lines are changing.” The future of AI may be “made in China,” as the New York Times suggested. First, China is spending billions on AI. Second, it has more people—and therefore more data (the new oil). Third, China doesn’t regulate privacy, so easy data access is another advantage. And, I would add, a trade war focused on twentieth-century manufacturing is unlikely to derail China’s efforts to gain an advantage in the twenty-first century.

McLean, Saudi America

Bethany McLean, the well-known financial journalist (she co-authored The Smartest Guys in the Room and All the Devils Are Here and wrote Shaky Ground), has turned her attention to shale oil and gas in Saudi America: The Truth About Fracking and How It’s Changing the World (Columbia Global Reports, 2018). In this 140-page piece she doesn’t address environmental concerns about fracking. Instead, her focus is on assessing the “breathless predictions about America’s future as an oil and gas colossus,” which, she argues, “has more to do with Wall Street than with geopolitics or geology.”

Central to her story is “America’s most reckless billionaire,” as Forbes once described the late Aubrey McClendon, the founder of Chesapeake Energy. He was a “flag waver for natural gas” and fracking, arguing that, with fracking, gas production was no longer wildcatting but manufacturing. He went all in, creating a vast web of debt for Chesapeake as well as himself personally. As gas prices dropped, so did Chesapeake’s stock , but he remained a true believer and doubled down, announcing deal after deal. “But the economics weren’t working anywhere,” and McClendon found himself in financial and legal hell. At the age of 56 he was killed when his car collided with a concrete wall at high speed. The state medical examiner ruled his death an accident.

As the economy has rebounded after the great recession, so has fracking, thanks in large measure to technology. Production at the Permian Basin increased from just shy of one million barrels of oil a day in 2010 to over 2.5 million barrels a day in 2017. The International Energy Agency predicts that, within a few years, output will be more than four million barrels a day. And the break-even cost has plunged from around $70 in 2008 to less than $50 today, some saying as low as $25, or even $15. But these break-even figures do not take into account the cyclical nature of service costs. As the price of oil rises and demand for services (such as renting rigs, hiring crews, purchasing sand) increases, so do the costs. One analysis claims that almost half of the reduction in break-even costs was due to a temporary plunge in service costs.

Wall Street has been supporting the comeback of shale, to the tune of about $70 billion in capital in 2015 and $60 billion in debt in 2017. “Wall Street’s willingness to fund money-losing shale operators is, in turn, a reflection of ultra-low interest rates.” Because, despite higher oil prices, most shale operators are still losing money; in the first quarter of 2018 only five companies generated more cash than they spent.

What is the future of the shale revolution? It’s hard to say. Shale wells deplete, global alliances shift, renewables are becoming less expensive. The administration’s push to allow drilling in previously prohibited areas might create an oversupply and crater prices. “If this comes about at a time of rising interest rates and the end of the era of cheap capital, we may soon begin talking about how the Trump Administration killed the shale revolution.” Or not. Basically, we don’t have a clue about what’s coming next.

Zeng, Smart Business

Ming Zeng’s Smart Business: What Alibaba’s Success Reveals about the Future of Strategy (Harvard Business Review Press, 2018) is a thought-provoking book. Written by the chief strategy officer at the Alibaba Group, it challenges traditional business models and offers an alternative based on data intelligence and network coordination.

As with all books about China, I’m always stunned by the magnitude of the numbers. For instance, on Single’s Day on November 11, 2017, Alibaba processed 1.5 billion transactions, totaling about $25 billion. At its peak during that day, Alibaba’s technology platforms processed 325,000 orders and 256,000 payments every second. By comparison, Visa’s stated capacity at about that time was 65,000 payments per second globally.

Zeng dispels the common notion that Alibaba is the Amazon of China. “Unlike Amazon, Alibaba is not even a retailer in the traditional sense—we don’t source or keep stock, and logistics services are carried out by third-party service providers. Instead, Alibaba is what you get if you take every function associated with retail and coordinate them online into a sprawling, data-driven network of sellers, marketers, service providers, logistics companies, and manufacturers. … Alibaba’s mandate is to apply cutting-edge technologies—from machine learning to the mobile internet and cloud computing—to revolutionize how business is done.”

The business model that Zeng describes stands in sharp contrast to that of traditional business. It’s a C2B model that demands constant innovation. And a lot of work for all of its participants. But, as the title says, it’s “smart,” and the rewards are sometimes staggering.

As I pondered Zeng’s thesis, a couple of obvious questions came to mind. First, is this a model that, with modifications, can extend beyond the digital retail space? Second, is it essentially a monopolistic framework? My best guesses: yes, and probably. And so, I believe that everyone who wants to make a mark in the business world should read Zeng’s book. They might want government regulators to skip it.