The world is complex and unpredictable, but humans prefer order, and cause and effect, so therefore tell stories that purport to explain what is simply random. Narratives pre-date writing. They help make events coherent and memorable, while arousing emotions in the listener. Behavioral biases, which all humans share, are in many cases essentially products of the stories we tell ourselves. The more detailed the story, the more entertaining it is and the more powerfully it can affect our emotions. We love stories. That can often be wonderful, but in decision making it can be dangerous.
In investing, there has been at least a little progress towards improving decision making by resisting the power of stories. Quantitative investors describe how they adhere to purely objective rules (rules and lines of code that, of course, they themselves have written) to govern their behavior and reduce bias. “Quantamentalists,” another breed of investor, allow some judgement to enter their decision making once they have established the framework. They do this in part in recognition that, as a rule, most humans don’t like rules. We suffer from what psychologists call “algorithm aversion,” i.e. preferring to go with our gut. That preference results from our need to remain in control, or at least to believe we are. Permitting human override of an algorithm may degrade the quality of its output, but in granting themselves the comfort of exercising some degree of control, decision makers likely improve their rate of adherence, for an overall improvement in outcomes. I fully expect self-driving cars to come with a steering wheel that will have no impact on direction of travel, but will allow the human passenger to feel more secure than if she were simply sitting back and giving herself over fully to the computer under the hood.
In his book The Success Equation, Michael Mauboussin writes extensively about the importance of a strong process and rules in activities where the immediate outcome is driven by luck and skill. He describes how it is possible to improve skill through what has become known as deliberate practice: repetitive, purposeful, and systematic repetition with immediate and specific feedback. Luck, however, can only be managed by having a strong process, with rules or standards constraining decision making and the urge to impute too much importance to our role in any one result. In activities such as investing or team sports—arenas where skill and luck both come into play—narrative is particularly seductive, making adherence to this recipe for success a constant battle.
The pandemic meant the stands were still empty at “Theater of Greens,” the home stadium for Plymouth Argyle this year, but the ideas were plentiful as to how to harness the theories of behavioral economics to put a more competitive squad on the field.
Richard Thaler, who won the Nobel Prize in Economics for his work in behavioral finance, has noted that 1980s basketball superstars like Larry Bird converted their three-point shot attempts at the same near-40% rate as 21st-century sharpshooters such as Steph Curry or James Harden. Yet, Bird took half as many three-point attempts as are normal today, favoring shots closer to the basket, which great players can hit roughly 50% of the time. It took 30 years for the three-point shot to become as popular as it is today or, in other words, for basketball to realize that 0.4 x 3 is greater than 0.5 x 2.
The Bill James Baseball Abstract, an annual compendium of baseball statistics that ultimately legitimized the science of baseball sabermetrics, was neglected for many years before a James disciple, Oakland Athletics General Manager Billy Beane, used the approach to identify underpriced skills for his small-market team, as popularized in Michael Lewis’s 2002 Moneyball. It was only then that James himself landed a job in the front office of the Boston Red Sox, helping the team finally overcome the “Curse of the Bambino,”1 and win its first World Series in 86 years.
Many baseball fans complain that the influence of Moneyball has been too great—players are chosen purely for their ability to walk or jack balls over the fence; defenders are all stacked on one side of the infield because that’s where the statistics say the vast majority of a given batter’s ground balls will be hit. “It’s no fun anymore!” Clearly, those fans prefer intuition and stories to success and efficiency. They also subscribe to the great man theory, that results are driven by individual managerial genius, the wizened skipper playing a hunch.2 And yet, organizations typically strive to create and adhere to processes that can improve their probability of success—to control luck.
I have been involved in two firms with many similarities, one being an investment manager and the other a football (soccer) club. An investment portfolio can, through careful construction, be more than the sum of its individual parts thanks to diversification. It may seem odd, but a football team is a portfolio. It, too, needs careful construction. You can’t simply rank order the most attractively priced available players and take the top 20 to form your squad. You would end up with a team made up only of defenders, and no goal scorers.
More than a decade after John Henry, the Red Sox owner who had hired Bill James, purchased Liverpool FC and brought Moneyball to the UK’s Premier League, systematic use of data analytics has finally worked its way throughout the ranks of English football. At my football club, Plymouth Argyle, which plays in the third tier of the leagues, we have set rules and standards to guide the decision-making of those who select the squad. Beginning with our philosophy, i.e., answering the question “What is the style of football we wish to play?,” we have defined the desired characteristics of each position. Our philosophy includes playing the ball forward from the back with quick passes along the ground to the opposition’s part of the field. Our goalkeeper must, therefore, be able to kick with both feet and play short passes while under pressure from the opposing forwards. We also prefer to play in a tactical lineup, known as a 3-5-2, with a pair of strikers at the front, which sacrifices some ability to dash down the wings in return for greater stability through the middle.
Defining the philosophy this way and defining how we will implement it enables us to search databases for players that match our criteria in each position, such as the defender we recently signed based on some impressive data around his defensive blocks, aerial duels won, and tackles per game. Then, in quantamental fashion, we can conduct research into aspects of players that can’t be quantified (yet!), such as their character, personality, or work ethic.
There are differences between assembling an optimal squad of football players and an investment portfolio. One is that securities prices are freely available, updated continuously, and can be compared with their hypothetical value generated by a model. That is not the case with football players. Transactions can only be made in two “transfer windows,” one midway through the season in January, the other during the summer before the next season starts. And no one has yet come up with a model that can assess players’ value in the way that we forecast cash flows and discount them to a present value to give an idea of what a share of stock is worth. I live in hope, though!
The market for football players is relatively efficient, but not completely so, and becomes less efficient the lower down the leagues you go. In this way, the relative inefficiencies of the market are parallel to those for, say, US large caps compared with those for emerging market small caps.
But surprisingly, perhaps, there are more similarities than differences. The market for football players is relatively efficient, but not completely so, and becomes less efficient the lower down the leagues you go. In the Premier League, the most watched football league in the world, there is a strong correlation between what you pay and the results you achieve. That is not the case lower down, so my club has a strong incentive to improve decision making, to exploit the behavioral biases and subsequent mispricing that is prevalent. In this way, the relative inefficiencies of the market are parallel to those for, say, US large caps compared with those for emerging market small caps. Similarly, though, as the potential gains to improved decision making increase, so does the resistance to replacing intuition with algorithms and process that reduces the degrees of freedom under which individuals operate. Those clubs which can overcome that resistance will generate a competitive advantage—an edge. I believe my football club has such an advantage, but I am on the alert for signs of backsliding—going along with “But that’s the way we’ve always done it!”
We have gone down this path at Plymouth Argyle because we want to spend the Club’s money wisely and compete by being smarter, not by spending more. At Harding Loevner we started down this path 25 years ago as decision making spread beyond the handful of founders to an expanded investment team. Each portfolio remained the product of two single decision makers but were now made with input from over 30 analysts. We had to learn how to structure a process that benefits from group wisdom but is not damaged by “groupthink.” The foundation of that was, and still is today, individual accountability and aggregation of individual decisions. Analysts drive the process and decide what stocks they will cover. They are free from direction by their colleagues, and free from direction by portfolio managers. They are not completely free, though. Just as Argyle’s team must recruit players who meet the objective standards we have established as being consistent with our playing philosophy, Harding Loevner analysts must confine their activities to securities of companies that they judge to conform with our investment philosophy, which focuses on growth, quality—which we have historically equated, in part, with a demonstrated track record of profitability—and price. When we look back at the last 25 years, we can see clearly that the arc of evolution of our investment process has been to define more clearly and rigorously the rules that restrict our analysts’ liberty to follow any company they chose. This has been highly effective at controlling biases and risk, and, overall has been good for returns.
Today, there are any number of voices, both internal and external, calling us away from the framework and structure that have served us well. The rise of the digital economy, and its steady proliferation of fast-growing but still-unprofitable businesses with compelling narratives, continually tests our reliance on an established track record as a guide to the future and the best indicator that a company has the culture and institutional strength to withstand cyclical downturns and shifting competitive landscape. Has the nature of growth changed? Have the kinds of characteristics that define quality shifted? Or are those merely the stories we tell ourselves to justify paying higher prices for the hot new fashion?
I don’t know the answers to those questions yet. I do know that, while it is necessary for portfolio managers to evolve along with markets, such evolution should take place deliberately and only after careful consideration. At Argyle we are at the early stages of translating our playing philosophy into a structured process for how we find players, and how they play on the field. Already, though, there have been occasions when we’ve had to adapt on the fly and adjust our typical tactical lineup. Football, like investing, is a game where the opposition can see what we do, and we must adapt to how they counter our plans. Players get injured, and replacement players may be less capable at implementing the approach. So, how we implement our philosophy must evolve, but we must be clear with ourselves that such adjustments are a change in process, not a change in philosophy. If we were to start changing our philosophy, we would surely be lost, tossed on a swirling sea of narrative.
1Purportedly begun in 1920 when, after he had led the club to three World Series titles in six years, the Sox traded George Herman “Babe” Ruth to the rival Yankees for $125,000 (about $1.7 million in today’s dollars, or roughly the salary of Boston’s current backup catcher, Kevin Plawecki).
2It could be said the great man theory is also a feature of investment marketing—how often do brand name investment managers attract large amounts of client assets and then collapse in ignominy?