During the offseason, prior to the 2002 Major League Baseball season, the Oakland Athletics lost three top talents to free agency. On top of that, as a small market franchise with a very low budget for payroll, they could not match the buying power of big city clubs. As a result, the team’s General Manager, Billy Beane, faced a daunting task — the Oakland A’s must break-even financially with cheap talent.
The effect he needed was ticket sales, and for that to happen, the cause would be winning a lot of games. In essence, Beane had to create another competitive team. While the traditional business model for spending money on player development and free agents seemed obvious and unavoidable, for the Oakland A’s, it wasn’t going to work.
To be successful, Beane had to call out the scouting department and replace them with a radical idea — one consistent with the reality of earning a profit. Because the ability and creativity of the A’s management was called out by responsible owners, Beane had to examine the premises of baseball’s traditional ‘best practices.’ He had to identify their contradictions.
THE BUREAUCRATIC STATE
The A’s solution was reliable, historical data sets for all minor league and college players, quantitative analysis to make practical use of it, and the independence to replace the traditional thinking of baseball’s media and management. The difference, as written in the book Moneyball by Michael Lewis, was that Beane was objective,
“They drafted players dismissed by their own scouts as too short or too skinny or too fat or too slow. They drafted pitchers who didn’t throw hard enough for the scouts and hitters who hadn’t enough power. They drafted kids in the first round who didn’t think they’d get drafted before the fifteenth. They drafted ballplayers.”
The pitchers got more outs, the batters did not, and it wasn’t fine art. It was winning baseball. Beane had replaced a chunk of his scouting department with quantitative research.
Proving the elegance of the profit motive, Beane signed players based on their productiveness, the franchise became financially viable, and the Oakland A’s won a lot of games for their fans. In fact, Beane’s loyalty to reality (aka honesty) did more for his communities than any “social investment.” That is poetic justice, and unsettling for Beane’s critics.
For the entrenched bureaucracy, the A’s new methods did not comport with the industry’s long-held propositions for winning — the subjective opinions of scouts and psychiatrists to unwind the errors that became exposed by the stress of the batter’s box. Primarily, for Beane to walk away from unreliable methods endemic to baseball; he had to risk alienating the established order.
That is Moneyball, and it revolutionized baseball’s hiring practices, for the better, for everyone.
RATIONALITY vs. RATIONALIZING
A prime example of bureaucratic hierarchies is government and their highly regulated industries. Nullifying the law of causality, those at the top are frequently insulated from the consequences of their actions. As author, statistician, and securities trader Nassim Taleb described in his book Skin in the Game,
“The Bob Rubin trade? A former Secretary of the US Treasury, collected more than $120 million in compensation from Citibank in the decade preceding the banking crash of 2008. When the bank, literally insolvent, was rescued by the taxpayer, he didn’t write any check — he invoked uncertainty as an excuse.”
This is also true for the investment management bureaucracy. Beating the market and filling Morningstar style boxes dominate their thinking. To boot, socially responsible investing depends on its own rationalizations (the common good), and in either case, they make excuses when unintended consequences become reality (diversification failures).
Here, it should be emphasized that rational ideas and actions are quite different from rationalizing unethical behavior. To be rational is to be true to the reality of evidence and a moral imperative. To rationalize is to avoid reality and make excuses.
With the latter, nothing is learned, knowledge does not expand, innovation is stymied, and society suffers when arbitrary behavior is rewarded. That happens when money, legal, and reputational consequences are not on the table. In any event, the laws of nature are certain, and to the extent they are known and understood, they are predictable. At the same time, when free will and the creative capacity of human action is in play, the future is uncertain.
For those cases, when knowledge is incomplete and events are distributed randomly, probability analysis becomes a useful procedure. To that end, the integration of historical data, financial resources, cash flow objectives, and analytical software is an objective way for baseball managers and individual investors to make strategy decisions.
On the other hand, macroeconomic forecasts, market predictions, ESG scores, fund rankings, stock picking, and arbitrary benchmarks are the preferred service model for traditional asset managers.
To illustrate, an article published in the March 15, 2021, issue of Advisor Perspectives (an online forum written by and for licensed financial advisors) seemed trustworthy. Co-authored by two professionals with PhDs, Certified Financial Planner (CFP) designations, and one Certified Financial Analyst (CFA) certificate, its headline was “The False Promise of US Historical Returns.”
As previously mentioned, input data for complex systems is easily manipulated to produce desired outcomes, and that was actually recommended at the conclusion to this article. Looking closely, it appears the authors were ambiguous with their conclusions or didn’t fully know the material. For example, in an effort to defend the current regime, it begins by offering some false premises and concludes with an inelegant solution,
- “US returns create an unrealistic picture of retirement outcomes.” The implication here is that a) the authors can produce a more realistic picture of retirement outcomes and, b) that the probability of outcomes is a predictor of the future. Neither are true.
- “US data are an anomaly among the broader global universe.” To be sure, property rights and objective law are uniquely American, but this implies that an advisor would unwittingly use US data to simulate a global portfolio. A competent one would not.
- “Our low-yield environment forebodes lower-than-average equity returns.” Lower than average? Compared to what? What is average? What competent advisor would use constant average return projections anyway? Over what time periods? Have there never been low yield environments in the ten decades that comprise historical US data sets? Does a high yield environment also forebode higher-than-average equity returns?
- “The projection typically assumes the withdrawal of a fixed amount each year from an investment portfolio.” Flexible and staggered withdrawal rates are important modeling variables. Competent advisers also include savings, outside sources of income, and future life events. Regardless, projections are the wrong concept. It’s about probabilities founded on the most reliable data.
There is more to this essay, some good, but it’s worth noting that the authors included this tautology: “While bond yields could increase, which would increase the yield on bonds, the increased income would be offset by the decrease in price.” No kidding.
In essence, this article is merely a tool for the self-preservation of industry analysts — they will not give due respect to the price mechanism of efficient markets. Because their purpose for being analysts is to publish price discrepancies, that would undermine the relevance of their employers. t’s not going to happen.
The article’s conclusion? The authors propose two sets of capital market assumptions, one that is purposefully subjective, short-term, and as credible as ESG scores. In other words, “inefficiency caused by sloppy data.” Heads, we win; tails, you lose. Socially responsible investing does the same thing.
To be fair, the use of historical data sounds backward looking, and loading them into a Monte Carlo engine is forward looking. As such, it seems like a contradiction, even for PhDs with CFA and CFP certificates (the scouting department). The proper method is with the practice of inductive reasoning — the historical evidence drives a probabilistic conclusion.
CALLED STRIKE THREE
What are they missing? Causality. It is not honest to tamper with the evidence of purely historical data sets by substituting part of it with subjective variables. To label them “forward looking assumptions” is merely rationalizing a scientific wild-assed guess (SWAG).
For what it’s worth, both authors are professors at the American College for Financial Services, and one is also employed by Morningstar, the largest mutual fund rating agency in the world. Their allegiances are obvious, and that’s OK, so long as we are informed. Whether its stock price or economic projections, diversification or diversity hiring, green energy or green washing, the overriding question is — will they achieve the investment objective?
In Modern Portfolio Theory, there are three potential assumption errors inherent to the capital asset pricing models — median return, standard deviation, and correlation. Furthermore, man-made complexity designed to protect entrenched bureaucracies only serves to add more of them. As Moneyball proved, honesty, productiveness, free markets, and profits are the socially responsible way to invest.
It's not fine art; it’s winning future values. To improve their likelihood of success, rational investors and baseball managers will avoid the arbitrary sacrifice and risk of traditional research.
the same time, despite anyone’s ‘beat the market’ mentality, investing is not a sport. There is no pitcher working to get batters out, and investing in social justice is bad psychiatry for managing the stress of society’s unearned guilt. Called strike three.