Why old mental models for investing no longer work
Don’t confuse investing with poker
“Swim upstream. Go the other way. Ignore the conventional wisdom. If everybody is doing it one way, there’s a good chance you can find your niche by going exactly in the opposite direction.” – Sam Walton, Founder of Walmart
The markets rebounded and returned to a more normal state in the first quarter. Or at least as “normal” as one can expect in today’s Trumptopian world. In hindsight, market panics, like the one experienced at the end of last year, are great times to find mispriced securities. Buy low, sell high…simple common sense, no? Unfortunately, if you rely on common sense, you are probably not accounting for the many cognitive biases at play. Opportunities will be available, because although times change, human behaviour does not. The behavioural tide will continue to ebb and flow between fear and greed. Indeed, the markets appear to be tipping back towards fear again as we write.
Investing is not easy. Just ask famed value investor Seth Klarman who ruminated, “Did we ever mention that investing is hard work – painstaking, relentless and, at times, confounding? Separating relevant signals from noise can be especially difficult. Endless patience, great discipline and steely resolve are required.” Indeed. It may be more challenging than ever because the competition to find opportunities has reached record levels1. Furthermore, today’s massive computing power has enabled algorithms to arbitrage away many of the pockets of profitable opportunities that existed historically.
To further complicate things, the world is changing fast. Many of today’s “norms” that we take for granted would have been be practically unimaginable just a few years ago. How do you “model” such an environment? In most cases, the data for tomorrow’s future business models does not exist. So what data does an algorithm crunch? As we noted last year, depending on the context, it might be different this time. We believe Charles Darwin stumbled upon a universal law, also applicable to investing, when he observed, “It is not the strongest of the species that survives, nor the most intelligent; it is the one most adaptable to change.” We are working hard to adapt.
Humility is one of the most important traits of successful investors. One important thing that has not changed in investing is the need to understand the thinking on the other side of your trade. As a default, one should assume that in most cases, the participant on the other side of the trade is very smart and probably knows just as much, if not more, than you do. And if you are unsure if you have a defendable edge, you probably don’t.
“If after ten minutes at the poker table you do not know who the patsy is – you are the patsy.” – Poker proverb
Commentators often like to compare investing to the game of poker. Skilled poker players look to play in “easy games” frequented by weaker players. They understand math and probability, study their opponents for “tells,” bluff when needed, and bet when they believe the odds are in their favour. They don’t need to wager with every hand. But bet sizing is important when they do.
Lady Luck will play a role, particularly in the short term. A champion poker player with a full house will still lose to a beginner with a four of a kind. But over the course of many evenings and countless hands, luck will tend to even out and usually leave the more skillful Champ with the bigger pot2. Similarly, we believe investing is also an activity where differential skill counts over the long term.
Although there are some valid parallels to poker and investing, there are also some problems with this analogy. Notably, poker is a linear game, which follows predefined rules and a path for the player with little, if any, deviation. After the cards are dealt in a Texas Hold’em poker game, a “four of a kind” beats a “full house” in 1934, still does today, and will continue to do so in the future. The underlying rules of the game are the same, and the number of cards is finite. It is a game that favours players who are both good at math and understand psychology. The time travelling poker champ of 1934 would likely do well in 2019.
In contrast, investing is a non-linear game. It is much more dynamic and complex with messier external considerations, such as evolving government regulations and potential disruption from trade wars. Sometimes a regulator will tell you that the investing equivalent of a “four of a kind” doesn’t count anymore. Too bad. There is no recourse.
The market is part of a complex adaptive system where the exact outcome is largely unknowable and unpredictable. In addition, the fundamental rules of the game can change over time. A hypothetical time travelling Ben Graham from 1934, widely regarded as the father of security analysis and value investing, would likely have been befuddled in 2019. Strategies that worked brilliantly after the Great Depression work less well today.
Focusing on the value of tangible assets doesn’t work as well in an environment like today where intangible assets account for the vast majority of incremental value creation. When investing, it is important to replace old mental models that may no longer work so well with new ones that fit the environment better.
Next time: New mental models for investing, and five “easy games” that might be worth exploring.
Felix Narhi, CFA, is Chief Investment Officer and Portfolio Manager at PenderFund Capital Management. He works alongside David Barr, Pender’s President, in setting the direction of Pender’s overall investment strategy. This article first appeared in the Pender blog. Used with permission.
Notes and Disclaimer
1. As one measure, according to its 2018 CFA Institute annual report, membership was 164,000 and 226,000 CFA candidates were enrolled for the June 2018 exams, both record figures.
2. New York federal judge Jack Weinstein ruled in 2012 that poker is indeed a game of skill. He based his decision on a study of 415 million hands of No Limit Texas Hold’em on PokerStars.com in the course of a year. The study showed a striking difference in earnings between the ten best and worst players.
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