Investing, by nature, makes it hard to separate good decisions from good luck. Uncertainty, randomness, and noise muddy results. Anything can happen in markets in the short run.
In addition, human nature drives us to be outcome-biased. We tend to judge decisions based on the outcome instead of on the quality of the decision made.
We see this often in sports. Fans praise coaches and players when the team wins. They criticize them if they lose.
- Wins = Good Decision
- Losses = Bad Decision
That’s the outcome bias. There’s no accounting for the riskiness or soundness of the strategy or decisions during the game. There’s no nuance.
Yet, sports are dominated by uncertainty. Any team has a chance to win any one game. This is how great teams lose to underdogs. Everything seems to fall in line for the lesser team and they come out on top.
But throughout a season, the teams that come out ahead year after year typically have a better process around assessing talent, practicing, game planning, and decision-making in games that give them an edge over other teams each season. They may not win every game but they win more often than not because of it.
Investing has a similar issue with uncertainty where random luck can confuse the results but sustained success comes from a great process. The best way to achieve a great process is to challenge and improve upon your existing one, as Bill Miller explained in a 2004 letter.
Investing is about making decisions with your money. All of those decisions are probabilistic. As former Treasury Secretary (and maybe future Chairman of the Fed) Robert Rubin emphasizes in his new book, nothing is certain – except uncertainty – and decision procedures need to reflect that. In a recent speech, Fed Chairman Greenspan makes much the same point, and elaborates on the difference between the most likely outcome and low probability but high impact outcomes.
Decision procedures and outcomes also need to be clearly distinguished. A decision is not bad or wrong because the outcome turns out badly. And a good decision is not the same as one that turns out well. A decision is bad if the process that engendered it was bad, regardless of the outcome. Bad outcomes — losing a lot of money in an invesment — can happen even if the process is sound; and good outcomes can occur even if the process is lousy. A market that is mostly efficient can distribute outcomes all over the place.
Thinking about poor decisions, those that result from bad reasoning, is useful. Decisions such as our buying Dell and Amazon on the IPO and selling them because they went up a lot and looked expensive: those were bad decisions. Not because Dell is, oh, almost a 400 bagger in the 15 or so years since it came public; or bubble and all, the buy and hold investor of Amazon is up about 40 times since the 1997 IPO. They were bad decisions because looking expensive and being expensive are not the same thing.
If you picture a 2×2 matrix, there are four possibilities for every investment decision:
The goal is the top left box. Good decisions with good outcomes lead to long-term success.
The secondary goal is to learn the correct lessons from bad decisions to avoid them in the future. Error reduction improves success rate and leads to better results over time.
That means being open to the possibilities of random luck on investment outcomes. If you recognize the role luck can play in good and bad decisions, you’re ahead of most investors.
You also have to study your mistakes. That means being critical of your reasoning behind bad decisions to improve your process and reduce errors.
Miller’s example at the end shows how he broke down two bad sell decisions. Outcome bias would drive us to assume both were bad decisions because of how they performed after he sold them but that’s not why it was a poor decision.
Value investing is often confused with buying companies with low multiples — where a low multiple is a good value (cheap) and a high multiple is a bad value (expensive). But that’s not the case.
Value investing is about taking advantage of mispriced expectations. The collective market’s expectations are embedded in every stock’s price.
A stock with low multiples is usually low because of low expectations about the company’s future. And stock with high multiples, generally, has high hopes for its future. How correct those expectations are is the question.
The wisdom of the crowds gets it close enough, most of the time but exceptions exist at the extremes. History has shown that most companies with the lowest multiples go on to exceed their pessimistic expectations, and companies trading at the highest multiples fail to live up to the optimism embedded in the price.
There are outliers, of course, like Miller’s examples that prove to be the exceptions to the rule. The optimism priced in turns out to be too conservative. The high multiple makes companies look expensive, but they’re bargains because they can sustain higher growth for longer than expected — and time and compounding take care of the rest.
Miller’s decision to sell was bad, not because of the outcome, but because he mistook a high multiple for expensive. In both cases, the market and Miller were not optimistic enough about the future of both companies.
More to the point, Miller’s lesson adds another check to the checklist of things to consider when making future decisions that lower the chance of mistakes over time.
Source:
Market Commentary 2004
Related Reading:
Seth Klarman: Why Investors Should Be More Like Athletes
Repeated Mistakes