What is your biggest investing mistake? Ask this question and you’ll recall the stock that gave you the biggest losses. It is intuitive. Losses get attributed to “I have made mistakes”. We throw them into the ‘Bad outcome’ mental bucket so we can learn what have we done wrong in order to avoid making the same mistakes again. This is the same for gains. We place them in the ‘Good outcome’ bucket because we made some good money. We made the right decision and we want to do more of it. However, the cause and effect relationship rarely works in a straight line because of luck and randomness.

Luck and randomness occur because the future is never certain. We have incomplete information as to what is going to happen tomorrow. In the stock market, a good decision can easily turn into losses at a stroke of a bad luck just as a good fortune can turn a bad decision into a windfall. We tend to underestimate the role of luck in influencing short-term outcomes. How do we know there is a high degree of luck involved in investing? One way is to ask: Can I lose this on purpose? Can you purposely lose a game of chess? You can. But in the stock market? That’s hard. There is a good chance you can make a profit picking stocks blindfolded. Therefore, relying on the outcomes—gains and losses—to make decisions can be disastrous for your long-term return. Looking at how much you have earned, or loss can mislead you. This is true when learning from others as well. A trader who earns a million dollars is different from a doctor who earns the same amount of money.

Imagine John is a doctor from your local suburb, while Jack works for a downtown mid-sized financial firm as a full-time equity trader. As a thought experiment, you get to observe how much they earn on average over their career. Further assume you can repeat their careers for a thousand times. Their lives will be different with each repeat. They might be married in one but not in another, moved to a different city, or take up distance running and so on. Lifestyle will be different. The only thing that remains the same is their profession—John as a doctor; Jack as a trader. As you sit down to compare how much they earn on average over their 1,000 careers, something jumps out at you.

You noticed extreme outcomes are not that uncommon in Jack’s career as a trader. In some of his careers, he becomes a multi-millionaire who is famous for making gutsy, timely, bet the farm calls. The opposite, however, is just as frequent. He suffered massive losses when the market took an unexpected turn against him. He was forced to liquidate all his open positions as a result of margin calls. One interesting thing you notice is when you line up all his incomes, it forms a uniform distribution.

John’s careers, on the other hand, paints a different picture. Or you can say, unexciting. There are extreme outcomes, but they are rare compared to Jack’s careers. Although he did end up broke for a few times, losing his medical license as a result of malpractice lawsuits. But that’s very rare. Unlike Jack who experiences big gains and losses over some of his careers, John’s incomes are fairly consistent throughout.

What explains the difference in incomes, or outcomes, between John and Jack? Also called fat tails in statistical term, extreme gains and losses are common when luck and randomness play a huge role in influencing the outcome. If you flip a coin for a number of times, you are bound to see one side turning up more often than the other side. *Extreme outcome *is the law of small numbers. Which is why there is a wide spread on how much Jack earns over his careers. If extreme outcome explains the behavior of luck, *reversion to the mean *reveals its personality. As you flip the coin towards 10,000 times, each side is likely to turn out half of the time. Similarly, Jack’s incomes revert towards the average of zero when combined together. Luck cancels out in the long run. In contrast, John’s incomes are less susceptible to wild swings due to the *persistency of skill*. Just as you would expect the performance of a 100m sprinter to be fairly consistent between races, how much John can earn as a doctor in each of his careers will be more or less the same as well.

To be clear, this is not to say the stock market is a futile game, or life is like a flip of a coin. It is just an emphasis that outcome is a poor indicator for evaluating decisions. Daniel Kahneman, a Nobel prize psychologist, sums up luck this way:

Success = talent + luck

Great success = a little more talent + a lot of luck

We tend to believe the difference in success is a matter of skills when it is luck that determine outcomes. We discount the role of luck because luck is an abstract idea that cannot be quantified or explained. There’s no cause, only statistics. Moreover, as we have learned, luck produce noisy feedback, which makes us all the more likely to discount its influence. Here, the effect becomes the cause—the existence of luck conceals its presence.

So, how can we improve our decision quality if we can’t rely on outcomes? We need a different mindset: A good and bad process bucket. Blackjack is a good example. In order to win the game, you have to get close to 21 points without exceeding it. And a basic strategy is to stand rather than ask for a hit if you are dealt a 17. Why? Because the chance of going bust is high, or 69%. You might get a good hand for being lucky, but it is a poor process because you’ll end up losing in the long run.

If the outcome buckets produce a deterministic mindset, the process buckets call for a probabilistic mindset. As Daniel Kahneman explains, “The difficulty we have with statistical regularities is that they call for a different approach. Instead of focusing on how the event at hand came to be, the statistical view relates it to what could have happened instead.” Thinking in probability is to consider what else can possibly happen. The focus is not on the *result* of a single outcome, but the expected value of all possible outcomes. That’s what we have done with John and Jack’s career. The possible outcomes of a real-world event are not as clear-cut as those in blackjack, of course. There are many unknown unknown. So the litmus test for a good thought process is whether you have spent more time considering what can possibly go wrong than what can possibly go right.

Process thinking is counterintuitive. We are not wired to think this way. But if you still find these difficult to follow, there is another simple solution: Stop following the market. If you don’t look at what’s happening in the market every day, you are less likely to suffer outcome bias.

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An Effective Capital Allocator – In investing, you are trying to predict an uncertain future where many possibilities can happen. This underlines the danger of satisficing by finding the most satisfactory explanation as supporting evidence to own a stock. We tend to extrapolate things will continue the way it is without considering other potential outcomes. Thinking in expected return allows you to develop a more accurate judgment by seeing things through multiple lenses instead of extrapolating through that rose-tinted glasses.

Bullish but Bearish – Expected value is the probable payoff of all possible outcomes. Warren Buffett explained using expected value to make decisions, “Take the probability of loss times the amount of possible loss from the probability of gain times the amount of possible gain. That is what we’re trying to do. It’s imperfect but that’s what it’s all about.”

Illusions of The Stock Market – Following the market opens the Pandora’s box of psychological misjudgment, like a nudge to a domino that triggers a chain reaction where biases quickly snowball into insurmountable mistakes.