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Being Smart Does Not Save You From Bias
Humans didn’t evolve for investing. Our brains are built for survival — notice a pattern, make a call, act. On the savannah, hesitation could get you killed.
In markets, that same wiring pushes you to do the exact thing you’re trying to avoid: react quickly to incomplete evidence and mistake motion for progress.
What makes this especially tricky is that intelligence doesn’t fix the bias problem. There is a study (See Stanovich, West, & Toplak, 2013) that found cognitive ability doesn’t reliability reduce susceptibility to bias. In fact, in many cases, smarter people simply get better at defending their decisions–especially after the fact–even when those decisions were flawed.
I’ve watched this play out time and again with incredibly smart people — engineers, physicians, attorneys, business owners, and senior executives. They’re used to problems having a correct answer, or at least an answer that can be improved with more effort. Markets don’t work that way. A confident forecast can be wrong. A tidy story can be nonsense. Factual statements can still be irrelevant to the decision at hand.
That’s how smart investors get themselves into trouble. They overestimate their ability to predict what comes next. They anchor on data points that are real but incomplete or irrelevant — performance over the wrong time period, a valuation statistic stripped of context, a geopolitical trend, a backtest that excludes taxes, costs, or the emotional difficulty of sticking with the strategy when it gets uncomfortable. Then they do what smart people do when they feel uncertain: they keep looking for more information.
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The Illusion of Knowledge: More Data, Worse Decisions
Seeking out more information feels like the responsible move. When evidence comes in shades of gray — as it often does in investing — it’s easy to believe that one more article, one more chart, or one more data point will finally turn uncertainty into a clear answer. But more information doesn’t always make you more accurate. Often it just makes you more confident.
In an often-cited 1973 study, Paul Slovic found that professional horse-racing handicappers became more confident as they were given more information, even though their predictive accuracy barely improved. The researchers asked them to pick winners using five facts about each horse; they were right about 17% of the time. Then the researchers gave them 10 facts. Accuracy barely moved, but the handicappers’ confidence jumped. At 20 and 40 facts, confidence kept climbing while accuracy stayed essentially flat.
That’s the trap investors step into every day. The extra data creates a sense of control, even when the decision hasn’t actually improved. At some point, information stops being input and starts becoming camouflage.
I see it most often when someone tries to engineer the optimal portfolio allocation. They read every outlook, compare competing forecasts, build spreadsheets, tweak assumptions, and keep refining the mix. The process looks rigorous — almost scientific. But the inputs are guesses stacked on guesses. The more variables you add, the more ways you can be precisely wrong.
Professionals do this too. Capital market assumption tables will show expected returns to the second decimal place, as if we can forecast the next decade with that kind of accuracy. The honest part of those tables isn’t the decimals. It’s the wide range of outcomes sitting behind them.
Sometimes people overanalyze decisions that barely matter, like choosing between two nearly identical ETFs. Same asset class, nearly identical fees, similar holdings — and yet investors will spend hours comparing five-year returns, trading volume, and tracking error, trying to “win” a decision that doesn’t have much to win.
Other times, people compare funds that were built for completely different jobs. The work feels like diligence. In reality, it’s anxiety dressed up as analysis.
In many of these situations, the real issue is that the question is fuzzy. If you can’t clearly define what you’re trying to solve — lower cost, improve diversification, reduce taxes, manage volatility — you won’t know which information matters.
Start by writing the decision in one sentence, then gather only the information that could realistically change your mind. That’s diligence. Everything else is just motion.
Markets also don’t give clean feedback. A careful decision can look wrong for years, and a sloppy one can look brilliant. That’s why you need a process that not only guides what you research before you act, but also tells you how you’ll judge the decision afterward.
Poker makes that easier to see.
The Importance of Process Over Outcome
Poker is a useful framework for evaluating decisions because the outcomes are driven by both skill and luck. Annie Duke, a former professional poker player and World Series of Poker bracelet winner, uses the term “resulting” to describe the habit of judging a decision by how it turned out instead of how the decision was made.(See Thinking in Bets by Annie Duke)
A reckless bet can win because the right card shows up. A well-reasoned bet can lose because it doesn’t. In poker, the decision happens before the river card hits the table. The outcome comes later.
Investing works the same way, except the feedback loop is slower and the cards stay hidden longer.
You can buy a fund for a bad reason and still make money. You can build a thoughtful portfolio and still underperform. You can make a concentrated, half-baked bet and get rewarded for it. The return tells you what happened. It does not automatically tell you whether the decision was good.
That’s the trap. If you start treating outcomes as proof of intelligence, you’ll reinforce whatever behavior happened to work most recently.
And the stretches that test you aren’t always one-year blips. They can run for five years. Ten years. Long enough that good investors start to feel foolish and impatient investors start to feel invincible.
When making a decision and judging the quality of that decision later, it’s essential to remember that we don’t control the outputs. We only control inputs.
Inputs are the things you get to choose. You can ground expectations in history. You can count costs and frictions. You can decide what role each position plays. You can size risk to something you can stick with when it gets uncomfortable. You can write down why you are making a decision before the outcome has a chance to rewrite your memory.
Outputs are the realized returns over a year or a decade. They’re influenced by luck, timing, starting valuations, interest rates, inflation, investor behavior, and the path markets take along the way.
The mistake is pretending the output gives a clean grade on the input.
It doesn’t.
A bad outcome does not automatically mean the decision was bad. A good outcome does not automatically mean the decision was good. That’s frustrating, but it’s also freeing. It means you don’t have to rebuild your entire philosophy every time the market hands you a disappointing year, and you shouldn’t crown yourself a genius every time the market rewards a risky decision.
This mismatch is why behavior does so much damage. Often, the damage doesn’t come from owning terrible investments. It comes from changing course at the wrong time. Red on a statement makes people want relief. Green makes them want more. Neither reaction tells you whether the original decision was sound.
The answer cannot be more confidence. Confidence is cheap. Confidence can come from a good argument, a good spreadsheet, or a lucky outcome.
Process is harder. Process forces you to ask, “What am I trying to solve? What would change my mind? What risks am I taking? How will I know later whether this was a good decision?”
That is the answer to the problem we’ve been talking about in this episode. Not more intelligence. Not more information. Not better storytelling after the fact.
A better process.
Introducing the Probabilistic Decision Protocol
And that’s exactly why I’m writing The Perfect Portfolio.
The book is not about pretending there’s one perfect portfolio that fits every person, every market, and every moment. That portfolio doesn’t exist. The point is to build a decision process that is durable enough to survive real life — uncertainty, emotions, market cycles, and the temptation to change your mind at the worst possible time.
One essential component of the book is what I call the Probablistic Decision Protocol.
The Probablistic Decision Protocol is a structured way to slow down major investment decisions. I’ll go deeper into the protocol in a future episode.
But if this topic resonates with you — if you’ve ever felt like you were working hard to make a good investment decision but still weren’t sure whether you were being disciplined or just overthinking — I’d love for you to follow along as the book comes together.
You can sign up to receive updates about The Perfect Portfolio using the link in the episode description, or by visiting theperfectportfoliobook.com.
Subscribers will get early chapter previews and excerpts, behind-the-scenes stories from the writing process, and invitations to subscriber-only webinars where I’ll go deeper on the ideas in the book.
Because the goal isn’t to feel certain. The goal is to make better decisions in the presence of uncertainty.
That’s what a real investment process is built to do.
Resources:
- Thinking in Bets by Annie Duke
- Behavioral Problems of Adhering to a Decision Policy, Slovic 1973
- The Perfect Portfolio Exclusive Update List
The Long Term Investor audio is edited by the team at The Podcast Consultant
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