The Paradox Nobody Wants to Admit
Jan 30, 2026
Two types of investors consistently destroy their returns. The first type concentrates heavily in their “best ideas,” convinced their research gives them an edge. They blow up spectacularly when a single position moves against them. The second type spreads capital across dozens of positions, seeking safety in numbers. They underperform index funds year after year while paying higher fees and doing more work.
Both errors look different on the surface. One appears reckless, the other cautious. But they stem from the same psychological root: an inability to accurately assess one’s own edge. The concentrated investor overestimates their information advantage. The over-diversified investor underestimates it—or never tries to measure it at all. Neither approach is based on honest self-assessment. Both are reactions to uncertainty dressed up as strategy.
The Overconfidence Problem
Most investors believe their best ideas deserve outsized positions. The logic feels sound: if you’ve done extensive research and have high conviction, why wouldn’t you bet big? The problem is that conviction and accuracy are not the same thing. Studies consistently show that confidence levels bear almost no relationship to actual predictive ability. Traders who feel most certain about a position are not more likely to be right—they’re simply more likely to size the position larger.
This overconfidence bias in edge estimation explains why so many “high-conviction” portfolios implode. The investor isn’t necessarily wrong about the company. They’re wrong about how much better their analysis is than the market’s collective wisdom. They mistake the feeling of certainty for an actual information advantage. When the position moves against them, they often double down—because their conviction hasn’t changed, even as the evidence has.
The mass psychology of markets makes this worse. During bull runs, concentrated bets that happen to work reinforce the belief that conviction equals edge. The investor attributes success to skill rather than favorable conditions. Then market conditions shift, the same sizing approach produces catastrophic losses, and a decade of gains evaporates in months.
Long-Term Capital Management: Nobel Prizes Meet Reality
If overconfidence could be solved by intelligence, Long-Term Capital Management would have succeeded. The hedge fund launched in 1994 with two Nobel laureates on its team, along with some of the sharpest minds on Wall Street. Their models were sophisticated. Their track record was exceptional—40% annual returns in the first years. Their position sizing was based on mathematical precision that retail investors couldn’t match.
By 1998, LTCM had leveraged its positions to roughly 25-to-1. The models said this was safe because the positions were hedged and correlations were stable. Then Russia defaulted on its debt. Correlations that had held for years suddenly broke. Positions that were supposed to offset each other moved in the same direction. Within weeks, LTCM lost $4.6 billion and required a Federal Reserve-coordinated bailout to prevent broader market contagion.
The lesson isn’t that models are useless. The lesson is that position sizing based on historical correlations assumes the future will resemble the past. It assumes your edge is real and measurable. LTCM’s team had more data, better models, and deeper expertise than almost anyone. They still sized their positions based on confidence in predictions that turned out to be wrong. If Nobel laureates can’t accurately assess their own edge, retail investors should be deeply skeptical of their ability to do so.
The Buffett Exception That Proves the Rule
Warren Buffett runs a concentrated portfolio. His largest positions often represent 30-40% of Berkshire’s equity holdings. This seems to contradict everything about the dangers of concentration. But Buffett’s approach differs from retail concentration in one critical way: he actually has an information advantage, and he knows what it is.
Buffett’s edge isn’t stock-picking in the traditional sense. It’s access to deals that retail investors can’t get—preferred stock with guaranteed dividends, warrants attached to investments, and the ability to negotiate terms directly with management. When Buffett bought $5 billion of Goldman Sachs during the 2008 crisis, he got 10% preferred dividends plus warrants. Regular investors buying Goldman stock on the same day got none of those protections.
Retail investors who concentrate like Buffett without Buffett’s structural advantages are not following his strategy. They’re imitating the visible output while lacking the invisible inputs. The psychology behind this error is straightforward: it feels good to believe you’ve identified what the market is missing. But feeling like you have an edge and actually having one are completely different things.
A Framework That Doesn’t Rely on Self-Assessment
If you can’t accurately assess your own edge—and you probably can’t—position sizing needs to be based on something more objective. Two factors work better than conviction: volatility and liquidity.
Volatility-based sizing adjusts position size inversely to price movement. A stock that swings 5% daily gets a smaller allocation than one that moves 1% daily, regardless of how confident you feel about either. This approach acknowledges that bigger swings create bigger risks of being stopped out or panicking at the wrong moment. It removes conviction from the equation entirely.
Liquidity-based sizing limits positions based on how easily you can exit. If a stock trades thin volume, a large position becomes a trap—you can’t sell without moving the price against yourself. Limiting position size to some fraction of average daily volume ensures you can exit within a reasonable timeframe without becoming your own worst counterparty.
Neither approach requires you to know your edge. Both impose external discipline that protects you from your own overconfidence. The Kelly Criterion—the mathematical formula for optimal bet sizing—looks elegant on paper. But it requires accurate inputs for win probability and payoff ratio. Garbage in, garbage out. Volatility and liquidity are measurable. Your edge is not.
The Structural Problem Nobody Solves
Most position sizing advice assumes you can rationally assess your own skill level, then size accordingly. This assumption is false. Decades of research on overconfidence bias show that people systematically overestimate their abilities, and that expertise makes this worse, not better. The more you know about a subject, the more confident you become—but your accuracy doesn’t increase at the same rate.
This creates an unsolvable problem at the heart of active investing. Optimal position sizing requires knowing your edge. Knowing your edge requires accurate self-assessment. Accurate self-assessment is psychologically impossible for most people. The investors who think they’ve solved this problem are often the ones most at risk.
The practical solution isn’t better formulas or more sophisticated models. It’s building systems that don’t require accurate self-assessment in the first place. Hard position limits. Volatility-based sizing. Forced diversification rules that kick in regardless of conviction. These approaches feel unsatisfying because they ignore your research and opinions. That’s precisely why they work. Your opinions about your own edge are the least reliable input in your entire investment process. Build a system that doesn’t need them.
