Luck vs. Skill
The recognition that short-term investment results are substantially driven by randomness, and that evaluating investment decisions requires examining the quality of the process rather than the quality of the outcome, since good processes can produce bad outcomes and vice versa.
“A good decision is one that's optimal given what was knowable at the time it was made. It cannot be evaluated based solely on the outcome.”
Concept Analysis
Definition & Origins
The luck-versus-skill question is one of the most intellectually honest themes in Marks' memo corpus — and one of the rarest in investment management discourse, where self-promotion is the norm. Marks engages directly and repeatedly with the uncomfortable reality that short-term investment results are substantially driven by randomness, and that the ability to distinguish skilled investors from lucky ones — including oneself — is genuinely difficult.
The intellectual lineage draws on probability theory, Michael Mauboussin's work on skill and luck, and Nassim Taleb's analysis of randomness in financial markets. But Marks' contribution is the practitioner's perspective: the honesty to apply this framework to his own career and Oaktree's own track record.
Core Ideas
Good decisions can produce bad outcomes, and bad decisions can produce good outcomes. This is the core insight. In any probabilistic endeavor, the short-term correlation between decision quality and outcome quality is weak. A sound investment thesis can produce a loss if low-probability negative scenarios materialize. A reckless bet can produce a gain if high-probability failure scenarios fail to materialize. Judging the quality of decisions by their outcomes — rather than by the quality of the process and the probabilities — is the most common analytical error in evaluating investment performance.
Survivorship bias distorts our perception of skill. The investment managers with the best track records may be the most skilled, or they may be the ones whose particular risk exposure happened to match the market environments of their careers. A manager who made aggressive growth bets in 1995-2000 and again in 2010-2021 would have a spectacular record — not because they were skillful, but because they were lucky about when they were born and what strategies were fashionable during their peak career years.
Large sample sizes are required to distinguish skill from luck. In a field where outcomes are probabilistic and careers span 20-30 years, the statistical evidence for distinguishing skill from luck is weaker than most practitioners acknowledge. Marks cites research suggesting that even 20-year track records are insufficient to statistically confirm alpha at conventional significance levels.
Process is the observable; outcomes are random. Because we cannot directly observe the probability distributions underlying investment decisions, the best we can do is evaluate the quality of the process: Was the thesis well-reasoned? Were the risks understood? Was the position sizing appropriate for the conviction level and uncertainty? A good process that produced a bad outcome is being incorrectly evaluated if judged only by results.
Intellectual honesty about luck is a competitive advantage. Counterintuitively, the investor who clearly sees the role of luck in their success is better positioned for long-term performance. They avoid overconfidence (the principal cause of concentrated losses), they maintain appropriate humility about position sizing, and they focus on improving process rather than celebrating outcomes.
Practical Application
The Getting Lucky memo (2014): Marks walks through a series of investment scenarios to illustrate how indistinguishable good luck and good skill can look ex post — and how the appropriate response is to evaluate each through the lens of process, not outcome.
Portfolio management implication: If outcomes are significantly random, position sizing should be calibrated to conviction level and analytical confidence — not to recent performance. A position that has performed well is not automatically more likely to continue performing well; a position that has performed poorly is not automatically more likely to reverse. The process evaluation, not the recent outcome, should drive sizing decisions.
Common Misconceptions
Misconception 1: Great track records prove skill. All investment track records are a mixture of skill, the factor exposures of the era, and the realization of probabilistic outcomes. Decomposing these components — especially over short periods — is genuinely difficult and requires more analytical rigor than simply looking at returns.
Misconception 2: Admitting luck means denying skill. Marks explicitly acknowledges that Oaktree's performance reflects genuine skill. His argument is not that skill doesn't matter — it is that skill and luck are both contributors to outcomes, and intellectual honesty requires acknowledging both.
Howard Marks' Own Words
"A good decision is one that a reasonable, intelligent person with the available information would make. It cannot be judged based solely on the outcome, because outcomes involve elements beyond the decision-maker's control."
"Every once in a while, someone makes a killing in a way that, if they're honest, they'll admit they didn't fully expect. The question is whether they draw the right lessons from it."
"I think about the best investors I've known, and I believe they share one trait: they know they don't always know. That humility is itself a competitive advantage."
Thought Evolution
Related Concepts
Key Memos
The definitive treatment; systematic analysis of how to distinguish a good decision from a lucky one
Connects luck to outcome evaluation; why process matters more than results
The foundational acknowledgment of epistemic uncertainty that underlies the luck discussion