Markku Kurtti is an engineer in the telecom world. His outsider quant take on portfolio construction is beautifully derived and intuitive.
I strongly recommend his interview with Corey Hoffstein:
🎙️Diversification is a Negatively Priced Lunch (Flirting with Models podcast)
His blog is also outstanding. I’ll point you to this post in particular:
How much skill a concentrated stock picker needs to beat a diversified benchmark? (17 min read)
I summarize key findings below (with the aid of an LLM). The “Moontower highlights” are direct quotes from my kindle.
The central theme:
For a stock picker to successfully manage a concentrated portfolio, they must generate sufficient alpha to overcome the inherent risks and volatility associated with fewer holdings.
Supporting points:
1) The Balance Between Concentration and Diversification
Concentrated portfolios inherently carry more risk due to idiosyncratic variance, or the unique risks associated with individual stocks. To overcome this risk, stock pickers need to generate enough alpha to offset the “variance drag” — the reduction in expected growth rate caused by high volatility.
🟡Moontower highlight: “Portfolio construction of a skilled stock picker is a compromise between enhancing alpha by concentration and mitigating idiosyncratic variance drag by diversification.”
2) Importance of Consistent Skill and Alpha Requirements by Stock Size:
Different types of stocks require varying levels of alpha to beat the benchmark. Larger, more stable stocks typically require less alpha than smaller, more volatile stocks. Consistency in skill is crucial, as erratic performance increases the minimum alpha required to compensate for the higher risk.
🟡Moontower highlight: “Assuming perfectly consistent stock picking skill over time, 10-stock big stocks portfolio has historically required roughly 0.5 percentage point (pp) annualized alpha, small stocks ~1pp and micro-caps ~2pp. High E/P, E/B, Mom and B/P styles, in the universe of all stocks, have required roughly ~1pp and low E/P, E/B, Mom and B/P styles north of ~2pp. Low E/P style (smallish growth stocks with low profitability) have required the highest 2.55pp alpha.”
3) Risk of Concentration Without Skill
Concentration magnifies returns but also heightens risks. Without genuine stock-picking skill, a concentrated portfolio becomes increasingly likely to underperform over time. The document cautions against relying too heavily on concentration to boost returns without sufficient alpha.
🟡Moontower highlight: “But concentration is risky. If you concentrate and don’t have genuine stock picking skill, time will be your enemy.”
4) Circle of Competence and Style Diversification
The post emphasizes the value of investing within one’s “circle of competence” — areas where the investor has the most knowledge or advantage. However, it also warns that focusing exclusively on a single style exposes investors to style risk.
5) Predictability of Variance Drag Over Return
Idiosyncratic variance drag, the penalty for concentrating in fewer stocks, is more predictable than expected returns.
🟡Moontower highlight: “Idiosyncratic variance drag differences are easier to predict than expected return differences. It is therefore safer to increase diversification, which reliably decreases minimum alpha requirement, than to increase portfolio concentration to enhance uncertain alpha.”
🟡Moontower reference: The idea that volatility is more predictable than returns is a foundational principle in portfolio management. See Know Nothing Sizing
6) Lottery Preference in High Variance Styles
Some investors are attracted to high-idiosyncratic-variance stocks with potential for lottery-like returns leading to lower forward-looking returns.
🟡Moontower highlight: “Some investors may prefer stocks that may pay off big and this is exactly what idiosyncratic variance delivers: large dispersion of returns among individual stocks.”
🟡Moontower reference: See A Recipe For Overpaying for a succinct explanation by Chris Schindler.
7) Takeaway on Diversification for Risk Management: Diversification not only reduces variance drag but also lessens reliance on unpredictable alpha.
🟡Moontower highlight: “Our take away is that idiosyncratic variance drag is much more predictable than expected return. More generally, it is easier to predict variance than mean return. It is therefore safer to diversify more as it will reliably bring down idiosyncratic variance drag compared to concentrating more in a hope of higher alpha.”
It’s a love letter to diversification mixing words and math. For what it’s worth, at SIG Jeff Yass also called diversification a free lunch.
I’m partial to my Sun/Rain example in You Don’t See The Whole Picture which is an even stronger statement — you are incinerating money by not diversifying but if you evaluate yourself by “resulting” you won’t see it. That’s because the highest bid for risk is the most efficient at absorbing it. This is deeply true in the derivatives world. In the broader investment landscape it’s confounded by info asymmetry, principal-agent conflict, and the comfort of (perceived) safety in herding.
If you want to get deeper into this idea see the back half of the moontower guide:
đźź°Understanding Risk-Neutral Probability (link)
But be aware…”diversification always means having to say you’re sorry” since something is always losing.
And sometimes almost everything loses. This was Wednesday. Eww.
