David Kaiser clarifies that "not adapting" refers to the core investment rules, not the portfolio itself. The rules (the "how") remain consistent, but applying them to a changing market naturally results in an evolving portfolio (the "what"). This avoids chasing trends while still adapting to market conditions.
The host, Andrew Walker, questions the long-term viability of simple rules-based investing. Since AI excels at following "if X, then Y" logic, any easily codifiable investment strategy faces the risk of being automated, its alpha competed away by faster, more powerful computer models.
Methodical Investment's David Kaiser suggests that the primary benefit of a rules-based system isn't just performance, but the psychological comfort it provides. It establishes a clear process (if X happens, do Y), removing emotional decision-making and making strategy easier to communicate, especially during volatile periods.
David Kaiser's system doesn't try to predict cyclical peaks. Instead, it mitigates the risk of buying hot cyclical stocks by owning a diversified portfolio and rebalancing consistently. This structural approach ensures that if the model over-allocates to a sector at its peak, the error is contained and corrected relatively quickly.
Methodical Investments uses an annual rebalancing cycle as a strategic choice. More frequent rebalancing doesn't allow value theses to fully develop and be recognized by the market. However, waiting longer than a year risks the portfolio drifting away from its core value characteristics, losing its margin of safety.
Drawing on a religious analogy, David Kaiser explains that striving for a "perfect" portfolio is a fool's errand. Instead, his rules-based approach is built on the idea of being human and fallible ("missing the mark"). The goal is a good, robust portfolio that can withstand errors, rather than a fragile, optimized-for-perfection one.
David Kaiser of Methodical Investments posits a contrarian view on AI's market impact. Instead of creating perfect efficiency, he argues AI and the data it processes might actually create more mispricings and inefficiencies. This provides opportunities for disciplined, rules-based strategies that don't constantly adapt to short-term noise.
Methodical Investments' model doesn't simply buy the cheapest stocks. It actively removes the extreme outliers from its consideration set. This rule acts as a fail-safe, recognizing that companies appearing exceptionally cheap on paper are often value traps, facing severe corporate governance issues, or are a result of data errors.
David Kaiser suggests that as AI becomes ubiquitous in investing, a "tiptoes at a parade" problem emerges where no one gains an edge. By intentionally not using AI to constantly evolve his process, he believes his firm can be differentiated. The alpha may lie in the systematic, old-school approach that AI-driven consensus overlooks.
Methodical Investments' rule to only hold profitable companies serves a dual purpose. Beyond seeking better performance, it ensures data integrity for their models. Metrics like P/E become more reliable and comparable across the portfolio when the denominator (earnings) is consistently positive, avoiding statistical noise from unprofitable firms.
David Kaiser reveals his model specifically limits exposure to financial stocks. Because financials frequently screen cheap on metrics like price-to-book, a pure value model can become dangerously over-concentrated in the sector. The limit is a pragmatic override to ensure diversification and avoid the unique, often hidden risks inherent in banks.