AI's primary value in pre-buy research isn't just accelerating diligence on promising ideas. It's about rapidly surfacing deal-breakers—like misaligned management incentives or existential risks—allowing analysts to discard flawed theses much earlier in the process and focus their deep research time more effectively.

Focusing on already-liquid stocks is often superior to buying illiquid but "transparently cheap" names. The fight for an illiquid company to gain market attention and liquidity is a significant, often underestimated, risk that can negate the perceived valuation advantage.

Counter to conventional value investing wisdom, a low Price-to-Earnings (P/E) ratio is often a "value trap" that exists for a valid, negative reason. A high P/E, conversely, is a more reliable indicator that a stock may be overvalued and worth selling. This suggests avoiding cheap stocks is more important than simply finding them.

Identifying a stock trading below its intrinsic value is only the first step. To avoid "value traps" (stocks that stay cheap forever), investors must also identify a specific catalyst that will unlock its value over a reasonable timeframe, typically 2-4 years.

Beyond simple quantitative screens, AI can now identify companies fitting complex, qualitative theses. For example, it can find "high-performing businesses with temporary, non-structural hiccups." This requires synthesizing business model quality, recent performance issues, and the nature of those issues—a task previously reliant on serendipity.

Despite decades of evidence, there is no agreement on why factors like "value" (cheap stocks outperforming) work. The debate is split between rational risk-based explanations (Fama's view that they are inherently riskier) and behavioral ones (Shiller's view that investors make systematic errors). This uncertainty persists at the core of quant investing.

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.

Anchoring valuation on a company's typical price-to-sales ratio helps identify buying opportunities when margins are temporarily depressed. This avoids the pitfalls of methods like the Magic Formula, which can mistakenly favor companies at their cyclical earnings peaks, leading to underperformance.