The 'company age' factor is not predictive on its own. MDT's decision tree model uses it to create context, asking different questions about young companies versus mature ones. For example, valuation proves to be a much more important factor for older, established businesses.

The firm discovered a reversal effect in stocks down 70-80%. The strategy's efficacy was confirmed when their own traders instinctively wanted to override these trades due to negative headlines. This emotional bias, even among professionals, is the inefficiency the model exploits.

The key to emulating professional investors isn't copying their trades but understanding their underlying strategies. Ackman uses concentration, Buffett waits for fear-driven discounts, and Wood bets on long-term innovation. Individual investors should focus on developing their own repeatable framework rather than simply following the moves of others.

WCM realized their portfolio became too correlated because their research pipeline itself was the root cause, with analysts naturally chasing what was working. To fix this, they built custom company categorization tools to force diversification at the idea generation stage, ensuring a broader set of opportunities is always available.

WCM avoids generic AI use cases. Instead, they've built a "research partner" AI model specifically tuned to codify and diagnose their core concepts of "moat trajectory" and "culture." This allows them to amplify their unique edge by systematically flagging changes across a vast universe of data, rather than just automating simple tasks.

Rather than building one deep, complex decision tree that would rely on increasingly smaller data subsets, MDT's model uses an ensemble method. It combines a 'forest' of many shallow trees, each with only two to five questions, to maintain statistical robustness while capturing complexity.

Instead of opaque 'black box' algorithms, MDT uses decision trees that allow their team to see and understand the logic behind every trade. This transparency is crucial for validating the model's decisions and identifying when a factor's effectiveness is decaying over time.

Financial models struggle to project sustained high growth rates (>30% YoY). Analysts naturally revert to the mean, causing them to undervalue companies that defy this and maintain high growth for years, creating an opportunity for investors who spot this persistence.

Instead of focusing on process, allocators should first ask managers fundamental questions like "What do you believe?" and "Why does this work?" to uncover their core investment philosophy. This simple test filters out the majority of firms that lack a deeply held, clearly articulated conviction about their edge.