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.
MDT deliberately avoids competing on acquiring novel, expensive datasets (informational edge). Instead, they focus on their analytical edge: applying sophisticated machine learning tools to long-history, high-quality standard datasets like financials and prices to find differentiated insights.
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 '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.
Daniel Mahr's first investing experience was successfully flipping dot-com IPOs. However, turning those wins into giant losses by straying from his original thesis taught him a formative lesson about the dangers of overconfidence and the necessity of a disciplined, systematic approach.
The firm doesn't just decide a factor is obsolete. Their process begins by observing within their transparent 'glass box' model that a factor (like book-to-price) is driving fewer and fewer trades. This observation prompts a formal backtest to confirm its removal won't harm performance.
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.