Keep pulling the thread on Daniel Mahr.
MDT's models discovered a powerful reversal effect where companies whose share prices have fallen 70-80% in a year can produce strong returns when combined with other value and quality characteristics.
MDT's strategy intentionally focuses on gaining an "analytical edge" through machine learning, rather than pursuing an "informational edge" by competing for new or alternative data sets.
MDT claims a competitive advantage from having used machine learning tools in its investment process since 2001, giving it a 24-year head start over new entrants.
MDT positions its investment strategies as a "glass box," emphasizing transparency in how its models work, in contrast to the "black box" label often applied to quant funds.
MDT's quantitative models use a unique factor called "company age," measured by how long a company has been publicly traded or filing financial statements.
MDT's research indicates that the predictive factors for stock performance differ significantly between young companies (within 10-20 years of IPO) and mature companies (50-100+ years old).
In MDT's models, valuation is a more important predictive factor for mature companies than for newly public companies.
MDT has observed that the "book to price" factor, used in its models since 1991, has lost its explanatory power over time due to the evolution of the economy towards intangible assets.
MDT's modeling approach has evolved from using a single decision tree to a "forest of trees" consisting of approximately one thousand individual trees to improve forecasting.
MDT's models confirm academic findings that companies engaging in significant financing activities, such as issuing debt or shares, tend to underperform.
MDT's models find that companies with high levels of financing can still outperform if they also exhibit strong, consistent momentum characteristics.
In MDT's models, the momentum factor is a more meaningful predictor of performance for newer companies compared to mature, long-established companies.