MDT Advisers

“We find, as the academics have, that companies that are engaged in significant amounts of financing tend to underperform, and those that don't have better outcomes.”

“At MDT, that is not the case. We like to position our investment strategies as being a glass box. There's a lot of machinery on the inside, but we can see into it. We can see how it's working and understand what's driving all of the decision making on a day-to…”

“Every night we download updated data from all of our vendors. We recalculate all of our characteristics. We run all the companies in the domestic equity market through our forests and have updated forecasts. Every portfolio that we run is re-optimized and gene…”

“We find that momentum typically is more meaningful when you're looking at newer companies than companies have been around for a long time.”

“Back in the OneTree day, we would print out the tree and tape it on the wall of our trading room. Every time we were reviewing a trade, we would simply walk through the sequence of questions On that paper tree on the wall to help inform what specifically was m…”

“The selection of factors is driven by the potential questions that can be asked, is driven by the investment team. That's a major area of focus for us on the research side. Once we present that list of factors to the algorithm, it's Completely mechanically det…”

“We train our models on roughly 50 years worth of data, which I say that to some potential investors and they're surprised. We think that market data from the 19 seventies and eighties is still useful for forecasting mispricing.”

“In 2002, we had made a big transition at MDT. For the first decade, the strategies were traditional factor tilting strategies. There was a formula that used a small number of characteristics and the portfolios would tilt toward them. Those strategies generated…”

“Large language models and ChatGPT specifically are not anything that we're presently making use of in our modeling.”

“More often than not, we test an idea and either it's not replicable when we look at it with our data set or something else in our model essentially captures the same underlying effect.”

“We tend to find momentum works better when it is consistent. When the stock price is rising in a consistent manner, it leads to better outcomes than companies that have one giant price move driving the momentum measurement.”

“Typically we ask between two and five questions in each tree. The reason we don't ask more questions is we found that as you ask questions deeper and deeper in the tree, you're working on smaller and smaller pools of data because the trees are customized to th…”

“At MDT, we've been using these machine learning tools since 2001. So we have a 24 year head start on someone who is new to the game.”

“This is precisely why this strategy works is because even quantitative investors who are intentionally trying to buy these stocks Find it hard to overcome the human emotions involved with buying a bad story.”

“We used book to price in our models. Going back to version one point O in 1991. But as markets evolved and more importantly, as the economy has changed, we saw less and less explanatory power to incorporating that in our model. And we had an intuitive sense of…”

“A number of years ago, we started adding price-based factors to our model, and the price-based factors found momentum effects, as was published in the academic literature and as we fully expected to see, but it also found some very powerful reversal effects wh…”

“And generally speaking, it's the strongest momentum companies that can generate good outcomes regardless of the financing.”

“One of the most unusual factors that we use, we call company age. We measure that simply as how long has the company been publicly traded and or filing financial statements.”