Daniel Mahr
Head of MDT Group, Federated Hermes. On 1 show, 1 appearance. The Shows tab opens the full record on each.
investorexecutivefederatedhermes.com ↗
Daniel Mahr oversees the MDT systematic equity team at Federated Hermes, which manages mutual funds, ETFs, and institutional accounts using proprietary quantitative models. He is known for MDT's glass box decision-tree framework, combining machine-learning algorithms with disciplined risk controls.
1shows
1appearances
26statements
2resolved
2supported
0contradicted
100%fully supported
Everything Daniel Mahr said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Mahr: Valuation matters far more for mature companies than new entrants
“Valuation is a lot more important for companies that have been around for a long time than a brand new entrant to the public markets.”
Mahr: Decades-long trend toward market efficiency broke in recent years
“It does feel like the markets are different in the last couple of years than they were a decade ago. If you asked me five years ago, are markets on a never ending trend towards efficiency? And is your job as a systematic investor It's going to get harder and h…”
Mahr: MDT trains its machine learning models on 50 years of data
“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.”
Mahr: MDT runs 'glass box' models with fully traceable daily decisions
“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…”
Mahr: Stocks down 70-80% yield strong returns when combined with value
“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…”
Mahr: Deep reversals work because investors emotionally avoid 'bad stories'
“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.”
Mahr: Most published finance research fails to replicate or add value
“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.”
Mahr: Factor selection and interaction at MDT is 100% algorithmic
“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…”
Mahr: Book-to-price lost explanatory power as the intangible economy expanded
“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…”
Mahr: High-financing companies tend to underperform the market
“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.”
Mahr: Strong Momentum Offsets Underperformance of High Financing
“And generally speaking, it's the strongest momentum companies that can generate good outcomes regardless of the financing.”
Mahr: Consistent Price Appreciation Yields Better Momentum Signals
“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.”
Mahr: Momentum Predicts Future Returns Better for Newer Companies
“We find that momentum typically is more meaningful when you're looking at newer companies than companies have been around for a long time.”
Mahr: LTCM Would Not Have Failed Without 50x Leverage
“If long-term capital management hadn't been running a fifty-x leveraged strategy, They wouldn't have ended up in the trouble that they ended up with.”
Mahr: 2007 Quant Quake was driven by crowding and added leverage
“The run on quant strategies that happened It was predicated by the fact that statistical arbitrage strategies had gotten more crowded over the years leading up to 2007. In response to that, certain managers began running those strategies with additional levera…”
MDT Advisers does not use LLMs or ChatGPT in investment modeling
“Large language models and ChatGPT specifically are not anything that we're presently making use of in our modeling.”
Mahr: ChatGPT stock backtests are untrustworthy due to look-ahead bias
“When you're running a back test through the better part of the last decade, ChatGPT knows that Nvidia became a multi-trillion dollar company. ChatGPT knows what the mega trends were in the economy and the market over those timeframes. It's not realistic to tru…”
Mahr: Traditional software engineering demand softened while AI talent soars
“In the same way that folks with data science backgrounds and AI knowledge are super in demand, the software programming space has hit a little bit of a soft patch. There's a lot of opportunities to hire great engineers these days.”
Mahr: MDT Advisers has used machine learning tools since 2001
“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.”
Mahr: MDT uses company age since IPO as a quantitative model factor
“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.”
Mahr: MDT limits decision trees to two to five questions to avoid fragmentation
“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…”
Mahr: Ensemble forests of shallow trees solve decision tree data scarcity
“You can quickly see that there's a sharp limit to how deep you want to make these trees. Fortunately, we have another approach to asking more questions about companies, which is rather than relying on a very deep tree, Relying on a forest of relatively shallow…”
Mahr: Dot-com brokerages distributed hot IPO shares first-come, first-served
“There were a lot of IPOs in that market environment, the dot-com bubble. They would go up a hundred percent, 200% or more on the day that they priced, and there were a small number of investment firms that would get allocations to these IPOs, and they would of…”
Mahr: MDT adopted decision trees after factor tilts struggled in 1998-1999
“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…”
One line per show, most statements first. The link opens Daniel's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
 CAPITAL ALLOCATORSLEDGER | Head of MDT Group, Federated Hermes | 1 | 26 | 100% 2/2 | full record on Capital Allocators → |