Our head of trading would come to me every day we bought a stock that was down 70 or 80% and

say, Dan, we need to override this trade.

The company's CEO just resigned and disgraced.

Dan, this is a one product, biotech company and they just missed their target.

It looks like the whole company's got nothing.

We have to override these trades. We can't do

them. That's when the light bulb goes off. 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.

I'm Ted Cydies, and this is Capital Allocators.

My guest on today's show is Daniel Marr, head of MDT, the $26 billion quantitative equity

investing group at Federated Hermes that oversees a suite of actively managed mutual funds,

ETFs, collective investment trusts, and separately managed accounts.

Dan joined the firm in 2002 as a junior analyst and took over leadership of the team six years later,

guiding its evolution through vast changes in data, computing power, and investment methodology.

Our conversation traces Dan's path from flipping IPOs as a college student to running machine

learning models across global equity markets.

We discussed the development of MDT's decision tree framework, a glass box approach to stock

selection that blends transparency with sophistication, and how the team balances analytical

rigor with human judgment. Dan explains lessons from two decades of modeling markets,

including the challenges of overfitting and underfitting data,

...