YouSaid · the spoken record

Daniel Mahr

lines on the record: 67
first: 2025-11-20
most recent: 2025-11-20
sittings or episodes: 1
sources: podcast

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“I've always been an incredibly competitive person. And when I was young, I would take setbacks very hard. Frankly, I see that in my kids too. They come by the competitiveness and it's hard for them. Every time a little thing goes wrong, I wish I knew earlier on the life is a journey and that no one wins everything. Often doors that seemed closed, open in time. Sometimes the path that you end up on as an alternative ends up being the right path. I try to stress that with my kids as much as I can when I see them having the same struggles I did.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“I would have never expected that 23 years on from graduating college that I would still be a quant working at MDT advisors. We go through an exercise every couple years. My graduating class where we publish a book on what everyone's been up to the past five years, I think it's just me and a fellow who's worked at Microsoft for 23 years who have gone down the career route and stayed in one place. It's been a phenomenal ride over the decades. The career that has managed to grow with me at every step where I needed it, I'm really fortunate that things turned out this way even though I would have never guessed it.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“They were both great mentors and helped me appreciate that success in investment management, it's not all about being the brightest and having the most genius ideas. There are a lot of geniuses who failed. It's not just about meticulousness and craftsmanship, but both of those things are very important to success in this business. I'm really indebted to David and Sarah.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“I've worked at MDT my entire career, and I was really fortunate to have two mentors from day one, David Goldsmith and Sarah Stahl. David was the founder of the Quant group and the CIO. Sarah was one of David's first hires who led analytical portfolio attribution effort here for many years. What was great about the two of them was that they were incredibly different from one another in terms of mentors. David was the mad scientist of our group. He would be thinking about algorithms 24-7 come in and tell us about the idea he had while he was in the shower. Sarah was also very brilliant in a less wild and unconstrained way. She was very meticulous, very focused on craftsmanship and understanding precisely what was driving the returns of our models.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“So, I am a homebrewer. Took that up shortly after we bought our first house, and I had enough space to store all of the equipment. We brew probably a dozen batches of beer a year, mostly trying to focus on what you don't find at the store all the time. My wife planted a sour cherry tree in our yard. Also at the time that we moved in. It's turned out to be wildly prolific. So we pull upwards of 80 pounds of cherries off that tree every year. I do her a favor by using some of them to brew interesting sour cherry beers.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“Markets are exciting every single day. I've been at this for 23 years. We have learned a lot and improved our models significantly over those decades. But you're never going to solve the financial markets. There's always new information out there. There are always curveballs coming from a macro perspective, risks that you had never seen before that all of a sudden manifest themselves. From my perspective, it's a great place to be in a really exciting place to be applying my technical background to.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“One of the most challenging things is on the team building side. 20 years ago, talented data science-oriented programmers were not in demand by every single other firm in the entire world. We had a much easier time finding junior analysts to join our team. Recruiting, given that the skill sets we're looking for are a lot more demand, has gotten more difficult. We've also tried to adapt and be more flexible in the types of people that we're looking for. In response to that, 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 a great engineers these days. We're strategically trying to lean into where do we see the market. For talent, presenting opportunities to us.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“Going to keep getting more powerful intersecting those two things, even if something seems far fetched today, that doesn't mean that it won't be 10 years from now and 20 years from now.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“We are doing a lot of research in the factor space. We used to use stock ownership to drive some of our factors and that stopped working at a certain point. But we're coming back around to the idea that knowing who owns the stocks that you're contemplating investing in might be able to tell you something about how to evaluate opportunities there. We are also looking in the AI space at ways that that can enhance productivity, but also idea generation. It's probably still a ways off before we're asking large language models to suggest stocks for the portfolio. It's important to be open-minded about the possibilities. Computers are going to keep getting faster data is going to keep getting more and more prevalent and accessible. The algorithms.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“Going on in the AI space, and we use a lot of proprietary software and tools in our investment process. One area in AI that is really appealing to us is the idea of software development co-pilots, the idea that AI can make and enhance software development at an organizational level. We're a small team with a lot of software and any ways in which we can improve efficiencies there valuable to us.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“Large language models and chat GPT specifically are not anything that we're presently making use of in our modeling. One of the big challenges for folks who are trying to use those types of models in a stock picking context is the problem of in sample versus out of sample, especially if you're using a commercial model. You don't have any control over what data that model was trained on. 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 trust a back test that ChatGPT generated. That said, there are exciting things.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“Differentiated insights about companies to have differentiated alpha sources. These algorithms are extremely data hungry. It's really important when you're building these machine learning models on noisy data like forecasting equity market returns to give them as much data as possible. 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 1970s and 80s is still useful for forecasting mispricing. It is in the sense that these machine learning tools become more robust the more data on more different market cycles in the context of investing that they're able to be trained on.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“Machine learning. The data that feeds our models is oriented towards the longest historical data and the highest quality data sets that are out there in the quant space, so financials, prices, analysts, estimates. It's not to say that the big data explosion doesn't have any value. It certainly does. And lots of people are a testament to that. But it's important to know what your edge is. And for us, it's using these sophisticated machine learning tools.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“In the markets, there are informational edges and there are analytical edges. It's not black and white between the two of those things. But generally speaking, when people get excited about big data, it's because they're excited about an informational edge. They're excited about finding some new source of information about companies that's going to drive returns that maybe a lot of people don't know about yet. There are a lot of investors who are successful pursuing informational edges, but it can be a little bit of an arms race. These new data sources can be expensive when too many people find out about them, depending on the size of the mispricing related to that data, the ability to generate returns can get diminished over time. We have intentionally focused not on that arms race, but on the analytical piece through the use of decision trees and”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“In that research piece, there's a wide swath of data on fundamentals and technical as a stock price. Then you've had this whole explosion of alternative data sets. I'd love to hear how you've thought about the value integration of alternative data into your research.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“Trading cost forecast miles, everything that we use is built in house. That gives us a lot of flexibility and breadth in terms of the idea generation and what we can consider doing in terms of making enhancements to the process. It's not all about the factors that go into the stockpicking, even though that's the most exciting part of research.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“That will impact a company and eventually probably will impact the data itself once it's updated. A company announces an acquisition. It can take upwards of a year of that deal closing. Once it closes, there's a lag until financial statements that reflect the deal are filed. We can get a jump on data by using our own eyes occasionally. The rest of the day for most of the team, the focus is on research. It's on idea generation and execution on those ideas of thinking about how do we improve various aspects of our model, being in the business since 1991, we pretty much use proprietary tooling for all the components of our process. Back in the 90s, there weren't third-party software vendors trying to sell you back to testing engines, risk models.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“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 reoptimized and generates a trade list. The first thing every day is the trade review process. We're not doing trade review from the perspective of interjecting our own subjective behavior on what trades we think should happen and which shouldn't. But what we're after in that process is making sure, number one, that the data is correct. Then number two, to be able to understand the dynamics of the model and what's driving our trading and also to make sure that there's not news out there in the marketplace that our data inputs do not see.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“Loved you to walk me through what your day looks like because there's aspects of what you're talking about that are seeing what the model does. And then there's other aspects of observing the outputs based on a trade blotter. So as you go through a typical day in your life of managing the portfolios, what does that path look like?”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“It could be the rise of retail trading and meme stocks and Robin Hood. It could be all of those things wrapped up in one. The good news is we don't need to know what's driving it. The important thing is having strategies that are active and that are able to take advantage of inefficiencies when they present themselves.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“Wouldn't say that we have felt a huge impact from pod shops per se. 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 going to get harder and harder and harder every single year? I would say absolutely. Because that's the way it had gone for decades. Traditional factor tilting became commonplace in order to have an edge. It got harder and harder every year. Something feels like it snapped in the last couple of years. I wish I knew what it was. It could be the rise of pot chops for all I know. It could be that we've hit a tipping point in terms of passive management in the equity space.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“The impact of leverage on trading strategies to what we were talking about before feels like it may exist in the hedge fund pod shops today. I'm curious if you've seen any changes in market structure that's impacting how you invest as those strategies have grown in size over the last bunch of years”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“We don't spend a lot of time worrying about what our competitors are up to, where we do tend to pay attention would be when competitors are publishing strategies that are specifically sounding like they're encroaching on our space, which is machine learning approach to traditionally looking investment fundamentally based investment portfolios.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“When we think about reflexivity in the quant space, there's a tendency to conflate natural fluctuations, good performance, bad performance with quant strategies, which can be true of any investment strategy with the impacts from running a strategy with leverage. When people talk about the most famous quant blow-ups of all time, long-term capital management, the quant quake in August of 2007, what they're highlighting are events that were caused by a period of underperformance for a quant strategy, but were magnified by the use of leverage in those strategies. If long-term capital management had been running a 50x leveraged strategy, they wouldn't have ended up in the trouble that they had. Up with. Similarly, in the quant quake, the paper that was published on that was written by”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“Them for reasons that have nothing to do with analyst forecasts, whether the analysts raise their forecasts a ton or whether they make modest updates can be irrelevant for certain of the trading that we're doing in our portfolios. That's the value of the glass box is being able to see how the decision making is being made allows us to be precise in terms of how we think about potentially stepping in and overriding the model.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“We take a data oriented view on that. We try to put all of the potential overrides that we might make to the decision making of the model as much as possible through the lens of data when we're thinking about trades, we're thinking about specifically what data inputs lead into what factors that are driving the decision making. When a company that we're trading has reported great earnings, we want to dig into, okay, well, how are those great earnings going to impact all of the factors in our model at the next level? How will those factors changing impact the decision making that comes out of the tree? It's often the case that we're trading something and they've just reported great earnings, but we are buying.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“Several of the things you've mentioned along the way, there is human judgment that's coming into play, whether that is the risk constraints in the model or news coming out about a company and say, well, that's not what the model is trying to signal. How do you think about the degree to which your human judgment should override anything that comes out of the model?”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“We have a trade off that's embedded in the optimization that captures that dynamic. Market impact specifically where it intersects with the portfolio construction is in terms of number ones or ultimate position sizing companies that are less liquid will tend to have smaller overall positions in them. But it also impacts the speed of trading to get to those positions. The biggest most liquid names in the world trade in some of our portfolios. It's easy to trade tens of billions of dollars of multi-trillion dollar stocks. Whereas some of our strategies are involved with small companies. We run small cap strategies. We run a micro cap strategy. In those spaces, we deal on some very illiquid stocks. And it's important there not only to think about Ultimate size of the position, but how quickly do you trade to those positions?”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“Unless we think that the improvement that we're getting from an alpha and risk perspective will compensate us for not only the visible costs of trading of spreads and commissions, but the less visible costs of market impact.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“Going to use technology. We have a portfolio optimizer that we've built that takes into account a couple of key things as it is constructing portfolios every day. It takes into account the alpha forecasts. It's a precise numerical forecast that comes out of the decision tree model. We're taking into account risk management. We use a set of hard risk constraints that are consistent across all of our portfolios, as well as statistical risk model predicting the volatility and the tracking error of a portfolio so that all else equal will prefer portfolios that have more consistent outcomes than portfolios that have volatile expected outcomes. We also take into account trading costs. We want to make sure that we're not repositioning the portfolio.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“You can imagine this toggling back and forth of questions between fundamental data, financing, to pricing data, momentum, and then fundamental data and back and forth that you could have these thousands and thousands of trees and different questions you could ask. How do you then create a portfolio from all those signals aggregating thousands of different trees?”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“The stopping rules on question asking are mechanical the whole process is mechanical. In our models, we stop for two reasons. One is we have a hard limit at five questions after you've asked five questions, you're done. There's also a limit that if you've asked a question and created a branch that has too small of a pool of data, that will also be a reason to stop. The questions don't have to break companies up 50-50. Occasionally we'll ask a question about extreme price returns, whether on the positive or negative side. Generally, there aren't that many companies that have extreme returns, but they're very interesting. We will see some questions that pull out relatively small.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“Typical questions would be about volatility. We tend to find momentum works better when it is consistent, when the stock price is rising in a consistent manner. Companies that have one giant price move driving the momentum measurement. Company age also comes into account there. We find that momentum typically is more meaningful when you're looking at newer companies than companies that have been around for a long time. They're generally higher growth businesses. are more often in industries that are evolving knowing that the sentiment is strong around those companies is an even more positive indicator of future returns than knowing that a company that's been around for 100 years had a good quarter.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“Down the other branch of the tree, though, it's not all going to be about momentum. We're going to find some strong, differentiated groups of companies down the other branch of the tree to pair with that particular set of high alpha stocks on the financing side.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“Good companies and companies that are going to underperform. Same on the non financing side. That's a very important thing that we don't give up on the high financing companies just because the odds are stacked against them. When the algorithm continues down that branch, what it finds are that a lot of companies with significant financing do underperform, but it finds that there is a class of stocks where they outperform despite the financing. And generally speaking, it's the strongest momentum companies that can generate good outcomes regardless of the financing. It makes intuitive sense when companies are looking at the strongest, highest growth companies, they don't punish them for a little bit of share issuance, which often takes the form of stock-based compensation for their employees.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“Questions they're asked in sequence and in context at the top of the tree, you're going to ask a question of all companies. You're going to want a question that is relevant to explaining returns for big companies, small companies, growth companies, value companies. A common question we'll ask at the top of the tree will be about a company's use of financing, whether they're issuing debt and or shares or buying those things back. That's a good question to ask of any company. 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. Down both of those branches, the algorithm proceeds in the same way. For the companies with a high level of financing, it tries to figure out what are the right questions to ask to find.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“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 the branch of the tree that you're working down. If you think about trees breaking up 50-50 at the second layer of the tree, each question is motivated on half of your original data. down another layer, it's a quarter, down ten layers each question is going to be motivated on one thousandth of the data, down 20 layers you would be operating on one millionth of the data. 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”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“The thousand trees. At the end of the day, you could go through that exercise, it would be tedious walking through tree by tree, whether anything has changed, specifically what, and dig in on the data updates that are driving every decision that happens in the portfolio.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“We started our decision tree journey with one tree over the years with faster processing power and more advanced algorithms. We've been able to improve the forecasting by relying on algorithms that employ a forest of trees. Back in the one tree 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 motivating every trade that happened in our portfolio. As you move to a forest of trees, we can't put a thousand paper trees on the wall anymore. We've built some tools, some analytical helpers to synthesize and summarize what's happening across”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“From what we present to the algorithm, how does that impact our research results? How does it impact the returns and the risk that we generate from our back test? And if we see that we can remove a factor from the model and have very little or no impact on portfolio outcomes over the course of decades, that gives us confidence that that's a factor that no longer needs to be there.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“It's very data driven. The process of removing a factor from the model, it's just the inverse of the process of adding a factor to the model. When we have a sense that a factor is working less well, generally that sense comes from the fact that we don't observe decision making being driven by that factor on a day-to-day basis. When we review our trades every morning, year after year, we see fewer and fewer trades that are being driven by this one factor. That's the value of the glass box and being able to understand what's driving the decision making. When we have that intuition that a factor has decreased in efficiency, we'll run that research project and say, well, the model seems to be making less use of this over time. What if we made zero use of it? What if we removed it from?”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“In the process of going from book to price being an important factor to not being in the model, what's the process to toggle on and off compared to decreasing its importance into the construction of the model?”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“We do occasionally remove factors from our modeling. The reasons that you do it are that first one where the factor no longer works for one reason or another, whether you were mistaken or whether markets have evolved. Occasionally we'll move a factor. If we add something new that captures a correlated underlying effect. An example of that first factor, we used book to price in our models going back to version 1.0 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 why that factor seemed to explain.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“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 determined. A lot of times we'll have an idea about a factor as a new idea help the model improve its forecasting and the decision trees will simply say nice try guys, but I don't find a lot of profitable questions to ask about this factor. I'm going to ignore it. In terms of how does it decide to use the factors in relation to all the other characteristics, that's 100% driven by the algorithm.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“Number of years since their IPO, why do we use this factor in our model? The power of the decision tree is that it allows you to make use of factors that don't explain returns on their own, but can give you context onto how to explain returns. What we find is that the important questions to ask about young companies, companies that are within 10, 15, 20 years of their IPO, are a little different than the important questions to ask of companies that have been around for 50, 80, 100 years. The decision tree gives you that framework to say if you're a young company, let's ask these questions. But if you've been around for 80 or 100 years, let's ask a different set of questions. Valuation is an important differentiator. Valuation is a lot more important for companies that have been around for a long time than for brand new entry.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“It's driven by machine learning, which leads us to differentiated view on factors to a lot of other investors. 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. It's an unusual factor. The best of my knowledge, few quant investors use that in their models. Also, very few, to my knowledge, traditional portfolio managers explicitly take the company's age into account when they're formulating their views. And there's a reason for why it's unusual, which is that on its own, company age tells you nothing about whether a company is going to outperform or underperform. Companies don't stop performing well because they've hit some mad.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“When you filled out your model, do you start with a couple of core factors that you believe true or all the time and then build from there as you build the model, how do you construct what those inputs are?”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“Two big sources of research ideas for our process. Certainly we read all of the academic and practitioner literature and the investment finance space. And occasionally we get some good ideas out of seeing what's published. 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. Where we find more value typically is when we generate ideas that are driven by our own observations on the behavior of our strategies. That's one of the advantages of having the long history we've been investing our strategies over 30 years now. The observations that we've made across multiple different Market cycles over those decades. I've informed meaningful enhancements to the process.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“In combination with other characteristics, such as value and quality could lead to strong outcomes. We didn't quite have a feeling for it, but we beat up the data and we convinced ourselves that it was worth implementing. But as soon as we started trading stocks that fit that profile, it immediately became obvious because our head of trading would come to me every day we bought a stock that was down 70 or 80 percent and say Damn, we need to override this trade, the company's CEO just resigned in disgrace. Damn, 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”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source
“The algorithms behind alpha go are not a decision tree. Machine learning, one of the advantages of that field is that it is able to discover insights on its own. And generally speaking, when we have a new research idea that makes it into the model, ex ante have figured out why this idea we expect it to add value or not. Occasionally we're modestly surprised by what comes out of the research process. 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 for companies who share prices were down 70 or 80 percent over the last year.”
2025-11-20 · Capital Allocators · Daniel Mahr – Glass Box Quant at MDT Advisers (EP.472) · IDENTIFIED FROM THE TRANSCRIPT · source