The Loomis Sayles Applied Integrated Quant (Applied IQ) Team powers our investment and research teams with quantitative expertise, advanced analytics and intuitive, proprietary tools and frameworks that help support long-term results.

Applied IQ combines proprietary technology, an investment-team focused approach and deep research to turn complex market data into practical, actionable insights. What sets the Applied IQ Team apart are its people and approach. Close collaboration with Loomis Sayles investors enables Applied IQ to deliver data-driven insights tailored to each team’s distinct needs, ensuring customized solutions that address challenges the teams may be facing.

“The most important thing is building really robust tools that get used to create alpha,” says Michael Crowell, Chief Investment Research Officer and Director of Applied Integrated Quant. “Our team believes these tools are only helpful when they are easily understood.” The team’s focus on practical application has fueled widespread adoption, establishing tools like the Corporate Health Index (CHIN) as bedrock features of fixed income investing at the firm.

Learn more about Applied IQ’s approach from Michael and colleagues Bobby Bakshi, CFA, Co-Director of Applied Integrated Quant and Bernardo Aumond, PhD, Co-Director of Applied Integrated Quant.

Q: How would you explain Alpha Tech to someone who didn’t work at Loomis Sayles?

Michael Crowell: Alpha Tech refers to the proprietary tools that we have developed at Loomis Sayles, particularly research tools that help drive alpha generation across our investment teams. It’s a key competitive advantage that we’ve tapped into. I think of our team as the Swiss army knife of Loomis Sayles: our whole mission is predicated on this idea that we can become very close to an investment team and learn to speak their language so that when they pose a problem, we understand why and what they’re facing. Everything our team does ultimately has an endpoint of an app or a technology outlet or deliverable. The big sea change, if you back up from the team’s origins until now, is that end point and delivering our internal stakeholders custom tools. There’s a partnership that we’ve really been able to harness working with our investment teams and research professionals, and we’ve leveraged our technology team as well to develop these tools.

Bernardo Aumond: Our team works very closely with our investment teams to understand their language and the challenges they are facing. With the help of our internal technology department, we can build proprietary systems and apps and deploy quantitative work in a way that most of our competitors aren’t able to do. It’s very rare that we develop and build something that lives in a spreadsheet somewhere. That was the approach and delivery mechanism in the early days that Mike alluded to. Many of our competitors are still using that method. Our work is very credit centric; we build tools that are useful across the board for our fixed income teams. Whether it’s a risk model or the Corporate Health Index (CHIN), these tools are optimized to serve the firm, which is especially rewarding because we’re able to solve challenges that have a broader impact.

Bobby Bakshi: You don’t win this battle by finding new frontiers; there are very few new frontiers. It’s a battle of inches, feet and implementation. Our work demands a lot of focus and partnership with the investment and research teams, spending time living in their shoes and trying to understand the problems they’re facing. What differentiates our Alpha Tech is solving their problem, not solving a general problem. What makes a difference is the implementation, the details, and the integration of these tools, like the CHIN into an investment process.

Q: How do you see Alpha Tech helping Loomis Sayles evolve as an organization over the next 100 years?

Bernardo Aumond: What we have done successfully is try to understand how the teams currently solve a problem. Using the same variables that they use or concepts that are important to them, we incorporate a lot of insight that already exists. The result of that is that it makes the tools a lot easier to be adopted. The way I view success with these tools is being able to step away and just let people use it. Portfolio Managers have a very high bar for talking about a process or a tool in front of a client. They are so well understood, and they need to be able to use the tool and know when it doesn’t work. If your tool shows up on a client deck, you are not going to be in the room to explain it, but the team is and that is very solid metric of success.

Michael Crowell: I could not think of a better signpost of success than having a member of the investment team speaking to a client and clearly demonstrating how they use the tool that we’ve built, it’s fully immersed in their process.

Bobby Bakshi: At some point what we do is really going to transform. If you look at the asset management industry, people are paying less for products and there’s a shift toward customization. Taking pieces of different concepts and embedding it into others so that each one is customized with what somebody needs. We have the technology and tools to enable that, so that leads to even more specialization.

Q: What role do you see Artificial Intelligence (AI) playing in this evolution?

Bobby Bakshi: We’ve always used AI, but it wasn’t always labeled as AI back then. Take the techniques of regression and classification trees—they are a very basic form of AI. It’s tough to build a model without some kind of machine learning; that’s always been there. The other bit is the natural language interface element. It’s adapting these natural language processing tools to understand the context of language and to do what humans do, but much more systematically. Finally, with LLMs (large language models), we can develop research models more quickly and continuously share intermediate results with investment teams via apps and dashboards. It brings the concept of “research in progress” to life and generates more model transparency and engagement with end users during the development phase. LLMs also help us reduce deployment cycles and increase researcher productivity.

Michael Crowell: I’m all about efficiency gains. Our investment teams will tell you with even some of the nascent tools we’ve developed here, it’s shaving hours of time off their day or time that they would have to delegate to an analyst or strategist on their team. One of my favorite things about AI is that it can write code. For our research projects, no matter what you’re going to do at some point you have to code the environment, which is time-consuming and can be prone to errors. Using AI, you can explore doing something in the best way possible and in the most efficient way possible.

Bernardo Aumond: The code is almost like the cost of doing business. The hard job and the most valuable job and skill is the imagination and creativity needed to identify the next important question you have to answer. When you free people to be able to do that first, you open the door to different opportunities and skillsets. The best quant can have a degree in history or can be a musician or an artist because you do not need to know how to program. So you may not need someone who has spent two thirds of their life learning to code, but instead has spent their life exercising creativity and curiosity and imagination. That is the most important thing to me: to decouple programming skills or math skills and have people who are creative thinkers, because it’s imagination that really makes impact.

IMPORTANT DISCLOSURE
 
The Credit Health Index (CHIN) is a macro tool created and managed by Loomis Sayles. The CHIN is currently managed by the Loomis Sayles Quantitative Research and Risk Analysis (QRRA) team. It is a proprietary framework that utilizes a combination of macro, financial market and policy variables to project us to corporate health. A higher reading indicates stronger corporate health whereas a lower reading indicates weaker corporate health.
 
Alpha: a measure of risk-adjusted performance. A positive alpha indicates outperformance and negative alpha indicates underperformance relative to the strategy’s level of systematic risk. “Alpha engine” alludes to efforts to promote positive risk-adjusted excess return.

This marketing communication is provided for informational purposes only and should not be construed as investment advice. Investment decisions should consider the individual circumstances of the particular investor. Any opinions or forecasts contained herein, reflect the subjective judgments and assumptions of the authors only, and do not necessarily reflect the views of Loomis, Sayles & Company, L.P. Investment recommendations may be inconsistent with these opinions. There is no assurance that developments will transpire, as forecasted and actual results will be different. Information, including that obtained from outside sources, is believed to be correct, but we cannot guarantee its accuracy. This information is subject to change at any time without notice.

Any investment that has the possibility for profits also has the possibility of losses, including loss of principal
 
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Learn more about the Applied Integrated Quant Team