The latest research shows that AI-derived algorithms combined with the input of skilled investment professionals can be more effective than any single approach.

Here is an excerpt from a report in Investment Magazine:

We think the future of fixed income investing requires moving beyond the active vs. quant stalemate. The ideal investment process starts with two familiar dimensions—top-down macroeconomic research and bottom-up fundamental security analysis.

The reasons for this division of labor are relatively straightforward: the performance of nearly every fixed income security (outside sovereign bonds) is influenced by unique mixtures of macroeconomic fundamentals—like inflation and stages of the credit cycle—and sources of bottom-up mispricing tied to individual credit issuers. The skills of a trained economist are different from a credit analyst who specializes in the micro economy of an industry.

Two additional dimensions—active fundamental and quantitative science—are also necessary for alpha generation. Factor based security selection and machine learning techniques bring new skills and fresh insights to fixed income investing. The goal of combining active with quantitative views, however, isn’t to mix them inside a portfolio like a kitchen blender.

In some instances, quantitative techniques can sharpen fundamental insights with greater precision. Other times, they can challenge assumptions made by fundamental credit analysts and portfolio managers. Through discussion, quantitative views might lead fundamental analysts toward a different conclusion or reconfirm their original hypothesis after a healthy debate and deeper inspection.

It’s been our experience that quantitative methods ultimately work best when combined with “traditional” human insights derived from the academic disciplines of macroeconomics and fundamental security analysis. Yes, machines offer much-needed precision in predicting asset prices, but we still need deep human expertise to make sense of market complexity. Machines aren’t good at assessing turning points in the business cycle or anticipating crowding behaviors from profit-driven traders.