Historically, investors have struggled to add meaningful alpha through security selection. A dynamic new credit scoring approach could change that.
Given the breadth and diversity of the corporate credit universe, hand picking individual securities can be inefficient. That’s why we’ve developed a powerful new scoring methodology that systematically harnesses quantitative and fundamental research to generate new sources of outperformance (alpha).
Size of Bond Universe Makes Security Selection a Challenge
The credit market’s complexity and sheer scale underscores the challenges of generating alpha through bond selection. The global corporate universe comprises nearly 20,000 securities, which can make security selection both time-consuming and overly subjective. Partly for this reason, many portfolio managers aren’t able to generate much alpha from security selection, and instead lean more on levers like beta timing and sector rotation.
We believe that’s a missed opportunity. In any market—but especially today’s—security selection has the potential to generate meaningful alpha. Historically, the heightened volatility and desynchronization between interest-rate regimes that we’re seeing now has contributed to increased dispersion and idiosyncratic opportunities at the issuer and security levels.
Fortunately, in our view, investment managers can generate potential alpha by combining quantitative methods with bottom-up fundamental research. The challenge lies in efficiently converting vast quantities of data into better investment outcomes.
Our approach systematically funnels both fundamental and quantitative inputs into a proprietary scoring model that ranks bonds by their attractiveness. We call this a “core score.”
Here’s how it works.
Balancing Fundamental and Quantitative Inputs
At the heart of the core-score methodology is traditional fundamental research. Credit analysts conduct thorough due diligence of issuers and securities, and assess various outcomes for each bond—including a base case, an upside case and a downside case. This range of outcomes is then used as a part of our fair-value model to determine a bond’s attractiveness relative to current market pricing. This informs the fundamental score for each security.
The second part of the process involves quantitative research. Analysts filter bonds through various predictive factors with demonstrable links to historical outperformance, such as momentum, relative value or default probability. Once securities are assessed through a factor lens, each bond is assigned a quantitative score that reflects our quantitative research team’s view of its attractiveness.
The fundamental and quantitative scores are then combined. The result is a single core score for each bond (Display). The core score informs our understanding of a bond’s return potential relative to its risk and allows portfolio managers to consistently implement our best ideas in client portfolios. We believe this increases the probability of generating alpha from security selection.
