Bloomberg announced the launch of Early Alerts, a ground breaking predictive model built by Bloomberg’s Enterprise Data business and available for analyzing the likelihood for future shifts in corporate credit spreads.

Early Alerts is a data driven output that uses Bloomberg’s proprietary library of fixed income data in concert with machine learning models to develop predictive insights that offer market participants an edge in an increasingly competitive market. The solution analyzes over 16,000 USD denominated corporate investment grade and high yield securities, providing scores over 1-day, 5-day, and 20-day horizons. The scores provide Bloomberg clients with valuable insight and an estimate for the likelihood a corporate security will undergo a significant credit spread tightening or widening over a specific time horizon – the higher the score, the greater the modeled probability.

“Fixed income markets, including corporate credit, are primed for the type of quantitative modeling, tooling and predictive power already available in other asset classes,” said Brad Foster, Global Head of Enterprise Content. “Early Alerts, offering probable indication of whether a corporate bond’s spread will widen or tighten, provides traders, asset managers, portfolio managers and risk managers a signal that easily compliments and enhances their existing process and a dataset that they can easily integrate into their models to more accurately anticipate market movement, further enabling them to meaningfully inform their investment strategy.”

Early Alerts is available to Bloomberg Enterprise Data clients, and initially covers USD denominated corporate issuers, with plans in the near future for increased regional expansion that covers issuance across more currencies.

“Our rich ongoing and historical datasets, robust back-testing process, machine learning and subject matter expertise help to collectively produce a signal that will enable our clients to add cutting edge predictive power to an asset class that has, to this point, lacked investment and access to such analytics,” added Foster.

Source: Bloomberg