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Investors can double alpha in the credit markets by using simple equity momentum strategies enhanced by applying machine learning with boosted regression trees.


Machine learning techniques have gained popularity in recent years but only to a limited extent in fixed-income research. This article shows some new work in the application of “boosted regression trees” for the equity momentum factor in the corporate bond market. We report significant performance gains to investors from using machine learning–driven forecasts, roughly doubling the alpha and information ratio of better known equity momentum strategies. In addition to past equity returns, we include size and liquidity of stocks and bonds in our model framework.

About the Authors

Hendrik Kaufmann

Hendrik Kaufmann is a portfolio manager and quantitative analyst at Deka Investment GmbH, Frankfurt, Germany.

Philip Messow

Philip Messow is an associate director and research analyst at Quoniam Asset Management GmbH, Frankfurt, Germany.

Jonas Vogt

Jonas Vogt is a professor of data science and finance at Baden-Württemberg Cooperative State University, Mannheim, Germany.