Machine learning applied to active fixed-income portfolio management: a Lasso logit approach

The use of quantitative methods constitutes a standard component of the institutional investors’ portfolio management toolkit. In the last decade, several empirical studies have employed probabilistic or classification models to predict stock market excess returns, model bond ratings and default probabilities, as well as to forecast yield curves. To the authors’ knowledge, little research exists into their application to active fixed-income management. This paper contributes to filling this gap by comparing a machine learning algorithm, the Lasso logit regression, with a passive (buy-and-hold) investment strategy in the construction of a duration management model for high-grade bond portfolios, specifically focusing on US treasury bonds. Additionally, a two-step procedure is proposed, together with a simple ensemble averaging aimed at minimising the potential overfitting of traditional machine learning algorithms. A method to select thresholds that translate probabilities into signals based on conditional probability distributions is also introduced. A large set of financial and economic variables is used as an input to obtain a signal for active duration management relative to a passive benchmark portfolio. As a first result, most of the variables selected by the model are related to financial flows and economic fundamentals, but the parameters seem to be unstable over time, thereby suggesting that the variable relevance may be time dependent. Backtesting of the model, which was carried out on a sovereign bond portfolio denominated in US dollars, resulted in a small but statistically significant outperformance of benchmark index in the out-of-sample dataset after controlling for overfitting. These results support the case for incorporating quantitative tools in the active portfolio management process for institutional investors, but paying special attention to potential overfitting and unstable parameters. Quantitative tools should be viewed as a complementary input to the qualitative and fundamental analysis, together with the portfolio manager’s expertise, in order to make better-informed investment decisions.