Market Timing Corporate Bonds with Machine learning – Random Forests
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Predicting Individual Corporate Bond Returns
Xin He; Guanhao Feng; Junbo L. Wang; Chunchi Wu
- HKCity University of Hong Kong
- University of Science and Technology of China
- Hunan University
- ?City University of Hong Kong (CityU)
- ?University of Science and Technology of China (USTC)
- MOCity University of Macau
- City University of Seattle
- ?City University of Hong Kong (CityU) - Department of Economics and Finance
- ?Dept. of Economics and Finance, City Univ. of HK
- University at Buffalo, State University of New York
- Buffalo State University
- ?SUNY at Buffalo - School of Management
- ?The State University of New York (SUNY) at Buffalo - School of Management
Strategy in a nutshell
This strategy predicts corporate bond returns using machine learning models, including random forest, lasso, and partial least squares. A panel approach combines bond-specific characteristics and aggregate economic predictors. The models are evaluated with out-of-sample R² metrics and Fama-MacBeth regressions, identifying the most influential predictors for public and private bonds across different rating and maturity groups. Market-timing is implemented by longing bonds with positive return predictions and shorting those with negative predictions, creating long-short portfolios optimized for risk-adjusted performance.
Economic rationale
The strategy’s effectiveness stems from improved data availability, long-span historical datasets, and the adoption of advanced machine learning techniques. By capturing nonlinear relationships and differentiating public versus private bond behaviors, it delivers robust return forecasts, enabling better-informed investment and risk management decisions across the corporate bond market.