Treasury Bond Price and Yield Curve Prediction via No Arbitrage Arguments and Machine Learning

This paper proposes a novel bond return (price or yield curve) prediction methodology, unifying the classical no arbitrage pricing framework, which is ubiquitous and serves as the fundamental theoretical building block in mathematical finance, and empirical asset (bond) pricing methodologies, e.g., (Bianchi, et al., 2020) for treasury bonds and (Gu, et al., 2021) for equities. The methodology can be viewed as a unification of theoretical and empirical asset pricing frameworks. Our method is mathematically and theoretically rigorous, arbitrage-free and meantime enjoys the flexibility offered by the empirical asset pricing framework, i.e., a potentially rich factor structure and accurate function approximations via machine learning regression. Real market backtesting studies show that our predictions are accurate, in the sense that the formulated equally- or optimally-weighted treasury bond portfolios in China exchange-based markets bear significant positive returns. The hit rate for weekly yield curve prediction reaches 57.00% and the related long-only trading strategy based on the prediction results in an annualized absolute return as high as 15.85% with Calmar ratio achieving 5.17 for equally weighted portfolios. The risk-adjusted returns are even higher for mean-variance optimal portfolios. As a by-product of our prediction framework, spot yield curves can be predicted accurately in an arbitrage-free manner