PT  - JOURNAL ARTICLE
AU  - Boyu Wu
AU  - Kevin J. DiCiurcio
AU  - Beatrice Yeo
AU  - Qian Wang
TI  - Forecasting US Equity and Bond Correlation—A Machine Learning Approach
AID  - 10.3905/jfds.2022.4.1.076
DP  - 2022 Jan 31
TA  - The Journal of Financial Data Science
PG  - 76--86
VI  - 4
IP  - 1
4099  - https://pm-research.com/content/4/1/76.short
4100  - https://pm-research.com/content/4/1/76.full
AB  - The stock–bond correlation is a cornerstone of every asset allocation decision, but estimating it reliably can prove to be challenging given the potential for co-movements to fluctuate significantly based on economic conditions. Using supervised machine learning techniques, this article presents a new approach for identifying key determinants of the correlation between US equity and bond returns, ultimately finding that inflation, alongside real yields, equity volatility, economic growth, and inflation uncertainty, predict changes in correlation dynamics overtime. Relative to the existing literature, the authors’ approach allows for the systematic detection of the main drivers of stock–bond correlation and uncovers the time variation in importance of each determinant across economic regimes. Upon conducting an out-of-sample portfolio evaluation, the authors show that the five factors with gradient boosting regression approach outperforms all other existing factor-based models in estimating both the trend and level of correlation, thereby offering an alternative robust solution for forecasting time-varying equity and bond co-movements that can be further applied to asset allocation decisions and risk management.