Bond Risk Premiums with Machine Learning (vol 34, pg 1046, 2021)
成果类型：
Correction
署名作者：
Bianchi, Daniele; Buchner, Matthias; Hoogteijling, Tobias; Tamoni, Andrea
署名单位：
University of London; Queen Mary University London; University of Warwick; Erasmus University Rotterdam; Erasmus University Rotterdam - Excl Erasmus MC; Rutgers University System; Rutgers University New Brunswick; Rutgers University Newark
刊物名称：
REVIEW OF FINANCIAL STUDIES
ISSN/ISSBN：
0893-9454
DOI：
10.1093/rfs/hhaa098
发表日期：
2021
页码：
1090
关键词：
摘要：
In this note we revisit the empirical results in after correcting for using information not available at the time the forecast was made. Although we note a decrease in out-of-sample , the revised analysis confirms that bond excess return predictability from neural networks remains statistically and economically significant.