科学研究

姜富伟，中央财经大学金融工程系主任、教授、博导，教育部青年长江学者，国家社科基金重大项目首席专家，黄大年教学团队核心成员，北京市海淀区政协委员，目前主要关注数字经济与金融科技交叉研究，在Journal of Financial Economics、Review of Financial Studies、Management Science、《管理世界》《金融研究》《经济学季刊》《管理科学学报》等发表论文50余篇，成果获评ESI经济管理类全球前1%最高被引用论文、RFS最高被引用论文、JFE最高被引用论文等，获《金融研究》优秀论文奖、国际金融管理协会最佳论文奖、亚洲金融协会最佳论文奖、中国金融工程学年会优秀论文奖、金融图书金羊奖等奖励荣誉，国家自然科学基金考核评价“特优”。学术观点被《哈佛商业评论》《清华金融评论》、CCTV、澎湃新闻等转载，担任Accounting and Finance副主编、Annals of Economics and Finance编委和30多本中外学术期刊审稿人。

In this paper we apply economic narratives to inflation forecasting using a large news corpus and machine learning algorithms. We measure economic narratives quantitatively from the full text content of over 880,000 Wall Street Journal articles and represent them as interpretable news topics. The results indicate that narrative-based forecasts are more accurate than the benchmarks both in-sample and out-of-sample, which perform especially well during recession periods. Narrative-based forecasts perform better in the long-run forecasting, suggesting that narratives help to capture the slowly-varying trend inflation objectives. Information about inflation expectations and prices of specific goods embedded in narratives contributes to its predictive power. Overall, we provide a novel representation of economic narratives and highlight the important role of economic narratives in inflation forecasting.