文澜金融论坛（第265期）

摘要：

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.

主讲人介绍：

姜富伟，中央财经大学教授、博导，金融工程系主任，教育部国家级人才计划入选者，研究领域包括资产定价、行为金融、金融科技等，在经济金融顶级期刊Journal of Financial Economics、Review of Financial Studies、Management Science、《管理世界》、《管理科学学报》、《金融研究》、《经济学季刊》等发表论文40余篇，成果被评为ESI全球前1%最高被引论文、RFS最高被引论文，主持的国家自然科学基金项目被评为特优，并被《哈佛商业评论》、《清华金融评论》等转载应用，荣获《金融研究》优秀论文奖、亚洲金融协会最佳论文奖、国际财务管理协会最佳论文奖等诸多学术奖励。担任SSCI来源期刊Accounting and Finance副主编、Annals of Economics and Finance编委和Management Science等30多本中英文学术期刊评审。